From b719fd69a5046b2048573fc314a5f802b427f5ac Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 11:25:45 -0700
Subject: [PATCH 1/7] chore(cockpit): remove the deep-agents sandboxes topic

The sandboxes topic demonstrated code execution through a `run_code` tool
that never executed anything: it regex-scraped `print()` calls out of the
snippet and returned a fabricated stdout. Rebuilding it on the real
framework would mean `deepagents`' LocalShellBackend, which is an
unsandboxed host shell, and every cockpit deep-agents topic deploys to the
shared public LangGraph deployment. A public demo with a real `execute`
tool is remote code execution on our own infrastructure. Real isolation is
a paid sandbox service or a container story, and that is a deliberate
future decision rather than a demo default.

Removes the topic and every list that named it: the capability registry,
the cockpit manifest and its docs-link table, ports, the Next route
modules, the examples middleware and production-smoke path lists, both
tsconfig project reference trees, the cockpit serve target, the CI smoke
project list, the deployment URL entry, and the generated shared
deployment manifest (regenerated, now 32 graphs).

The manifest guard's capability count moves 42 -> 41. That assertion is
what caught the removal, and reinstating the registry entry turns
`nx test cockpit` red again through the #910 wiring guard, so both guards
stay mutation-capable with the topic gone.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .github/workflows/ci.yml                      |    2 +-
 apps/cockpit/e2e/production-smoke.spec.ts     |    1 -
 apps/cockpit/project.json                     |    7 -
 apps/cockpit/scripts/capability-registry.ts   |    1 -
 apps/cockpit/src/lib/route-resolution.ts      |    2 -
 apps/cockpit/tsconfig.json                    |    3 -
 cockpit/deep-agents/footprint.spec.ts         |    1 -
 .../angular/e2e/da-sandboxes.spec.ts          |    8 -
 .../angular/e2e/fixtures/da-sandboxes.json    |   12 -
 .../angular/e2e/global-setup-impl.ts          |   14 -
 .../angular/e2e/manual/sandboxes.manual.ts    |   21 -
 .../angular/e2e/playwright.config.ts          |   22 -
 .../sandboxes/angular/e2e/tsconfig.json       |   32 -
 .../sandboxes/angular/package.json            |   12 -
 .../sandboxes/angular/project.json            |  101 -
 .../sandboxes/angular/prompts/sandboxes.md    |    5 -
 .../sandboxes/angular/proxy.conf.mjs          |   14 -
 .../sandboxes/angular/src/app/app.config.ts   |   17 -
 .../angular/src/app/sandboxes.component.ts    |  195 --
 .../src/app/views/code-execution.component.ts |   97 -
 .../environments/environment.development.ts   |   11 -
 .../angular/src/environments/environment.ts   |   11 -
 .../sandboxes/angular/src/index.html          |   12 -
 .../sandboxes/angular/src/index.ts            |   35 -
 .../sandboxes/angular/src/main.cockpit.ts     |    6 -
 .../deep-agents/sandboxes/angular/src/main.ts |    6 -
 .../sandboxes/angular/src/styles.css          |    9 -
 .../sandboxes/angular/tsconfig.app.json       |   28 -
 .../sandboxes/angular/tsconfig.json           |   23 -
 .../deep-agents/sandboxes/python/.gitignore   |    3 -
 .../sandboxes/python/docs/guide.md            |  167 --
 .../sandboxes/python/langgraph.json           |   10 -
 .../deep-agents/sandboxes/python/package.json |    7 -
 .../deep-agents/sandboxes/python/project.json |   30 -
 .../sandboxes/python/prompts/sandboxes.md     |   10 -
 .../sandboxes/python/pyproject.toml           |   21 -
 .../deep-agents/sandboxes/python/src/graph.py |  102 -
 .../deep-agents/sandboxes/python/src/index.ts |   44 -
 .../sandboxes/python/tsconfig.json            |   13 -
 cockpit/deep-agents/sandboxes/python/uv.lock  | 1718 -----------------
 cockpit/ports.mjs                             |    1 -
 deployment-urls.json                          |    1 -
 deployments/shared-dev/langgraph.json         |    2 -
 libs/cockpit-docs/src/lib/docs-bundle.spec.ts |    1 -
 libs/cockpit-registry/src/lib/docs-links.ts   |    2 -
 .../cockpit-registry/src/lib/manifest.spec.ts |    3 +-
 libs/cockpit-registry/src/lib/manifest.ts     |    1 -
 .../cockpit-runtime-bridge-coverage.spec.mjs  |    6 +-
 scripts/examples-middleware.ts                |    1 -
 scripts/update-angular-environments.ts        |    1 -
 tsconfig.json                                 |    3 -
 51 files changed, 5 insertions(+), 2850 deletions(-)
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/da-sandboxes.spec.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/fixtures/da-sandboxes.json
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/global-setup-impl.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/manual/sandboxes.manual.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/playwright.config.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/e2e/tsconfig.json
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/package.json
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/project.json
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/prompts/sandboxes.md
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/proxy.conf.mjs
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/app/app.config.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/app/sandboxes.component.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/app/views/code-execution.component.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/index.html
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/index.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/main.cockpit.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/main.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/src/styles.css
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/tsconfig.app.json
 delete mode 100644 cockpit/deep-agents/sandboxes/angular/tsconfig.json
 delete mode 100644 cockpit/deep-agents/sandboxes/python/.gitignore
 delete mode 100644 cockpit/deep-agents/sandboxes/python/docs/guide.md
 delete mode 100644 cockpit/deep-agents/sandboxes/python/langgraph.json
 delete mode 100644 cockpit/deep-agents/sandboxes/python/package.json
 delete mode 100644 cockpit/deep-agents/sandboxes/python/project.json
 delete mode 100644 cockpit/deep-agents/sandboxes/python/prompts/sandboxes.md
 delete mode 100644 cockpit/deep-agents/sandboxes/python/pyproject.toml
 delete mode 100644 cockpit/deep-agents/sandboxes/python/src/graph.py
 delete mode 100644 cockpit/deep-agents/sandboxes/python/src/index.ts
 delete mode 100644 cockpit/deep-agents/sandboxes/python/tsconfig.json
 delete mode 100644 cockpit/deep-agents/sandboxes/python/uv.lock

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 26d55d5da..51a6d741a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -271,7 +271,7 @@ jobs:
           node-version: 22
           cache: npm
       - run: npm ci
-      - run: npx nx run-many -t smoke --projects=cockpit-ag-ui-interrupts-python,cockpit-ag-ui-streaming-python,cockpit-deep-agents-planning-python,cockpit-deep-agents-filesystem-python,cockpit-deep-agents-subagents-python,cockpit-deep-agents-memory-python,cockpit-deep-agents-skills-python,cockpit-deep-agents-sandboxes-python,cockpit-langgraph-persistence-python,cockpit-langgraph-durable-execution-python,cockpit-langgraph-streaming-python,cockpit-langgraph-interrupts-python,cockpit-langgraph-memory-python,cockpit-langgraph-subgraphs-python,cockpit-langgraph-time-travel-python,cockpit-langgraph-deployment-runtime-python,cockpit-chat-messages-python,cockpit-render-spec-rendering-python,cockpit-runtimes-microsoft-agent-framework-python,cockpit-runtimes-aws-strands-python --skip-nx-cache
+      - run: npx nx run-many -t smoke --projects=cockpit-ag-ui-interrupts-python,cockpit-ag-ui-streaming-python,cockpit-deep-agents-planning-python,cockpit-deep-agents-filesystem-python,cockpit-deep-agents-subagents-python,cockpit-deep-agents-memory-python,cockpit-deep-agents-skills-python,cockpit-langgraph-persistence-python,cockpit-langgraph-durable-execution-python,cockpit-langgraph-streaming-python,cockpit-langgraph-interrupts-python,cockpit-langgraph-memory-python,cockpit-langgraph-subgraphs-python,cockpit-langgraph-time-travel-python,cockpit-langgraph-deployment-runtime-python,cockpit-chat-messages-python,cockpit-render-spec-rendering-python,cockpit-runtimes-microsoft-agent-framework-python,cockpit-runtimes-aws-strands-python --skip-nx-cache
 
   cockpit-deploy-smoke:
     name: Cockpit — deploy smoke dry-run
diff --git a/apps/cockpit/e2e/production-smoke.spec.ts b/apps/cockpit/e2e/production-smoke.spec.ts
index 5635e6f75..8d088239e 100644
--- a/apps/cockpit/e2e/production-smoke.spec.ts
+++ b/apps/cockpit/e2e/production-smoke.spec.ts
@@ -35,7 +35,6 @@ const CHAT_CAPABILITIES = [
   'deep-agents/subagents',
   'deep-agents/memory',
   'deep-agents/skills',
-  'deep-agents/sandboxes',
   'chat/tool-calls',
   'chat/subagents',
   'chat/threads',
diff --git a/apps/cockpit/project.json b/apps/cockpit/project.json
index 11b2ff607..d16a34802 100644
--- a/apps/cockpit/project.json
+++ b/apps/cockpit/project.json
@@ -156,13 +156,6 @@
         "cwd": "."
       }
     },
-    "serve-sandboxes": {
-      "executor": "nx:run-commands",
-      "options": {
-        "command": "npx tsx apps/cockpit/scripts/serve-example.ts --capability=da-sandboxes",
-        "cwd": "."
-      }
-    },
     "serve-all": {
       "executor": "nx:run-commands",
       "options": {
diff --git a/apps/cockpit/scripts/capability-registry.ts b/apps/cockpit/scripts/capability-registry.ts
index 1c5ddb8ea..ff45dd078 100644
--- a/apps/cockpit/scripts/capability-registry.ts
+++ b/apps/cockpit/scripts/capability-registry.ts
@@ -56,7 +56,6 @@ export const capabilities: readonly Capability[] = [
   { id: 'da-subagents', product: 'deep-agents', topic: 'subagents', angularProject: 'cockpit-deep-agents-subagents-angular', port: 4312, pythonPort: 5312, pythonDir: 'cockpit/deep-agents/subagents/python', graphName: 'subagents' },
   { id: 'da-memory', product: 'deep-agents', topic: 'memory', angularProject: 'cockpit-deep-agents-memory-angular', port: 4313, pythonPort: 5313, pythonDir: 'cockpit/deep-agents/memory/python', graphName: 'da-memory' },
   { id: 'da-skills', product: 'deep-agents', topic: 'skills', angularProject: 'cockpit-deep-agents-skills-angular', port: 4314, pythonPort: 5314, pythonDir: 'cockpit/deep-agents/skills/python', graphName: 'da-skills' },
-  { id: 'da-sandboxes', product: 'deep-agents', topic: 'sandboxes', angularProject: 'cockpit-deep-agents-sandboxes-angular', port: 4315, pythonPort: 5315, pythonDir: 'cockpit/deep-agents/sandboxes/python', graphName: 'da-sandboxes' },
   // Render capabilities
   { id: 'r-spec-rendering', product: 'render', topic: 'spec-rendering', angularProject: 'cockpit-render-spec-rendering-angular', port: 4401, pythonPort: 5401, pythonDir: 'cockpit/render/spec-rendering/python', graphName: 'r-spec-rendering' },
   { id: 'r-element-rendering', product: 'render', topic: 'element-rendering', angularProject: 'cockpit-render-element-rendering-angular', port: 4402, pythonPort: 5402, pythonDir: 'cockpit/render/element-rendering/python', graphName: 'r-element-rendering' },
diff --git a/apps/cockpit/src/lib/route-resolution.ts b/apps/cockpit/src/lib/route-resolution.ts
index 0013018db..380a8bb1d 100644
--- a/apps/cockpit/src/lib/route-resolution.ts
+++ b/apps/cockpit/src/lib/route-resolution.ts
@@ -24,7 +24,6 @@ import { deepAgentsPlanningPythonModule } from '../../../../cockpit/deep-agents/
 import { deepAgentsFilesystemPythonModule } from '../../../../cockpit/deep-agents/filesystem/python/src/index';
 import { deepAgentsSubagentsPythonModule } from '../../../../cockpit/deep-agents/subagents/python/src/index';
 import { deepAgentsSkillsPythonModule } from '../../../../cockpit/deep-agents/skills/python/src/index';
-import { deepAgentsSandboxesPythonModule } from '../../../../cockpit/deep-agents/sandboxes/python/src/index';
 import { renderSpecRenderingPythonModule } from '../../../../cockpit/render/spec-rendering/python/src/index';
 import { renderElementRenderingPythonModule } from '../../../../cockpit/render/element-rendering/python/src/index';
 import { renderStateManagementPythonModule } from '../../../../cockpit/render/state-management/python/src/index';
@@ -133,7 +132,6 @@ export const capabilityModules: RegisteredCapabilityModule[] = [
   deepAgentsFilesystemPythonModule,
   deepAgentsSubagentsPythonModule,
   deepAgentsSkillsPythonModule,
-  deepAgentsSandboxesPythonModule,
   renderSpecRenderingPythonModule,
   renderElementRenderingPythonModule,
   renderStateManagementPythonModule,
diff --git a/apps/cockpit/tsconfig.json b/apps/cockpit/tsconfig.json
index 805565a26..67aed5d6f 100644
--- a/apps/cockpit/tsconfig.json
+++ b/apps/cockpit/tsconfig.json
@@ -29,9 +29,6 @@
   "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
   "exclude": ["node_modules"],
   "references": [
-    {
-      "path": "../../cockpit/deep-agents/sandboxes/python"
-    },
     {
       "path": "../../cockpit/deep-agents/skills/python"
     },
diff --git a/cockpit/deep-agents/footprint.spec.ts b/cockpit/deep-agents/footprint.spec.ts
index 79d3db5ff..bdca60932 100644
--- a/cockpit/deep-agents/footprint.spec.ts
+++ b/cockpit/deep-agents/footprint.spec.ts
@@ -8,7 +8,6 @@ const topicNames = [
   'subagents',
   'memory',
   'skills',
-  'sandboxes',
 ] as const;
 
 const pageNames = ['overview', 'build', 'prompts', 'code', 'testing'] as const;
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/da-sandboxes.spec.ts b/cockpit/deep-agents/sandboxes/angular/e2e/da-sandboxes.spec.ts
deleted file mode 100644
index 2f0e8137a..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/da-sandboxes.spec.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { test, expect } from '@playwright/test';
-import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
-
-test('da-sandboxes: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
-});
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/fixtures/da-sandboxes.json b/cockpit/deep-agents/sandboxes/angular/e2e/fixtures/da-sandboxes.json
deleted file mode 100644
index 8766ff233..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/fixtures/da-sandboxes.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "fixtures": [
-    {
-      "match": {
-        "userMessage": "Hello"
-      },
-      "response": {
-        "content": "Hello! How can I help you today?"
-      }
-    }
-  ]
-}
\ No newline at end of file
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/global-setup-impl.ts b/cockpit/deep-agents/sandboxes/angular/e2e/global-setup-impl.ts
deleted file mode 100644
index 447a8d3a4..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/global-setup-impl.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { resolve } from 'node:path';
-import { portsFor } from '../../../../../cockpit/ports.mjs';
-import { createGlobalSetup } from '@threadplane-internal/e2e-harness';
-
-const ports = portsFor('cockpit-deep-agents-sandboxes-angular');
-
-export default createGlobalSetup({
-  langgraphCwd: 'cockpit/deep-agents/sandboxes/python',
-  langgraphPort: ports.langgraph,
-  angularProject: 'cockpit-deep-agents-sandboxes-angular',
-  angularPort: ports.angular,
-  fixturesDir: resolve(__dirname, 'fixtures'),
-});
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/manual/sandboxes.manual.ts b/cockpit/deep-agents/sandboxes/angular/e2e/manual/sandboxes.manual.ts
deleted file mode 100644
index f177b6e36..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/manual/sandboxes.manual.ts
+++ /dev/null
@@ -1,21 +0,0 @@
-import { expect, test } from '@playwright/test';
-
-test.describe('Deep Agents Sandboxes Example', () => {
-  test.beforeEach(async ({ page }) => {
-    await page.goto('http://localhost:4315');
-    await page.waitForSelector('app-sandboxes', { state: 'attached' });
-  });
-
-  test('renders the chat interface with execution log sidebar', async ({ page }) => {
-    await expect(page.locator('chat')).toBeVisible();
-    await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
-    await expect(page.locator('text=No code executed yet')).toBeVisible();
-  });
-
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'Write a Python script that prints the first 5 Fibonacci numbers.');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
-  });
-});
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/playwright.config.ts b/cockpit/deep-agents/sandboxes/angular/e2e/playwright.config.ts
deleted file mode 100644
index 3c4ef9ac4..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/playwright.config.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { defineConfig, devices } from '@playwright/test';
-import { portsFor } from '../../../../../cockpit/ports.mjs';
-
-const { angular: angularPort } = portsFor('cockpit-deep-agents-sandboxes-angular');
-
-
-export default defineConfig({
-  testDir: '.',
-  testMatch: '**/*.spec.ts',
-  fullyParallel: false,
-  workers: 1,
-  retries: process.env.CI ? 2 : 0,
-  reporter: process.env.CI ? [['list'], ['html', { open: 'never' }]] : 'list',
-  use: {
-    baseURL: `http://localhost:${angularPort}`,
-    trace: 'retain-on-failure',
-  },
-  projects: [{ name: 'chromium', use: { ...devices['Desktop Chrome'] } }],
-  globalSetup: './global-setup-impl.ts',
-  globalTeardown: require.resolve('../../../../../libs/e2e-harness/src/global-teardown'),
-});
diff --git a/cockpit/deep-agents/sandboxes/angular/e2e/tsconfig.json b/cockpit/deep-agents/sandboxes/angular/e2e/tsconfig.json
deleted file mode 100644
index 0fc9befb1..000000000
--- a/cockpit/deep-agents/sandboxes/angular/e2e/tsconfig.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-  "compilerOptions": {
-    "target": "ES2022",
-    "module": "ES2022",
-    "moduleResolution": "Bundler",
-    "esModuleInterop": true,
-    "strict": true,
-    "skipLibCheck": true,
-    "noEmit": true,
-    "types": [
-      "node"
-    ],
-    "baseUrl": "../../../../..",
-    "paths": {
-      "@threadplane-internal/e2e-harness": [
-        "libs/e2e-harness/src/index.ts"
-      ],
-      "@threadplane-internal/e2e-harness/global-teardown": [
-        "libs/e2e-harness/src/global-teardown.ts"
-      ]
-    },
-    "allowJs": true
-  },
-  "include": [
-    "**/*.ts"
-  ],
-  "exclude": [
-    "node_modules",
-    "test-results",
-    "playwright-report"
-  ]
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/package.json b/cockpit/deep-agents/sandboxes/angular/package.json
deleted file mode 100644
index 91a2f4398..000000000
--- a/cockpit/deep-agents/sandboxes/angular/package.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "name": "@threadplane/cockpit-deep-agents-sandboxes-angular",
-  "private": true,
-  "version": "0.0.1",
-  "peerDependencies": {
-    "@threadplane/chat": "*",
-    "@threadplane/langgraph": "*",
-    "@langchain/langgraph-sdk": "^0.0.36"
-  },
-  "license": "MIT",
-  "sideEffects": false
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/project.json b/cockpit/deep-agents/sandboxes/angular/project.json
deleted file mode 100644
index 8ca08e864..000000000
--- a/cockpit/deep-agents/sandboxes/angular/project.json
+++ /dev/null
@@ -1,101 +0,0 @@
-{
-  "name": "cockpit-deep-agents-sandboxes-angular",
-  "$schema": "../../../../../../node_modules/nx/schemas/project-schema.json",
-  "sourceRoot": "cockpit/deep-agents/sandboxes/angular/src",
-  "projectType": "application",
-  "targets": {
-    "build": {
-      "executor": "@angular/build:application",
-      "outputs": [
-        "{options.outputPath.base}"
-      ],
-      "options": {
-        "outputPath": {
-          "base": "dist/cockpit/deep-agents/sandboxes/angular",
-          "browser": ""
-        },
-        "browser": "cockpit/deep-agents/sandboxes/angular/src/main.ts",
-        "tsConfig": "cockpit/deep-agents/sandboxes/angular/tsconfig.app.json",
-        "styles": [
-          "cockpit/deep-agents/sandboxes/angular/src/styles.css"
-        ]
-      },
-      "configurations": {
-        "production": {
-          "budgets": [
-            {
-              "type": "initial",
-              "maximumWarning": "1mb",
-              "maximumError": "1.5mb"
-            },
-            {
-              "type": "anyComponentStyle",
-              "maximumWarning": "10kb",
-              "maximumError": "16kb"
-            }
-          ],
-          "outputHashing": "none"
-        },
-        "development": {
-          "optimization": false,
-          "extractLicenses": false,
-          "sourceMap": true,
-          "fileReplacements": [
-            {
-              "replace": "cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts",
-              "with": "cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts"
-            }
-          ]
-        },
-        "cockpit": {
-          "optimization": false,
-          "extractLicenses": false,
-          "sourceMap": true,
-          "fileReplacements": [
-            {
-              "replace": "cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts",
-              "with": "cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts"
-            }
-          ],
-          "browser": "cockpit/deep-agents/sandboxes/angular/src/main.cockpit.ts"
-        }
-      },
-      "defaultConfiguration": "production"
-    },
-    "serve": {
-      "continuous": true,
-      "executor": "@angular/build:dev-server",
-      "configurations": {
-        "production": {
-          "buildTarget": "cockpit-deep-agents-sandboxes-angular:build:production"
-        },
-        "development": {
-          "buildTarget": "cockpit-deep-agents-sandboxes-angular:build:development"
-        },
-        "cockpit": {
-          "buildTarget": "cockpit-deep-agents-sandboxes-angular:build:cockpit"
-        }
-      },
-      "defaultConfiguration": "development",
-      "options": {
-        "proxyConfig": "cockpit/deep-agents/sandboxes/angular/proxy.conf.mjs"
-      }
-    },
-    "smoke": {
-      "executor": "nx:run-commands",
-      "options": {
-        "command": "npx tsx -e \"import { deepAgentsSandboxesAngularModule } from './cockpit/deep-agents/sandboxes/angular/src/index.ts'; const mod = deepAgentsSandboxesAngularModule; if (mod.id !== 'deep-agents-sandboxes-angular') throw new Error('Unexpected id: ' + mod.id); if (mod.title !== 'Deep Agents Sandboxes (Angular)') throw new Error('Unexpected title: ' + mod.title); console.log(JSON.stringify({ id: mod.id, title: mod.title }));\""
-      }
-    },
-    "e2e": {
-      "executor": "@nx/playwright:playwright",
-      "options": {
-        "config": "cockpit/deep-agents/sandboxes/angular/e2e/playwright.config.ts"
-      }
-    }
-  },
-  "tags": [
-    "scope:cockpit-e2e",
-    "scope:cockpit-examples"
-  ]
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/prompts/sandboxes.md b/cockpit/deep-agents/sandboxes/angular/prompts/sandboxes.md
deleted file mode 100644
index 988b6bf80..000000000
--- a/cockpit/deep-agents/sandboxes/angular/prompts/sandboxes.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Deep Agents Sandboxes (Angular)
-
-This capability demonstrates a deep agent executing code and shell commands inside an isolated sandbox environment using the `@threadplane/chat` Angular component library. The `<chat-debug>` component surfaces every sandbox invocation — the code submitted, the execution environment, stdout/stderr, and exit codes — giving developers complete visibility into agent-driven code execution.
-
-Key components used: `<chat-debug>`. Sandbox execution events appear as trace nodes labelled with the runtime (Python, Node.js, shell, etc.), with expandable panels showing the exact code submitted and the full execution output, making it straightforward to reproduce and debug agent-generated code.
diff --git a/cockpit/deep-agents/sandboxes/angular/proxy.conf.mjs b/cockpit/deep-agents/sandboxes/angular/proxy.conf.mjs
deleted file mode 100644
index 5ad229af3..000000000
--- a/cockpit/deep-agents/sandboxes/angular/proxy.conf.mjs
+++ /dev/null
@@ -1,14 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { portsFor } from '../../../../cockpit/ports.mjs';
-
-const { langgraph } = portsFor('cockpit-deep-agents-sandboxes-angular');
-
-export default {
-  "/api": {
-    target: `http://localhost:${langgraph}`,
-    secure: false,
-    changeOrigin: true,
-    pathRewrite: {"^/api":""},
-    ws: true,
-  },
-};
diff --git a/cockpit/deep-agents/sandboxes/angular/src/app/app.config.ts b/cockpit/deep-agents/sandboxes/angular/src/app/app.config.ts
deleted file mode 100644
index c20520be0..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/app/app.config.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
-import { environment } from '../environments/environment';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: environment.langGraphApiUrl,
-      assistantId: environment.streamingAssistantId,
-    }),
-    provideChat({}),
-    provideRender({}),
-  ],
-};
diff --git a/cockpit/deep-agents/sandboxes/angular/src/app/sandboxes.component.ts b/cockpit/deep-agents/sandboxes/angular/src/app/sandboxes.component.ts
deleted file mode 100644
index e497c8b21..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/app/sandboxes.component.ts
+++ /dev/null
@@ -1,195 +0,0 @@
-import { Component, computed } from '@angular/core';
-import { ChatComponent, views } from '@threadplane/chat';
-import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
-import { injectAgent } from '@threadplane/langgraph';
-import { signalStateStore } from '@threadplane/render';
-import { CodeExecutionComponent } from './views/code-execution.component';
-
-/**
- * Represents a parsed code execution: the code that was run and its output.
- */
-interface CodeExecution {
-  id: string;
-  code: string;
-  stdout: string;
-  stderr: string;
-  exitStatus: number;
-}
-
-/**
- * SandboxesComponent demonstrates a coding agent that executes Python code.
- *
- * The agent writes and runs code snippets to solve problems using a
- * `run_code` tool. The sidebar displays a real-time log of code executions
- * derived from `stream.messages()`, showing the code, stdout, and stderr
- * for each invocation.
- */
-@Component({
-  selector: 'app-sandboxes',
-  standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
-  template: `
-    <example-chat-layout sidebarWidth="20rem">
-      <chat main [agent]="agent" [views]="ui" [store]="uiStore" class="flex-1 min-w-0" />
-      <div sidebar class="panel">
-        <h3 class="cap">Execution Output</h3>
-        @if (executions().length === 0) {
-          <p class="empty">No code executed yet</p>
-        }
-        @for (exec of executions(); track exec.id) {
-          <div class="exec-card">
-            <div class="exec-label">Code</div>
-            <pre class="code">{{ exec.code }}</pre>
-            @if (exec.stdout) {
-              <div class="exec-label exec-label--success">stdout</div>
-              <pre class="code code--success">{{ exec.stdout }}</pre>
-            }
-            @if (exec.stderr) {
-              <div class="exec-label exec-label--error">stderr</div>
-              <pre class="code code--error">{{ exec.stderr }}</pre>
-            }
-          </div>
-        }
-      </div>
-    </example-chat-layout>
-  `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.75rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
-
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .exec-card {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 0.75rem;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-    }
-
-    .exec-label {
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-    }
-
-    .exec-label--success {
-      color: var(--tplane-chat-success);
-    }
-
-    .exec-label--error {
-      color: var(--tplane-chat-error-text);
-    }
-
-    .code {
-      margin: 0;
-      padding: 0.5rem;
-      overflow-x: auto;
-      white-space: pre-wrap;
-      overflow-wrap: anywhere;
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface);
-      color: var(--tplane-chat-text);
-      font: var(--tplane-chat-font-size-xs) / 1.5 var(--tplane-chat-font-mono);
-    }
-
-    .code--success {
-      color: var(--tplane-chat-success);
-    }
-
-    .code--error {
-      color: var(--tplane-chat-error-text);
-    }
-  `],
-})
-export class SandboxesComponent {
-  readonly ui = views({ 'code-execution': CodeExecutionComponent });
-  readonly uiStore = signalStateStore({});
-
-  protected readonly agent = injectAgent();
-
-  /**
-   * Derived signal: extracts code executions from the message stream.
-   *
-   * Scans AI messages for tool_calls with name `run_code`, then pairs each
-   * with its corresponding tool result message. Tool results are parsed as
-   * JSON with {stdout, stderr, exit_status} fields.
-   */
-  protected readonly executions = computed<CodeExecution[]>(() => {
-    const msgs = this.agent.langGraphMessages();
-    const resultMap = new Map<string, { stdout: string; stderr: string; exitStatus: number }>();
-
-    // Build a lookup of tool_call_id -> parsed result from tool messages
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function'
-        ? msg._getType()
-        : (msg as unknown as Record<string, string>)['type'] ?? '';
-      if (type === 'tool') {
-        const toolCallId = (msg as unknown as Record<string, string>)['tool_call_id'];
-        if (toolCallId) {
-          const raw = typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content);
-          try {
-            const parsed = JSON.parse(raw);
-            resultMap.set(toolCallId, {
-              stdout: parsed.stdout ?? '',
-              stderr: parsed.stderr ?? '',
-              exitStatus: parsed.exit_status ?? 0,
-            });
-          } catch {
-            resultMap.set(toolCallId, { stdout: raw, stderr: '', exitStatus: 0 });
-          }
-        }
-      }
-    }
-
-    // Extract run_code tool_calls from AI messages
-    const executions: CodeExecution[] = [];
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function'
-        ? msg._getType()
-        : (msg as unknown as Record<string, string>)['type'] ?? '';
-      if (type === 'ai') {
-        const toolCalls = (msg as unknown as Record<string, unknown[]>)['tool_calls'];
-        if (Array.isArray(toolCalls)) {
-          for (const tc of toolCalls) {
-            const call = tc as { id: string; name: string; args: Record<string, unknown> };
-            if (call.name === 'run_code') {
-              const result = resultMap.get(call.id);
-              executions.push({
-                id: call.id,
-                code: (call.args['code'] as string) ?? '',
-                stdout: result?.stdout ?? '',
-                stderr: result?.stderr ?? '',
-                exitStatus: result?.exitStatus ?? 0,
-              });
-            }
-          }
-        }
-      }
-    }
-
-    return executions;
-  });
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/src/app/views/code-execution.component.ts b/cockpit/deep-agents/sandboxes/angular/src/app/views/code-execution.component.ts
deleted file mode 100644
index 7ce120c48..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/app/views/code-execution.component.ts
+++ /dev/null
@@ -1,97 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'code-execution',
-  standalone: true,
-  template: `
-    <div class="exec-view">
-      <div class="exec-view__head">
-        Code Execution
-      </div>
-      <pre class="exec-view__body code">{{ code() }}</pre>
-      @if (stdout()) {
-        <div class="exec-view__section">
-          <div class="exec-view__label exec-view__label--success">stdout</div>
-          <pre class="code code--success">{{ stdout() }}</pre>
-        </div>
-      }
-      @if (stderr()) {
-        <div class="exec-view__section">
-          <div class="exec-view__label exec-view__label--error">stderr</div>
-          <pre class="code code--error">{{ stderr() }}</pre>
-        </div>
-      }
-    </div>
-  `,
-  styles: [`
-    .exec-view {
-      margin: 0.5rem 0;
-      overflow: hidden;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-    }
-
-    .exec-view__head {
-      padding: 0.5rem 1rem;
-      border-bottom: 1px solid var(--tplane-chat-separator);
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-    }
-
-    .exec-view__body {
-      padding: 1rem;
-      background: var(--tplane-chat-surface-alt);
-      color: var(--tplane-chat-text);
-    }
-
-    .exec-view__section {
-      padding: 0.5rem 1rem;
-      border-top: 1px solid var(--tplane-chat-separator);
-    }
-
-    .exec-view__label {
-      margin-bottom: 0.25rem;
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-    }
-
-    .exec-view__label--success {
-      color: var(--tplane-chat-success);
-    }
-
-    .exec-view__label--error {
-      color: var(--tplane-chat-error-text);
-    }
-
-    .code {
-      margin: 0;
-      padding: 0.5rem;
-      overflow-x: auto;
-      white-space: pre-wrap;
-      overflow-wrap: anywhere;
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface);
-      color: var(--tplane-chat-text);
-      font: var(--tplane-chat-font-size-xs) / 1.5 var(--tplane-chat-font-mono);
-    }
-
-    .code--success {
-      color: var(--tplane-chat-success);
-    }
-
-    .code--error {
-      color: var(--tplane-chat-error-text);
-    }
-  `],
-})
-export class CodeExecutionComponent {
-  readonly code = input<string>('');
-  readonly stdout = input<string>('');
-  readonly stderr = input<string>('');
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts b/cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts
deleted file mode 100644
index 9e402f5dd..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/environments/environment.development.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-/**
- * Development environment configuration.
- *
- * Points to a local LangGraph server started with:
- *   cd cockpit/deep-agents/sandboxes/python && langgraph dev
- */
-export const environment = {
-  production: false,
-  langGraphApiUrl: 'http://localhost:4315/api',
-  streamingAssistantId: 'da-sandboxes',
-};
diff --git a/cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts b/cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts
deleted file mode 100644
index 002cdb18f..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/environments/environment.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-/**
- * Production environment configuration.
- *
- * Uses relative /api URL — Vercel middleware proxies to LangGraph Cloud
- * and injects the x-api-key header server-side.
- */
-export const environment = {
-  production: true,
-  langGraphApiUrl: '/api',
-  streamingAssistantId: 'da-sandboxes',
-};
diff --git a/cockpit/deep-agents/sandboxes/angular/src/index.html b/cockpit/deep-agents/sandboxes/angular/src/index.html
deleted file mode 100644
index 2cbbe4ec1..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/index.html
+++ /dev/null
@@ -1,12 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-  <meta charset="utf-8" />
-  <title>Sandboxes — Deep Agents Angular</title>
-  <base href="/" />
-  <meta name="viewport" content="width=device-width, initial-scale=1" />
-</head>
-<body>
-  <app-sandboxes></app-sandboxes>
-</body>
-</html>
diff --git a/cockpit/deep-agents/sandboxes/angular/src/index.ts b/cockpit/deep-agents/sandboxes/angular/src/index.ts
deleted file mode 100644
index 776699753..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/index.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-export interface CockpitCapabilityModule {
-  id: string;
-  manifestIdentity: {
-    product: 'deep-agents';
-    section: 'core-capabilities';
-    topic: 'sandboxes';
-    page: 'overview';
-    language: 'angular';
-  };
-  title: string;
-  docsPath: string;
-  promptAssetPaths: string[];
-  codeAssetPaths: string[];
-}
-
-export const deepAgentsSandboxesAngularModule: CockpitCapabilityModule = {
-  id: 'deep-agents-sandboxes-angular',
-  manifestIdentity: {
-    product: 'deep-agents',
-    section: 'core-capabilities',
-    topic: 'sandboxes',
-    page: 'overview',
-    language: 'angular',
-  },
-  title: 'Deep Agents Sandboxes (Angular)',
-  // No `deep-agents` library exists on the website yet; the empty string is
-  // the "no published docs page" sentinel and renders no Docs link.
-  docsPath: '',
-  promptAssetPaths: [
-    'cockpit/deep-agents/sandboxes/angular/prompts/sandboxes.md',
-  ],
-  codeAssetPaths: [
-    'cockpit/deep-agents/sandboxes/angular/src/app.component.ts',
-  ],
-};
diff --git a/cockpit/deep-agents/sandboxes/angular/src/main.cockpit.ts b/cockpit/deep-agents/sandboxes/angular/src/main.cockpit.ts
deleted file mode 100644
index f6d17ef94..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/main.cockpit.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { appConfig } from './app/app.config';
-import { SandboxesComponent } from './app/sandboxes.component';
-import { bootstrapWithCockpitHarness } from '@threadplane/cockpit-telemetry';
-
-bootstrapWithCockpitHarness(SandboxesComponent, appConfig);
diff --git a/cockpit/deep-agents/sandboxes/angular/src/main.ts b/cockpit/deep-agents/sandboxes/angular/src/main.ts
deleted file mode 100644
index cce3571c2..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/main.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { bootstrapWithCockpitHarness } from '@threadplane/cockpit-telemetry';
-import { appConfig } from './app/app.config';
-import { SandboxesComponent } from './app/sandboxes.component';
-
-void bootstrapWithCockpitHarness(SandboxesComponent, appConfig).catch(console.error);
diff --git a/cockpit/deep-agents/sandboxes/angular/src/styles.css b/cockpit/deep-agents/sandboxes/angular/src/styles.css
deleted file mode 100644
index e536b44a9..000000000
--- a/cockpit/deep-agents/sandboxes/angular/src/styles.css
+++ /dev/null
@@ -1,9 +0,0 @@
-@import "@threadplane/example-layouts/theme.css";
-
-html, body {
-  height: 100%;
-  margin: 0;
-  background: var(--tplane-chat-bg);
-  color: var(--tplane-chat-text);
-  font-family: var(--tplane-chat-font-family);
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/tsconfig.app.json b/cockpit/deep-agents/sandboxes/angular/tsconfig.app.json
deleted file mode 100644
index ef34262b1..000000000
--- a/cockpit/deep-agents/sandboxes/angular/tsconfig.app.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "outDir": "../../../../dist/out-tsc",
-    "lib": ["es2022", "dom"],
-    "types": [],
-    "emitDeclarationOnly": false
-  },
-  "files": ["src/main.ts"],
-  "include": ["src/**/*.ts"],
-  "references": [
-    {
-      "path": "../../../../libs/cockpit-telemetry"
-    },
-    {
-      "path": "../../../../libs/example-layouts"
-    },
-    {
-      "path": "../../../../libs/render"
-    },
-    {
-      "path": "../../../../libs/chat"
-    },
-    {
-      "path": "../../../../libs/langgraph"
-    }
-  ]
-}
diff --git a/cockpit/deep-agents/sandboxes/angular/tsconfig.json b/cockpit/deep-agents/sandboxes/angular/tsconfig.json
deleted file mode 100644
index dedbb5609..000000000
--- a/cockpit/deep-agents/sandboxes/angular/tsconfig.json
+++ /dev/null
@@ -1,23 +0,0 @@
-{
-  "extends": "../../../../tsconfig.base.json",
-  "compilerOptions": {
-    "noPropertyAccessFromIndexSignature": false,
-    "experimentalDecorators": true,
-    "module": "preserve",
-    "emitDeclarationOnly": false,
-    "composite": false,
-    "lib": ["es2022", "dom"],
-    "skipLibCheck": true,
-    "strict": false
-  },
-  "angularCompilerOptions": {
-    "enableI18nLegacyMessageIdFormat": false,
-    "strictInjectionParameters": false,
-    "strictInputAccessModifiers": false
-  },
-  "files": [],
-  "include": [],
-  "references": [
-    { "path": "./tsconfig.app.json" }
-  ]
-}
diff --git a/cockpit/deep-agents/sandboxes/python/.gitignore b/cockpit/deep-agents/sandboxes/python/.gitignore
deleted file mode 100644
index 1a72538cd..000000000
--- a/cockpit/deep-agents/sandboxes/python/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-.langgraph_api/
-__pycache__/
-.venv/
diff --git a/cockpit/deep-agents/sandboxes/python/docs/guide.md b/cockpit/deep-agents/sandboxes/python/docs/guide.md
deleted file mode 100644
index 065287009..000000000
--- a/cockpit/deep-agents/sandboxes/python/docs/guide.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# Code Execution Sandbox with Angular
-
-<Summary>
-Build a chat interface that shows real-time code execution logs using `provideAgent()` and
-`injectAgent()` from `@threadplane/langgraph`. The agent writes Python code and runs it in a sandbox, and the
-sidebar displays each execution as a log entry with code input, stdout output, and exit status.
-</Summary>
-
-<Prompt>
-Add a code execution log sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.messages()` to access tool call data from the `run_code` tool, derive `executionLogs` with `computed()`, and bind them to the sidebar beside the `<chat>` component from `@threadplane/chat`.
-</Prompt>
-
-<Steps>
-<Step title="Configure the provider">
-
-Set up `provideAgent()` in your app config with the LangGraph API URL:
-
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'da-sandboxes',
-    }),
-  ],
-};
-```
-
-This makes the configured agent available to all `injectAgent()` calls in your app.
-
-</Step>
-<Step title="Create the streaming resource">
-
-In your component, call `injectAgent()` to retrieve the configured sandboxes agent:
-
-```typescript
-// sandboxes.component.ts
-import { injectAgent } from '@threadplane/langgraph';
-
-export class SandboxesComponent {
-  protected readonly stream = injectAgent();
-}
-```
-
-The resource manages the connection, message history, loading state, and errors automatically.
-
-</Step>
-<Step title="Derive execution logs with computed()">
-
-Use Angular's `computed()` to extract execution log entries from `run_code` tool calls in `stream.messages()`:
-
-```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
-
-interface ExecutionLog {
-  code: string;
-  stdout: string;
-  exitStatus: number;
-}
-
-export class SandboxesComponent {
-  protected readonly stream = injectAgent();
-
-  executionLogs = computed(() => {
-    const msgs = this.stream.messages();
-    const logs: ExecutionLog[] = [];
-    for (const m of msgs) {
-      if ((m as any).tool_calls) {
-        for (const tc of (m as any).tool_calls) {
-          if (tc.name === 'run_code' && tc.output) {
-            const parsed = JSON.parse(tc.output);
-            logs.push({
-              code: tc.args?.code ?? '',
-              stdout: parsed.stdout ?? '',
-              exitStatus: parsed.exit_status ?? 0,
-            });
-          }
-        }
-      }
-    }
-    return logs;
-  });
-}
-```
-
-Each `run_code` call becomes a log entry. The `output` field contains a JSON-encoded result with `stdout`, `stderr`, and `exit_status`.
-
-<Tip>
-The tool result (`tc.output`) is populated after the tool finishes executing. Before that, the entry has no result yet — useful for showing a "running" state.
-</Tip>
-
-</Step>
-<Step title="Build the template with execution log sidebar">
-
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
-
-```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>Execution Logs</h3>
-    @for (log of executionLogs(); track $index) {
-      <div>
-        <span [style.background]="log.exitStatus === 0 ? '#d1fae5' : '#fee2e2'">
-          exit {{ log.exitStatus }}
-        </span>
-        <pre>{{ log.code }}</pre>
-        @if (log.stdout) {
-          <div>stdout</div>
-          <pre>{{ log.stdout }}</pre>
-        }
-      </div>
-    }
-    @empty {
-      <p>Ask the agent to write and run Python code.</p>
-    }
-</aside>
-```
-
-Each log entry shows the code that was executed, the exit status badge, and the stdout output.
-
-</Step>
-<Step title="The LangGraph sandboxes backend">
-
-The backend defines a `run_code` tool that simulates Python execution and uses a tool-calling loop:
-
-```python
-# graph.py
-import json
-from langchain_core.tools import tool
-from langgraph.prebuilt import ToolNode
-
-@tool
-def run_code(code: str) -> str:
-    """Execute a Python code snippet in a sandbox.
-
-    Returns a JSON object with stdout, stderr, and exit_status.
-    """
-    # In demo mode, simulates print() calls and checks syntax
-    stdout = simulate_execution(code)
-    return json.dumps({
-        "stdout": stdout,
-        "stderr": "",
-        "exit_status": 0,
-    })
-
-llm = ChatOpenAI(model="gpt-5-mini").bind_tools([run_code])
-tool_node = ToolNode([run_code])
-```
-
-The agent writes code, calls `run_code`, receives the JSON result, and uses the output to formulate its answer. The frontend parses the JSON to populate the execution log sidebar.
-
-<Tip>
-In production, replace the simulated execution with a real sandboxed runner (e.g., a Docker container, AWS Lambda, or E2B). The frontend component doesn't change — only the tool implementation.
-</Tip>
-
-</Step>
-</Steps>
-
-<Tip>
-The `@empty` block in `@for` renders when no code has been executed yet — a clean placeholder before the user submits their first coding request.
-</Tip>
diff --git a/cockpit/deep-agents/sandboxes/python/langgraph.json b/cockpit/deep-agents/sandboxes/python/langgraph.json
deleted file mode 100644
index 024b3b92f..000000000
--- a/cockpit/deep-agents/sandboxes/python/langgraph.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-  "graphs": {
-    "da-sandboxes": "./src/graph.py:graph"
-  },
-  "dependencies": [
-    "."
-  ],
-  "python_version": "3.12",
-  "env": ".env"
-}
diff --git a/cockpit/deep-agents/sandboxes/python/package.json b/cockpit/deep-agents/sandboxes/python/package.json
deleted file mode 100644
index 42f8e2995..000000000
--- a/cockpit/deep-agents/sandboxes/python/package.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "name": "@threadplane/cockpit-deep-agents-sandboxes-python",
-  "private": true,
-  "version": "0.0.1",
-  "license": "MIT",
-  "sideEffects": false
-}
diff --git a/cockpit/deep-agents/sandboxes/python/project.json b/cockpit/deep-agents/sandboxes/python/project.json
deleted file mode 100644
index 19c44de65..000000000
--- a/cockpit/deep-agents/sandboxes/python/project.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
-  "name": "cockpit-deep-agents-sandboxes-python",
-  "$schema": "../../../../../../node_modules/nx/schemas/project-schema.json",
-  "sourceRoot": "cockpit/deep-agents/sandboxes/python/src",
-  "projectType": "library",
-  "targets": {
-    "build": {
-      "executor": "@nx/js:tsc",
-      "outputs": [
-        "{workspaceRoot}/dist/cockpit/deep-agents/sandboxes/python"
-      ],
-      "options": {
-        "outputPath": "dist/cockpit/deep-agents/sandboxes/python",
-        "main": "cockpit/deep-agents/sandboxes/python/src/index.ts",
-        "tsConfig": "cockpit/deep-agents/sandboxes/python/tsconfig.json"
-      }
-    },
-    "smoke": {
-      "executor": "nx:run-commands",
-      "options": {
-        "command": "npx tsx -e \"import { deepAgentsSandboxesPythonModule } from './cockpit/deep-agents/sandboxes/python/src/index.ts'; const mod = deepAgentsSandboxesPythonModule; if (mod.id !== 'deep-agents-sandboxes-python') throw new Error('Unexpected id: ' + mod.id); if (mod.title !== 'Deep Agents Sandboxes (Python)') throw new Error('Unexpected title: ' + mod.title); console.log(JSON.stringify({ id: mod.id, title: mod.title }));\""
-      }
-    }
-  },
-  "tags": [
-    "scope:cockpit-e2e",
-    "scope:cockpit-examples",
-    "scope:cockpit-smoke"
-  ]
-}
diff --git a/cockpit/deep-agents/sandboxes/python/prompts/sandboxes.md b/cockpit/deep-agents/sandboxes/python/prompts/sandboxes.md
deleted file mode 100644
index acd9cb8e4..000000000
--- a/cockpit/deep-agents/sandboxes/python/prompts/sandboxes.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Coding Agent
-
-You are a coding agent that writes and executes Python code to solve problems.
-
-When a user asks you to compute something, generate data, or solve a problem programmatically:
-1. Write a clear Python code snippet that solves the problem.
-2. Use the `run_code` tool to execute the code.
-3. Interpret the output and explain the result to the user.
-
-Write clean, readable code with comments where helpful. Keep snippets focused and minimal.
diff --git a/cockpit/deep-agents/sandboxes/python/pyproject.toml b/cockpit/deep-agents/sandboxes/python/pyproject.toml
deleted file mode 100644
index 50c36a0bf..000000000
--- a/cockpit/deep-agents/sandboxes/python/pyproject.toml
+++ /dev/null
@@ -1,21 +0,0 @@
-[project]
-name = "cockpit-deep-agents-sandboxes"
-version = "0.1.0"
-requires-python = ">=3.12"
-dependencies = [
-    "langgraph>=0.3",
-    "langchain-openai>=0.3",
-    "langsmith>=0.2",
-]
-
-[tool.uv]
-dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
-]
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.hatch.build.targets.wheel]
-packages = ["src"]
diff --git a/cockpit/deep-agents/sandboxes/python/src/graph.py b/cockpit/deep-agents/sandboxes/python/src/graph.py
deleted file mode 100644
index 52ad0b902..000000000
--- a/cockpit/deep-agents/sandboxes/python/src/graph.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""
-Deep Agents Sandboxes Graph
-
-Demonstrates a coding agent that writes and executes Python code to solve
-problems. The agent uses a `run_code` tool that simulates Python execution
-in demo mode, returning realistic stdout output and exit status. Tool calls
-are visible via stream.messages(), powering the execution log sidebar.
-"""
-
-import json
-import re
-from pathlib import Path
-from typing import Annotated
-import operator
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, BaseMessage, AIMessage
-from langchain_core.tools import tool
-
-PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
-
-
-class SandboxesState(dict):
-    messages: Annotated[list[BaseMessage], operator.add]
-
-
-@tool
-def run_code(code: str) -> str:
-    """Execute a Python code snippet in a sandbox and return the result.
-
-    In demo mode, simulates execution by parsing print() calls and simple
-    expressions. Returns a JSON object with stdout, stderr, and exit_status.
-
-    Args:
-        code: The Python code to execute.
-    """
-    stdout_lines = []
-    exit_status = 0
-
-    # Simulate print() calls
-    print_pattern = re.compile(r'print\(([^)]+)\)')
-    for match in print_pattern.finditer(code):
-        arg = match.group(1).strip()
-        # Evaluate simple string literals and f-strings in a limited context
-        try:
-            value = eval(arg, {"__builtins__": {"range": range, "len": len, "str": str, "int": int, "float": float}})  # noqa: S307
-            stdout_lines.append(str(value))
-        except Exception:
-            # Fallback: strip quotes from string literals
-            cleaned = arg.strip("'\"")
-            stdout_lines.append(cleaned)
-
-    # If no print calls, try to detect syntax errors
-    try:
-        compile(code, "<sandbox>", "exec")
-    except SyntaxError as e:
-        return json.dumps({
-            "stdout": "",
-            "stderr": f"SyntaxError: {e}",
-            "exit_status": 1,
-        })
-
-    stdout = "\n".join(stdout_lines) if stdout_lines else "(no output)"
-    return json.dumps({
-        "stdout": stdout,
-        "stderr": "",
-        "exit_status": exit_status,
-    })
-
-
-def build_sandboxes_graph():
-    tools = [run_code]
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True).bind_tools(tools)
-    tool_node = ToolNode(tools)
-
-    async def agent(state: SandboxesState) -> dict:
-        """Write and run code to solve the user's problem."""
-        system_prompt = (PROMPTS_DIR / "sandboxes.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    def should_continue(state: SandboxesState) -> str:
-        last = state["messages"][-1]
-        if isinstance(last, AIMessage) and last.tool_calls:
-            return "tools"
-        return END
-
-    graph = StateGraph(SandboxesState)
-    graph.add_node("agent", agent)
-    graph.add_node("tools", tool_node)
-    graph.set_entry_point("agent")
-    graph.add_conditional_edges("agent", should_continue, {
-        "tools": "tools",
-        END: END,
-    })
-    graph.add_edge("tools", "agent")
-    return graph.compile()
-
-
-graph = build_sandboxes_graph()
diff --git a/cockpit/deep-agents/sandboxes/python/src/index.ts b/cockpit/deep-agents/sandboxes/python/src/index.ts
deleted file mode 100644
index 1fe53f0e9..000000000
--- a/cockpit/deep-agents/sandboxes/python/src/index.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-export interface CockpitCapabilityModule {
-  id: string;
-  manifestIdentity: {
-    product: 'deep-agents';
-    section: 'core-capabilities';
-    topic: 'sandboxes';
-    page: 'overview';
-    language: 'python';
-  };
-  title: string;
-  docsPath: string;
-  promptAssetPaths: string[];
-  codeAssetPaths: string[];
-  backendAssetPaths: string[];
-  docsAssetPaths: string[];
-  runtimeUrl?: string;
-  devPort?: number;
-}
-
-export const deepAgentsSandboxesPythonModule: CockpitCapabilityModule = {
-  id: 'deep-agents-sandboxes-python',
-  manifestIdentity: {
-    product: 'deep-agents',
-    section: 'core-capabilities',
-    topic: 'sandboxes',
-    page: 'overview',
-    language: 'python',
-  },
-  title: 'Deep Agents Sandboxes (Python)',
-  // No `deep-agents` library exists on the website yet; the empty string is
-  // the "no published docs page" sentinel and renders no Docs link.
-  docsPath: '',
-  promptAssetPaths: ['cockpit/deep-agents/sandboxes/python/prompts/sandboxes.md'],
-  codeAssetPaths: [
-    'cockpit/deep-agents/sandboxes/angular/src/app/sandboxes.component.ts',
-    'cockpit/deep-agents/sandboxes/angular/src/app/app.config.ts',
-  ],
-  backendAssetPaths: [
-    'cockpit/deep-agents/sandboxes/python/src/graph.py',
-  ],
-  docsAssetPaths: ['cockpit/deep-agents/sandboxes/python/docs/guide.md'],
-  runtimeUrl: 'deep-agents/sandboxes',
-  devPort: 4315,
-};
diff --git a/cockpit/deep-agents/sandboxes/python/tsconfig.json b/cockpit/deep-agents/sandboxes/python/tsconfig.json
deleted file mode 100644
index b3f92951b..000000000
--- a/cockpit/deep-agents/sandboxes/python/tsconfig.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "extends": "../../../../tsconfig.base.json",
-  "compilerOptions": {
-    "baseUrl": "../../../../../../",
-    "jsx": "react-jsx",
-    "outDir": "../../../../../../dist/out-tsc",
-    "declaration": true,
-    "emitDeclarationOnly": false,
-    "types": ["node", "react"]
-  },
-  "include": ["src/**/*.ts"],
-  "exclude": ["src/**/*.spec.ts"]
-}
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deleted file mode 100644
index 04ce62901..000000000
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-]
diff --git a/cockpit/ports.mjs b/cockpit/ports.mjs
index cf9a9e055..a95109a41 100644
--- a/cockpit/ports.mjs
+++ b/cockpit/ports.mjs
@@ -51,7 +51,6 @@ export const PORTS = Object.freeze({
   'cockpit-deep-agents-filesystem-angular': { angular: 4311, langgraph: 5311 },
   'cockpit-deep-agents-memory-angular': { angular: 4313, langgraph: 5313 },
   'cockpit-deep-agents-planning-angular': { angular: 4310, langgraph: 5310 },
-  'cockpit-deep-agents-sandboxes-angular': { angular: 4315, langgraph: 5315 },
   'cockpit-deep-agents-skills-angular': { angular: 4314, langgraph: 5314 },
   'cockpit-deep-agents-subagents-angular': { angular: 4312, langgraph: 5312 },
   'cockpit-langgraph-client-tools-angular': { angular: 4308, langgraph: 5308 },
diff --git a/deployment-urls.json b/deployment-urls.json
index 59105bca8..f3bc07241 100644
--- a/deployment-urls.json
+++ b/deployment-urls.json
@@ -12,6 +12,5 @@
   "subagents": "https://cockpit-dev-219a15942c545a00a03a9a41905d7fc2.us.langgraph.app",
   "da-memory": "https://cockpit-dev-219a15942c545a00a03a9a41905d7fc2.us.langgraph.app",
   "skills": "https://cockpit-dev-219a15942c545a00a03a9a41905d7fc2.us.langgraph.app",
-  "sandboxes": "https://cockpit-dev-219a15942c545a00a03a9a41905d7fc2.us.langgraph.app",
   "chat": "https://cockpit-dev-219a15942c545a00a03a9a41905d7fc2.us.langgraph.app"
 }
diff --git a/deployments/shared-dev/langgraph.json b/deployments/shared-dev/langgraph.json
index 1fe9ac64c..b155966d0 100644
--- a/deployments/shared-dev/langgraph.json
+++ b/deployments/shared-dev/langgraph.json
@@ -14,7 +14,6 @@
     "subagents": "./deps/da-subagents/src/graph.py:graph",
     "da-memory": "./deps/da-memory/src/graph.py:graph",
     "da-skills": "./deps/da-skills/src/graph.py:graph",
-    "da-sandboxes": "./deps/da-sandboxes/src/graph.py:graph",
     "r-spec-rendering": "./deps/r-spec-rendering/src/graph.py:graph",
     "r-element-rendering": "./deps/r-element-rendering/src/graph.py:graph",
     "r-state-management": "./deps/r-state-management/src/graph.py:graph",
@@ -49,7 +48,6 @@
     "./deps/da-filesystem",
     "./deps/da-memory",
     "./deps/da-planning",
-    "./deps/da-sandboxes",
     "./deps/da-skills",
     "./deps/da-subagents",
     "./deps/deployment-runtime",
diff --git a/libs/cockpit-docs/src/lib/docs-bundle.spec.ts b/libs/cockpit-docs/src/lib/docs-bundle.spec.ts
index 2551b3d6f..32b9b5319 100644
--- a/libs/cockpit-docs/src/lib/docs-bundle.spec.ts
+++ b/libs/cockpit-docs/src/lib/docs-bundle.spec.ts
@@ -74,7 +74,6 @@ describe('docs bundle matrix coverage', () => {
       ['deep-agents', 'core-capabilities', 'subagents'],
       ['deep-agents', 'core-capabilities', 'memory'],
       ['deep-agents', 'core-capabilities', 'skills'],
-      ['deep-agents', 'core-capabilities', 'sandboxes'],
       ['langgraph', 'getting-started', 'overview'],
       ['langgraph', 'core-capabilities', 'persistence'],
       ['langgraph', 'core-capabilities', 'durable-execution'],
diff --git a/libs/cockpit-registry/src/lib/docs-links.ts b/libs/cockpit-registry/src/lib/docs-links.ts
index 3002bf2fa..7e8054cd1 100644
--- a/libs/cockpit-registry/src/lib/docs-links.ts
+++ b/libs/cockpit-registry/src/lib/docs-links.ts
@@ -41,7 +41,6 @@ export const COCKPIT_DOCS_LINKS: Readonly<Record<string, string>> = {
   'deep-agents/core-capabilities/subagents': NO_COCKPIT_DOCS_LINK,
   'deep-agents/core-capabilities/memory': NO_COCKPIT_DOCS_LINK,
   'deep-agents/core-capabilities/skills': NO_COCKPIT_DOCS_LINK,
-  'deep-agents/core-capabilities/sandboxes': NO_COCKPIT_DOCS_LINK,
 
   // langgraph
   'langgraph/getting-started/overview': '/docs/langgraph/getting-started/introduction',
@@ -116,7 +115,6 @@ export const COCKPIT_TOPICS_WITHOUT_DOCS: readonly string[] = [
   'deep-agents/core-capabilities/subagents',
   'deep-agents/core-capabilities/memory',
   'deep-agents/core-capabilities/skills',
-  'deep-agents/core-capabilities/sandboxes',
 ];
 
 /**
diff --git a/libs/cockpit-registry/src/lib/manifest.spec.ts b/libs/cockpit-registry/src/lib/manifest.spec.ts
index 9fc9584f3..7cdcb870d 100644
--- a/libs/cockpit-registry/src/lib/manifest.spec.ts
+++ b/libs/cockpit-registry/src/lib/manifest.spec.ts
@@ -12,7 +12,6 @@ const expectedTopics = {
     ['core-capabilities', 'subagents'],
     ['core-capabilities', 'memory'],
     ['core-capabilities', 'skills'],
-    ['core-capabilities', 'sandboxes'],
   ],
   langgraph: [
     ['getting-started', 'overview'],
@@ -107,7 +106,7 @@ describe('cockpitManifest', () => {
       (entry) => entry.entryKind === 'capability'
     );
 
-    expect(capabilityEntries).toHaveLength(42);
+    expect(capabilityEntries).toHaveLength(41);
 
     for (const entry of capabilityEntries) {
       expect(entry.supportedLanguages).toEqual(['python']);
diff --git a/libs/cockpit-registry/src/lib/manifest.ts b/libs/cockpit-registry/src/lib/manifest.ts
index ade482e78..6fc425fad 100644
--- a/libs/cockpit-registry/src/lib/manifest.ts
+++ b/libs/cockpit-registry/src/lib/manifest.ts
@@ -15,7 +15,6 @@ const APPROVED_TOPICS = {
       'subagents',
       'memory',
       'skills',
-      'sandboxes',
     ],
   },
   langgraph: {
diff --git a/scripts/cockpit-runtime-bridge-coverage.spec.mjs b/scripts/cockpit-runtime-bridge-coverage.spec.mjs
index 111da36fd..ad207b66d 100644
--- a/scripts/cockpit-runtime-bridge-coverage.spec.mjs
+++ b/scripts/cockpit-runtime-bridge-coverage.spec.mjs
@@ -316,13 +316,13 @@ describe('entryPointProblems', () => {
 });
 
 describe('Cockpit Angular runtime bridge coverage', () => {
-  test('the capability registry contains exactly 42 unique Angular projects', async () => {
+  test('the capability registry contains exactly 41 unique Angular projects', async () => {
     const projects = await registryAngularProjects();
 
-    assert.equal(projects.length, 42, 'expected exactly 42 registry Angular projects');
+    assert.equal(projects.length, 41, 'expected exactly 41 registry Angular projects');
     assert.equal(
       new Set(projects).size,
-      42,
+      41,
       'expected every registry Angular project to be unique',
     );
   });
diff --git a/scripts/examples-middleware.ts b/scripts/examples-middleware.ts
index f6e871370..d043d2b8c 100644
--- a/scripts/examples-middleware.ts
+++ b/scripts/examples-middleware.ts
@@ -34,7 +34,6 @@ const ACTIVE_PRODUCT_PATHS = new Set([
   'deep-agents/subagents',
   'deep-agents/memory',
   'deep-agents/skills',
-  'deep-agents/sandboxes',
   'chat/messages',
   'chat/input',
   'chat/interrupts',
diff --git a/scripts/update-angular-environments.ts b/scripts/update-angular-environments.ts
index cfe9ace89..c0b728058 100644
--- a/scripts/update-angular-environments.ts
+++ b/scripts/update-angular-environments.ts
@@ -28,7 +28,6 @@ const capabilities = [
   { dir: 'cockpit/deep-agents/subagents/angular', assistantId: 'subagents', field: 'streamingAssistantId' },
   { dir: 'cockpit/deep-agents/memory/angular', assistantId: 'da-memory', field: 'streamingAssistantId' },
   { dir: 'cockpit/deep-agents/skills/angular', assistantId: 'da-skills', field: 'streamingAssistantId' },
-  { dir: 'cockpit/deep-agents/sandboxes/angular', assistantId: 'da-sandboxes', field: 'streamingAssistantId' },
   { dir: 'cockpit/chat/messages/angular', assistantId: 'c-messages', urlKey: 'streaming', field: 'streamingAssistantId' },
   { dir: 'cockpit/chat/input/angular', assistantId: 'c-input', urlKey: 'streaming', field: 'streamingAssistantId' },
   { dir: 'cockpit/chat/interrupts/angular', assistantId: 'c-interrupts', urlKey: 'streaming', field: 'streamingAssistantId' },
diff --git a/tsconfig.json b/tsconfig.json
index 6a492b483..8ba07e16a 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -15,9 +15,6 @@
     {
       "path": "./cockpit/deep-agents/filesystem/python"
     },
-    {
-      "path": "./cockpit/deep-agents/sandboxes/python"
-    },
     {
       "path": "./cockpit/deep-agents/subagents/python"
     },

From 7eba913ce17edd75a800b8b9b867984d9042e12d Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 11:35:30 -0700
Subject: [PATCH 2/7] feat(cockpit): rebuild the deep-agents planning topic on
 deepagents
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The old graph was a two-node StateGraph that asked the model for a JSON
array, stored it under a `plan` key, and then rewrote every entry to
`complete` in the second node. Nothing in it came from `deepagents`, and
the sidebar never showed a step in progress because no step ever was.

This is `create_deep_agent` with `TodoListMiddleware`. The model gets the
real `write_todos` tool and the graph gets a real `todos` key, so the panel
projects state the agent actually maintains: rows move pending ->
in_progress -> completed one call at a time, and the list can be revised
mid-run.

A todo in deepagents 0.7.11 is exactly `{content, status}` — no id, no
present-tense label — so the panel tracks rows by index and normalizes
unknown statuses to `pending`.

The system prompt does real work here. TodoListMiddleware supplies a tool,
not a policy: with the earlier wording the model fanned out six parallel
lookups and never wrote a todo at all. The prompt now requires `write_todos`
as the first action and one call per status transition.

Verified live against `langgraph dev` with a real key: the recorded run
produces four distinct todo snapshots advancing through the list. The
committed aimock fixture is that run, eight entries disambiguated by
turnIndex with the hasToolResult continuations ordered ahead of the opening
turn. The e2e asserts the differentiated surface — four rows, three
completed, one in progress, none left pending — and goes red when the
status binding is broken.

Also drops the unused `@threadplane/render` view registrations the old demo
carried and points the descriptors at the files that now exist.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../planning/angular/e2e/da-planning.spec.ts  |   31 +-
 .../angular/e2e/fixtures/da-planning.json     |  123 +-
 .../angular/e2e/manual/planning.manual.ts     |   21 +-
 .../planning/angular/prompts/planning.md      |    4 +-
 .../planning/angular/src/app/app.config.ts    |    2 -
 .../angular/src/app/planning.component.ts     |  280 +-
 .../src/app/views/checkbox-row.component.ts   |   50 -
 .../src/app/views/plan-checklist.component.ts |   31 -
 .../deep-agents/planning/angular/src/index.ts |    3 +-
 .../deep-agents/planning/python/docs/guide.md |  191 +-
 .../planning/python/prompts/planning.md       |   32 +-
 .../planning/python/pyproject.toml            |    5 +-
 .../deep-agents/planning/python/src/graph.py  |  142 +-
 cockpit/deep-agents/planning/python/uv.lock   | 2294 +++++++++++------
 14 files changed, 1976 insertions(+), 1233 deletions(-)
 delete mode 100644 cockpit/deep-agents/planning/angular/src/app/views/checkbox-row.component.ts
 delete mode 100644 cockpit/deep-agents/planning/angular/src/app/views/plan-checklist.component.ts

diff --git a/cockpit/deep-agents/planning/angular/e2e/da-planning.spec.ts b/cockpit/deep-agents/planning/angular/e2e/da-planning.spec.ts
index f58d1fc9a..d509b771b 100644
--- a/cockpit/deep-agents/planning/angular/e2e/da-planning.spec.ts
+++ b/cockpit/deep-agents/planning/angular/e2e/da-planning.spec.ts
@@ -2,7 +2,32 @@
 import { test, expect } from '@playwright/test';
 import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
 
-test('da-planning: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
+// Matches the welcome suggestion in planning.component.ts and the fixture's
+// `userMessage` match in e2e/fixtures/da-planning.json.
+const PROMPT =
+  'Plan a dispatch brief for a flight from KSFO to KASE: check field elevation, runway length, and weather at both ends, then tell me if there is anything the crew should know.';
+
+test('da-planning: the todo list renders and its rows move off pending', async ({ page }) => {
+  const bubble = await submitAndWaitForResponse(page, PROMPT);
+
+  // The panel is a pure projection of `state.todos`, which only exists because
+  // TodoListMiddleware is installed and the model called `write_todos`. Four
+  // rows is the plan the recorded run wrote.
+  const rows = page.locator('[data-testid="todo-row"]');
+  await expect(rows).toHaveCount(4);
+
+  // The differentiated assertion: statuses actually advanced. The recorded run
+  // finishes the first three steps and leaves the summary step in progress, so
+  // nothing is left at `pending` and the completed count is non-zero. A graph
+  // that emitted a static list, or a panel that ignored `status`, fails here.
+  await expect(page.locator('[data-testid="todo-row"][data-status="completed"]')).toHaveCount(3);
+  await expect(page.locator('[data-testid="todo-row"][data-status="in_progress"]')).toHaveCount(1);
+  await expect(page.locator('[data-testid="todo-row"][data-status="pending"]')).toHaveCount(0);
+  await expect(page.getByTestId('todo-progress')).toHaveText('3 of 4 complete');
+
+  // The plan text came from the agent, not from the component.
+  await expect(rows.first()).toContainText(/elevation/i);
+
+  const finalText = await bubble.innerText();
+  expect(finalText.toLowerCase()).toMatch(/ksfo|kase|runway|elevation|weather/);
 });
diff --git a/cockpit/deep-agents/planning/angular/e2e/fixtures/da-planning.json b/cockpit/deep-agents/planning/angular/e2e/fixtures/da-planning.json
index e968eb99a..12717a17c 100644
--- a/cockpit/deep-agents/planning/angular/e2e/fixtures/da-planning.json
+++ b/cockpit/deep-agents/planning/angular/e2e/fixtures/da-planning.json
@@ -2,17 +2,130 @@
   "fixtures": [
     {
       "match": {
-        "userMessage": "Hello"
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 1
       },
       "response": {
-        "content": "[{\"title\":\"Respond with a friendly greeting\",\"status\":\"pending\"},{\"title\":\"Ask how I can help\",\"status\":\"pending\"},{\"title\":\"Give examples of what I can do\",\"status\":\"pending\"},{\"title\":\"Suggest next steps/questions\",\"status\":\"pending\"}]\n\nStep 1 \u2014 Respond with a friendly greeting (completed):\nHello! Nice to meet you.\n\nStep 2 \u2014 Ask how I can help (completed):\nHow can I help you today?\n\nStep 3 \u2014 Give examples of what I can do (completed):\n- Answer questions or explain concepts\n- Help write or edit email, essays, or code\n- Brainstorm ideas or outline plans\n- Summarize articles or long text\n- Translate or proofread text\n- Solve math problems or debug code\n\nStep 4 \u2014 Suggest next steps/questions (completed):\nYou can tell me what you need right now (a question, a task, or a topic). If you prefer, pick one of the examples above and I\u2019ll get started."
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KSFO\"}"
+          },
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KASE\"}"
+          }
+        ]
       }
     },
     {
-      "match": {},
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 2
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "write_todos",
+            "arguments": "{\"todos\":[{\"content\":\"Check field elevation at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Check longest runway length at KSFO and KASE.\",\"status\":\"in_progress\"},{\"content\":\"Check current weather at KSFO and KASE.\",\"status\":\"pending\"},{\"content\":\"Summarize findings and note any operational considerations for the crew.\",\"status\":\"pending\"}]}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 3
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KSFO\"}"
+          },
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KASE\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 4
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "write_todos",
+            "arguments": "{\"todos\":[{\"content\":\"Check field elevation at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Check longest runway length at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Check current weather at KSFO and KASE.\",\"status\":\"in_progress\"},{\"content\":\"Summarize findings and note any operational considerations for the crew.\",\"status\":\"pending\"}]}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 5
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_weather",
+            "arguments": "{\"airport\": \"KSFO\"}"
+          },
+          {
+            "name": "lookup_weather",
+            "arguments": "{\"airport\": \"KASE\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 6
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "write_todos",
+            "arguments": "{\"todos\":[{\"content\":\"Check field elevation at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Check longest runway length at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Check current weather at KSFO and KASE.\",\"status\":\"completed\"},{\"content\":\"Summarize findings and note any operational considerations for the crew.\",\"status\":\"in_progress\"}]}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": true,
+        "turnIndex": 7
+      },
+      "response": {
+        "content": "Dispatch brief for KSFO-KASE:\n\n- KSFO field elevation: 13 ft; longest runway: 11,870 ft; current weather: marine layer, 800 ft overcast, 4 SM visibility, wind 280 at 14.\n- KASE field elevation: 7,820 ft; longest runway: 8,006 ft; current weather: few clouds at 12,000 ft, 10 SM visibility, wind 300 at 6, mountain wave advisory in effect.\n\nCrew considerations: Expect low ceilings and reduced visibility on departure from KSFO. KASE has a high elevation and a relatively short runway for the altitude, so performance calculations are critical. Be aware of potential turbulence and windshear on approach due to the mountain wave advisory at Aspen."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Plan a dispatch brief for a flight from KSFO to KASE",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
       "response": {
-        "content": "Plan (4 steps)\n1. Ask what you need.\n2. Clarify scope, format, and any constraints (deadline, length, tone, audience).\n3. Offer examples of how I can help and suggest a concrete first deliverable.\n4. Confirm and begin work.\n\nExecution\nStep 1 \u2014 Ask what you need:\nWhat would you like me to do right now?\n\nStep 2 \u2014 Clarify scope and constraints:\nIf you already have something specific, tell me: the goal, desired format (email, essay, code, plan, summary, slides, etc.), length, tone (formal/casual), audience, and any deadline. If you don\u2019t have constraints, say so.\n\nStep 3 \u2014 Examples and a suggested first deliverable:\nHere are quick examples of tasks I can do \u2014 pick one or describe your own:\n- Write or edit an email (provide recipient/context and key points).\n- Draft an essay or article (topic, thesis, word count).\n- Create a project plan or roadmap (objective, timeline).\n- Summarize an article or document (paste text or upload).\n- Debug or write code (language, repo/files, expected behavior).\n- Brainstorm ideas (product names, marketing angles, meeting agendas).\n\nSuggested first deliverable: I can produce a short sample (e.g., a 150\u2013300 word draft, a 5-item plan, or a 1-paragraph summary) so you can confirm style before I expand.\n\nStep 4 \u2014 Confirm and begin:\nTell me which task you want and any constraints, or choose one of the examples above. I\u2019ll start immediately."
+        "toolCalls": [
+          {
+            "name": "write_todos",
+            "arguments": "{\"todos\":[{\"content\":\"Check field elevation at KSFO and KASE.\",\"status\":\"in_progress\"},{\"content\":\"Check longest runway length at KSFO and KASE.\",\"status\":\"pending\"},{\"content\":\"Check current weather at KSFO and KASE.\",\"status\":\"pending\"},{\"content\":\"Summarize findings and note any operational considerations for the crew.\",\"status\":\"pending\"}]}"
+          }
+        ]
       }
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/cockpit/deep-agents/planning/angular/e2e/manual/planning.manual.ts b/cockpit/deep-agents/planning/angular/e2e/manual/planning.manual.ts
index e90e4bdc9..19470cf83 100644
--- a/cockpit/deep-agents/planning/angular/e2e/manual/planning.manual.ts
+++ b/cockpit/deep-agents/planning/angular/e2e/manual/planning.manual.ts
@@ -1,3 +1,8 @@
+// SPDX-License-Identifier: MIT
+//
+// Live-model manual check. Not run by CI (playwright matches *.spec.ts only).
+//   npx nx run cockpit:serve-planning
+//   npx playwright test cockpit/deep-agents/planning/angular/e2e/manual
 import { expect, test } from '@playwright/test';
 
 test.describe('Deep Agents Planning Example', () => {
@@ -6,16 +11,20 @@ test.describe('Deep Agents Planning Example', () => {
     await page.waitForSelector('app-planning', { state: 'attached' });
   });
 
-  test('renders the chat interface with plan sidebar', async ({ page }) => {
+  test('renders the chat interface with an empty plan panel', async ({ page }) => {
     await expect(page.locator('chat')).toBeVisible();
     await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
     await expect(page.locator('text=No plan yet')).toBeVisible();
   });
 
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'What are the steps to build a REST API?');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
+  test('writes a todo list and works it to completion', async ({ page }) => {
+    await page.getByRole('button', { name: 'Dispatch brief: KSFO to KASE' }).click();
+
+    const rows = page.locator('[data-testid="todo-row"]');
+    await expect(rows.first()).toBeVisible({ timeout: 60000 });
+    await expect(page.locator('[data-testid="todo-row"][data-status="completed"]').first()).toBeVisible({
+      timeout: 120000,
+    });
+    await expect(page.locator('[data-testid="todo-progress"]')).toBeVisible();
   });
 });
diff --git a/cockpit/deep-agents/planning/angular/prompts/planning.md b/cockpit/deep-agents/planning/angular/prompts/planning.md
index 680494039..b8086259f 100644
--- a/cockpit/deep-agents/planning/angular/prompts/planning.md
+++ b/cockpit/deep-agents/planning/angular/prompts/planning.md
@@ -1,5 +1,5 @@
 # Deep Agents Planning (Angular)
 
-This capability demonstrates how a deep agent decomposes complex tasks into structured plans using the `@threadplane/chat` Angular component library. The `<chat-debug>` component exposes the agent's internal reasoning trace — goal decomposition, sub-task generation, and dependency resolution — so developers can inspect planning decisions in real time.
+This capability renders the todo list that `deepagents` keeps on the graph state. `TodoListMiddleware` gives the model a `write_todos` tool; every call replaces the whole `todos` array, and the Angular sidebar is a `computed()` projection of it, so rows move from pending to in progress to completed while the agent works.
 
-Key components used: `<chat-debug>`. The debug panel renders the full agent thought trace alongside the final response, making it easy to understand how the planning agent broke down the user's request and in which order it intends to tackle each sub-task.
+Key integration points: `injectAgent().value()` for the graph state, and the `<chat>` composition from `@threadplane/chat` for the conversation. A todo is `{ content, status }` — there is no identifier, so the panel tracks rows by index.
diff --git a/cockpit/deep-agents/planning/angular/src/app/app.config.ts b/cockpit/deep-agents/planning/angular/src/app/app.config.ts
index c20520be0..67b6e650c 100644
--- a/cockpit/deep-agents/planning/angular/src/app/app.config.ts
+++ b/cockpit/deep-agents/planning/angular/src/app/app.config.ts
@@ -2,7 +2,6 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideAgent } from '@threadplane/langgraph';
 import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
 import { environment } from '../environments/environment';
 
 export const appConfig: ApplicationConfig = {
@@ -12,6 +11,5 @@ export const appConfig: ApplicationConfig = {
       assistantId: environment.streamingAssistantId,
     }),
     provideChat({}),
-    provideRender({}),
   ],
 };
diff --git a/cockpit/deep-agents/planning/angular/src/app/planning.component.ts b/cockpit/deep-agents/planning/angular/src/app/planning.component.ts
index 51ab09504..098cd26e3 100644
--- a/cockpit/deep-agents/planning/angular/src/app/planning.component.ts
+++ b/cockpit/deep-agents/planning/angular/src/app/planning.component.ts
@@ -1,154 +1,192 @@
+// SPDX-License-Identifier: MIT
 import { Component, computed } from '@angular/core';
-import { ChatComponent, views } from '@threadplane/chat';
+import { ChatComponent, ChatWelcomeSuggestionComponent } from '@threadplane/chat';
 import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
 import { injectAgent } from '@threadplane/langgraph';
-import { signalStateStore } from '@threadplane/render';
-import { PlanChecklistComponent } from './views/plan-checklist.component';
-import { CheckboxRowComponent } from './views/checkbox-row.component';
 
 /**
- * Represents a single step in an agent-generated plan.
+ * One entry of the `todos` list written by the `write_todos` tool.
+ *
+ * `deepagents` 0.7.11 emits exactly two fields. There is no identifier and no
+ * separate present-tense label, so the panel tracks rows by index.
  */
-interface PlanStep {
-  title: string;
-  status: 'pending' | 'in_progress' | 'complete';
+interface Todo {
+  content: string;
+  status: 'pending' | 'in_progress' | 'completed';
 }
 
+const TODO_STATUSES: ReadonlyArray<Todo['status']> = ['pending', 'in_progress', 'completed'];
+
+const SUGGESTIONS = [
+  // value matches cockpit/deep-agents/planning/angular/e2e/da-planning.spec.ts PROMPT.
+  {
+    label: 'Dispatch brief: KSFO to KASE',
+    value:
+      'Plan a dispatch brief for a flight from KSFO to KASE: check field elevation, runway length, and weather at both ends, then tell me if there is anything the crew should know.',
+    description: 'The agent writes a todo list first, then works it one step at a time.',
+  },
+] as const;
+
 /**
- * PlanningComponent demonstrates agent task decomposition.
- *
- * The agent receives a complex task, breaks it into ordered steps,
- * and executes them sequentially. The sidebar displays a live checklist
- * of plan steps derived from the `plan` array in the graph state.
+ * PlanningComponent renders the todo list `TodoListMiddleware` keeps on the
+ * graph state.
  *
- * Key integration points:
- * - `stream.value()` exposes the full graph state, including the `plan` array
- * - `planSteps` is derived from `stream.value()` for reactive sidebar rendering
- * - Step status icons update in real time as the agent progresses
+ * The agent's `write_todos` tool replaces the whole list on every call, and the
+ * graph streams each replacement as a `values` update. `injectAgent().value()`
+ * exposes that state, so the panel is a pure projection of `todos` — no local
+ * copy, no reconciliation. Watching it during a run is the point of this
+ * capability: rows move from pending to in progress to completed, and the agent
+ * revises the list mid-run when a lookup turns up something the first plan
+ * missed.
  */
-
 @Component({
   selector: 'app-planning',
   standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
+  imports: [ChatComponent, ChatWelcomeSuggestionComponent, ExampleChatLayoutComponent],
   template: `
-    <example-chat-layout sidebarWidth="18rem">
-      <chat main [agent]="agent" [views]="ui" [store]="uiStore" class="flex-1 min-w-0" />
+    <example-chat-layout sidebarWidth="20rem">
+      <chat main [agent]="agent" class="flex-1 min-w-0">
+        <div chatWelcomeSuggestions>
+          @for (s of suggestions; track s.value) {
+            <chat-welcome-suggestion
+              [label]="s.label"
+              [value]="s.value"
+              [description]="s.description"
+              (selected)="send($event)"
+            />
+          }
+        </div>
+      </chat>
       <div sidebar class="panel">
         <h3 class="cap">Plan</h3>
-        @if (planSteps().length === 0) {
+        @if (todos().length === 0) {
           <p class="empty">No plan yet</p>
+        } @else {
+          <p class="progress" data-testid="todo-progress">
+            {{ completedCount() }} of {{ todos().length }} complete
+          </p>
         }
-        @for (step of planSteps(); track step.title) {
-          <div class="plan-row">
-            <span class="plan-icon">
-              @if (step.status === 'complete') {
-                <span class="plan-icon--complete">&#10003;</span>
-              } @else if (step.status === 'in_progress') {
-                <span class="plan-icon--active">&#9696;</span>
-              } @else {
-                <span>&#9675;</span>
+        @for (todo of todos(); track $index) {
+          <div class="todo" data-testid="todo-row" [attr.data-status]="todo.status">
+            <span class="todo__icon">
+              @switch (todo.status) {
+                @case ('completed') {
+                  <span class="todo__icon--done">&#10003;</span>
+                }
+                @case ('in_progress') {
+                  <span class="todo__icon--active">&#9696;</span>
+                }
+                @default {
+                  <span>&#9675;</span>
+                }
               }
             </span>
-            <span class="plan-title">{{ step.title }}</span>
+            <span class="todo__text">{{ todo.content }}</span>
           </div>
         }
       </div>
     </example-chat-layout>
   `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
-
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .plan-row {
-      display: flex;
-      align-items: flex-start;
-      gap: 0.5rem;
-      padding: 0.375rem 0.5rem;
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-      font-size: var(--tplane-chat-font-size-sm);
-    }
-
-    .plan-icon {
-      flex: 0 0 auto;
-      margin-top: 0.125rem;
-      color: var(--tplane-chat-separator);
-      font-size: 1rem;
-      line-height: 1;
-    }
-
-    .plan-icon--complete {
-      color: var(--tplane-chat-success);
-    }
-
-    .plan-icon--active {
-      display: inline-block;
-      color: var(--tplane-chat-text-muted);
-      animation: spin 1s linear infinite;
-    }
-
-    .plan-title {
-      color: var(--tplane-chat-text);
-    }
-
-    @keyframes spin {
-      to {
-        transform: rotate(360deg);
+  styles: [
+    `
+      .panel {
+        display: flex;
+        flex-direction: column;
+        gap: 0.5rem;
+        padding: 1rem;
+        background: var(--tplane-chat-bg);
+        color: var(--tplane-chat-text);
+      }
+
+      .cap {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        font-weight: 700;
+        letter-spacing: 0.12em;
+        text-transform: uppercase;
+      }
+
+      .empty,
+      .progress {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
+
+      .empty {
+        font-style: italic;
+      }
+
+      .todo {
+        display: flex;
+        align-items: flex-start;
+        gap: 0.5rem;
+        padding: 0.375rem 0.5rem;
+        border-radius: var(--tplane-chat-radius-card);
+        background: var(--tplane-chat-surface-alt);
+        font-size: var(--tplane-chat-font-size-sm);
+      }
+
+      .todo__icon {
+        flex: 0 0 auto;
+        margin-top: 0.125rem;
+        color: var(--tplane-chat-separator);
+        font-size: 1rem;
+        line-height: 1;
       }
-    }
-  `],
+
+      .todo__icon--done {
+        color: var(--tplane-chat-success);
+      }
+
+      .todo__icon--active {
+        display: inline-block;
+        color: var(--tplane-chat-text-muted);
+        animation: spin 1s linear infinite;
+      }
+
+      .todo[data-status='completed'] .todo__text {
+        color: var(--tplane-chat-text-muted);
+        text-decoration: line-through;
+      }
+
+      .todo__text {
+        color: var(--tplane-chat-text);
+      }
+
+      @keyframes spin {
+        to {
+          transform: rotate(360deg);
+        }
+      }
+    `,
+  ],
 })
 export class PlanningComponent {
-  readonly ui = views({ 'plan-checklist': PlanChecklistComponent, 'checkbox-row': CheckboxRowComponent });
-  readonly uiStore = signalStateStore({});
-
-  /**
-   * The streaming resource connected to the planning graph.
-   *
-   * The graph returns a `plan` array alongside messages in its state.
-   * Each plan entry has a `title` and `status` that drive the sidebar checklist.
-   */
   protected readonly agent = injectAgent();
 
-  /**
-   * Reactive list of plan steps derived from the graph state.
-   *
-   * The Python graph stores the plan as `state.plan` — an array of objects
-   * with `title` and `status` fields. This signal re-computes whenever
-   * the stream state changes.
-   */
-  protected readonly planSteps = computed<PlanStep[]>(() => {
-    const val = this.agent.value() as Record<string, unknown>;
-    const plan = val?.['plan'];
-    if (!Array.isArray(plan)) return [];
-    return plan.map((step: Record<string, unknown>) => ({
-      title: String(step['title'] ?? ''),
-      status: (['pending', 'in_progress', 'complete'].includes(step['status'] as string)
-        ? step['status']
-        : 'pending') as PlanStep['status'],
-    }));
+  protected readonly suggestions = SUGGESTIONS;
+
+  /** Live projection of `state.todos`, normalized against unknown statuses. */
+  protected readonly todos = computed<Todo[]>(() => {
+    const todos = (this.agent.value() as Record<string, unknown> | undefined)?.['todos'];
+    if (!Array.isArray(todos)) return [];
+    return todos.map((todo) => {
+      const entry = todo as Record<string, unknown>;
+      const status = entry['status'] as Todo['status'];
+      return {
+        content: String(entry['content'] ?? ''),
+        status: TODO_STATUSES.includes(status) ? status : 'pending',
+      };
+    });
   });
+
+  protected readonly completedCount = computed(
+    () => this.todos().filter((todo) => todo.status === 'completed').length,
+  );
+
+  protected send(text: string): void {
+    void this.agent.submit({ message: text });
+  }
 }
diff --git a/cockpit/deep-agents/planning/angular/src/app/views/checkbox-row.component.ts b/cockpit/deep-agents/planning/angular/src/app/views/checkbox-row.component.ts
deleted file mode 100644
index 71ce9650b..000000000
--- a/cockpit/deep-agents/planning/angular/src/app/views/checkbox-row.component.ts
+++ /dev/null
@@ -1,50 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'checkbox-row',
-  standalone: true,
-  template: `
-    <label class="check-row">
-      <input
-        type="checkbox"
-        [checked]="checked()"
-        (change)="toggle()"
-        class="check-row__box"
-      />
-      <span class="check-row__label" [class.check-row__label--checked]="checked()">
-        {{ label() }}
-      </span>
-    </label>
-  `,
-  styles: [`
-    .check-row {
-      display: flex;
-      align-items: center;
-      gap: 0.5rem;
-      padding: 0.25rem 0;
-      color: var(--tplane-chat-text);
-      cursor: pointer;
-      font-size: var(--tplane-chat-font-size-sm);
-    }
-
-    .check-row__box {
-      width: 1rem;
-      height: 1rem;
-      accent-color: var(--tplane-chat-primary);
-    }
-
-    .check-row__label--checked {
-      opacity: 0.5;
-      text-decoration: line-through;
-    }
-  `],
-})
-export class CheckboxRowComponent {
-  readonly label = input<string>('');
-  readonly checked = input<boolean>(false);
-  readonly emit = input<(event: string) => void>(() => {});
-
-  toggle(): void {
-    this.emit()('toggle');
-  }
-}
diff --git a/cockpit/deep-agents/planning/angular/src/app/views/plan-checklist.component.ts b/cockpit/deep-agents/planning/angular/src/app/views/plan-checklist.component.ts
deleted file mode 100644
index c91a713c1..000000000
--- a/cockpit/deep-agents/planning/angular/src/app/views/plan-checklist.component.ts
+++ /dev/null
@@ -1,31 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'plan-checklist',
-  standalone: true,
-  template: `
-    <div class="checklist">
-      <h4 class="checklist__title">{{ title() }}</h4>
-      <ng-content />
-    </div>
-  `,
-  styles: [`
-    .checklist {
-      margin: 0.5rem 0;
-      padding: 1rem;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-    }
-
-    .checklist__title {
-      margin: 0 0 0.5rem;
-      color: var(--tplane-chat-text);
-      font-size: var(--tplane-chat-font-size-sm);
-      font-weight: 700;
-    }
-  `],
-})
-export class PlanChecklistComponent {
-  readonly title = input<string>('Plan');
-}
diff --git a/cockpit/deep-agents/planning/angular/src/index.ts b/cockpit/deep-agents/planning/angular/src/index.ts
index abf649fcf..5f845f9d9 100644
--- a/cockpit/deep-agents/planning/angular/src/index.ts
+++ b/cockpit/deep-agents/planning/angular/src/index.ts
@@ -30,6 +30,7 @@ export const deepAgentsPlanningAngularModule: CockpitCapabilityModule = {
     'cockpit/deep-agents/planning/angular/prompts/planning.md',
   ],
   codeAssetPaths: [
-    'cockpit/deep-agents/planning/angular/src/app.component.ts',
+    'cockpit/deep-agents/planning/angular/src/app/planning.component.ts',
+    'cockpit/deep-agents/planning/angular/src/app/app.config.ts',
   ],
 };
diff --git a/cockpit/deep-agents/planning/python/docs/guide.md b/cockpit/deep-agents/planning/python/docs/guide.md
index 7a03d36e7..1cc48622b 100644
--- a/cockpit/deep-agents/planning/python/docs/guide.md
+++ b/cockpit/deep-agents/planning/python/docs/guide.md
@@ -1,152 +1,131 @@
-# Task Decomposition with Angular
+# Planning with Deep Agents
 
 <Summary>
-Build a chat interface that shows real-time task decomposition using `provideAgent()` and
-`injectAgent()` from `@threadplane/langgraph`. The agent breaks complex requests into ordered steps, and the
-sidebar displays each step's status as the agent works through them.
+Render the live todo list a `deepagents` agent keeps while it works. `TodoListMiddleware`
+gives the model a `write_todos` tool and puts a `todos` array on the graph state; the Angular
+panel is a `computed()` projection of that array, so rows move from pending to in progress to
+completed as the agent works, and the list changes shape when the agent revises its plan.
 </Summary>
 
 <Prompt>
-Add a task planning sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.value()` to access the agent's plan state, derive `planSteps` with `computed()`, and bind them to the sidebar beside the `<chat>` component from `@threadplane/chat`.
+Add a live plan panel beside the `<chat>` component. Read `todos` off `injectAgent().value()`,
+where each entry is `{ content, status }` with status one of `pending`, `in_progress`, or
+`completed`, and render one row per todo with a status icon.
 </Prompt>
 
+<Callout type="info" title="Provider setup lives in the LangGraph quickstart">
+This guide assumes `provideAgent()` is already configured. If it is not, work through the
+[LangGraph quickstart](/docs/langgraph/getting-started/quickstart) first — every Deep Agents
+capability uses the same provider, the same `injectAgent()` call, and the same `<chat>`
+composition.
+</Callout>
+
 <Steps>
-<Step title="Configure the provider">
+<Step title="Build the agent on TodoListMiddleware">
 
-Set up `provideAgent()` in your app config with the LangGraph API URL:
+`create_deep_agent` assembles a LangGraph agent from middleware. `TodoListMiddleware` is the
+one that matters here: it registers the `write_todos` tool and declares the `todos` key on the
+state schema.
 
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'da-planning',
-    }),
-  ],
-};
+```python
+# src/graph.py
+from deepagents import create_deep_agent
+from langchain.agents.middleware import TodoListMiddleware
+from langchain_openai import ChatOpenAI
+
+graph = create_deep_agent(
+    model=ChatOpenAI(model="gpt-4.1", temperature=0),
+    tools=[lookup_field_elevation, lookup_runway_length, lookup_weather],
+    system_prompt=(PROMPTS_DIR / "planning.md").read_text(),
+    middleware=[TodoListMiddleware()],
+)
 ```
 
-This makes the configured agent available to all `injectAgent()` calls in your app.
+The middleware is listed explicitly rather than left to the `create_deep_agent` defaults, so
+the source says which component owns `todos`.
 
 </Step>
-<Step title="Create the streaming resource">
+<Step title="Tell the model to plan first">
 
-In your component, call `injectAgent()` to retrieve the configured planning agent:
+`TodoListMiddleware` supplies the model with a tool, not with a policy. Without instruction the
+model will happily fan out six parallel lookups and never write a todo. The system prompt is
+what makes the plan visible:
 
-```typescript
-// planning.component.ts
-import { injectAgent } from '@threadplane/langgraph';
+```markdown
+Your first action on any request is a call to `write_todos`. Do not call a
+lookup tool before the todo list exists. Write one todo per step.
 
-export class PlanningComponent {
-  protected readonly stream = injectAgent();
-}
+Mark exactly one todo `in_progress` before you start it and mark it `completed`
+the moment it is done. Call `write_todos` again for each transition.
 ```
 
-The resource manages the connection, message history, loading state, and errors automatically.
+Each `write_todos` call replaces the entire list. There is no partial update, so the panel never
+has to reconcile anything.
 
 </Step>
-<Step title="Derive plan steps with computed()">
+<Step title="Project the todos into a signal">
 
-Use Angular's `computed()` to reactively derive the plan steps from `stream.value()`:
+`injectAgent().value()` is the latest graph state. Derive the rows with `computed()` and
+normalize the status, because a state key is not a typed contract:
 
 ```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
-
-interface PlanStep {
-  title: string;
-  status: 'pending' | 'running' | 'complete';
+// planning.component.ts
+interface Todo {
+  content: string;
+  status: 'pending' | 'in_progress' | 'completed';
 }
 
-export class PlanningComponent {
-  protected readonly stream = injectAgent();
-
-  planSteps = computed(() => {
-    const val = this.stream.value() as { plan?: PlanStep[] } | undefined;
-    return val?.plan ?? [];
+protected readonly agent = injectAgent();
+
+protected readonly todos = computed<Todo[]>(() => {
+  const todos = (this.agent.value() as Record<string, unknown> | undefined)?.['todos'];
+  if (!Array.isArray(todos)) return [];
+  return todos.map((todo) => {
+    const entry = todo as Record<string, unknown>;
+    const status = entry['status'] as Todo['status'];
+    return {
+      content: String(entry['content'] ?? ''),
+      status: TODO_STATUSES.includes(status) ? status : 'pending',
+    };
   });
-}
+});
 ```
 
-`stream.value()` contains the latest graph state. The planning graph stores steps under a `plan` key, updating statuses as each step is completed.
-
-<Tip>
-`stream.value()` is a signal that updates reactively as new state arrives from the server. `computed()` recalculates `planSteps` automatically whenever the stream updates.
-</Tip>
-
 </Step>
-<Step title="Build the template with plan sidebar">
+<Step title="Render the panel">
 
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
+A todo carries no identifier, so track by index rather than by content — the content of a row
+can change when the agent rewrites a step.
 
 ```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>Task Plan</h3>
-    @for (step of planSteps(); track $index) {
-      <div>
-        <span [style.background]="step.status === 'complete' ? '#10b981' : '#d1d5db'"></span>
-        <span [style.textDecoration]="step.status === 'complete' ? 'line-through' : 'none'">
-          {{ step.title }}
-        </span>
-      </div>
+<example-chat-layout sidebarWidth="20rem">
+  <chat main [agent]="agent" class="flex-1 min-w-0" />
+  <div sidebar class="panel">
+    <h3 class="cap">Plan</h3>
+    @if (todos().length === 0) {
+      <p class="empty">No plan yet</p>
     }
-    @empty {
-      <p>Ask a complex question to see the plan.</p>
+    @for (todo of todos(); track $index) {
+      <div class="todo" [attr.data-status]="todo.status">
+        <span class="todo__text">{{ todo.content }}</span>
+      </div>
     }
-</aside>
+  </div>
+</example-chat-layout>
 ```
 
-The sibling panel renders steps reactively as the agent updates the plan state.
-
-</Step>
-<Step title="The LangGraph planning backend">
-
-The backend uses a two-node graph: one to decompose the task into steps, another to execute them:
-
-```python
-# graph.py
-from langgraph.graph import StateGraph, END
-
-def build_planning_graph():
-    async def create_plan(state: PlanningState) -> dict:
-        """Decompose the task into ordered steps."""
-        # LLM returns a JSON array of steps
-        plan = [{"title": "Step 1", "status": "pending"}, ...]
-        return {"plan": plan, "messages": [response]}
-
-    async def execute_plan(state: PlanningState) -> dict:
-        """Execute the plan and mark steps complete."""
-        plan = [dict(s, status="complete") for s in state["plan"]]
-        return {"plan": plan, "messages": [response]}
-
-    graph = StateGraph(PlanningState)
-    graph.add_node("create_plan", create_plan)
-    graph.add_node("execute_plan", execute_plan)
-    graph.set_entry_point("create_plan")
-    graph.add_edge("create_plan", "execute_plan")
-    graph.add_edge("execute_plan", END)
-    return graph.compile()
-```
-
-Each node updates the `plan` list in state. The frontend sees these updates via `stream.value()` as the graph executes.
-
-<Tip>
-For real step-by-step execution, add one node per step and update the step's `status` field to `"running"` at the start and `"complete"` at the end. The frontend will show progress in real time.
-</Tip>
+Styling off `[attr.data-status]` keeps the status mapping in CSS instead of spreading a second
+copy of the enum through the template.
 
 </Step>
 </Steps>
 
 <Tip>
-The `@empty` block in `@for` renders when the plan array is empty — a clean way to show a placeholder before the user submits their first complex question.
+The shape of a todo in `deepagents` 0.7.11 is exactly `{ content, status }`. There is no `id`
+and no separate present-tense label, so do not build a panel that depends on one.
 </Tip>
 
 <Related>
-- [Chat Tool Calls](/chat/core-capabilities/tool-calls/overview/python) — Learn how ChatToolCallsComponent renders tool call activity
+- [Deep Agents Subagents](/deep-agents/core-capabilities/subagents/overview/python) — the same agent delegating work to child agents
+- [Deep Agents Filesystem](/deep-agents/core-capabilities/filesystem/overview/python) — the file tree the agent writes into while it works
 </Related>
diff --git a/cockpit/deep-agents/planning/python/prompts/planning.md b/cockpit/deep-agents/planning/python/prompts/planning.md
index 5f3cc36cb..1f5721f09 100644
--- a/cockpit/deep-agents/planning/python/prompts/planning.md
+++ b/cockpit/deep-agents/planning/python/prompts/planning.md
@@ -1,7 +1,29 @@
-# Task Planning Agent
+# Flight Planning Agent
 
-You are a planning agent that decomposes complex tasks into ordered steps.
-When given a task, first create a plan with 3-5 clear steps, then execute
-each step to provide a comprehensive response.
+You are a flight-planning assistant for a regional dispatch desk.
 
-Think step by step. Break down the problem before solving it.
+## Plan before you work
+
+Your first action on any request is a call to `write_todos`. Do not call a
+lookup tool before the todo list exists. Write one todo per step.
+
+Mark exactly one todo `in_progress` before you start it and mark it `completed`
+the moment it is done. Call `write_todos` again for each transition — do not
+batch several completions into one call, and do not call `write_todos` in
+parallel with itself.
+
+Revise the list when the data tells you something new. If a lookup reveals a
+constraint the original plan did not account for — a runway that is short for
+the field elevation, weather that forces an alternate — append a todo for it
+rather than folding it silently into an existing step.
+
+## Data
+
+Use `lookup_field_elevation`, `lookup_runway_length`, and `lookup_weather` for
+all airport data. Call one tool at a time so the todo list stays in step with
+the work. Never invent numbers.
+
+## Answering
+
+Write the final answer in the message after your last `write_todos` call, and
+keep it to a few sentences.
diff --git a/cockpit/deep-agents/planning/python/pyproject.toml b/cockpit/deep-agents/planning/python/pyproject.toml
index 32ce88e40..76229208f 100644
--- a/cockpit/deep-agents/planning/python/pyproject.toml
+++ b/cockpit/deep-agents/planning/python/pyproject.toml
@@ -3,14 +3,15 @@ name = "cockpit-deep-agents-planning"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = [
-    "langgraph>=0.3",
+    "deepagents==0.7.11",
     "langchain-openai>=0.3",
+    "langgraph>=0.3",
     "langsmith>=0.2",
 ]
 
 [tool.uv]
 dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
+    "langgraph-cli[inmem]>=0.4",
 ]
 
 [build-system]
diff --git a/cockpit/deep-agents/planning/python/src/graph.py b/cockpit/deep-agents/planning/python/src/graph.py
index 29643b9e2..38c0a48cd 100644
--- a/cockpit/deep-agents/planning/python/src/graph.py
+++ b/cockpit/deep-agents/planning/python/src/graph.py
@@ -1,65 +1,93 @@
-"""
-Deep Agents Planning Graph
+"""Deep Agents planning capability: the real `deepagents` todo loop.
+
+`create_deep_agent` with `TodoListMiddleware` gives the model a `write_todos`
+tool and a `todos` key on the graph state. Every call to that tool replaces the
+whole list, so the frontend reads `agent.value()['todos']` and re-renders the
+checklist — including the mid-run revisions the agent makes when a lookup turns
+up something the original plan did not account for.
 
-Demonstrates agent task decomposition. The agent receives a complex
-task, breaks it into ordered steps, and executes them sequentially.
-The plan state is visible to the frontend via stream.value().
+Each todo is `{"content": str, "status": "pending" | "in_progress" |
+"completed"}`. There is no stable identifier on a todo, so the panel tracks by
+index rather than by id.
 """
 
-import json
 from pathlib import Path
-from typing import TypedDict
-from langgraph.graph import StateGraph, END
+
+from deepagents import create_deep_agent
+from langchain.agents.middleware import TodoListMiddleware
+from langchain_core.tools import tool
 from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, BaseMessage
 
 PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
 
-
-class PlanStep(TypedDict):
-    title: str
-    status: str  # 'pending' | 'running' | 'complete'
-
-
-class PlanningState(TypedDict):
-    messages: list[BaseMessage]
-    plan: list[PlanStep]
-
-
-def build_planning_graph():
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
-
-    async def create_plan(state: PlanningState) -> dict:
-        """Decompose the task into ordered steps."""
-        system_prompt = (PROMPTS_DIR / "planning.md").read_text()
-        plan_prompt = system_prompt + "\n\nReturn a JSON array of steps: [{\"title\": \"step name\", \"status\": \"pending\"}]"
-        messages = [SystemMessage(content=plan_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-
-        try:
-            plan = json.loads(response.content)
-            if not isinstance(plan, list):
-                plan = [{"title": "Process request", "status": "pending"}]
-        except (json.JSONDecodeError, TypeError):
-            plan = [{"title": "Process request", "status": "pending"}]
-
-        return {"plan": plan, "messages": [response]}
-
-    async def execute_plan(state: PlanningState) -> dict:
-        """Execute the plan and generate a response."""
-        plan = [dict(s, status="complete") for s in state.get("plan", [])]
-        system_prompt = (PROMPTS_DIR / "planning.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-        return {"plan": plan, "messages": [response]}
-
-    graph = StateGraph(PlanningState)
-    graph.add_node("create_plan", create_plan)
-    graph.add_node("execute_plan", execute_plan)
-    graph.set_entry_point("create_plan")
-    graph.add_edge("create_plan", "execute_plan")
-    graph.add_edge("execute_plan", END)
-    return graph.compile()
-
-
-graph = build_planning_graph()
+FIELD_ELEVATION_FT = {
+    "KSFO": 13,
+    "KDEN": 5434,
+    "KJFK": 13,
+    "KASE": 7820,
+    "KLAX": 125,
+    "KBOS": 20,
+}
+
+RUNWAY_LENGTH_FT = {
+    "KSFO": 11870,
+    "KDEN": 16000,
+    "KJFK": 14511,
+    "KASE": 8006,
+    "KLAX": 12091,
+    "KBOS": 10083,
+}
+
+WEATHER = {
+    "KSFO": "Marine layer, 800 ft overcast, visibility 4 SM, wind 280 at 14.",
+    "KDEN": "Clear, visibility 10 SM, wind 350 at 22 gusting 31.",
+    "KJFK": "Broken 3500 ft, visibility 10 SM, wind 040 at 9.",
+    "KASE": "Few 12000 ft, visibility 10 SM, wind 300 at 6, mountain wave advisory.",
+    "KLAX": "Clear, visibility 10 SM, wind 260 at 8.",
+    "KBOS": "Overcast 1200 ft, visibility 6 SM, wind 120 at 17.",
+}
+
+
+@tool
+def lookup_field_elevation(airport: str) -> str:
+    """Return the field elevation in feet for a four-letter ICAO airport code."""
+    elevation = FIELD_ELEVATION_FT.get(airport.upper())
+    if elevation is None:
+        return f"No field elevation on file for {airport.upper()}."
+    return f"{airport.upper()} field elevation is {elevation} ft."
+
+
+@tool
+def lookup_runway_length(airport: str) -> str:
+    """Return the longest runway length in feet for a four-letter ICAO airport code."""
+    length = RUNWAY_LENGTH_FT.get(airport.upper())
+    if length is None:
+        return f"No runway data on file for {airport.upper()}."
+    return f"{airport.upper()} longest runway is {length} ft."
+
+
+@tool
+def lookup_weather(airport: str) -> str:
+    """Return the current field conditions for a four-letter ICAO airport code."""
+    conditions = WEATHER.get(airport.upper())
+    if conditions is None:
+        return f"No observation on file for {airport.upper()}."
+    return f"{airport.upper()}: {conditions}"
+
+
+def build_planning_agent():
+    """Build the planning agent.
+
+    `TodoListMiddleware` is passed explicitly rather than relying on the
+    `create_deep_agent` default set so the capability shows exactly which
+    middleware puts `todos` on the state.
+    """
+    return create_deep_agent(
+        model=ChatOpenAI(model="gpt-4.1", temperature=0),
+        tools=[lookup_field_elevation, lookup_runway_length, lookup_weather],
+        system_prompt=(PROMPTS_DIR / "planning.md").read_text(),
+        middleware=[TodoListMiddleware()],
+    )
+
+
+graph = build_planning_agent()
diff --git a/cockpit/deep-agents/planning/python/uv.lock b/cockpit/deep-agents/planning/python/uv.lock
index 10df9e01b..0a809714d 100644
--- a/cockpit/deep-agents/planning/python/uv.lock
+++ b/cockpit/deep-agents/planning/python/uv.lock
@@ -1,190 +1,304 @@
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
 [[package]]
 name = "annotated-types"
-version = "0.7.0"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
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+]
+
+[[package]]
+name = "anthropic"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "docstring-parser" },
+    { name = "httpx2" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b4/50/463166f02179ab279edb61de1589a6f69cb3838d6a2fb6f2c92a3f8042f1/anthropic-1.3.0.tar.gz", hash = "sha256:6873492a77ede8849a161ab1bc78bc9a1e492a006d0b5bb4c57ac77845df838a", size = 1148177, upload-time = "2026-09-01T17:37:10.392Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/2c/5d/7863a9961d320c23787c7b594956afe4e878f9c0ae2376b11a20e416791d/anthropic-1.3.0-py3-none-any.whl", hash = "sha256:e7e7dbebf9f3c84a23954ab989378af6ae10a4d1804c81e9fea4b5ced695ce75", size = 1296959, upload-time = "2026-09-01T17:37:08.525Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "4.13.0"
+version = "4.14.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" }
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 source = { editable = "." }
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 ]
 
 [[package]]

From d94c0e4158ccddc83e350287dd7be26ca6fc0773 Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 11:41:22 -0700
Subject: [PATCH 3/7] feat(cockpit): rebuild the deep-agents filesystem topic
 on deepagents

The old graph declared two tools that returned strings: `read_file` gave
back "[simulated file content]" and `write_file` reported a byte count for
bytes it never wrote. The sidebar logged those calls, so the panel was a
record of an agent pretending to use a filesystem.

This is `create_deep_agent` on `StateBackend`, which is the choice that
makes the capability renderable at all: the agent's files live on the graph
state under `files`, so every write streams to the client as a `values`
update and the panel is a projection of the live workspace rather than a
replay of tool calls. An edit that rewrites a file shows as one changed
file, not two log entries.

A `FilesystemPermission` in `interrupt` mode covers `/reports/**`, so a
write there pauses the run and `<chat-interrupt-panel>` renders the
approval. The pending path is read off the interrupt payload
(`action_requests[].args.file_path`) and shown in the tree as a ghost row
before the file exists, which is why the tree and the approval belong in
one sidebar.

Resuming takes `{ decisions: [{ type: 'approve' }] }`. A bare string or list
is a server-side TypeError, not a validation error the browser can show.

Verified live against `langgraph dev` with a real key: the notes file
streams into `values.files`, the run parks at
`HumanInTheLoopMiddleware.after_model`, and resuming with the decisions
payload lands `/reports/kase-runway.md`. The committed fixture is that run,
four turns; the e2e drives the same pause-and-approve cycle and goes red
when the ghost row is removed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../angular/e2e/da-filesystem.spec.ts         |   39 +-
 .../angular/e2e/fixtures/da-filesystem.json   |   57 +-
 .../angular/e2e/manual/filesystem.manual.ts   |   23 +-
 .../filesystem/angular/prompts/filesystem.md  |    4 +-
 .../filesystem/angular/src/app/app.config.ts  |    2 -
 .../angular/src/app/filesystem.component.ts   |  385 ++-
 .../src/app/views/file-preview.component.ts   |   65 -
 .../filesystem/angular/src/index.ts           |    3 +-
 .../filesystem/python/docs/guide.md           |  199 +-
 .../filesystem/python/prompts/filesystem.md   |   26 +-
 .../filesystem/python/pyproject.toml          |    5 +-
 .../filesystem/python/src/graph.py            |  123 +-
 cockpit/deep-agents/filesystem/python/uv.lock | 2294 +++++++++++------
 13 files changed, 1995 insertions(+), 1230 deletions(-)
 delete mode 100644 cockpit/deep-agents/filesystem/angular/src/app/views/file-preview.component.ts

diff --git a/cockpit/deep-agents/filesystem/angular/e2e/da-filesystem.spec.ts b/cockpit/deep-agents/filesystem/angular/e2e/da-filesystem.spec.ts
index 790beebd1..d694bde80 100644
--- a/cockpit/deep-agents/filesystem/angular/e2e/da-filesystem.spec.ts
+++ b/cockpit/deep-agents/filesystem/angular/e2e/da-filesystem.spec.ts
@@ -1,8 +1,39 @@
 // SPDX-License-Identifier: MIT
 import { test, expect } from '@playwright/test';
-import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
+import {
+  clickInterruptActionAndWaitFinal,
+  sendPromptAndWaitForInterrupt,
+} from '@threadplane-internal/e2e-harness';
 
-test('da-filesystem: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
+// Matches the welcome suggestion in filesystem.component.ts and the fixture's
+// `userMessage` match in e2e/fixtures/da-filesystem.json.
+const PROMPT =
+  'Work up a runway suitability note for KASE. Save your raw lookups to /notes/kase-data.md, then write the finished note to /reports/kase-runway.md.';
+
+test('da-filesystem: the workspace fills in, the report write pauses, and approving lands it', async ({
+  page,
+}) => {
+  await sendPromptAndWaitForInterrupt(page, PROMPT);
+
+  // `/notes/` is unrestricted, so that write completed and reached the tree as
+  // real state — this only renders because the agent runs on StateBackend and
+  // its files stream on `values.files`.
+  await expect(page.locator('[data-testid="file-row"][data-path="/notes/kase-data.md"]')).toBeVisible();
+  await expect(page.getByTestId('file-preview')).toContainText(/7,?820|8,?006|elevation/i);
+
+  // `/reports/**` carries a FilesystemPermission in interrupt mode, so the
+  // second write did NOT complete. The tree shows it as a pending ghost row
+  // read off the interrupt payload, and no such file exists in state yet.
+  const pendingRow = page.locator('[data-testid="file-row"][data-path="/reports/kase-runway.md"]');
+  await expect(pendingRow).toHaveAttribute('data-pending', 'true');
+  await expect(pendingRow).toContainText('awaiting approval');
+
+  // Two directories, because the ghost row groups under its own prefix.
+  await expect(page.locator('[data-testid="file-dir"]')).toHaveCount(2);
+
+  // Approving resumes with { decisions: [{ type: 'approve' }] }; the write then
+  // completes and the row stops being pending.
+  await clickInterruptActionAndWaitFinal(page, 'Accept');
+  await expect(pendingRow).not.toHaveAttribute('data-pending', 'true');
+  await expect(page.locator('[data-testid="file-row"]')).toHaveCount(2);
 });
diff --git a/cockpit/deep-agents/filesystem/angular/e2e/fixtures/da-filesystem.json b/cockpit/deep-agents/filesystem/angular/e2e/fixtures/da-filesystem.json
index a5bccfada..d1c774b7d 100644
--- a/cockpit/deep-agents/filesystem/angular/e2e/fixtures/da-filesystem.json
+++ b/cockpit/deep-agents/filesystem/angular/e2e/fixtures/da-filesystem.json
@@ -2,11 +2,62 @@
   "fixtures": [
     {
       "match": {
-        "userMessage": "Hello"
+        "userMessage": "Work up a runway suitability note for KASE",
+        "hasToolResult": true,
+        "turnIndex": 1
       },
       "response": {
-        "content": "Hello! How can I help you today? I can read or write files in the workspace\u2014tell me what you'd like to do (e.g., \"read file /path/to/file\" or \"write file /path/to/file with content ...\")."
+        "toolCalls": [
+          {
+            "name": "write_file",
+            "arguments": "{\"file_path\":\"/notes/kase-data.md\",\"content\":\"KASE Field Elevation: 7,820 ft\\nKASE Longest Runway: 8,006 ft\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Work up a runway suitability note for KASE",
+        "hasToolResult": true,
+        "turnIndex": 2
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "write_file",
+            "arguments": "{\"file_path\":\"/reports/kase-runway.md\",\"content\":\"Runway Suitability Note: KASE (Aspen/Pitkin County)\\n\\n- Field elevation is 7,820 ft, which is considered high altitude and will impact aircraft performance (longer takeoff/landing distances required).\\n- The longest runway is 8,006 ft, which is adequate for many business jets and regional aircraft, but performance calculations are critical due to elevation and possible temperature effects.\\n- Operators should verify aircraft-specific requirements and consider density altitude, especially in warm weather or with heavy loads.\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Work up a runway suitability note for KASE",
+        "hasToolResult": true,
+        "turnIndex": 3
+      },
+      "response": {
+        "content": "I saved the raw data lookups for KASE (field elevation and runway length) to /notes/kase-data.md. The finished runway suitability note is in /reports/kase-runway.md."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Work up a runway suitability note for KASE",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KASE\"}"
+          },
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KASE\"}"
+          }
+        ]
       }
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/cockpit/deep-agents/filesystem/angular/e2e/manual/filesystem.manual.ts b/cockpit/deep-agents/filesystem/angular/e2e/manual/filesystem.manual.ts
index 6d192e231..4039ed53e 100644
--- a/cockpit/deep-agents/filesystem/angular/e2e/manual/filesystem.manual.ts
+++ b/cockpit/deep-agents/filesystem/angular/e2e/manual/filesystem.manual.ts
@@ -1,3 +1,8 @@
+// SPDX-License-Identifier: MIT
+//
+// Live-model manual check. Not run by CI (playwright matches *.spec.ts only).
+//   npx nx run cockpit:serve-filesystem
+//   npx playwright test cockpit/deep-agents/filesystem/angular/e2e/manual
 import { expect, test } from '@playwright/test';
 
 test.describe('Deep Agents Filesystem Example', () => {
@@ -6,16 +11,20 @@ test.describe('Deep Agents Filesystem Example', () => {
     await page.waitForSelector('app-filesystem', { state: 'attached' });
   });
 
-  test('renders the chat interface with file operations sidebar', async ({ page }) => {
+  test('renders the chat interface with an empty workspace', async ({ page }) => {
     await expect(page.locator('chat')).toBeVisible();
     await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
-    await expect(page.locator('text=No file operations yet')).toBeVisible();
+    await expect(page.locator('text=No files yet')).toBeVisible();
   });
 
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'Read the file at workspace/hello.txt');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
+  test('writes notes, pauses on the report, and lands it after approval', async ({ page }) => {
+    await page.getByRole('button', { name: 'Runway note for KASE' }).click();
+
+    await expect(page.locator('[data-testid="file-row"]').first()).toBeVisible({ timeout: 120000 });
+    await expect(page.locator('chat-interrupt-panel')).toBeVisible({ timeout: 120000 });
+    await expect(page.locator('text=awaiting approval')).toBeVisible();
+
+    await page.locator('chat-interrupt-panel').getByRole('button', { name: /^Accept$/ }).click();
+    await expect(page.locator('[data-testid="file-row"]')).toHaveCount(2, { timeout: 120000 });
   });
 });
diff --git a/cockpit/deep-agents/filesystem/angular/prompts/filesystem.md b/cockpit/deep-agents/filesystem/angular/prompts/filesystem.md
index 066e5f722..e3a27d143 100644
--- a/cockpit/deep-agents/filesystem/angular/prompts/filesystem.md
+++ b/cockpit/deep-agents/filesystem/angular/prompts/filesystem.md
@@ -1,5 +1,5 @@
 # Deep Agents Filesystem (Angular)
 
-This capability demonstrates a deep agent that reads, writes, and navigates a sandboxed filesystem using the `@threadplane/chat` Angular component library. The `<chat-debug>` component surfaces every filesystem tool call — including path, arguments, and result — so developers can follow the agent's file operations step by step.
+This capability renders the agent's workspace, not a log of its file operations. `StateBackend` puts every file the agent writes on the graph state under `files`, keyed by absolute path, so the Angular sidebar is a projection of the live workspace: an edit that rewrites an existing file shows as one changed file rather than two log entries.
 
-Key components used: `<chat-debug>`. Each tool invocation (read_file, write_file, list_dir, etc.) appears as a collapsible trace node, giving full visibility into how the agent interacts with the filesystem without cluttering the end-user chat view.
+A `FilesystemPermission` in `interrupt` mode covers `/reports/**`, so a write there pauses the run and `<chat-interrupt-panel>` from `@threadplane/chat` renders the approval. The pending path is read off the interrupt payload and shown in the tree as a ghost row before the file exists. Resuming takes `{ decisions: [{ type: 'approve' }] }`.
diff --git a/cockpit/deep-agents/filesystem/angular/src/app/app.config.ts b/cockpit/deep-agents/filesystem/angular/src/app/app.config.ts
index c20520be0..67b6e650c 100644
--- a/cockpit/deep-agents/filesystem/angular/src/app/app.config.ts
+++ b/cockpit/deep-agents/filesystem/angular/src/app/app.config.ts
@@ -2,7 +2,6 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideAgent } from '@threadplane/langgraph';
 import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
 import { environment } from '../environments/environment';
 
 export const appConfig: ApplicationConfig = {
@@ -12,6 +11,5 @@ export const appConfig: ApplicationConfig = {
       assistantId: environment.streamingAssistantId,
     }),
     provideChat({}),
-    provideRender({}),
   ],
 };
diff --git a/cockpit/deep-agents/filesystem/angular/src/app/filesystem.component.ts b/cockpit/deep-agents/filesystem/angular/src/app/filesystem.component.ts
index d45142cb3..415a6e97c 100644
--- a/cockpit/deep-agents/filesystem/angular/src/app/filesystem.component.ts
+++ b/cockpit/deep-agents/filesystem/angular/src/app/filesystem.component.ts
@@ -1,180 +1,289 @@
-import { Component, computed } from '@angular/core';
-import { ChatComponent, views } from '@threadplane/chat';
+// SPDX-License-Identifier: MIT
+import { Component, computed, signal } from '@angular/core';
+import {
+  ChatComponent,
+  ChatInterruptPanelComponent,
+  ChatWelcomeSuggestionComponent,
+} from '@threadplane/chat';
+import type { InterruptAction } from '@threadplane/chat';
 import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
 import { injectAgent } from '@threadplane/langgraph';
-import { signalStateStore } from '@threadplane/render';
-import { FilePreviewComponent } from './views/file-preview.component';
 
-/**
- * Represents a file operation extracted from agent tool calls.
- */
-interface FileOperation {
-  type: 'read' | 'write';
+/** One entry of the `files` map `StateBackend` keeps on the graph state. */
+interface WorkspaceFile {
   path: string;
-  preview: string;
+  directory: string;
+  name: string;
+  contents: string;
+  /** True while a write to this path is waiting on human approval. */
+  pending: boolean;
 }
 
+const SUGGESTIONS = [
+  // value matches cockpit/deep-agents/filesystem/angular/e2e/da-filesystem.spec.ts PROMPT.
+  {
+    label: 'Runway note for KASE',
+    value:
+      'Work up a runway suitability note for KASE. Save your raw lookups to /notes/kase-data.md, then write the finished note to /reports/kase-runway.md.',
+    description: 'Notes are written straight through; the report pauses for your approval.',
+  },
+] as const;
+
 /**
- * FilesystemComponent demonstrates agent file operations.
+ * FilesystemComponent renders the agent's workspace, not a log of its actions.
+ *
+ * `StateBackend` puts every file the agent writes on the graph state under
+ * `files`, keyed by absolute path, so the panel is a projection of the live
+ * workspace rather than a replay of `write_file` tool calls. The distinction
+ * matters: an edit that rewrites an existing file shows up as one changed file
+ * here, and as two entries in a call log.
  *
- * The agent can read and write files using tool calls. The sidebar displays
- * a live log of file operations derived from `stream.messages()`, filtering
- * for `read_file` and `write_file` tool calls.
+ * A `FilesystemPermission` in `interrupt` mode covers `/reports/**`, so a write
+ * there pauses the run. The pending path is read off the interrupt payload and
+ * shown in the tree as a ghost row before it exists, which is the whole reason
+ * to render the tree and the approval together.
  *
- * Key integration points:
- * - `stream.messages()` exposes the full message history including tool calls
- * - `fileOps` filters messages for file-related tool calls and extracts metadata
- * - Operations appear in the sidebar in real time as the agent works
+ * `deepagents` resumes on `{ decisions: [{ type: 'approve' | 'reject' }] }`. A
+ * bare string or list is a server-side TypeError.
  */
 @Component({
   selector: 'app-filesystem',
   standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
+  imports: [
+    ChatComponent,
+    ChatInterruptPanelComponent,
+    ChatWelcomeSuggestionComponent,
+    ExampleChatLayoutComponent,
+  ],
   template: `
-    <example-chat-layout sidebarWidth="18rem">
-      <chat main [agent]="agent" [views]="ui" [store]="uiStore" class="flex-1 min-w-0" />
+    <example-chat-layout sidebarWidth="22rem">
+      <chat main [agent]="agent" class="flex-1 min-w-0">
+        <div chatWelcomeSuggestions>
+          @for (s of suggestions; track s.value) {
+            <chat-welcome-suggestion
+              [label]="s.label"
+              [value]="s.value"
+              [description]="s.description"
+              (selected)="send($event)"
+            />
+          }
+        </div>
+      </chat>
       <div sidebar class="panel">
-        <h3 class="cap">File Operations</h3>
-        @if (fileOps().length === 0) {
-          <p class="empty">No file operations yet</p>
+        <h3 class="cap">Workspace</h3>
+        @if (files().length === 0) {
+          <p class="empty">No files yet</p>
         }
-        @for (op of fileOps(); track $index) {
-          <div class="file-op">
-            <div class="file-op__head">
-              <span class="op-badge"
-                    [class.op-badge--read]="op.type === 'read'"
-                    [class.op-badge--write]="op.type === 'write'">
-                {{ op.type === 'read' ? 'READ' : 'WRITE' }}
-              </span>
-              <span class="file-path">{{ op.path }}</span>
-            </div>
-            @if (op.preview) {
-              <p class="preview">{{ op.preview }}</p>
+        @for (group of tree(); track group.directory) {
+          <div class="dir" data-testid="file-dir">
+            <span class="dir__name">{{ group.directory }}</span>
+            @for (file of group.files; track file.path) {
+              <button
+                type="button"
+                class="file"
+                data-testid="file-row"
+                [attr.data-path]="file.path"
+                [attr.data-pending]="file.pending ? 'true' : null"
+                [class.file--selected]="selectedPath() === file.path"
+                (click)="select(file.path)"
+              >
+                <span class="file__name">{{ file.name }}</span>
+                @if (file.pending) {
+                  <span class="file__badge">awaiting approval</span>
+                }
+              </button>
             }
           </div>
         }
+        @if (selectedFile(); as file) {
+          <div class="preview" data-testid="file-preview">
+            <span class="cap">{{ file.path }}</span>
+            <pre class="preview__body">{{ file.contents }}</pre>
+          </div>
+        }
+        <h3 class="cap">Approval</h3>
+        <chat-interrupt-panel [agent]="agent" (action)="onInterruptAction($event)" />
       </div>
     </example-chat-layout>
   `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
+  styles: [
+    `
+      .panel {
+        display: flex;
+        flex-direction: column;
+        gap: 0.75rem;
+        padding: 1rem;
+        background: var(--tplane-chat-bg);
+        color: var(--tplane-chat-text);
+      }
 
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
+      .cap {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        font-weight: 700;
+        letter-spacing: 0.12em;
+        text-transform: uppercase;
+      }
 
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
+      .empty {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-sm);
+        font-style: italic;
+      }
 
-    .file-op {
-      display: flex;
-      flex-direction: column;
-      gap: 0.25rem;
-      padding: 0.375rem 0.5rem;
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-      font-size: var(--tplane-chat-font-size-sm);
-    }
+      .dir {
+        display: flex;
+        flex-direction: column;
+        gap: 0.125rem;
+      }
 
-    .file-op__head {
-      display: flex;
-      align-items: center;
-      gap: 0.5rem;
-      min-width: 0;
-    }
+      .dir__name {
+        color: var(--tplane-chat-text-muted);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
 
-    .op-badge {
-      display: inline-flex;
-      align-items: center;
-      flex: 0 0 auto;
-      border-radius: var(--tplane-chat-radius-button);
-      padding: 0.125rem 0.375rem;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 600;
-    }
+      .file {
+        display: flex;
+        align-items: center;
+        justify-content: space-between;
+        gap: 0.5rem;
+        margin-left: 0.75rem;
+        padding: 0.25rem 0.5rem;
+        border: 0;
+        border-radius: var(--tplane-chat-radius-card);
+        background: var(--tplane-chat-surface-alt);
+        color: var(--tplane-chat-text);
+        cursor: pointer;
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+        text-align: left;
+      }
 
-    .op-badge--read {
-      background: color-mix(in srgb, var(--tplane-chat-primary) 12%, var(--tplane-chat-surface));
-      color: var(--tplane-chat-primary);
-    }
+      .file--selected {
+        outline: 1px solid var(--tplane-chat-primary);
+      }
 
-    .op-badge--write {
-      background: var(--tplane-chat-warning-bg);
-      color: var(--tplane-chat-warning-text);
-    }
+      .file[data-pending='true'] {
+        opacity: 0.65;
+        font-style: italic;
+      }
 
-    .file-path {
-      min-width: 0;
-      overflow: hidden;
-      color: var(--tplane-chat-text);
-      font-size: var(--tplane-chat-font-size-xs);
-      text-overflow: ellipsis;
-      white-space: nowrap;
-    }
+      .file__badge {
+        color: var(--tplane-chat-text-muted);
+        font-family: var(--tplane-chat-font-family);
+        font-style: normal;
+      }
 
-    .preview {
-      margin: 0;
-      overflow: hidden;
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-      text-overflow: ellipsis;
-      white-space: nowrap;
-    }
-  `],
+      .preview__body {
+        max-height: 14rem;
+        margin: 0.25rem 0 0;
+        overflow: auto;
+        padding: 0.5rem;
+        border-radius: var(--tplane-chat-radius-card);
+        background: var(--tplane-chat-surface-alt);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+        white-space: pre-wrap;
+      }
+    `,
+  ],
 })
 export class FilesystemComponent {
-  readonly ui = views({ 'file-preview': FilePreviewComponent });
-  readonly uiStore = signalStateStore({});
-
-  /**
-   * The streaming resource connected to the filesystem graph.
-   *
-   * The graph uses `read_file` and `write_file` tool calls that appear
-   * in `stream.messages()`. We filter and display them in the sidebar.
-   */
   protected readonly agent = injectAgent();
 
+  protected readonly suggestions = SUGGESTIONS;
+
+  private readonly manualSelection = signal<string | null>(null);
+
   /**
-   * Reactive list of file operations derived from the message history.
+   * The path a pending `write_file` approval would create.
    *
-   * Scans all messages for tool calls named `read_file` or `write_file`,
-   * extracts the file path and a short result preview for sidebar display.
+   * The interrupt payload is `{ action_requests: [{ name, args }] }`; the path
+   * lives on `args.file_path`.
    */
-  protected readonly fileOps = computed<FileOperation[]>(() => {
-    const msgs = this.agent.langGraphMessages();
-    const ops: FileOperation[] = [];
-    for (const msg of msgs) {
-      const m = msg as unknown as Record<string, unknown>;
-      if (!('tool_calls' in m) || !Array.isArray(m['tool_calls'])) continue;
-      for (const tc of m['tool_calls'] as Array<Record<string, unknown>>) {
-        const name = tc['name'] as string | undefined;
-        if (name !== 'read_file' && name !== 'write_file') continue;
-        const args = (tc['args'] ?? {}) as Record<string, unknown>;
-        const path = String(args['path'] ?? args['file_path'] ?? '');
-        const content = String(args['content'] ?? args['result'] ?? '');
-        ops.push({
-          type: name === 'read_file' ? 'read' : 'write',
-          path,
-          preview: content.slice(0, 80),
-        });
+  protected readonly pendingPath = computed<string | null>(() => {
+    for (const interrupt of this.agent.langGraphInterrupts() ?? []) {
+      const value = (interrupt as { value?: unknown }).value as
+        | { action_requests?: Array<{ name?: string; args?: Record<string, unknown> }> }
+        | undefined;
+      for (const request of value?.action_requests ?? []) {
+        const path = request.args?.['file_path'];
+        if (typeof path === 'string') return path;
       }
     }
-    return ops;
+    return null;
   });
+
+  /** Live projection of `state.files`, plus a ghost row for a pending write. */
+  protected readonly files = computed<WorkspaceFile[]>(() => {
+    const raw = (this.agent.value() as Record<string, unknown> | undefined)?.['files'];
+    const entries = new Map<string, string>();
+    if (raw && typeof raw === 'object') {
+      for (const [path, contents] of Object.entries(raw as Record<string, unknown>)) {
+        entries.set(path, typeof contents === 'string' ? contents : JSON.stringify(contents));
+      }
+    }
+    const pending = this.pendingPath();
+    if (pending && !entries.has(pending)) entries.set(pending, '');
+
+    return [...entries.entries()]
+      .map(([path, contents]) => {
+        const slash = path.lastIndexOf('/');
+        return {
+          path,
+          directory: slash > 0 ? path.slice(0, slash) : '/',
+          name: path.slice(slash + 1),
+          contents,
+          pending: path === pending,
+        };
+      })
+      .sort((a, b) => a.path.localeCompare(b.path));
+  });
+
+  /** Files grouped by directory, so the panel reads as a tree. */
+  protected readonly tree = computed(() => {
+    const groups = new Map<string, WorkspaceFile[]>();
+    for (const file of this.files()) {
+      const bucket = groups.get(file.directory) ?? [];
+      bucket.push(file);
+      groups.set(file.directory, bucket);
+    }
+    return [...groups.entries()]
+      .sort(([a], [b]) => a.localeCompare(b))
+      .map(([directory, files]) => ({ directory, files }));
+  });
+
+  /** The clicked file, defaulting to the most recently written one. */
+  protected readonly selectedPath = computed<string | null>(() => {
+    const manual = this.manualSelection();
+    const files = this.files();
+    if (manual && files.some((file) => file.path === manual)) return manual;
+    const settled = files.filter((file) => !file.pending);
+    return settled.at(-1)?.path ?? files.at(0)?.path ?? null;
+  });
+
+  protected readonly selectedFile = computed<WorkspaceFile | null>(() => {
+    const path = this.selectedPath();
+    return this.files().find((file) => file.path === path) ?? null;
+  });
+
+  protected select(path: string): void {
+    this.manualSelection.set(path);
+  }
+
+  protected onInterruptAction(action: InterruptAction): void {
+    if (action === 'accept') {
+      void this.agent.submit({ resume: { decisions: [{ type: 'approve' }] } });
+    } else if (action === 'ignore') {
+      void this.agent.submit({ resume: { decisions: [{ type: 'reject' }] } });
+    }
+    // 'edit' and 'respond' would need the tool args echoed back; out of scope here.
+  }
+
+  protected send(text: string): void {
+    void this.agent.submit({ message: text });
+  }
 }
diff --git a/cockpit/deep-agents/filesystem/angular/src/app/views/file-preview.component.ts b/cockpit/deep-agents/filesystem/angular/src/app/views/file-preview.component.ts
deleted file mode 100644
index 485886728..000000000
--- a/cockpit/deep-agents/filesystem/angular/src/app/views/file-preview.component.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'file-preview',
-  standalone: true,
-  template: `
-    <div class="file-card">
-      <div class="file-card__head">
-        <span class="file-card__path">{{ path() }}</span>
-        <span class="file-card__size">{{ size() }}</span>
-      </div>
-      <pre class="file-card__body">{{ content() }}</pre>
-    </div>
-  `,
-  styles: [`
-    .file-card {
-      margin: 0.5rem 0;
-      overflow: hidden;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-    }
-
-    .file-card__head {
-      display: flex;
-      align-items: center;
-      justify-content: space-between;
-      gap: 0.5rem;
-      padding: 0.5rem 1rem;
-      border-bottom: 1px solid var(--tplane-chat-separator);
-      background: var(--tplane-chat-bg);
-    }
-
-    .file-card__path {
-      min-width: 0;
-      overflow: hidden;
-      color: var(--tplane-chat-text);
-      font: var(--tplane-chat-font-size-sm) / 1.5 var(--tplane-chat-font-mono);
-      text-overflow: ellipsis;
-      white-space: nowrap;
-    }
-
-    .file-card__size {
-      flex: 0 0 auto;
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-    }
-
-    .file-card__body {
-      margin: 0;
-      padding: 1rem;
-      overflow-x: auto;
-      background: var(--tplane-chat-surface-alt);
-      color: var(--tplane-chat-text);
-      font: var(--tplane-chat-font-size-xs) / 1.5 var(--tplane-chat-font-mono);
-      overflow-wrap: anywhere;
-      white-space: pre-wrap;
-    }
-  `],
-})
-export class FilePreviewComponent {
-  readonly path = input<string>('');
-  readonly content = input<string>('');
-  readonly size = input<string>('');
-}
diff --git a/cockpit/deep-agents/filesystem/angular/src/index.ts b/cockpit/deep-agents/filesystem/angular/src/index.ts
index a45db3b1d..493125d36 100644
--- a/cockpit/deep-agents/filesystem/angular/src/index.ts
+++ b/cockpit/deep-agents/filesystem/angular/src/index.ts
@@ -30,6 +30,7 @@ export const deepAgentsFilesystemAngularModule: CockpitCapabilityModule = {
     'cockpit/deep-agents/filesystem/angular/prompts/filesystem.md',
   ],
   codeAssetPaths: [
-    'cockpit/deep-agents/filesystem/angular/src/app.component.ts',
+    'cockpit/deep-agents/filesystem/angular/src/app/filesystem.component.ts',
+    'cockpit/deep-agents/filesystem/angular/src/app/app.config.ts',
   ],
 };
diff --git a/cockpit/deep-agents/filesystem/python/docs/guide.md b/cockpit/deep-agents/filesystem/python/docs/guide.md
index 8bfd2fe24..acd504a6e 100644
--- a/cockpit/deep-agents/filesystem/python/docs/guide.md
+++ b/cockpit/deep-agents/filesystem/python/docs/guide.md
@@ -1,152 +1,135 @@
-# File Operations with Angular
+# Filesystem with Deep Agents
 
 <Summary>
-Build a chat interface that shows real-time file operation logs using `provideAgent()` and
-`injectAgent()` from `@threadplane/langgraph`. The agent reads and writes files using tool calls, and the
-sidebar displays each operation as it happens.
+Render the agent's workspace and gate the writes that matter. `StateBackend` keeps the
+agent's files on the graph state under `files`, so a file tree is a `computed()` projection
+of live state rather than a replay of `write_file` calls. A `FilesystemPermission` in
+`interrupt` mode pauses writes under a chosen prefix for human approval.
 </Summary>
 
 <Prompt>
-Add a file operations sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.messages()` to access tool call data, derive `toolCallEntries` with `computed()`, and bind them to the sidebar beside the `<chat>` component from `@threadplane/chat`.
+Add a workspace panel beside the `<chat>` component. Read the `files` map off
+`injectAgent().value()`, group the paths into directories, and render
+`<chat-interrupt-panel>` for the write approvals.
 </Prompt>
 
+<Callout type="info" title="Provider setup lives in the LangGraph quickstart">
+This guide assumes `provideAgent()` is already configured. If it is not, work through the
+[LangGraph quickstart](/docs/langgraph/getting-started/quickstart) first.
+</Callout>
+
 <Steps>
-<Step title="Configure the provider">
+<Step title="Choose StateBackend, deliberately">
 
-Set up `provideAgent()` in your app config with the LangGraph API URL:
+`create_deep_agent` always installs `FilesystemMiddleware`, so the agent always has
+`ls`, `read_file`, `write_file`, and `edit_file`. What decides whether a UI can render the
+workspace is the backend. `StateBackend` stores files on the graph state, which means every
+write arrives at the client as a `values` update:
 
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'da-filesystem',
-    }),
-  ],
-};
+```python
+from deepagents import create_deep_agent
+from deepagents.backends import StateBackend
+
+graph = create_deep_agent(
+    model=ChatOpenAI(model="gpt-4.1", temperature=0),
+    tools=[lookup_field_elevation, lookup_runway_length],
+    system_prompt=(PROMPTS_DIR / "filesystem.md").read_text(),
+    backend=StateBackend(),
+)
 ```
 
-This makes the configured agent available to all `injectAgent()` calls in your app.
+A backend that writes anywhere else — a host directory, a remote store — streams nothing
+onto the state, and a panel bound to `files` stays empty no matter how busy the agent is.
 
 </Step>
-<Step title="Create the streaming resource">
+<Step title="Interrupt the writes that leave the desk">
 
-In your component, call `injectAgent()` to retrieve the configured filesystem agent:
+`FilesystemPermission` is a rule over operations and path patterns. In `interrupt` mode a
+matching call pauses for approval instead of executing:
 
-```typescript
-// filesystem.component.ts
-import { injectAgent } from '@threadplane/langgraph';
+```python
+from deepagents.middleware import FilesystemPermission
 
-export class FilesystemComponent {
-  protected readonly stream = injectAgent();
-}
+permissions=[
+    FilesystemPermission(operations=["write"], paths=["/reports/**"], mode="interrupt"),
+]
 ```
 
-The resource manages the connection, message history, loading state, and errors automatically.
+Anchor the pattern with a literal prefix. Bulk tools (`ls`, `glob`, `grep`) decide whether to
+fire based on whether their search subtree could overlap the anchored prefix, so a fully
+unanchored pattern collapses to `/` and fires on every listing.
 
 </Step>
-<Step title="Derive tool call entries with computed()">
+<Step title="Project the file map into a tree">
 
-Use Angular's `computed()` to reactively extract tool calls from `stream.messages()`:
+`files` is a flat map from absolute path to contents. Split each key on its last slash to get
+a directory grouping:
 
 ```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
-
-interface ToolCallEntry {
-  name: string;
-  args: string;
-  result?: string;
-}
-
-export class FilesystemComponent {
-  protected readonly stream = injectAgent();
-
-  toolCallEntries = computed(() => {
-    const msg = this.stream.messages();
-    const calls: ToolCallEntry[] = [];
-    for (const m of msg) {
-      if ((m as any).tool_calls) {
-        for (const tc of (m as any).tool_calls) {
-          calls.push({ name: tc.name, args: JSON.stringify(tc.args), result: tc.output });
-        }
-      }
+protected readonly files = computed<WorkspaceFile[]>(() => {
+  const raw = (this.agent.value() as Record<string, unknown> | undefined)?.['files'];
+  const entries = new Map<string, string>();
+  if (raw && typeof raw === 'object') {
+    for (const [path, contents] of Object.entries(raw as Record<string, unknown>)) {
+      entries.set(path, typeof contents === 'string' ? contents : JSON.stringify(contents));
     }
-    return calls;
+  }
+  return [...entries.entries()].map(([path, contents]) => {
+    const slash = path.lastIndexOf('/');
+    return { path, directory: slash > 0 ? path.slice(0, slash) : '/', name: path.slice(slash + 1), contents };
   });
-}
+});
 ```
 
-`stream.messages()` is a signal that updates as messages arrive. `computed()` re-derives the tool call list automatically whenever new messages come in.
+</Step>
+<Step title="Show the pending write before it exists">
 
-<Tip>
-Tool calls appear inside AI messages as `tool_calls`. Each entry has a `name` (e.g. `read_file`), `args` (the tool inputs), and `output` (the tool result, populated after the tool runs).
-</Tip>
+The interrupt payload is `{ action_requests: [{ name, args }] }`, and for `write_file` the
+target path is `args.file_path`. Reading it off `langGraphInterrupts()` lets the tree show the
+file as a ghost row while the approval is open:
 
-</Step>
-<Step title="Build the template with file operations sidebar">
-
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
-
-```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>File Operations</h3>
-    @for (entry of toolCallEntries(); track $index) {
-      <div>
-        <span>{{ entry.name === 'read_file' ? '📖' : '✏️' }}</span>
-        <div>
-          <div>{{ getFilePath(entry.args) }}</div>
-          <div>{{ entry.name === 'read_file' ? 'read' : 'write' }}{{ entry.result ? ' · done' : ' · running…' }}</div>
-        </div>
-      </div>
-    }
-    @empty {
-      <p>Ask the agent to read or write a file.</p>
+```typescript
+protected readonly pendingPath = computed<string | null>(() => {
+  for (const interrupt of this.agent.langGraphInterrupts() ?? []) {
+    const value = (interrupt as { value?: unknown }).value as
+      | { action_requests?: Array<{ args?: Record<string, unknown> }> }
+      | undefined;
+    for (const request of value?.action_requests ?? []) {
+      const path = request.args?.['file_path'];
+      if (typeof path === 'string') return path;
     }
-</aside>
+  }
+  return null;
+});
 ```
 
-The sibling panel renders operations reactively as the agent calls tools.
-
 </Step>
-<Step title="The LangGraph filesystem backend">
+<Step title="Resume with a decisions payload">
 
-The backend uses a tool-calling agent loop with `read_file` and `write_file` tools:
+`deepagents` expects a structured decision, not a bare string:
 
-```python
-# graph.py
-from langchain_core.tools import tool
-from langgraph.prebuilt import ToolNode
-
-@tool
-def read_file(path: str) -> str:
-    """Read a file's contents. Only reads from the workspace directory."""
-    return f"Contents of {path}: [simulated file content]"
-
-@tool
-def write_file(path: str, content: str) -> str:
-    """Write content to a file in the workspace."""
-    return f"Successfully wrote {len(content)} bytes to {path}"
-
-# Bind tools to the LLM
-llm = ChatOpenAI(model="gpt-5-mini").bind_tools([read_file, write_file])
+```typescript
+protected onInterruptAction(action: InterruptAction): void {
+  if (action === 'accept') {
+    void this.agent.submit({ resume: { decisions: [{ type: 'approve' }] } });
+  } else if (action === 'ignore') {
+    void this.agent.submit({ resume: { decisions: [{ type: 'reject' }] } });
+  }
+}
 ```
 
-The agent node invokes the LLM, which may emit tool calls. A conditional edge routes to the `ToolNode` when tool calls are present, then loops back to the agent. The frontend sees each tool call in `stream.messages()`.
-
-<Tip>
-`ToolNode` from `langgraph.prebuilt` handles dispatching tool calls to the right function and returning results as `ToolMessage` nodes automatically.
-</Tip>
+Passing a bare list, or a plain string, raises a `TypeError` on the server rather than a
+validation error you can see in the browser.
 
 </Step>
 </Steps>
 
 <Tip>
-The `@empty` block in `@for` renders when no tool calls have been made yet — a clean way to show a placeholder before the user submits their first request.
+The pending row and the approval panel belong in the same sidebar. Reviewing a write is much
+easier when the tree already shows where the file is about to land.
 </Tip>
+
+<Related>
+- [Deep Agents Planning](/deep-agents/core-capabilities/planning/overview/python) — the todo list the agent keeps while it files
+- [Chat Interrupts](/chat/core-capabilities/interrupts/overview/python) — the interrupt panel on its own
+</Related>
diff --git a/cockpit/deep-agents/filesystem/python/prompts/filesystem.md b/cockpit/deep-agents/filesystem/python/prompts/filesystem.md
index 38d4e0475..72bec545a 100644
--- a/cockpit/deep-agents/filesystem/python/prompts/filesystem.md
+++ b/cockpit/deep-agents/filesystem/python/prompts/filesystem.md
@@ -1,7 +1,23 @@
-# Filesystem Agent
+# Dispatch Filing Agent
 
-You are a filesystem agent that helps users read and write files.
-When the user asks you to read a file, use the `read_file` tool with the file path.
-When the user asks you to write a file, use the `write_file` tool with the path and content.
+You are a flight dispatcher who keeps the paperwork for each trip in a small
+virtual workspace.
 
-Always confirm what you did after each file operation.
+## Where files go
+
+- `/notes/` holds scratch work: raw lookups, calculations, anything you want to
+  reread later. Writing here is unrestricted.
+- `/reports/` holds documents that leave the desk. Every write under `/reports/`
+  is reviewed by a human before it lands, so expect to be paused there.
+
+Use `write_file` to create a document, `read_file` to reread one, `ls` to see
+what exists, and `edit_file` to revise. Always write real content — never a
+placeholder.
+
+## Working a request
+
+Gather data with `lookup_field_elevation` and `lookup_runway_length` first, write
+the working notes under `/notes/`, and only then write the finished document
+under `/reports/`. Keep each file short: a heading and a handful of lines.
+
+When you are done, tell the user which files you wrote in one or two sentences.
diff --git a/cockpit/deep-agents/filesystem/python/pyproject.toml b/cockpit/deep-agents/filesystem/python/pyproject.toml
index 4fb68f2ac..903113da2 100644
--- a/cockpit/deep-agents/filesystem/python/pyproject.toml
+++ b/cockpit/deep-agents/filesystem/python/pyproject.toml
@@ -3,14 +3,15 @@ name = "cockpit-deep-agents-filesystem"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = [
-    "langgraph>=0.3",
+    "deepagents==0.7.11",
     "langchain-openai>=0.3",
+    "langgraph>=0.3",
     "langsmith>=0.2",
 ]
 
 [tool.uv]
 dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
+    "langgraph-cli[inmem]>=0.4",
 ]
 
 [build-system]
diff --git a/cockpit/deep-agents/filesystem/python/src/graph.py b/cockpit/deep-agents/filesystem/python/src/graph.py
index 8e3083077..b5948e9cd 100644
--- a/cockpit/deep-agents/filesystem/python/src/graph.py
+++ b/cockpit/deep-agents/filesystem/python/src/graph.py
@@ -1,64 +1,85 @@
-"""
-Deep Agents Filesystem Graph
+"""Deep Agents filesystem capability: a real agent-visible file tree.
+
+`create_deep_agent` always installs `FilesystemMiddleware`. What this
+capability adds is the two things that make the file tree worth rendering:
+
+1. `StateBackend` — the agent's files live on the graph state under `files`,
+   so every write streams to the client as a `values` update. A backend that
+   stores files anywhere else (a host directory, a remote store) writes nothing
+   to the state and the panel would stay empty.
+2. A `FilesystemPermission` in `interrupt` mode on `/reports/**` — writes under
+   that prefix pause the run for human approval instead of completing, which is
+   what the interrupt panel renders.
 
-Demonstrates agent file operations using tool calls. The agent can
-read and write files, and the frontend displays each operation in
-the sidebar via stream.messages().
+Resuming an approval takes `{"decisions": [{"type": "approve"}]}`. A bare list
+is a server-side TypeError.
 """
 
 from pathlib import Path
-from typing import TypedDict
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode
-from langchain_openai import ChatOpenAI
+
+from deepagents import create_deep_agent
+from deepagents.backends import StateBackend
+from deepagents.middleware import FilesystemPermission
 from langchain_core.tools import tool
-from langchain_core.messages import SystemMessage, BaseMessage
+from langchain_openai import ChatOpenAI
 
 PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
 
-
-@tool
-def read_file(path: str) -> str:
-    """Read a file's contents. Only reads from the workspace directory."""
-    return f"Contents of {path}: [simulated file content]"
+FIELD_ELEVATION_FT = {
+    "KSFO": 13,
+    "KDEN": 5434,
+    "KJFK": 13,
+    "KASE": 7820,
+    "KLAX": 125,
+    "KBOS": 20,
+}
+
+RUNWAY_LENGTH_FT = {
+    "KSFO": 11870,
+    "KDEN": 16000,
+    "KJFK": 14511,
+    "KASE": 8006,
+    "KLAX": 12091,
+    "KBOS": 10083,
+}
 
 
 @tool
-def write_file(path: str, content: str) -> str:
-    """Write content to a file in the workspace."""
-    return f"Successfully wrote {len(content)} bytes to {path}"
-
+def lookup_field_elevation(airport: str) -> str:
+    """Return the field elevation in feet for a four-letter ICAO airport code."""
+    elevation = FIELD_ELEVATION_FT.get(airport.upper())
+    if elevation is None:
+        return f"No field elevation on file for {airport.upper()}."
+    return f"{airport.upper()} field elevation is {elevation} ft."
 
-class FilesystemState(TypedDict):
-    messages: list[BaseMessage]
 
-
-def build_filesystem_graph():
-    tools = [read_file, write_file]
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True).bind_tools(tools)
-
-    async def agent(state: FilesystemState) -> dict:
-        """Run the agent — may emit tool calls."""
-        system_prompt = (PROMPTS_DIR / "filesystem.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    def should_continue(state: FilesystemState) -> str:
-        last = state["messages"][-1]
-        if hasattr(last, "tool_calls") and last.tool_calls:
-            return "tools"
-        return END
-
-    tool_node = ToolNode(tools)
-
-    graph = StateGraph(FilesystemState)
-    graph.add_node("agent", agent)
-    graph.add_node("tools", tool_node)
-    graph.set_entry_point("agent")
-    graph.add_conditional_edges("agent", should_continue, {"tools": "tools", END: END})
-    graph.add_edge("tools", "agent")
-    return graph.compile()
-
-
-graph = build_filesystem_graph()
+@tool
+def lookup_runway_length(airport: str) -> str:
+    """Return the longest runway length in feet for a four-letter ICAO airport code."""
+    length = RUNWAY_LENGTH_FT.get(airport.upper())
+    if length is None:
+        return f"No runway data on file for {airport.upper()}."
+    return f"{airport.upper()} longest runway is {length} ft."
+
+
+def build_filesystem_agent():
+    """Build the filesystem agent.
+
+    `permissions` reaches `FilesystemMiddleware`, which pairs with
+    `HumanInTheLoopMiddleware` to raise the interrupt. Anchor the pattern with a
+    literal prefix: bulk tools (`ls`, `glob`, `grep`) decide whether to fire
+    based on whether their search subtree could overlap the anchored prefix, so
+    an unanchored pattern over-fires on every listing.
+    """
+    return create_deep_agent(
+        model=ChatOpenAI(model="gpt-4.1", temperature=0),
+        tools=[lookup_field_elevation, lookup_runway_length],
+        system_prompt=(PROMPTS_DIR / "filesystem.md").read_text(),
+        backend=StateBackend(),
+        permissions=[
+            FilesystemPermission(operations=["write"], paths=["/reports/**"], mode="interrupt"),
+        ],
+    )
+
+
+graph = build_filesystem_agent()
diff --git a/cockpit/deep-agents/filesystem/python/uv.lock b/cockpit/deep-agents/filesystem/python/uv.lock
index bdec1a6ca..3a2904588 100644
--- a/cockpit/deep-agents/filesystem/python/uv.lock
+++ b/cockpit/deep-agents/filesystem/python/uv.lock
@@ -1,190 +1,304 @@
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
 [[package]]
 name = "annotated-types"
-version = "0.7.0"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
+[[package]]
+name = "anthropic"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "docstring-parser" },
+    { name = "httpx2" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b4/50/463166f02179ab279edb61de1589a6f69cb3838d6a2fb6f2c92a3f8042f1/anthropic-1.3.0.tar.gz", hash = "sha256:6873492a77ede8849a161ab1bc78bc9a1e492a006d0b5bb4c57ac77845df838a", size = 1148177, upload-time = "2026-09-01T17:37:10.392Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/5d/7863a9961d320c23787c7b594956afe4e878f9c0ae2376b11a20e416791d/anthropic-1.3.0-py3-none-any.whl", hash = "sha256:e7e7dbebf9f3c84a23954ab989378af6ae10a4d1804c81e9fea4b5ced695ce75", size = 1296959, upload-time = "2026-09-01T17:37:08.525Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "4.13.0"
+version = "4.14.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/61/cc/a381afa6efea9f496eff839d4a6a1aed3bfafc7b3ab4b0d1b243a12573dd/anyio-4.14.2.tar.gz", hash = "sha256:cfa139f3ed1a23ee8f88a145ddb5ac7605b8bbfd8592baacd7ce3d8bb4313c7f", size = 260176, upload-time = "2026-07-12T20:29:07.082Z" }
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 source = { editable = "." }
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 [[package]]

From d39c413fca1548646721b4168df2a08f5e2c9026 Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 12:18:19 -0700
Subject: [PATCH 4/7] feat(cockpit): rebuild the deep-agents subagents topic on
 deepagents
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The old graph called three `@tool` functions that each ran one ainvoke on a
chat model inside the parent process. There were no child graphs, so the
demo named after subagents rendered no subagent cards at all — the tracker
had no `tools:<call_id>` namespace to match, and the whole thing showed as
three generic tool chips.

This is `create_deep_agent` with two declared `SubAgent` specialists, which
installs `SubAgentMiddleware` and its `task` tool. Every dispatch is a real
child graph in its own namespace, seeded with the orchestrator's
`description` before its first token, so attribution is structural rather
than inferred from message ordering. That is what makes concurrent children
render correctly.

The orchestrator gets no lookup tools of its own, so it cannot answer
without delegating, and the prompt asks for every dispatch in a single turn
when a request spans more than one airport. A live run against
`langgraph dev` fans out four ways — field data and weather for KASE and
KDEN — and the four children produce four distinct transcripts with no
cross-wiring.

Two fixtures and two specs: the fan-out (ten recorded turns; four cards,
labelled by `subagent_type`, each expanded to check it carries its own
child's transcript) and a single dispatch (four turns, one card).

One correction to the brief this was built from: `task` is already the
SubagentTracker's DEFAULT dispatch-tool name, so a deepagents graph needs no
client configuration for the cards to appear. Removing
`subagentToolNames: ['task']` leaves the e2e green. The option is kept as
documentation and the guide says so; overriding it with a name the graph
never calls is the mutation that turns the cards back into tool chips, and
that does go red.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../angular/e2e/da-subagents-fanout.spec.ts   |   47 +
 .../angular/e2e/da-subagents-single.spec.ts   |   29 +
 .../angular/e2e/da-subagents.spec.ts          |    8 -
 .../e2e/fixtures/da-subagents-fanout.json     |  149 ++
 .../e2e/fixtures/da-subagents-single.json     |   58 +
 .../angular/e2e/fixtures/da-subagents.json    |   18 -
 .../angular/e2e/manual/subagents.manual.ts    |   21 +-
 .../subagents/angular/prompts/subagents.md    |    4 +-
 .../subagents/angular/src/app/app.config.ts   |   10 +-
 .../angular/src/app/subagents.component.ts    |  250 +-
 .../subagents/angular/src/index.ts            |    3 +-
 .../subagents/python/docs/guide.md            |  184 +-
 .../subagents/python/prompts/subagents.md     |   30 +-
 .../subagents/python/pyproject.toml           |    5 +-
 .../deep-agents/subagents/python/src/graph.py |  217 +-
 cockpit/deep-agents/subagents/python/uv.lock  | 2294 +++++++++++------
 16 files changed, 2066 insertions(+), 1261 deletions(-)
 create mode 100644 cockpit/deep-agents/subagents/angular/e2e/da-subagents-fanout.spec.ts
 create mode 100644 cockpit/deep-agents/subagents/angular/e2e/da-subagents-single.spec.ts
 delete mode 100644 cockpit/deep-agents/subagents/angular/e2e/da-subagents.spec.ts
 create mode 100644 cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-fanout.json
 create mode 100644 cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-single.json
 delete mode 100644 cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents.json

diff --git a/cockpit/deep-agents/subagents/angular/e2e/da-subagents-fanout.spec.ts b/cockpit/deep-agents/subagents/angular/e2e/da-subagents-fanout.spec.ts
new file mode 100644
index 000000000..f1216e87d
--- /dev/null
+++ b/cockpit/deep-agents/subagents/angular/e2e/da-subagents-fanout.spec.ts
@@ -0,0 +1,47 @@
+// SPDX-License-Identifier: MIT
+import { test, expect } from '@playwright/test';
+import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
+
+// Matches the welcome suggestion in subagents.component.ts and the fixture in
+// e2e/fixtures/da-subagents-fanout.json.
+const PROMPT =
+  'Brief me on KASE and KDEN: I need field data for both and the current weather at both.';
+
+test('da-subagents: a parallel fan-out lands as four separate cards', async ({ page }) => {
+  const bubble = await submitAndWaitForResponse(page, PROMPT);
+
+  // The orchestrator issued four `task` calls in ONE turn, so four child graphs
+  // streamed concurrently. Each ran in its own `tools:<call_id>` namespace,
+  // which is what lets the SubagentTracker keep them apart — a tracker guessing
+  // from message order would fold concurrent children into one card.
+  await expect(page.locator('chat-subagent-card')).toHaveCount(4, { timeout: 30_000 });
+  await expect(page.getByTestId('dispatch-count')).toHaveText('4 dispatched, 0 running');
+
+  // Both specialists were used, and each card is labelled with the
+  // `subagent_type` the orchestrator chose.
+  const cards = page.locator('chat-subagent-card');
+  const labels = (await cards.locator('.sac__name').allInnerTexts()).sort();
+  expect(labels).toEqual([
+    'field-researcher',
+    'field-researcher',
+    'weather-analyst',
+    'weather-analyst',
+  ]);
+
+  // Cards collapse when their child finishes. Expanding them shows each
+  // child's OWN transcript — the numbers the researcher looked up and the
+  // conditions the analyst read. Concurrent children that had been folded
+  // together would not produce four distinct transcripts.
+  for (const card of await cards.all()) {
+    await card.getByRole('button', { expanded: false }).first().click();
+  }
+  const cardText = (await cards.allInnerTexts()).join('\n').toLowerCase();
+  expect(cardText).toContain('kase');
+  expect(cardText).toContain('kden');
+  expect(cardText).toMatch(/7,?820|8,?006/);
+  expect(cardText).toMatch(/visibility|wind|gust/);
+
+  // The orchestrator wrote its own briefing rather than pasting a child's.
+  const finalText = await bubble.innerText();
+  expect(finalText.toLowerCase()).toMatch(/kase|kden/);
+});
diff --git a/cockpit/deep-agents/subagents/angular/e2e/da-subagents-single.spec.ts b/cockpit/deep-agents/subagents/angular/e2e/da-subagents-single.spec.ts
new file mode 100644
index 000000000..e7d7bbb1f
--- /dev/null
+++ b/cockpit/deep-agents/subagents/angular/e2e/da-subagents-single.spec.ts
@@ -0,0 +1,29 @@
+// SPDX-License-Identifier: MIT
+import { test, expect } from '@playwright/test';
+import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
+
+// Matches the welcome suggestion in subagents.component.ts and the fixture in
+// e2e/fixtures/da-subagents-single.json.
+const PROMPT = 'What is the field data for KSFO?';
+
+test('da-subagents: a single dispatch renders one card with the child transcript', async ({
+  page,
+}) => {
+  const bubble = await submitAndWaitForResponse(page, PROMPT);
+
+  // One `task` call, one child graph, one persistent card. The card is the
+  // proof that `subagentToolNames: ['task']` is wired: without it the same
+  // dispatch renders as a generic tool chip.
+  await expect(page.locator('chat-subagent-card')).toHaveCount(1, { timeout: 30_000 });
+  await expect(page.getByTestId('dispatch-count')).toHaveText('1 dispatched, 0 running');
+
+  // The card is labelled with the `subagent_type` the orchestrator chose, and
+  // expanding it shows the child's own transcript with the numbers it looked up.
+  const card = page.locator('chat-subagent-card').first();
+  await expect(card.locator('.sac__name')).toHaveText('field-researcher');
+  await card.getByRole('button', { expanded: false }).first().click();
+  await expect(card).toContainText(/11,?870|elevation/i);
+
+  const finalText = await bubble.innerText();
+  expect(finalText.toLowerCase()).toContain('ksfo');
+});
diff --git a/cockpit/deep-agents/subagents/angular/e2e/da-subagents.spec.ts b/cockpit/deep-agents/subagents/angular/e2e/da-subagents.spec.ts
deleted file mode 100644
index ba008b0f0..000000000
--- a/cockpit/deep-agents/subagents/angular/e2e/da-subagents.spec.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-// SPDX-License-Identifier: MIT
-import { test, expect } from '@playwright/test';
-import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
-
-test('da-subagents: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
-});
diff --git a/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-fanout.json b/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-fanout.json
new file mode 100644
index 000000000..2540c4d08
--- /dev/null
+++ b/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-fanout.json
@@ -0,0 +1,149 @@
+{
+  "fixtures": [
+    {
+      "match": {
+        "userMessage": "Analyze the current weather conditions at Denver International Airport (KDEN) and explain the operational impact for departures and arrivals.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "Denver International Airport (KDEN) is currently experiencing clear skies, 10 statute miles visibility, and strong north winds at 22 knots gusting to 31 knots. These gusty winds may require increased attention during takeoff and landing, potentially causing delays or runway changes, but otherwise conditions are favorable for operations."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for Denver International Airport (KDEN). Return both values clearly.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "Denver International Airport (KDEN) has a field elevation of 5,434 feet and its longest runway is 16,000 feet. This runway length is more than sufficient for a mid-size business jet at that elevation."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Analyze the current weather conditions at Aspen/Pitkin County Airport (KASE) and explain the operational impact for departures and arrivals.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "Aspen/Pitkin County Airport (KASE) is currently reporting a few clouds at 12,000 feet, visibility 10 statute miles, wind from 300° at 6 knots, and a mountain wave advisory. While the weather is generally VFR and suitable for operations, the mountain wave advisory indicates potential turbulence and wind shear, which could impact both departures and arrivals, especially for smaller aircraft and during approach or climb-out phases."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for Aspen/Pitkin County Airport (KASE). Return both values clearly.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "Aspen/Pitkin County Airport (KASE) has a field elevation of 7,820 feet and its longest runway is 8,006 feet. At this elevation, the runway is just adequate for most mid-size business jets, but performance margins may be tight, especially in hot weather or at maximum weights."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Brief me on KASE and KDEN: I need field data for both and the current weather at both.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "Aspen/Pitkin County Airport (KASE) sits at a high elevation of 7,820 feet with an 8,006-foot runway, which is adequate but leaves little margin for performance, especially in hot or heavy conditions. The current weather at KASE is VFR with good visibility, but a mountain wave advisory means turbulence and wind shear could affect arrivals and departures. Denver International Airport (KDEN) is at 5,434 feet and has a very long 16,000-foot runway, providing ample room for operations. Weather at KDEN is clear, but strong, gusty north winds may require extra caution during takeoff and landing, though overall conditions are favorable."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Brief me on KASE and KDEN: I need field data for both and the current weather at both.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "task",
+            "arguments": "{\"description\": \"Gather the field elevation and runway length for Aspen/Pitkin County Airport (KASE). Return both values clearly.\", \"subagent_type\": \"field-researcher\"}"
+          },
+          {
+            "name": "task",
+            "arguments": "{\"description\": \"Gather the field elevation and runway length for Denver International Airport (KDEN). Return both values clearly.\", \"subagent_type\": \"field-researcher\"}"
+          },
+          {
+            "name": "task",
+            "arguments": "{\"description\": \"Analyze the current weather conditions at Aspen/Pitkin County Airport (KASE) and explain the operational impact for departures and arrivals.\", \"subagent_type\": \"weather-analyst\"}"
+          },
+          {
+            "name": "task",
+            "arguments": "{\"description\": \"Analyze the current weather conditions at Denver International Airport (KDEN) and explain the operational impact for departures and arrivals.\", \"subagent_type\": \"weather-analyst\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Analyze the current weather conditions at Denver International Airport (KDEN) and explain the operational impact for departures and arrivals.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_weather",
+            "arguments": "{\"airport\":\"KDEN\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for Denver International Airport (KDEN). Return both values clearly.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KDEN\"}"
+          },
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KDEN\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Analyze the current weather conditions at Aspen/Pitkin County Airport (KASE) and explain the operational impact for departures and arrivals.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_weather",
+            "arguments": "{\"airport\":\"KASE\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for Aspen/Pitkin County Airport (KASE). Return both values clearly.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KASE\"}"
+          },
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KASE\"}"
+          }
+        ]
+      }
+    }
+  ]
+}
diff --git a/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-single.json b/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-single.json
new file mode 100644
index 000000000..626aba0ed
--- /dev/null
+++ b/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents-single.json
@@ -0,0 +1,58 @@
+{
+  "fixtures": [
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for San Francisco International Airport (KSFO). Return both values clearly labeled.",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "San Francisco International Airport (KSFO) has a field elevation of 13 feet and its longest runway is 11,870 feet. This runway length is more than sufficient for a mid-size business jet at that elevation."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "What is the field data for KSFO?",
+        "hasToolResult": true,
+        "turnIndex": 1
+      },
+      "response": {
+        "content": "San Francisco International Airport (KSFO) has a field elevation of 13 feet above sea level. Its longest runway measures 11,870 feet, which is ample for most aircraft operations, including mid-size business jets."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "What is the field data for KSFO?",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "task",
+            "arguments": "{\"description\":\"Gather the field elevation and runway length for San Francisco International Airport (KSFO). Return both values clearly labeled.\",\"subagent_type\":\"field-researcher\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Gather the field elevation and runway length for San Francisco International Airport (KSFO). Return both values clearly labeled.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\": \"KSFO\"}"
+          },
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\": \"KSFO\"}"
+          }
+        ]
+      }
+    }
+  ]
+}
diff --git a/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents.json b/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents.json
deleted file mode 100644
index 88d1f4888..000000000
--- a/cockpit/deep-agents/subagents/angular/e2e/fixtures/da-subagents.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-  "fixtures": [
-    {
-      "match": {
-        "userMessage": "Hello"
-      },
-      "response": {
-        "toolCalls": [
-          {
-            "name": "research_agent",
-            "arguments": "{\"topic\":\"How to respond to a simple greeting like 'Hello' in a friendly, concise way; suggested phrasing, tone, and follow-up questions to engage user\"}",
-            "id": "call_JAuyEoJ5ILdin2rB6kRACMpw"
-          }
-        ]
-      }
-    }
-  ]
-}
\ No newline at end of file
diff --git a/cockpit/deep-agents/subagents/angular/e2e/manual/subagents.manual.ts b/cockpit/deep-agents/subagents/angular/e2e/manual/subagents.manual.ts
index 7cfa4d1e2..b286be523 100644
--- a/cockpit/deep-agents/subagents/angular/e2e/manual/subagents.manual.ts
+++ b/cockpit/deep-agents/subagents/angular/e2e/manual/subagents.manual.ts
@@ -1,3 +1,8 @@
+// SPDX-License-Identifier: MIT
+//
+// Live-model manual check. Not run by CI (playwright matches *.spec.ts only).
+//   npx nx run cockpit:serve-subagents
+//   npx playwright test cockpit/deep-agents/subagents/angular/e2e/manual
 import { expect, test } from '@playwright/test';
 
 test.describe('Deep Agents Subagents Example', () => {
@@ -6,16 +11,18 @@ test.describe('Deep Agents Subagents Example', () => {
     await page.waitForSelector('app-subagents', { state: 'attached' });
   });
 
-  test('renders the chat interface with subagents sidebar', async ({ page }) => {
+  test('renders the chat interface with the specialist roster', async ({ page }) => {
     await expect(page.locator('chat')).toBeVisible();
     await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
-    await expect(page.locator('text=No delegations yet')).toBeVisible();
+    await expect(page.locator('text=field-researcher')).toBeVisible();
+    await expect(page.locator('text=weather-analyst')).toBeVisible();
   });
 
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'Research the history of the internet and summarize it.');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
+  test('fans out to several specialists at once', async ({ page }) => {
+    await page.getByRole('button', { name: 'Two airports at once' }).click();
+
+    await expect(page.locator('chat-subagent-card').first()).toBeVisible({ timeout: 120000 });
+    await expect(page.locator('chat-subagent-card')).toHaveCount(4, { timeout: 180000 });
+    await expect(page.getByTestId('dispatch-count')).toBeVisible();
   });
 });
diff --git a/cockpit/deep-agents/subagents/angular/prompts/subagents.md b/cockpit/deep-agents/subagents/angular/prompts/subagents.md
index 2b9758228..8e410872a 100644
--- a/cockpit/deep-agents/subagents/angular/prompts/subagents.md
+++ b/cockpit/deep-agents/subagents/angular/prompts/subagents.md
@@ -1,5 +1,5 @@
 # Deep Agents Subagents (Angular)
 
-This capability demonstrates an orchestrator agent that delegates work to specialised subagents using the `@threadplane/chat` Angular component library. The `<chat-debug>` component shows the full delegation trace — which subagent was called, with what instructions, and what it returned — giving developers complete observability into multi-agent coordination.
+This capability renders real child agents. `SubAgentMiddleware` gives the orchestrator a `task` tool taking `{ description, subagent_type }`, and each dispatch runs as its own graph in a `tools:<call_id>` namespace. Attribution is therefore structural rather than inferred: the SubagentTracker matches namespaces, so four children streaming at once land in four separate `<chat-subagent-card>` elements instead of interleaving into one.
 
-Key components used: `<chat-debug>`. Each subagent invocation appears as a collapsible trace node labelled with the subagent's identity, making it straightforward to audit delegation chains, spot redundant calls, and verify that each subagent received the correct context.
+`task` is the SubagentTracker's default dispatch-tool name, so a `deepagents` graph needs no client configuration for the cards to appear; `provideAgent()` names it explicitly as documentation. The sidebar reads `agent.subagents()` for a live dispatch and running count.
diff --git a/cockpit/deep-agents/subagents/angular/src/app/app.config.ts b/cockpit/deep-agents/subagents/angular/src/app/app.config.ts
index c20520be0..59d026135 100644
--- a/cockpit/deep-agents/subagents/angular/src/app/app.config.ts
+++ b/cockpit/deep-agents/subagents/angular/src/app/app.config.ts
@@ -2,7 +2,6 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideAgent } from '@threadplane/langgraph';
 import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
 import { environment } from '../environments/environment';
 
 export const appConfig: ApplicationConfig = {
@@ -10,8 +9,15 @@ export const appConfig: ApplicationConfig = {
     provideAgent({
       apiUrl: environment.langGraphApiUrl,
       assistantId: environment.streamingAssistantId,
+      // `SubAgentMiddleware` dispatches every child through one tool named
+      // `task`, carrying `{description, subagent_type}`. That name is also the
+      // SubagentTracker's default, so this line changes nothing at runtime —
+      // it is here to say out loud which tool call means "a child agent
+      // started". Set it when your dispatch tool is named something else;
+      // overriding it with a name the graph never calls is what turns the
+      // cards back into generic tool chips.
+      subagentToolNames: ['task'],
     }),
     provideChat({}),
-    provideRender({}),
   ],
 };
diff --git a/cockpit/deep-agents/subagents/angular/src/app/subagents.component.ts b/cockpit/deep-agents/subagents/angular/src/app/subagents.component.ts
index a1f5c859b..865cfbcd3 100644
--- a/cockpit/deep-agents/subagents/angular/src/app/subagents.component.ts
+++ b/cockpit/deep-agents/subagents/angular/src/app/subagents.component.ts
@@ -1,177 +1,129 @@
+// SPDX-License-Identifier: MIT
 import { Component, computed } from '@angular/core';
-import { ChatComponent } from '@threadplane/chat';
+import { ChatComponent, ChatWelcomeSuggestionComponent } from '@threadplane/chat';
 import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
 import { injectAgent } from '@threadplane/langgraph';
 
-/**
- * Delegation status derived from matching tool calls with tool result messages.
- */
-interface Delegation {
-  /** Tool call ID used to track request/response pairing. */
-  id: string;
-  /** Name of the delegated agent (tool name). */
-  agent: string;
-  /** Execution status: running until a matching tool result arrives. */
-  status: 'running' | 'complete' | 'error';
-  /** Human-readable status text. */
-  statusText: string;
-}
+const SUGGESTIONS = [
+  // values match cockpit/deep-agents/subagents/angular/e2e/*.spec.ts prompts.
+  {
+    label: 'Two airports at once',
+    value: 'Brief me on KASE and KDEN: I need field data for both and the current weather at both.',
+    description: 'Four dispatches in a single turn — the specialists run in parallel.',
+  },
+  {
+    label: 'One airport',
+    value: 'What is the field data for KSFO?',
+    description: 'A single dispatch to the field researcher.',
+  },
+] as const;
 
 /**
- * SubagentsComponent demonstrates the Deep Agents subagent delegation pattern.
+ * SubagentsComponent shows real child agents, not a tool-call log.
  *
- * The orchestrator agent receives a task and delegates subtasks to specialist
- * subagents via tool calls. The sidebar tracks each delegation by scanning
- * `stream.messages()` for AI tool_calls and matching ToolMessage results.
+ * `SubAgentMiddleware` runs each `task` dispatch as its own graph in a
+ * `tools:<call_id>` namespace, so a child's tokens arrive tagged with which
+ * dispatch produced them. Attribution is therefore structural: the tracker
+ * matches namespaces, and does not have to guess from message ordering. That
+ * is what makes parallel fan-out render correctly — four children streaming at
+ * once land in four separate cards rather than interleaving into one.
+ *
+ * The cards are rendered inline by the `<chat>` composition and persist after
+ * completion. The sidebar keeps the roster and a live dispatch count so the
+ * fan-out is visible as a number, not only as a wall of cards.
  */
 @Component({
   selector: 'app-subagents',
   standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
+  imports: [ChatComponent, ChatWelcomeSuggestionComponent, ExampleChatLayoutComponent],
   template: `
-    <example-chat-layout sidebarWidth="18rem">
-      <chat main [agent]="agent" class="flex-1 min-w-0" />
+    <example-chat-layout sidebarWidth="20rem">
+      <chat main [agent]="agent" class="flex-1 min-w-0">
+        <div chatWelcomeSuggestions>
+          @for (s of suggestions; track s.value) {
+            <chat-welcome-suggestion
+              [label]="s.label"
+              [value]="s.value"
+              [description]="s.description"
+              (selected)="send($event)"
+            />
+          }
+        </div>
+      </chat>
       <div sidebar class="panel">
-        <h3 class="cap">Delegations</h3>
-        @if (delegations().length === 0) {
-          <p class="empty">No delegations yet</p>
-        }
-        @for (d of delegations(); track d.id) {
-          <div class="delegation">
-            <span class="dot"
-                  [class.dot--running]="d.status === 'running'"
-                  [class.dot--complete]="d.status === 'complete'"
-                  [class.dot--error]="d.status === 'error'">
-            </span>
-            <span class="agent">{{ d.agent }}</span>
-            <span class="status">{{ d.statusText }}</span>
-          </div>
-        }
+        <h3 class="cap">Dispatches</h3>
+        <p class="count" data-testid="dispatch-count">
+          {{ dispatchCount() }} dispatched, {{ runningCount() }} running
+        </p>
+        <h3 class="cap">Specialists</h3>
+        <ul class="roster">
+          <li><span class="roster__name">field-researcher</span> — elevation and runway length</li>
+          <li><span class="roster__name">weather-analyst</span> — conditions and operational impact</li>
+        </ul>
       </div>
     </example-chat-layout>
   `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
-
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
-
-    .delegation {
-      display: flex;
-      align-items: center;
-      gap: 0.5rem;
-      padding: 0.25rem 0;
-      font-size: var(--tplane-chat-font-size-sm);
-    }
-
-    .dot {
-      flex: 0 0 auto;
-      width: 0.5rem;
-      height: 0.5rem;
-      border-radius: var(--tplane-chat-radius-launcher);
-    }
-
-    .dot--running {
-      background: var(--tplane-chat-warning-text);
-    }
+  styles: [
+    `
+      .panel {
+        display: flex;
+        flex-direction: column;
+        gap: 0.75rem;
+        padding: 1rem;
+        background: var(--tplane-chat-bg);
+        color: var(--tplane-chat-text);
+      }
 
-    .dot--complete {
-      background: var(--tplane-chat-success);
-    }
+      .cap {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        font-weight: 700;
+        letter-spacing: 0.12em;
+        line-height: var(--tplane-chat-line-height-tight);
+        text-transform: uppercase;
+      }
 
-    .dot--error {
-      background: var(--tplane-chat-error-text);
-    }
+      .count {
+        margin: 0;
+        color: var(--tplane-chat-text);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
 
-    .agent {
-      min-width: 0;
-      overflow: hidden;
-      color: var(--tplane-chat-text);
-      font-weight: 600;
-      text-overflow: ellipsis;
-      white-space: nowrap;
-    }
+      .roster {
+        display: flex;
+        flex-direction: column;
+        gap: 0.25rem;
+        margin: 0;
+        padding: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        line-height: var(--tplane-chat-line-height);
+        list-style: none;
+      }
 
-    .status {
-      margin-left: auto;
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-    }
-  `],
+      .roster__name {
+        color: var(--tplane-chat-text);
+        font-family: var(--tplane-chat-font-mono);
+      }
+    `,
+  ],
 })
 export class SubagentsComponent {
-  /**
-   * The streaming resource connected to the subagents orchestrator graph.
-   */
   protected readonly agent = injectAgent();
 
-  /**
-   * Reactive delegation list derived from messages.
-   *
-   * Scans all messages for AI tool_calls, then checks for matching
-   * ToolMessage results (by tool_call_id) to determine completion status.
-   */
-  protected readonly delegations = computed<Delegation[]>(() => {
-    const msgs = this.agent.langGraphMessages();
-    const toolResultIds = new Set<string>();
-    const errorResultIds = new Set<string>();
+  protected readonly suggestions = SUGGESTIONS;
 
-    // Collect all tool result message IDs and detect errors
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function' ? msg._getType() : (msg as any).type;
-      if (type === 'tool') {
-        const toolCallId = (msg as any).tool_call_id;
-        if (toolCallId) {
-          toolResultIds.add(toolCallId);
-          const status = (msg as any).status;
-          if (status === 'error') {
-            errorResultIds.add(toolCallId);
-          }
-        }
-      }
-    }
+  private readonly dispatches = computed(() => [...this.agent.subagents().values()]);
 
-    // Extract tool calls from AI messages and match with results
-    const delegations: Delegation[] = [];
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function' ? msg._getType() : (msg as any).type;
-      if (type === 'ai') {
-        const toolCalls = (msg as any).tool_calls as Array<{ id: string; name: string }> | undefined;
-        if (toolCalls?.length) {
-          for (const tc of toolCalls) {
-            const isError = errorResultIds.has(tc.id);
-            const isComplete = toolResultIds.has(tc.id);
-            delegations.push({
-              id: tc.id,
-              agent: tc.name,
-              status: isError ? 'error' : isComplete ? 'complete' : 'running',
-              statusText: isError ? 'error' : isComplete ? 'done' : 'running',
-            });
-          }
-        }
-      }
-    }
+  protected readonly dispatchCount = computed(() => this.dispatches().length);
+
+  protected readonly runningCount = computed(
+    () => this.dispatches().filter((subagent) => subagent.status() === 'running').length,
+  );
 
-    return delegations;
-  });
+  protected send(text: string): void {
+    void this.agent.submit({ message: text });
+  }
 }
diff --git a/cockpit/deep-agents/subagents/angular/src/index.ts b/cockpit/deep-agents/subagents/angular/src/index.ts
index e0ef72e22..85e182103 100644
--- a/cockpit/deep-agents/subagents/angular/src/index.ts
+++ b/cockpit/deep-agents/subagents/angular/src/index.ts
@@ -30,6 +30,7 @@ export const deepAgentsSubagentsAngularModule: CockpitCapabilityModule = {
     'cockpit/deep-agents/subagents/angular/prompts/subagents.md',
   ],
   codeAssetPaths: [
-    'cockpit/deep-agents/subagents/angular/src/app.component.ts',
+    'cockpit/deep-agents/subagents/angular/src/app/subagents.component.ts',
+    'cockpit/deep-agents/subagents/angular/src/app/app.config.ts',
   ],
 };
diff --git a/cockpit/deep-agents/subagents/python/docs/guide.md b/cockpit/deep-agents/subagents/python/docs/guide.md
index 875b797e2..b2a438db3 100644
--- a/cockpit/deep-agents/subagents/python/docs/guide.md
+++ b/cockpit/deep-agents/subagents/python/docs/guide.md
@@ -1,155 +1,111 @@
-# Child Agent Delegation with Angular
+# Subagents with Deep Agents
 
 <Summary>
-Build a chat interface that shows real-time subagent activity using `provideAgent()` and
-`injectAgent()` from `@threadplane/langgraph`. An orchestrator agent delegates subtasks to specialist child
-agents, and the sidebar displays each subagent's status and message count as they stream.
+Render real child agents. `SubAgentMiddleware` dispatches through a single `task` tool, and
+each dispatch runs as its own graph in a `tools:<call_id>` namespace — so several children
+streaming at the same time stay in separate cards. `task` is the SubagentTracker's default
+dispatch-tool name, so the Angular side needs no configuration for this to work.
 </Summary>
 
 <Prompt>
-Add a subagent activity sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.subagents()` to access the live Map of child agent streams, derive `subagentEntries` with `computed()`, and render them beside the `<chat>` component.
+Render subagent dispatches beside the `<chat>` component: `task` tool calls already register
+as subagents, so read `agent.subagents()` for a live dispatch and running count.
 </Prompt>
 
+<Callout type="info" title="Provider setup lives in the LangGraph quickstart">
+This guide assumes `provideAgent()` is already configured. If it is not, work through the
+[LangGraph quickstart](/docs/langgraph/getting-started/quickstart) first.
+</Callout>
+
 <Steps>
-<Step title="Configure the provider">
+<Step title="Declare the specialists">
 
-Set up `provideAgent()` in your app config with the LangGraph API URL:
+A `SubAgent` is a name, a description the orchestrator reads when choosing, a system prompt,
+and the tools that child may use. Giving the orchestrator no lookup tools of its own is what
+forces delegation:
 
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'subagents',
-    }),
-  ],
-};
+```python
+from deepagents import SubAgent, create_deep_agent
+
+FIELD_RESEARCHER: SubAgent = {
+    "name": "field-researcher",
+    "description": "Gathers field elevation and runway length for one airport.",
+    "system_prompt": "You research airport field data for a dispatch desk. ...",
+    "tools": [lookup_field_elevation, lookup_runway_length],
+}
+
+graph = create_deep_agent(
+    model=ChatOpenAI(model="gpt-4.1", temperature=0),
+    system_prompt=(PROMPTS_DIR / "subagents.md").read_text(),
+    subagents=[FIELD_RESEARCHER, WEATHER_ANALYST],
+)
 ```
 
-This makes the configured agent available to all `injectAgent()` calls in your app.
+Passing `subagents` is what installs `SubAgentMiddleware` and, with it, the `task` tool.
 
 </Step>
-<Step title="Create the streaming resource">
+<Step title="Ask for parallel work explicitly">
 
-In your component, call `injectAgent()` to retrieve the configured subagents agent:
+The model will serialize dispatches unless told not to. One line in the orchestrator prompt
+changes the shape of the run:
 
-```typescript
-// subagents.component.ts
-import { injectAgent } from '@threadplane/langgraph';
-
-export class SubagentsComponent {
-  protected readonly stream = injectAgent();
-}
+```markdown
+When a request covers more than one airport or more than one kind of data,
+issue every dispatch you need **in a single turn** so the specialists work in
+parallel. Do not wait for one to report before sending the next.
 ```
 
-The resource manages the connection, message history, loading state, and all subagent streams automatically.
-
 </Step>
-<Step title="Derive subagent entries with computed()">
+<Step title="Know which tool call means a child started">
 
-Use Angular's `computed()` to convert the subagents Map into a renderable array:
+`task` is an ordinary tool call on the wire. What makes it render as a child agent is that the
+SubagentTracker recognizes the name — and `task` is its default, so a `deepagents` graph needs
+no client configuration at all. Naming it explicitly is worth doing anyway, as documentation:
 
 ```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
-
-export class SubagentsComponent {
-  protected readonly stream = injectAgent();
-
-  subagentEntries = computed(() => Array.from(this.stream.subagents().entries()));
-}
+provideAgent({
+  apiUrl: environment.langGraphApiUrl,
+  assistantId: environment.streamingAssistantId,
+  // The default. Set it when your dispatch tool is named something else.
+  subagentToolNames: ['task'],
+});
 ```
 
-`stream.subagents()` returns a `Map<string, SubagentStreamRef>` keyed by tool call ID. Each `SubagentStreamRef` exposes `status`, `messages`, and `values` as Angular Signals.
-
-<Tip>
-`stream.subagents()` is a signal that updates whenever a new subagent is spawned or an existing one changes status. `computed()` recalculates `subagentEntries` automatically on each update.
-</Tip>
+The SubagentTracker registers the dispatch from the tool call — including the `subagent_type`
+argument, which becomes the card's name — and then matches the child's `tools:<call_id>`
+namespace. Because the dispatch is registered before the child emits its first token,
+attribution never depends on message ordering, which is exactly why concurrent children do not
+bleed into each other.
 
 </Step>
-<Step title="Build the template with subagent sidebar">
-
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
-
-```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>Subagents</h3>
-    @for (entry of subagentEntries(); track entry[0]) {
-      <div>
-        <span>{{ entry[1].status() }}</span>
-        <span>{{ entry[0].slice(0, 8) }}…</span>
-        <div>{{ entry[1].messages().length }} messages</div>
-      </div>
-    }
-    @empty {
-      <p>Ask a question to see subagent activity.</p>
-    }
-</aside>
-```
+<Step title="Let the cards render inline, and count them in the sidebar">
 
-Each `entry` is a `[toolCallId, SubagentStreamRef]` tuple. The tool call ID identifies which specialist was invoked; the ref's signals update in real time.
+The `<chat>` composition renders each dispatch as a `<chat-subagent-card>` in the conversation
+and keeps it, collapsed, after completion. A separate active-only tray would duplicate that, so
+the sidebar is better spent on something the cards do not show — how wide the fan-out went:
 
-</Step>
-<Step title="The LangGraph orchestrator backend">
+```typescript
+private readonly dispatches = computed(() => [...this.agent.subagents().values()]);
 
-The backend uses an orchestrator node that binds specialist tools and a conditional edge to run them:
+protected readonly dispatchCount = computed(() => this.dispatches().length);
 
-```python
-# graph.py
-from langgraph.graph import StateGraph, END
-from langchain_core.tools import tool
-
-@tool
-async def research_agent(topic: str) -> str:
-    """Spawn a research subagent to gather information on a topic."""
-    # Delegate to a specialist LLM
-    ...
-
-@tool
-async def analysis_agent(content: str) -> str:
-    """Spawn an analysis subagent to analyze and synthesize information."""
-    ...
-
-def build_subagents_graph():
-    orchestrator_llm = llm.bind_tools([research_agent, analysis_agent, summary_agent])
-
-    async def orchestrate(state):
-        response = await orchestrator_llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    def should_continue(state):
-        last = state["messages"][-1]
-        if last.tool_calls:
-            return "run_subagents"
-        return END
-
-    graph = StateGraph(SubagentsState)
-    graph.add_node("orchestrate", orchestrate)
-    graph.add_node("run_subagents", run_subagents)
-    graph.add_conditional_edges("orchestrate", should_continue, ...)
-    graph.add_edge("run_subagents", "respond")
-    return graph.compile()
+protected readonly runningCount = computed(
+  () => this.dispatches().filter((subagent) => subagent.status() === 'running').length,
+);
 ```
 
-Each tool call emits a `ToolMessage` that the frontend tracks as a subagent stream entry.
-
-<Tip>
-For real parallel subagent execution, use `asyncio.gather()` inside `run_subagents` to invoke all tools concurrently. The frontend's `stream.subagents()` will show multiple subagents running simultaneously.
-</Tip>
+Note that `status` is itself a signal on the `Subagent` record, so it is called, not read.
 
 </Step>
 </Steps>
 
 <Tip>
-The `@empty` block in `@for` renders when no subagents have been spawned yet — a clean placeholder before the first message is submitted.
+Give every dispatch a `description` that names its subject and says what you want back. It is
+both the child's opening instruction and the label a reader sees on the card, so a vague
+description costs twice.
 </Tip>
 
 <Related>
-- [Chat Subagents](/chat/core-capabilities/subagents/overview/python) — Learn how ChatSubagentsComponent visualizes nested agent delegation
+- [Deep Agents Planning](/deep-agents/core-capabilities/planning/overview/python) — the orchestrator's own todo list
+- [Chat Subagents](/chat/core-capabilities/subagents/overview/python) — the card components on their own
 </Related>
diff --git a/cockpit/deep-agents/subagents/python/prompts/subagents.md b/cockpit/deep-agents/subagents/python/prompts/subagents.md
index 1222a5335..64371c086 100644
--- a/cockpit/deep-agents/subagents/python/prompts/subagents.md
+++ b/cockpit/deep-agents/subagents/python/prompts/subagents.md
@@ -1,12 +1,24 @@
-# Orchestrator Agent
+# Dispatch Orchestrator
 
-You are an orchestrator agent that delegates research and analysis tasks to
-specialist subagents. When given a complex question, break it into subtasks
-and delegate each to the appropriate specialist:
+You are the lead dispatcher. You do not look up airport data yourself — you
+delegate it.
 
-- Use `research_agent` to gather factual information on a topic
-- Use `analysis_agent` to analyze and find patterns in collected information
-- Use `summary_agent` to synthesize findings into a clear final answer
+## Delegation
 
-Always delegate before responding directly. Coordinate your subagents to
-produce a comprehensive, well-reasoned answer.
+Use the `task` tool to hand work to a specialist:
+
+- `field-researcher` gathers field elevation and runway length for one airport.
+- `weather-analyst` reads the current conditions for one airport and says what
+  they mean for a departure or arrival.
+
+Give each dispatch a `description` that names the airport and says exactly what
+you want back. One airport per dispatch — never ask a specialist to cover two.
+
+When a request covers more than one airport or more than one kind of data,
+issue every dispatch you need **in a single turn** so the specialists work in
+parallel. Do not wait for one to report before sending the next.
+
+## Reporting
+
+When every specialist has reported, write a short briefing in your own words —
+three or four sentences. Do not paste a specialist's report verbatim.
diff --git a/cockpit/deep-agents/subagents/python/pyproject.toml b/cockpit/deep-agents/subagents/python/pyproject.toml
index 13c49a205..cd841b191 100644
--- a/cockpit/deep-agents/subagents/python/pyproject.toml
+++ b/cockpit/deep-agents/subagents/python/pyproject.toml
@@ -3,14 +3,15 @@ name = "cockpit-deep-agents-subagents"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = [
-    "langgraph>=0.3",
+    "deepagents==0.7.11",
     "langchain-openai>=0.3",
+    "langgraph>=0.3",
     "langsmith>=0.2",
 ]
 
 [tool.uv]
 dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
+    "langgraph-cli[inmem]>=0.4",
 ]
 
 [build-system]
diff --git a/cockpit/deep-agents/subagents/python/src/graph.py b/cockpit/deep-agents/subagents/python/src/graph.py
index a66f73341..da34fce12 100644
--- a/cockpit/deep-agents/subagents/python/src/graph.py
+++ b/cockpit/deep-agents/subagents/python/src/graph.py
@@ -1,115 +1,118 @@
-"""
-Deep Agents Subagents Graph
+"""Deep Agents subagents capability: real child agents behind the `task` tool.
+
+`SubAgentMiddleware` gives the orchestrator a `task` tool taking
+`{description, subagent_type}`. Each dispatch runs a real child graph in its own
+`tools:<call_id>` namespace, and the child is seeded with the orchestrator's
+`description` before its first token.
 
-Demonstrates the orchestrator + subagent delegation pattern. The orchestrator
-receives a task and delegates to specialist subagent tools. Each tool call
-represents a child agent invocation whose progress streams independently to
-the frontend via stream.subagents().
+That ordering is what lets the frontend attribute child output structurally
+rather than by guessing: the SubagentTracker registers the dispatch from the
+`task` call and matches the child namespace exactly. On the Angular side the
+only wiring needed is `subagentToolNames: ['task']`.
+
+Parallel fan-out works: two `task` calls in one turn produce two children with
+distinct namespaces, distinct transcripts, and no cross-wiring.
 """
 
 from pathlib import Path
-from typing import TypedDict, Annotated
-import operator
-from langgraph.graph import StateGraph, END
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, BaseMessage, AIMessage, ToolMessage
+
+from deepagents import SubAgent, create_deep_agent
 from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
 
 PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
 
-
-class SubagentsState(TypedDict):
-    messages: Annotated[list[BaseMessage], operator.add]
-
-
-def build_subagents_graph():
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
-
-    @tool
-    async def research_agent(topic: str) -> str:
-        """Spawn a research subagent to gather information on a topic."""
-        research_llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
-        response = await research_llm.ainvoke([
-            SystemMessage(content="You are a research specialist. Provide concise, factual information."),
-            {"role": "human", "content": f"Research this topic and provide key facts: {topic}"},
-        ])
-        return str(response.content)
-
-    @tool
-    async def analysis_agent(content: str) -> str:
-        """Spawn an analysis subagent to analyze and synthesize information."""
-        analysis_llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
-        response = await analysis_llm.ainvoke([
-            SystemMessage(content="You are an analysis specialist. Identify patterns, draw insights, and synthesize information clearly."),
-            {"role": "human", "content": f"Analyze this content and provide key insights: {content}"},
-        ])
-        return str(response.content)
-
-    @tool
-    async def summary_agent(findings: str) -> str:
-        """Spawn a summary subagent to produce a final coherent response."""
-        summary_llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
-        response = await summary_llm.ainvoke([
-            SystemMessage(content="You are a summarization specialist. Produce clear, well-structured summaries."),
-            {"role": "human", "content": f"Summarize these findings into a concise final answer: {findings}"},
-        ])
-        return str(response.content)
-
-    tools = [research_agent, analysis_agent, summary_agent]
-    tools_by_name = {t.name: t for t in tools}
-    orchestrator_llm = llm.bind_tools(tools)
-
-    async def orchestrate(state: SubagentsState) -> dict:
-        """Orchestrator decides which subagents to invoke."""
-        system_prompt = (PROMPTS_DIR / "subagents.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await orchestrator_llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    async def run_subagents(state: SubagentsState) -> dict:
-        """Execute each tool call as a subagent invocation."""
-        last_message = state["messages"][-1]
-        if not isinstance(last_message, AIMessage) or not last_message.tool_calls:
-            return {"messages": []}
-
-        tool_messages = []
-        for tool_call in last_message.tool_calls:
-            tool_fn = tools_by_name.get(tool_call["name"])
-            if tool_fn:
-                result = await tool_fn.ainvoke(tool_call["args"])
-                tool_messages.append(
-                    ToolMessage(
-                        content=str(result),
-                        tool_call_id=tool_call["id"],
-                    )
-                )
-        return {"messages": tool_messages}
-
-    async def respond(state: SubagentsState) -> dict:
-        """Produce the final response after all subagents have completed."""
-        system_prompt = (PROMPTS_DIR / "subagents.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await orchestrator_llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    def should_continue(state: SubagentsState) -> str:
-        last_message = state["messages"][-1]
-        if isinstance(last_message, AIMessage) and last_message.tool_calls:
-            return "run_subagents"
-        return END
-
-    graph = StateGraph(SubagentsState)
-    graph.add_node("orchestrate", orchestrate)
-    graph.add_node("run_subagents", run_subagents)
-    graph.add_node("respond", respond)
-    graph.set_entry_point("orchestrate")
-    graph.add_conditional_edges("orchestrate", should_continue, {
-        "run_subagents": "run_subagents",
-        END: END,
-    })
-    graph.add_edge("run_subagents", "respond")
-    graph.add_edge("respond", END)
-    return graph.compile()
-
-
-graph = build_subagents_graph()
+FIELD_ELEVATION_FT = {
+    "KSFO": 13,
+    "KDEN": 5434,
+    "KJFK": 13,
+    "KASE": 7820,
+    "KLAX": 125,
+    "KBOS": 20,
+}
+
+RUNWAY_LENGTH_FT = {
+    "KSFO": 11870,
+    "KDEN": 16000,
+    "KJFK": 14511,
+    "KASE": 8006,
+    "KLAX": 12091,
+    "KBOS": 10083,
+}
+
+WEATHER = {
+    "KSFO": "Marine layer, 800 ft overcast, visibility 4 SM, wind 280 at 14.",
+    "KDEN": "Clear, visibility 10 SM, wind 350 at 22 gusting 31.",
+    "KJFK": "Broken 3500 ft, visibility 10 SM, wind 040 at 9.",
+    "KASE": "Few 12000 ft, visibility 10 SM, wind 300 at 6, mountain wave advisory.",
+    "KLAX": "Clear, visibility 10 SM, wind 260 at 8.",
+    "KBOS": "Overcast 1200 ft, visibility 6 SM, wind 120 at 17.",
+}
+
+
+@tool
+def lookup_field_elevation(airport: str) -> str:
+    """Return the field elevation in feet for a four-letter ICAO airport code."""
+    elevation = FIELD_ELEVATION_FT.get(airport.upper())
+    if elevation is None:
+        return f"No field elevation on file for {airport.upper()}."
+    return f"{airport.upper()} field elevation is {elevation} ft."
+
+
+@tool
+def lookup_runway_length(airport: str) -> str:
+    """Return the longest runway length in feet for a four-letter ICAO airport code."""
+    length = RUNWAY_LENGTH_FT.get(airport.upper())
+    if length is None:
+        return f"No runway data on file for {airport.upper()}."
+    return f"{airport.upper()} longest runway is {length} ft."
+
+
+@tool
+def lookup_weather(airport: str) -> str:
+    """Return the current field conditions for a four-letter ICAO airport code."""
+    conditions = WEATHER.get(airport.upper())
+    if conditions is None:
+        return f"No observation on file for {airport.upper()}."
+    return f"{airport.upper()}: {conditions}"
+
+
+FIELD_RESEARCHER: SubAgent = {
+    "name": "field-researcher",
+    "description": "Gathers field elevation and runway length for one airport.",
+    "system_prompt": (
+        "You research airport field data for a dispatch desk. Use "
+        "lookup_field_elevation and lookup_runway_length for the airport you were "
+        "given. Reply with two sentences: the numbers, and whether the runway is "
+        "long enough for a mid-size business jet at that elevation."
+    ),
+    "tools": [lookup_field_elevation, lookup_runway_length],
+}
+
+WEATHER_ANALYST: SubAgent = {
+    "name": "weather-analyst",
+    "description": "Reads the current conditions for one airport and calls out the operational impact.",
+    "system_prompt": (
+        "You read weather for a dispatch desk. Use lookup_weather for the airport "
+        "you were given. Reply with two sentences: the conditions, and what they "
+        "mean for a departure or arrival there."
+    ),
+    "tools": [lookup_weather],
+}
+
+
+def build_subagents_agent():
+    """Build the orchestrator.
+
+    Passing `subagents` is what installs `SubAgentMiddleware` and, with it, the
+    `task` tool. The orchestrator gets no lookup tools of its own so it has no
+    way to answer without delegating.
+    """
+    return create_deep_agent(
+        model=ChatOpenAI(model="gpt-4.1", temperature=0),
+        system_prompt=(PROMPTS_DIR / "subagents.md").read_text(),
+        subagents=[FIELD_RESEARCHER, WEATHER_ANALYST],
+    )
+
+
+graph = build_subagents_agent()
diff --git a/cockpit/deep-agents/subagents/python/uv.lock b/cockpit/deep-agents/subagents/python/uv.lock
index 9ff186dae..66ab537ec 100644
--- a/cockpit/deep-agents/subagents/python/uv.lock
+++ b/cockpit/deep-agents/subagents/python/uv.lock
@@ -1,190 +1,304 @@
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
 [[package]]
 name = "annotated-types"
-version = "0.7.0"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
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 source = { editable = "." }
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 ]
 
 [[package]]

From 35b318c480617ab12d7b2cf49f429c1cd76082bc Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 12:29:42 -0700
Subject: [PATCH 5/7] feat(cockpit): rebuild the deep-agents memory topic on
 deepagents
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The old graph ran a second model call after every turn asking for "any new
facts about the user as a JSON object", stuffed the result into an
`agent_memory` dict on the thread's state, and pasted it back into the next
system prompt. It was hand-rolled fact extraction, and because the dict
lived on the thread it remembered nothing across conversations — the one
thing memory is for.

This is `MemoryMiddleware` over `StoreBackend`. The agent owns
`/memories/AGENTS.md`: the middleware loads it into the system prompt every
turn and the model rewrites it with `edit_file` when it learns something
durable. Nothing in the application parses the conversation. `StoreBackend`
on a fixed namespace puts the file in LangGraph's store rather than on the
thread, so a brand new thread starts already knowing. The prompt says
plainly what must never be written there, credentials included.

Reading it back needed work. `memory_contents` is annotated
`PrivateStateAttr`, so it is absent from the `values` stream — verified,
not assumed: a live run's values events carry `messages` and nothing else.
`MemoryVisibilityMiddleware` republishes the key as a `custom` stream event,
which reaches `agent.customEvents()`.

A finding worth recording: the settle-time fallback alone is enough to fill
the panel. `@threadplane/langgraph` projects the latest checkpoint into
`value()` at run completion, and `memory_contents` IS on the checkpoint, so
removing the visibility middleware left the first version of this e2e green.
The panel now reports which of the two sources it is showing, and the spec
asserts `live` — so the middleware is load-bearing for the live path and the
mutation goes red with `Received: "checkpoint"`.

Verified live against `langgraph dev` with a real key across two threads:
the first writes the file, the second loads it and answers from it.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../memory/angular/e2e/da-memory.spec.ts      |   41 +-
 .../angular/e2e/fixtures/da-memory.json       |   46 +-
 .../angular/e2e/manual/memory.manual.ts       |   24 +-
 .../memory/angular/prompts/memory.md          |    4 +-
 .../memory/angular/src/app/app.config.ts      |    2 -
 .../angular/src/app/memory.component.ts       |  264 +-
 .../deep-agents/memory/angular/src/index.ts   |    3 +-
 .../deep-agents/memory/python/docs/guide.md   |  190 +-
 .../memory/python/prompts/memory.md           |   31 +-
 .../deep-agents/memory/python/pyproject.toml  |    5 +-
 .../deep-agents/memory/python/src/graph.py    |  140 +-
 cockpit/deep-agents/memory/python/uv.lock     | 2294 +++++++++++------
 12 files changed, 1929 insertions(+), 1115 deletions(-)

diff --git a/cockpit/deep-agents/memory/angular/e2e/da-memory.spec.ts b/cockpit/deep-agents/memory/angular/e2e/da-memory.spec.ts
index fa230fd6c..1f53a39f5 100644
--- a/cockpit/deep-agents/memory/angular/e2e/da-memory.spec.ts
+++ b/cockpit/deep-agents/memory/angular/e2e/da-memory.spec.ts
@@ -2,7 +2,42 @@
 import { test, expect } from '@playwright/test';
 import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
 
-test('da-memory: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
+// Match the welcome suggestions in memory.component.ts and the fixture in
+// e2e/fixtures/da-memory.json.
+const TEACH =
+  'I fly a Citation CJ3 out of KASE, and I always want briefings in bullet points. Please remember that.';
+const RECALL = 'What do you already know about my operation?';
+
+test('da-memory: the agent writes its memory file and a new thread still has it', async ({
+  page,
+}) => {
+  // First conversation: the agent edits /memories/AGENTS.md itself. Nothing in
+  // this app parses the conversation for facts.
+  await submitAndWaitForResponse(page, TEACH);
+
+  // `submitAndWaitForResponse` navigates, so this second call runs on a FRESH
+  // page load and therefore a fresh thread. The memory file is not on the
+  // thread — StoreBackend put it in the LangGraph store — so this asserts
+  // cross-thread persistence, not that the panel remembers its own DOM.
+  await submitAndWaitForResponse(page, RECALL);
+
+  // The panel only fills in because the graph republishes `memory_contents` as
+  // a custom stream event. The key is PrivateStateAttr, so it never reaches the
+  // `values` stream and a panel bound to agent.value() alone would be empty
+  // during the run.
+  const file = page.locator('[data-testid="memory-file"][data-path="/memories/AGENTS.md"]');
+  await expect(file).toBeVisible({ timeout: 30_000 });
+
+  // The contents are what the agent wrote in the FIRST conversation.
+  await expect(file).toContainText('Citation CJ3');
+  await expect(file).toContainText('KASE');
+  await expect(file).toContainText(/bullet points/i);
+  await expect(page.getByTestId('memory-line').first()).toContainText('Crew notes');
+
+  // The panel is showing the LIVE source, which only exists because the graph
+  // republishes `memory_contents` as a custom stream event. Without that
+  // middleware the panel still fills in, but only from the settle-time
+  // checkpoint hydration — so this assertion is what keeps the live path
+  // honest.
+  await expect(page.getByTestId('memory-source')).toHaveAttribute('data-source', 'live');
 });
diff --git a/cockpit/deep-agents/memory/angular/e2e/fixtures/da-memory.json b/cockpit/deep-agents/memory/angular/e2e/fixtures/da-memory.json
index 4bc44176f..b2b0de1ae 100644
--- a/cockpit/deep-agents/memory/angular/e2e/fixtures/da-memory.json
+++ b/cockpit/deep-agents/memory/angular/e2e/fixtures/da-memory.json
@@ -2,17 +2,53 @@
   "fixtures": [
     {
       "match": {
-        "userMessage": "Hello"
+        "userMessage": "I fly a Citation CJ3 out of KASE, and I always want briefings in bullet points. Please remember that.",
+        "hasToolResult": true,
+        "turnIndex": 1
       },
       "response": {
-        "content": "Hi \u2014 hello! How can I help you today?"
+        "toolCalls": [
+          {
+            "name": "write_file",
+            "arguments": "{\"file_path\":\"/memories/AGENTS.md\",\"content\":\"## Crew notes\\n- Aircraft: Citation CJ3\\n- Home base: KASE\\n- Briefing preference: Always provide briefings in bullet points\\n\"}"
+          }
+        ]
       }
     },
     {
-      "match": {},
+      "match": {
+        "userMessage": "I fly a Citation CJ3 out of KASE, and I always want briefings in bullet points. Please remember that.",
+        "hasToolResult": true,
+        "turnIndex": 2
+      },
+      "response": {
+        "content": "I've saved the following to my memory:\n- You fly a Citation CJ3 based at KASE.\n- You want all briefings provided in bullet points.\n\nI'll use this information for all future requests."
+      }
+    },
+    {
+      "match": {
+        "userMessage": "I fly a Citation CJ3 out of KASE, and I always want briefings in bullet points. Please remember that.",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "ls",
+            "arguments": "{\"path\":\"/memories\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "What do you already know about my operation?",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
       "response": {
-        "content": "{}"
+        "content": "Here’s what I have in my notes about your operation:\n\n- Aircraft: Citation CJ3\n- Home base: KASE (Aspen)\n- Briefing preference: Always provide briefings in bullet points\n\nIf anything has changed or you’d like to add more details (like crew names, typical routes, or special procedures), just let me know!"
       }
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/cockpit/deep-agents/memory/angular/e2e/manual/memory.manual.ts b/cockpit/deep-agents/memory/angular/e2e/manual/memory.manual.ts
index 21d64cec6..5dfef1aad 100644
--- a/cockpit/deep-agents/memory/angular/e2e/manual/memory.manual.ts
+++ b/cockpit/deep-agents/memory/angular/e2e/manual/memory.manual.ts
@@ -1,3 +1,8 @@
+// SPDX-License-Identifier: MIT
+//
+// Live-model manual check. Not run by CI (playwright matches *.spec.ts only).
+//   npx nx run cockpit:serve-memory
+//   npx playwright test cockpit/deep-agents/memory/angular/e2e/manual
 import { expect, test } from '@playwright/test';
 
 test.describe('Deep Agents Memory Example', () => {
@@ -6,16 +11,21 @@ test.describe('Deep Agents Memory Example', () => {
     await page.waitForSelector('app-da-memory', { state: 'attached' });
   });
 
-  test('renders the chat interface with memory sidebar', async ({ page }) => {
+  test('renders the chat interface with an empty memory panel', async ({ page }) => {
     await expect(page.locator('chat')).toBeVisible();
     await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
-    await expect(page.locator('text=No facts learned yet')).toBeVisible();
+    await expect(page.locator('text=Nothing remembered yet')).toBeVisible();
   });
 
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'My name is Alex and I love hiking.');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
+  test('remembers across a reload', async ({ page }) => {
+    await page.getByRole('button', { name: 'Tell it about your operation' }).click();
+    await expect(page.locator('chat-message[data-role="assistant"]').last()).toBeVisible({
+      timeout: 120000,
+    });
+
+    await page.reload();
+    await page.getByRole('button', { name: 'Start over and ask what it knows' }).click();
+    await expect(page.locator('[data-testid="memory-file"]')).toBeVisible({ timeout: 120000 });
+    await expect(page.locator('[data-testid="memory-line"]').first()).toBeVisible();
   });
 });
diff --git a/cockpit/deep-agents/memory/angular/prompts/memory.md b/cockpit/deep-agents/memory/angular/prompts/memory.md
index 848113832..b93314e93 100644
--- a/cockpit/deep-agents/memory/angular/prompts/memory.md
+++ b/cockpit/deep-agents/memory/angular/prompts/memory.md
@@ -1,5 +1,5 @@
 # Deep Agents Memory (Angular)
 
-This capability demonstrates how a deep agent stores, retrieves, and updates long-term memories across sessions using the `@threadplane/chat` Angular component library. The `<chat-debug>` component reveals every memory read and write operation — including the memory key, value, and retrieval score — so developers can verify that the agent is building and using its knowledge store correctly.
+This capability shows the file the agent keeps about you. `MemoryMiddleware` loads `/memories/AGENTS.md` into the system prompt at the start of every turn and the agent rewrites it with `edit_file` when it learns something durable — nothing in the Angular app parses the conversation for facts. `StoreBackend` puts that file in LangGraph's store rather than on the thread, so a brand new thread starts already knowing.
 
-Key components used: `<chat-debug>`. Memory tool calls (store_memory, retrieve_memories, delete_memory) appear as collapsible trace nodes, giving full visibility into how the agent's persistent knowledge base evolves over the course of a session and across session boundaries.
+Reading it back needs a detour, and that is the interesting part. `memory_contents` is annotated `PrivateStateAttr`, so it never reaches the `values` stream. The graph republishes it as a `custom` stream event, which arrives on `agent.customEvents()`; the key IS on the checkpoint, so `agent.value()` covers a reopened thread once the client has hydrated it.
diff --git a/cockpit/deep-agents/memory/angular/src/app/app.config.ts b/cockpit/deep-agents/memory/angular/src/app/app.config.ts
index c20520be0..67b6e650c 100644
--- a/cockpit/deep-agents/memory/angular/src/app/app.config.ts
+++ b/cockpit/deep-agents/memory/angular/src/app/app.config.ts
@@ -2,7 +2,6 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideAgent } from '@threadplane/langgraph';
 import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
 import { environment } from '../environments/environment';
 
 export const appConfig: ApplicationConfig = {
@@ -12,6 +11,5 @@ export const appConfig: ApplicationConfig = {
       assistantId: environment.streamingAssistantId,
     }),
     provideChat({}),
-    provideRender({}),
   ],
 };
diff --git a/cockpit/deep-agents/memory/angular/src/app/memory.component.ts b/cockpit/deep-agents/memory/angular/src/app/memory.component.ts
index 44dcefd95..a4923b30f 100644
--- a/cockpit/deep-agents/memory/angular/src/app/memory.component.ts
+++ b/cockpit/deep-agents/memory/angular/src/app/memory.component.ts
@@ -1,111 +1,225 @@
+// SPDX-License-Identifier: MIT
 import { Component, computed } from '@angular/core';
-import { ChatComponent } from '@threadplane/chat';
+import { ChatComponent, ChatWelcomeSuggestionComponent } from '@threadplane/chat';
 import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
 import { injectAgent } from '@threadplane/langgraph';
 
+/** Name of the custom stream event the graph's visibility middleware emits. */
+const MEMORY_EVENT = 'deep_agents.memory';
+
+interface MemoryFile {
+  path: string;
+  contents: string;
+  lines: string[];
+}
+
+const SUGGESTIONS = [
+  // values match cockpit/deep-agents/memory/angular/e2e/da-memory.spec.ts prompts.
+  {
+    label: 'Tell it about your operation',
+    value:
+      'I fly a Citation CJ3 out of KASE, and I always want briefings in bullet points. Please remember that.',
+    description: 'The agent edits its own memory file.',
+  },
+  {
+    label: 'Start over and ask what it knows',
+    value: 'What do you already know about my operation?',
+    description: 'Reload first: a new thread still loads the same memory.',
+  },
+] as const;
+
 /**
- * MemoryComponent demonstrates persistent agent memory across sessions.
+ * MemoryComponent shows the file the agent keeps about you.
  *
- * The agent extracts facts about the user from each conversation turn
- * and stores them in `agent_memory` (or `memory`) state. The sidebar
- * displays all learned facts as key-value pairs with a live count.
+ * The memory is not a state key this app maintains. `MemoryMiddleware` loads
+ * `/memories/AGENTS.md` into the system prompt at the start of every turn and
+ * the agent rewrites it with `edit_file` when it learns something durable.
+ * `StoreBackend` puts that file in LangGraph's store rather than on the thread,
+ * so a brand new thread starts already knowing.
  *
- * Key integration points:
- * - `stream.value()` exposes the full graph state including the memory dict
- * - `memoryEntries` is derived reactively for sidebar rendering
- * - Facts appear as the agent learns them during conversation
+ * Reading it back needs a detour. `MemoryMiddleware` annotates `memory_contents`
+ * with `PrivateStateAttr`, which keeps it out of the `values` stream entirely,
+ * so a panel bound to `agent.value()` alone would stay empty during a live run.
+ * Two sources cover the two cases:
+ *
+ * - live: the graph republishes the key as a `custom` stream event, which
+ *   arrives on `agent.customEvents()`;
+ * - settled: the key IS present in the thread's checkpoint, so once the client
+ *   has hydrated a thread, `agent.value()` carries it.
+ *
+ * The custom event wins when both are present, because it is the fresher of
+ * the two during a run.
  */
 @Component({
   selector: 'app-da-memory',
   standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
+  imports: [ChatComponent, ChatWelcomeSuggestionComponent, ExampleChatLayoutComponent],
   template: `
-    <example-chat-layout sidebarWidth="18rem">
-      <chat main [agent]="agent" class="flex-1 min-w-0" />
-      <div sidebar class="panel">
-        <h3 class="cap">
-          Learned Facts
-          @if (memoryEntries().length > 0) {
-            <span class="count">({{ memoryEntries().length }})</span>
+    <example-chat-layout sidebarWidth="22rem">
+      <chat main [agent]="agent" class="flex-1 min-w-0">
+        <div chatWelcomeSuggestions>
+          @for (s of suggestions; track s.value) {
+            <chat-welcome-suggestion
+              [label]="s.label"
+              [value]="s.value"
+              [description]="s.description"
+              (selected)="send($event)"
+            />
           }
-        </h3>
-        @if (memoryEntries().length === 0) {
-          <p class="empty">No facts learned yet</p>
+        </div>
+      </chat>
+      <div sidebar class="panel">
+        <h3 class="cap">Agent Memory</h3>
+        <p class="source" data-testid="memory-source" [attr.data-source]="memorySource()">
+          {{ memorySource() === 'live' ? 'streamed live' : memorySource() === 'checkpoint' ? 'from checkpoint' : 'no source yet' }}
+        </p>
+        @if (memoryFiles().length === 0) {
+          <p class="empty">Nothing remembered yet</p>
         }
-        @for (entry of memoryEntries(); track entry[0]) {
-          <div class="fact-row">
-            <span class="fact-key">{{ entry[0] }}:</span>
-            <span class="fact-value"> {{ entry[1] }}</span>
+        @for (file of memoryFiles(); track file.path) {
+          <div class="mem" data-testid="memory-file" [attr.data-path]="file.path">
+            <span class="mem__path">{{ file.path }}</span>
+            @for (line of file.lines; track $index) {
+              <p class="mem__line" data-testid="memory-line">{{ line }}</p>
+            }
           </div>
         }
+        <p class="hint">
+          This file lives in the LangGraph store, not on the thread. Reload the page to start a
+          new conversation and it will still be here.
+        </p>
       </div>
     </example-chat-layout>
   `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
+  styles: [
+    `
+      .panel {
+        display: flex;
+        flex-direction: column;
+        gap: 0.75rem;
+        padding: 1rem;
+        background: var(--tplane-chat-bg);
+        color: var(--tplane-chat-text);
+      }
 
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
+      .cap {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        font-weight: 700;
+        letter-spacing: 0.12em;
+        text-transform: uppercase;
+      }
 
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
+      .source {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
 
-    .fact-row {
-      padding: 0.25rem 0;
-      font-size: var(--tplane-chat-font-size-sm);
-    }
+      .empty {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-sm);
+        font-style: italic;
+      }
 
-    .fact-key {
-      color: var(--tplane-chat-text);
-      font-weight: 600;
-    }
+      .mem {
+        display: flex;
+        flex-direction: column;
+        gap: 0.125rem;
+        padding: 0.5rem;
+        border-radius: var(--tplane-chat-radius-card);
+        background: var(--tplane-chat-surface-alt);
+      }
 
-    .fact-value {
-      color: var(--tplane-chat-text-muted);
-    }
+      .mem__path {
+        color: var(--tplane-chat-text-muted);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
 
-    .count {
-      margin-left: 0.25rem;
-      font-variant-numeric: tabular-nums;
-    }
-  `],
+      .mem__line {
+        margin: 0;
+        color: var(--tplane-chat-text);
+        font-size: var(--tplane-chat-font-size-sm);
+      }
+
+      .hint {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        line-height: var(--tplane-chat-line-height);
+      }
+    `,
+  ],
 })
 export class MemoryComponent {
+  protected readonly agent = injectAgent();
+
+  protected readonly suggestions = SUGGESTIONS;
+
+  /** Latest `memory_contents` announced on the custom stream. */
+  private readonly liveMemory = computed<Record<string, string> | null>(() => {
+    for (const event of [...this.agent.customEvents()].reverse()) {
+      if (event.name !== MEMORY_EVENT) continue;
+      const contents = (event.data as { memory_contents?: unknown } | undefined)?.[
+        'memory_contents'
+      ];
+      if (contents && typeof contents === 'object') return contents as Record<string, string>;
+    }
+    return null;
+  });
+
   /**
-   * The streaming resource connected to the memory graph.
+   * `memory_contents` off the hydrated thread state.
    *
-   * The graph returns an `agent_memory` (or `memory`) dict alongside messages
-   * in its state. We derive a reactive signal from `stream.value()` for display.
+   * The key is private to the `values` stream but is written to the
+   * checkpoint, so this covers a reopened thread where no custom event has
+   * fired in this session.
    */
-  protected readonly agent = injectAgent();
+  private readonly settledMemory = computed<Record<string, string> | null>(() => {
+    const contents = (this.agent.value() as Record<string, unknown> | undefined)?.[
+      'memory_contents'
+    ];
+    return contents && typeof contents === 'object'
+      ? (contents as Record<string, string>)
+      : null;
+  });
 
   /**
-   * Reactive list of [key, value] memory entries derived from the graph state.
+   * Which of the two sources the panel is currently showing.
    *
-   * Checks for `agent_memory` first, then falls back to `memory`.
-   * This signal re-computes whenever the stream state changes.
+   * `live` means the graph's visibility middleware announced the key on the
+   * custom stream during this run. `checkpoint` means only the settle-time
+   * hydration has it — which is what a reopened thread looks like, and also
+   * what the panel degrades to if the middleware is removed.
    */
-  protected readonly memoryEntries = computed(() => {
-    const val = this.agent.value() as Record<string, unknown>;
-    const mem = val?.['agent_memory'] ?? val?.['memory'];
-    if (!mem || typeof mem !== 'object') return [];
-    return Object.entries(mem as Record<string, string>);
+  protected readonly memorySource = computed<'live' | 'checkpoint' | 'none'>(() => {
+    const live = this.liveMemory();
+    if (live && Object.keys(live).length > 0) return 'live';
+    return this.settledMemory() ? 'checkpoint' : 'none';
   });
+
+  protected readonly memoryFiles = computed<MemoryFile[]>(() => {
+    const live = this.liveMemory();
+    const source = live && Object.keys(live).length > 0 ? live : this.settledMemory();
+    if (!source) return [];
+    return Object.entries(source)
+      .filter(([, contents]) => typeof contents === 'string' && contents.trim().length > 0)
+      .map(([path, contents]) => ({
+        path,
+        contents,
+        lines: contents
+          .split('\n')
+          .map((line) => line.trim())
+          .filter((line) => line.length > 0),
+      }))
+      .sort((a, b) => a.path.localeCompare(b.path));
+  });
+
+  protected send(text: string): void {
+    void this.agent.submit({ message: text });
+  }
 }
diff --git a/cockpit/deep-agents/memory/angular/src/index.ts b/cockpit/deep-agents/memory/angular/src/index.ts
index 0a845b735..396b7d0b4 100644
--- a/cockpit/deep-agents/memory/angular/src/index.ts
+++ b/cockpit/deep-agents/memory/angular/src/index.ts
@@ -30,6 +30,7 @@ export const deepAgentsMemoryAngularModule: CockpitCapabilityModule = {
     'cockpit/deep-agents/memory/angular/prompts/memory.md',
   ],
   codeAssetPaths: [
-    'cockpit/deep-agents/memory/angular/src/app.component.ts',
+    'cockpit/deep-agents/memory/angular/src/app/memory.component.ts',
+    'cockpit/deep-agents/memory/angular/src/app/app.config.ts',
   ],
 };
diff --git a/cockpit/deep-agents/memory/python/docs/guide.md b/cockpit/deep-agents/memory/python/docs/guide.md
index 9242a8682..de4c6747e 100644
--- a/cockpit/deep-agents/memory/python/docs/guide.md
+++ b/cockpit/deep-agents/memory/python/docs/guide.md
@@ -1,146 +1,126 @@
-# Persistent Agent Memory with Angular
+# Memory with Deep Agents
 
 <Summary>
-Build a chat interface where the agent remembers facts about the user across turns using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. The agent stores learned facts in `agent_memory` state, and the sidebar displays them in real time.
+Give an agent a memory file it maintains itself, keep that file in LangGraph's store so it
+outlives the thread, and render it. The interesting part is reading it back:
+`memory_contents` is annotated `PrivateStateAttr`, so it never appears in the `values` stream
+and needs a deliberate channel.
 </Summary>
 
 <Prompt>
-Add a memory sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.value()` to access the agent's `agent_memory` state, derive `memoryEntries` with `computed()`, and bind them to the sidebar beside the `<chat>` component from `@threadplane/chat`.
+Add an agent-memory panel beside the `<chat>` component. Read `memory_contents` from
+`agent.customEvents()` for the live view and from `agent.value()` for a reopened thread, and
+render each remembered line.
 </Prompt>
 
-<Steps>
-<Step title="Configure the provider">
-
-Set up `provideAgent()` in your app config with the LangGraph API URL:
+<Callout type="info" title="Provider setup lives in the LangGraph quickstart">
+This guide assumes `provideAgent()` is already configured. If it is not, work through the
+[LangGraph quickstart](/docs/langgraph/getting-started/quickstart) first.
+</Callout>
 
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'da-memory',
-    }),
-  ],
-};
-```
+<Steps>
+<Step title="Point memory at a store, not at the thread">
 
-This makes the configured agent available to all `injectAgent()` calls in your app.
+`memory=[...]` installs `MemoryMiddleware`, which loads those files into the system prompt on
+every turn and instructs the model to keep them current with `edit_file`. The backend decides
+how long that survives:
 
-</Step>
-<Step title="Create the streaming resource">
-
-In your component, call `injectAgent()` to retrieve the configured memory agent:
+```python
+from deepagents import create_deep_agent
+from deepagents.backends import StoreBackend
 
-```typescript
-// memory.component.ts
-import { injectAgent } from '@threadplane/langgraph';
+MEMORY_NAMESPACE = ("cockpit", "deep-agents-memory")
 
-export class MemoryComponent {
-  protected readonly stream = injectAgent();
-}
+graph = create_deep_agent(
+    model=ChatOpenAI(model="gpt-4.1", temperature=0),
+    system_prompt=(PROMPTS_DIR / "memory.md").read_text(),
+    backend=StoreBackend(namespace=lambda _runtime: MEMORY_NAMESPACE),
+    memory=["/memories/AGENTS.md"],
+)
 ```
 
-The resource manages the connection, message history, loading state, and errors automatically.
+`StoreBackend` writes into LangGraph's `BaseStore`, which is shared across threads;
+`StateBackend` would put the file on the thread's own state, where a new conversation would
+never see it. `store=None` means "resolve the store from the graph execution context", which
+the LangGraph server supplies. Scope the namespace per user in anything real — a fixed tuple
+means every visitor shares one memory.
 
 </Step>
-<Step title="Derive memory entries with computed()">
+<Step title="Let the agent decide what to remember">
 
-Use Angular's `computed()` to reactively derive the memory key/value pairs from `stream.value()`:
+Nothing in the application parses the conversation for facts. The system prompt is the policy:
 
-```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
+```markdown
+`/memories/AGENTS.md` is yours. It is loaded into your context at the start of
+every conversation, including conversations you have not had yet.
 
-export class MemoryComponent {
-  protected readonly stream = injectAgent();
+Write to it with `edit_file` whenever the user tells you something durable:
+a home base, a fleet type, a standing preference, a correction.
 
-  memoryEntries = computed(() => {
-    const val = this.stream.value() as { agent_memory?: Record<string, string> } | undefined;
-    return Object.entries(val?.agent_memory ?? {});
-  });
-}
+Do not record one-off requests, small talk, or anything stale next week.
+Never record credentials of any kind.
 ```
 
-`stream.value()` contains the latest graph state. The memory graph stores learned facts under `agent_memory`, updating the dict as the agent extracts new information.
-
-<Tip>
-`stream.value()` is a signal that updates reactively as new state arrives from the server. `computed()` recalculates `memoryEntries` automatically whenever the stream updates.
-</Tip>
+The last line matters. A memory file is a persistent, model-writable document, so say plainly
+what must never go in it.
 
 </Step>
-<Step title="Build the template with memory sidebar">
-
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
-
-```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>Learned Facts</h3>
-    @for (entry of memoryEntries(); track entry[0]) {
-      <div>
-        <div style="font-weight: 600;">{{ entry[0] }}</div>
-        <div>{{ entry[1] }}</div>
-      </div>
-    }
-    @empty {
-      <p>Tell the agent something about yourself to see it remember.</p>
-    }
-</aside>
+<Step title="Republish the private key as a custom event">
+
+`MemoryMiddleware` annotates `memory_contents` with `PrivateStateAttr`. That keeps it out of
+the `values` stream — correct for a transcript, and the reason a panel bound to
+`agent.value()` shows nothing while the agent is working. A small middleware announces it on a
+channel the client does receive:
+
+```python
+class MemoryVisibilityMiddleware(AgentMiddleware):
+    def _emit(self, state):
+        contents = state.get("memory_contents")
+        if contents is None:
+            return
+        try:
+            writer = get_stream_writer()
+        except (RuntimeError, KeyError):
+            return
+        writer({"name": MEMORY_EVENT, "data": {"memory_contents": contents}})
+
+    def after_model(self, state, runtime):
+        self._emit(state)
+        return None
 ```
 
-The sibling panel renders memory entries reactively as the agent learns new facts.
+This is an application-side shim, not a framework change: the key stays private on the state
+and is simply announced alongside it.
 
 </Step>
-<Step title="The LangGraph memory backend">
+<Step title="Read both sources, and know which one you are on">
 
-The backend uses a two-node graph: one to generate a response, another to extract new facts:
+A custom event is a live signal — it is not replayed when a thread is reopened. The key IS
+written to the checkpoint, though, and `@threadplane/langgraph` projects the latest checkpoint
+into `value()` when a run completes. So there are two sources, and it is worth telling them
+apart:
 
-```python
-# graph.py
-from langgraph.graph import StateGraph, END
-
-def build_memory_graph():
-    async def generate(state: MemoryState) -> dict:
-        """Generate a response, using remembered facts in the prompt."""
-        memory = state.get("agent_memory", {})
-        # Inject memory into system prompt...
-        response = await llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    async def extract_facts(state: MemoryState) -> dict:
-        """Extract new facts from the conversation and update agent_memory."""
-        new_facts = {}  # LLM returns JSON dict of facts
-        current_memory = dict(state.get("agent_memory", {}))
-        current_memory.update(new_facts)
-        return {"agent_memory": current_memory}
-
-    graph = StateGraph(MemoryState)
-    graph.add_node("generate", generate)
-    graph.add_node("extract_facts", extract_facts)
-    graph.set_entry_point("generate")
-    graph.add_edge("generate", "extract_facts")
-    graph.add_edge("extract_facts", END)
-    return graph.compile()
+```typescript
+protected readonly memorySource = computed<'live' | 'checkpoint' | 'none'>(() => {
+  const live = this.liveMemory();
+  if (live && Object.keys(live).length > 0) return 'live';
+  return this.settledMemory() ? 'checkpoint' : 'none';
+});
 ```
 
-Each turn, `generate` uses existing memory to personalize its response, then `extract_facts` updates `agent_memory` with anything new the user shared. The frontend sees these updates via `stream.value()`.
-
-<Tip>
-Because LangGraph persists state across turns in a thread, `agent_memory` accumulates facts over the entire session. The agent becomes progressively more personalized as the conversation continues.
-</Tip>
+Without the middleware the panel still fills in, just a beat later and only at settle. With it,
+the panel updates while the agent is still writing.
 
 </Step>
 </Steps>
 
 <Tip>
-The `@empty` block in `@for` renders when the memory dict is empty — a clean way to show a placeholder before the user shares any personal information.
+Test cross-thread memory by starting a genuinely new thread, not by clearing the panel. A
+reload that creates a new thread and still shows the file is the only assertion that proves the
+store, rather than the component, is doing the remembering.
 </Tip>
 
 <Related>
-- [Chat Threads](/chat/core-capabilities/threads/overview/python) — Learn how ChatThreadsComponent manages conversation threads
+- [Deep Agents Skills](/deep-agents/core-capabilities/skills/overview/python) — the same private-state visibility problem, for `skills_metadata`
+- [Deep Agents Filesystem](/deep-agents/core-capabilities/filesystem/overview/python) — the state-backed workspace that does stream
 </Related>
diff --git a/cockpit/deep-agents/memory/python/prompts/memory.md b/cockpit/deep-agents/memory/python/prompts/memory.md
index 2f2fe29a6..4ad2e6435 100644
--- a/cockpit/deep-agents/memory/python/prompts/memory.md
+++ b/cockpit/deep-agents/memory/python/prompts/memory.md
@@ -1,11 +1,26 @@
-# Memory Agent
+# Dispatch Desk with Memory
 
-You are a helpful assistant that learns and remembers facts about the user across
-conversations. You have access to a memory of what you have learned about the user
-in previous turns (shown below if available).
+You are a dispatcher who works with the same crews week after week, so you keep
+notes about them.
 
-Use what you remember to personalize your responses. If the user shares something
-new about themselves — their name, preferences, goals, or background — acknowledge
-it naturally and incorporate it into your reply.
+## Your memory file
 
-Be concise and friendly. Do not explicitly list back the user's facts unless asked.
+`/memories/AGENTS.md` is yours. It is loaded into your context at the start of
+every conversation, including conversations you have not had yet, and it is the
+only thing about you that survives a new thread.
+
+Write to it with `edit_file` whenever the user tells you something durable:
+
+- a home base, a fleet type, an operating limitation
+- a standing preference about how they want briefings written
+- a correction to something you got wrong
+
+Keep it as a short markdown list under a `## Crew notes` heading. One line per
+fact. Do not record one-off requests, small talk, or anything that will be stale
+next week. Never record credentials of any kind.
+
+## Answering
+
+Use what you remember. If a fact in your memory file answers part of the
+request, say so rather than asking again. When you write something new to
+memory, say in one short sentence what you saved.
diff --git a/cockpit/deep-agents/memory/python/pyproject.toml b/cockpit/deep-agents/memory/python/pyproject.toml
index a1eaee44e..2b7148de2 100644
--- a/cockpit/deep-agents/memory/python/pyproject.toml
+++ b/cockpit/deep-agents/memory/python/pyproject.toml
@@ -3,14 +3,15 @@ name = "cockpit-deep-agents-memory"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = [
-    "langgraph>=0.3",
+    "deepagents==0.7.11",
     "langchain-openai>=0.3",
+    "langgraph>=0.3",
     "langsmith>=0.2",
 ]
 
 [tool.uv]
 dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
+    "langgraph-cli[inmem]>=0.4",
 ]
 
 [build-system]
diff --git a/cockpit/deep-agents/memory/python/src/graph.py b/cockpit/deep-agents/memory/python/src/graph.py
index 5137ee2ce..d18283a0c 100644
--- a/cockpit/deep-agents/memory/python/src/graph.py
+++ b/cockpit/deep-agents/memory/python/src/graph.py
@@ -1,78 +1,92 @@
+"""Deep Agents memory capability: cross-thread memory the agent writes itself.
+
+Two pieces make this work:
+
+* `memory=["/memories/AGENTS.md"]` installs `MemoryMiddleware`, which loads that
+  file into the system prompt on every turn and tells the model to keep it up to
+  date with `edit_file`. The agent authors its own memory; nothing here parses
+  the conversation for facts.
+* `StoreBackend` on a fixed namespace puts the file in LangGraph's `BaseStore`
+  rather than on the thread's state, so it survives into the next thread. That
+  is the whole point: a new conversation starts already knowing.
+
+The visibility problem: `MemoryMiddleware` annotates `memory_contents` with
+`PrivateStateAttr`, so it never appears in the `values` stream and a panel bound
+to `agent.value()` would stay empty. `MemoryVisibilityMiddleware` below
+republishes it as a `custom` stream event, which does reach the client. Custom
+events are live-only, so the client also hydrates from `getState` when a thread
+is reopened.
 """
-Deep Agents Memory Graph
 
-Demonstrates persistent agent memory across sessions. The agent extracts
-facts about the user from each conversation turn and stores them in the
-`agent_memory` dict in state. Subsequent turns include remembered facts
-in the system prompt, giving the agent context about the user.
-"""
-
-import json
-import re
 from pathlib import Path
-from typing import TypedDict
-from langgraph.graph import StateGraph, END
+from typing import Any
+
+from deepagents import create_deep_agent
+from deepagents.backends import StoreBackend
+from langchain.agents.middleware import AgentMiddleware
 from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, BaseMessage
+from langgraph.config import get_stream_writer
 
 PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
 
+MEMORY_FILE = "/memories/AGENTS.md"
 
-class MemoryState(TypedDict):
-    messages: list[BaseMessage]
-    agent_memory: dict[str, str]
-
-
-def build_memory_graph():
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True)
+#: Fixed namespace: every thread of this demo shares one memory. A real
+#: deployment would scope it, e.g. `lambda rt: (rt.server_info.user.identity,)`.
+MEMORY_NAMESPACE = ("cockpit", "deep-agents-memory")
 
-    async def generate(state: MemoryState) -> dict:
-        """Generate a response using remembered facts in the system prompt."""
-        system_prompt = (PROMPTS_DIR / "memory.md").read_text()
+#: Custom stream event name the Angular panel listens for.
+MEMORY_EVENT = "deep_agents.memory"
 
-        memory = state.get("agent_memory", {})
-        if memory:
-            facts_text = "\n".join(f"- {k}: {v}" for k, v in memory.items())
-            system_prompt += f"\n\n## What you remember about the user\n{facts_text}"
 
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-        return {"messages": [response]}
+class MemoryVisibilityMiddleware(AgentMiddleware):
+    """Republish `memory_contents` as a `custom` stream event.
 
-    async def extract_facts(state: MemoryState) -> dict:
-        """Parse the latest exchange for new facts to remember about the user."""
-        system_prompt = (
-            "You are a fact extractor. Given a conversation, extract any new facts "
-            "about the user as a JSON object where keys are short fact labels and "
-            "values are concise descriptions. Only include facts explicitly stated by "
-            "the user. If no new facts are present, return an empty object {}."
-        )
+    `PrivateStateAttr` keeps the key out of the `values` stream, which is right
+    for a transcript and wrong for a panel. This is a demo-side shim: the key
+    stays private on the state and is simply announced alongside it.
+    """
 
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
+    @property
+    def name(self) -> str:
+        return "MemoryVisibilityMiddleware"
 
-        current_memory = dict(state.get("agent_memory", {}))
+    def _emit(self, state: dict[str, Any]) -> None:
+        contents = state.get("memory_contents")
+        if contents is None:
+            return
         try:
-            raw = response.content
-            # Strip markdown code fences if present
-            match = re.search(r"```(?:json)?\s*([\s\S]*?)```", raw)
-            if match:
-                raw = match.group(1)
-            new_facts = json.loads(raw.strip())
-            if isinstance(new_facts, dict):
-                current_memory.update(new_facts)
-        except (json.JSONDecodeError, TypeError):
-            pass
-
-        return {"agent_memory": current_memory}
-
-    graph = StateGraph(MemoryState)
-    graph.add_node("generate", generate)
-    graph.add_node("extract_facts", extract_facts)
-    graph.set_entry_point("generate")
-    graph.add_edge("generate", "extract_facts")
-    graph.add_edge("extract_facts", END)
-    return graph.compile()
-
-
-graph = build_memory_graph()
+            writer = get_stream_writer()
+        except (RuntimeError, KeyError):
+            # No streaming context. The value is still on the checkpoint, which
+            # is what the client's getState fallback reads.
+            return
+        writer({"name": MEMORY_EVENT, "data": {"memory_contents": contents}})
+
+    def after_model(self, state: dict[str, Any], runtime: Any) -> None:  # noqa: ANN401, ARG002
+        self._emit(state)
+        return None
+
+    def after_agent(self, state: dict[str, Any], runtime: Any) -> None:  # noqa: ANN401, ARG002
+        self._emit(state)
+        return None
+
+
+def build_memory_agent():
+    """Build the memory agent.
+
+    `store=None` on `StoreBackend` means "resolve the store from the graph
+    execution context", which is what the LangGraph server supplies. The
+    namespace factory ignores its runtime argument on purpose, so every thread
+    of this demo reads and writes the same memory.
+    """
+    return create_deep_agent(
+        model=ChatOpenAI(model="gpt-4.1", temperature=0),
+        system_prompt=(PROMPTS_DIR / "memory.md").read_text(),
+        backend=StoreBackend(namespace=lambda _runtime: MEMORY_NAMESPACE),
+        memory=[MEMORY_FILE],
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+
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+graph = build_memory_agent()
diff --git a/cockpit/deep-agents/memory/python/uv.lock b/cockpit/deep-agents/memory/python/uv.lock
index e876be1c3..0fd42348a 100644
--- a/cockpit/deep-agents/memory/python/uv.lock
+++ b/cockpit/deep-agents/memory/python/uv.lock
@@ -1,190 +1,304 @@
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 source = { editable = "." }
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 ]
 
 [[package]]

From c3d824da30bcc88df0f61265436fc839bfbf9965 Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 12:38:15 -0700
Subject: [PATCH 6/7] feat(cockpit): rebuild the deep-agents skills topic on
 deepagents
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The old graph had three tools named calculator, word_count, and summarize.
None of them was a skill in any sense the word carries: they were tools with
descriptions, all of them in the prompt all of the time, and `calculator`
was a bare `eval`.

This is `SkillsMiddleware` over two real skills authored to the agentskills
format. Each is a folder with a `SKILL.md` whose YAML frontmatter carries a
name and a description; only that frontmatter goes into the system prompt.
The body stays on the filesystem until a request matches, and
`runway-analysis` deliberately points at `reference/margins.md`, so a second
read happens only when the agent is genuinely following the procedure.

A live run asks whether a mid-size jet can operate out of KASE. The agent
looks up the field data, reads the SKILL.md, reads the margin table, and
computes 4,800 x (1 + 0.14 x 7.82) = 10,057 ft against KASE's 8,006 ft
runway. Every number in that answer came from a file it chose to open.

Where the skills live was the design constraint. These topics deploy to a
shared public LangGraph deployment, and `FilesystemBackend` documents itself
as inappropriate for servers — the same reasoning that retired sandboxes. So
the bundled skills are seeded into a process-local InMemoryStore at import
and mounted read-only at `/skills/` through a CompositeBackend, with
everything the agent writes falling through to StateBackend.

One trap worth the comment it carries: CompositeBackend strips the route
prefix before delegating. Seeding the store at `/skills/runway-analysis/...`
surfaces to the agent as `/skills/skills/runway-analysis/...` and the skill
scan silently finds nothing — which is exactly what the first live run did.

`skills_metadata` is PrivateStateAttr, so the index reaches the panel through
the same custom-event shim as memory. What the agent opened needs no shim:
`read_file` is an ordinary tool call. The e2e asserts the differentiated
half — runway-analysis opened with two files, weather-brief left closed —
and goes red with zero skills when `skills=` is dropped.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../skills/angular/e2e/da-skills.spec.ts      |   46 +-
 .../angular/e2e/fixtures/da-skills.json       |   68 +-
 .../angular/e2e/manual/skills.manual.ts       |   22 +-
 .../skills/angular/prompts/skills.md          |    4 +-
 .../skills/angular/src/app/app.config.ts      |    2 -
 .../angular/src/app/skills.component.ts       |  359 +--
 .../app/views/calculator-result.component.ts  |   56 -
 .../app/views/word-count-result.component.ts  |   56 -
 .../deep-agents/skills/angular/src/index.ts   |    3 +-
 .../deep-agents/skills/python/docs/guide.md   |  219 +-
 .../skills/python/prompts/skills.md           |   19 +-
 .../deep-agents/skills/python/pyproject.toml  |    5 +-
 .../python/skills/runway-analysis/SKILL.md    |   27 +
 .../runway-analysis/reference/margins.md      |   16 +
 .../python/skills/weather-brief/SKILL.md      |   24 +
 .../deep-agents/skills/python/src/graph.py    |  242 +-
 cockpit/deep-agents/skills/python/uv.lock     | 2294 +++++++++++------
 17 files changed, 2116 insertions(+), 1346 deletions(-)
 delete mode 100644 cockpit/deep-agents/skills/angular/src/app/views/calculator-result.component.ts
 delete mode 100644 cockpit/deep-agents/skills/angular/src/app/views/word-count-result.component.ts
 create mode 100644 cockpit/deep-agents/skills/python/skills/runway-analysis/SKILL.md
 create mode 100644 cockpit/deep-agents/skills/python/skills/runway-analysis/reference/margins.md
 create mode 100644 cockpit/deep-agents/skills/python/skills/weather-brief/SKILL.md

diff --git a/cockpit/deep-agents/skills/angular/e2e/da-skills.spec.ts b/cockpit/deep-agents/skills/angular/e2e/da-skills.spec.ts
index 3e500b9de..5e914f88a 100644
--- a/cockpit/deep-agents/skills/angular/e2e/da-skills.spec.ts
+++ b/cockpit/deep-agents/skills/angular/e2e/da-skills.spec.ts
@@ -2,7 +2,47 @@
 import { test, expect } from '@playwright/test';
 import { submitAndWaitForResponse } from '@threadplane-internal/e2e-harness';
 
-test('da-skills: hello prompt produces assistant turn', async ({ page }) => {
-  const bubble = await submitAndWaitForResponse(page, 'Hello');
-  await expect(bubble).toBeVisible();
+// Matches the welcome suggestion in skills.component.ts and the fixture in
+// e2e/fixtures/da-skills.json.
+const PROMPT = 'Can a mid-size jet operate out of KASE?';
+
+test('da-skills: the index loads up front and only the matching skill is opened', async ({
+  page,
+}) => {
+  const bubble = await submitAndWaitForResponse(page, PROMPT);
+
+  // Both skills are in the index. That index is `skills_metadata`, which is
+  // PrivateStateAttr and therefore absent from the `values` stream — it is here
+  // only because the graph republishes it as a custom stream event.
+  await expect(page.locator('[data-testid="skill"]')).toHaveCount(2);
+  await expect(page.getByTestId('skills-source')).toHaveAttribute('data-source', 'live');
+  await expect(page.locator('[data-testid="skill"][data-name="runway-analysis"]')).toContainText(
+    /runway suitability/i,
+  );
+
+  // Progressive disclosure: the request matched runway-analysis, so the agent
+  // read that skill's files and left weather-brief's body on disk. A demo that
+  // pasted every skill into the prompt would open both, or neither.
+  await expect(
+    page.locator('[data-testid="skill"][data-name="runway-analysis"]'),
+  ).toHaveAttribute('data-opened', 'true');
+  await expect(
+    page.locator('[data-testid="skill"][data-name="weather-brief"]'),
+  ).toHaveAttribute('data-opened', 'false');
+
+  // Two reads inside the matched skill: the SKILL.md, then the reference table
+  // it points at. The second read is the part that does not happen unless the
+  // agent is genuinely following the skill's procedure.
+  const opened = page
+    .locator('[data-testid="skill"][data-name="runway-analysis"]')
+    .locator('[data-testid="skill-open"]');
+  await expect(opened).toHaveCount(2);
+  await expect(opened.nth(0)).toHaveText('/skills/runway-analysis/SKILL.md');
+  await expect(opened.nth(1)).toHaveText('/skills/runway-analysis/reference/margins.md');
+
+  // The answer used the margin table rather than a guess: 4,800 x (1 + 0.14 x
+  // 7.82) rounds to 10,057 ft, which is longer than KASE's 8,006 ft runway.
+  const finalText = await bubble.innerText();
+  expect(finalText).toMatch(/10,?057/);
+  expect(finalText.toLowerCase()).toContain('runway-analysis');
 });
diff --git a/cockpit/deep-agents/skills/angular/e2e/fixtures/da-skills.json b/cockpit/deep-agents/skills/angular/e2e/fixtures/da-skills.json
index 210889f44..7cb19d5ca 100644
--- a/cockpit/deep-agents/skills/angular/e2e/fixtures/da-skills.json
+++ b/cockpit/deep-agents/skills/angular/e2e/fixtures/da-skills.json
@@ -2,11 +2,73 @@
   "fixtures": [
     {
       "match": {
-        "userMessage": "Hello"
+        "userMessage": "Can a mid-size jet operate out of KASE?",
+        "hasToolResult": true,
+        "turnIndex": 1
       },
       "response": {
-        "content": "Hello! How can I help you today? I can evaluate math expressions, count words in a text, or summarize content \u2014 which would you like?"
+        "toolCalls": [
+          {
+            "name": "lookup_runway_length",
+            "arguments": "{\"airport\":\"KASE\"}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Can a mid-size jet operate out of KASE?",
+        "hasToolResult": true,
+        "turnIndex": 2
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "read_file",
+            "arguments": "{\"file_path\":\"/skills/runway-analysis/SKILL.md\",\"limit\":1000}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Can a mid-size jet operate out of KASE?",
+        "hasToolResult": true,
+        "turnIndex": 3
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "read_file",
+            "arguments": "{\"file_path\":\"/skills/runway-analysis/reference/margins.md\",\"limit\":1000}"
+          }
+        ]
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Can a mid-size jet operate out of KASE?",
+        "hasToolResult": true,
+        "turnIndex": 4
+      },
+      "response": {
+        "content": "A mid-size jet requires 10,057 ft of runway at KASE (Aspen), but the longest available is only 8,006 ft. \n\nMargin applied: mid-size jet, 4,800 ft sea-level balanced field, 14% penalty per 1,000 ft elevation. Verdict: The runway is not long enough for a mid-size jet at this elevation. (Used: runway-analysis skill)"
+      }
+    },
+    {
+      "match": {
+        "userMessage": "Can a mid-size jet operate out of KASE?",
+        "hasToolResult": false,
+        "turnIndex": 0
+      },
+      "response": {
+        "toolCalls": [
+          {
+            "name": "lookup_field_elevation",
+            "arguments": "{\"airport\":\"KASE\"}"
+          }
+        ]
       }
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/cockpit/deep-agents/skills/angular/e2e/manual/skills.manual.ts b/cockpit/deep-agents/skills/angular/e2e/manual/skills.manual.ts
index 05fd2b405..0199f64ef 100644
--- a/cockpit/deep-agents/skills/angular/e2e/manual/skills.manual.ts
+++ b/cockpit/deep-agents/skills/angular/e2e/manual/skills.manual.ts
@@ -1,3 +1,8 @@
+// SPDX-License-Identifier: MIT
+//
+// Live-model manual check. Not run by CI (playwright matches *.spec.ts only).
+//   npx nx run cockpit:serve-skills
+//   npx playwright test cockpit/deep-agents/skills/angular/e2e/manual
 import { expect, test } from '@playwright/test';
 
 test.describe('Deep Agents Skills Example', () => {
@@ -6,16 +11,19 @@ test.describe('Deep Agents Skills Example', () => {
     await page.waitForSelector('app-skills', { state: 'attached' });
   });
 
-  test('renders the chat interface with skill invocation sidebar', async ({ page }) => {
+  test('renders the chat interface with an empty skill index', async ({ page }) => {
     await expect(page.locator('chat')).toBeVisible();
     await expect(page.locator('textarea[name="messageText"]')).toBeVisible();
-    await expect(page.locator('text=No skills invoked yet')).toBeVisible();
+    await expect(page.locator('text=No skills loaded')).toBeVisible();
   });
 
-  test('sends a message and receives a response', async ({ page }) => {
-    await page.fill('textarea[name="messageText"]', 'What is 42 times 7?');
-    await page.click('button[type="submit"]');
-    await expect(page.locator('.chat-md').first()).toBeVisible({ timeout: 30000 });
-    await expect(page.locator('.chat-md').first()).not.toBeEmpty({ timeout: 30000 });
+  test('opens only the skill the request matches', async ({ page }) => {
+    await page.getByRole('button', { name: 'Mid-size jet at KASE' }).click();
+
+    await expect(page.locator('[data-testid="skill"]')).toHaveCount(2, { timeout: 120000 });
+    await expect(
+      page.locator('[data-testid="skill"][data-name="runway-analysis"]'),
+    ).toHaveAttribute('data-opened', 'true', { timeout: 120000 });
+    await expect(page.locator('[data-testid="skill-open"]').first()).toBeVisible();
   });
 });
diff --git a/cockpit/deep-agents/skills/angular/prompts/skills.md b/cockpit/deep-agents/skills/angular/prompts/skills.md
index 73842304c..94f25cbf1 100644
--- a/cockpit/deep-agents/skills/angular/prompts/skills.md
+++ b/cockpit/deep-agents/skills/angular/prompts/skills.md
@@ -1,5 +1,5 @@
 # Deep Agents Skills (Angular)
 
-This capability demonstrates a deep agent that selects and executes reusable skill modules — pre-packaged sequences of tool calls — using the `@threadplane/chat` Angular component library. The `<chat-debug>` component shows which skill was invoked, the parameters it received, and the intermediate steps it performed, giving developers full traceability into skill dispatch and execution.
+This capability shows progressive disclosure happening. A skill is a folder with a `SKILL.md` whose YAML frontmatter carries a name and a description; `SkillsMiddleware` puts only that frontmatter in the system prompt and leaves the body on the filesystem until a request matches. A reference file inside the skill stays unread until the `SKILL.md` sends the agent to it.
 
-Key components used: `<chat-debug>`. Skill invocations appear as named trace nodes with expandable step-by-step sub-traces, making it easy to audit skill selection logic, identify skill failures, and verify that parameter binding between the agent and each skill is correct.
+The sidebar therefore has two halves. The index comes from `skills_metadata`, which is annotated `PrivateStateAttr` and so never reaches the `values` stream — the graph republishes it as a `custom` stream event, and the checkpoint covers a reopened thread. The files the agent actually opened come from the ordinary tool-call stream, because `read_file` is not private at all.
diff --git a/cockpit/deep-agents/skills/angular/src/app/app.config.ts b/cockpit/deep-agents/skills/angular/src/app/app.config.ts
index c20520be0..67b6e650c 100644
--- a/cockpit/deep-agents/skills/angular/src/app/app.config.ts
+++ b/cockpit/deep-agents/skills/angular/src/app/app.config.ts
@@ -2,7 +2,6 @@
 import { ApplicationConfig } from '@angular/core';
 import { provideAgent } from '@threadplane/langgraph';
 import { provideChat } from '@threadplane/chat';
-import { provideRender } from '@threadplane/render';
 import { environment } from '../environments/environment';
 
 export const appConfig: ApplicationConfig = {
@@ -12,6 +11,5 @@ export const appConfig: ApplicationConfig = {
       assistantId: environment.streamingAssistantId,
     }),
     provideChat({}),
-    provideRender({}),
   ],
 };
diff --git a/cockpit/deep-agents/skills/angular/src/app/skills.component.ts b/cockpit/deep-agents/skills/angular/src/app/skills.component.ts
index b272ac218..870113716 100644
--- a/cockpit/deep-agents/skills/angular/src/app/skills.component.ts
+++ b/cockpit/deep-agents/skills/angular/src/app/skills.component.ts
@@ -1,206 +1,241 @@
+// SPDX-License-Identifier: MIT
 import { Component, computed } from '@angular/core';
-import { ChatComponent, views } from '@threadplane/chat';
+import { ChatComponent, ChatWelcomeSuggestionComponent } from '@threadplane/chat';
 import { ExampleChatLayoutComponent } from '@threadplane/example-layouts';
 import { injectAgent } from '@threadplane/langgraph';
-import { signalStateStore } from '@threadplane/render';
-import { CalculatorResultComponent } from './views/calculator-result.component';
-import { WordCountResultComponent } from './views/word-count-result.component';
 
-/**
- * Represents a matched skill invocation: tool call paired with its result.
- */
-interface SkillInvocation {
-  id: string;
+/** Name of the custom stream event the graph's visibility middleware emits. */
+const SKILLS_EVENT = 'deep_agents.skills';
+
+/** One entry of `skills_metadata`, parsed from a SKILL.md frontmatter block. */
+interface SkillEntry {
   name: string;
-  args: Record<string, unknown>;
-  result: string | undefined;
+  description: string;
+  path: string;
+  /** Directory the skill lives in, used to match files the agent opened. */
+  root: string;
+  /** Files under this skill the agent has read during the run. */
+  opened: string[];
 }
 
+const SUGGESTIONS = [
+  // value matches cockpit/deep-agents/skills/angular/e2e/da-skills.spec.ts PROMPT.
+  {
+    label: 'Mid-size jet at KASE',
+    value: 'Can a mid-size jet operate out of KASE?',
+    description: 'Matches the runway-analysis skill, which sends the agent to its margin table.',
+  },
+] as const;
+
 /**
- * SkillsComponent demonstrates a multi-skill agent with specialized tools.
+ * SkillsComponent shows progressive disclosure happening.
+ *
+ * `SkillsMiddleware` loads only each SKILL.md's frontmatter — a name and a
+ * description — into the system prompt. The body stays on the filesystem until
+ * a request matches, and a reference file inside the skill stays unread until
+ * the SKILL.md sends the agent to it. The panel therefore has two halves: the
+ * index the model was given up front, and the files it actually opened.
  *
- * The agent can calculate math expressions, count words, and summarize text
- * by selecting the appropriate skill tool for each user request.
+ * The index is not on the `values` stream: `skills_metadata` is annotated
+ * `PrivateStateAttr`. The graph republishes it as a `custom` stream event, and
+ * the checkpoint carries it for a reopened thread — the same two-source pattern
+ * as the memory capability.
  *
- * The sidebar displays a real-time log of skill invocations derived from
- * `stream.messages()`, matching AI tool_calls with their corresponding
- * tool result messages.
+ * The opened files come from the ordinary tool-call stream, because `read_file`
+ * is not private at all.
  */
 @Component({
   selector: 'app-skills',
   standalone: true,
-  imports: [ChatComponent, ExampleChatLayoutComponent],
+  imports: [ChatComponent, ChatWelcomeSuggestionComponent, ExampleChatLayoutComponent],
   template: `
-    <example-chat-layout sidebarWidth="18rem">
-      <chat main [agent]="agent" [views]="ui" [store]="uiStore" class="flex-1 min-w-0" />
+    <example-chat-layout sidebarWidth="22rem">
+      <chat main [agent]="agent" class="flex-1 min-w-0">
+        <div chatWelcomeSuggestions>
+          @for (s of suggestions; track s.value) {
+            <chat-welcome-suggestion
+              [label]="s.label"
+              [value]="s.value"
+              [description]="s.description"
+              (selected)="send($event)"
+            />
+          }
+        </div>
+      </chat>
       <div sidebar class="panel">
-        <h3 class="cap">Skill Invocations</h3>
-        @if (invocations().length === 0) {
-          <p class="empty">No skills invoked yet</p>
+        <h3 class="cap">Skill Index</h3>
+        <p class="source" data-testid="skills-source" [attr.data-source]="skillsSource()">
+          {{ skillsSource() === 'live' ? 'streamed live' : skillsSource() === 'checkpoint' ? 'from checkpoint' : 'no source yet' }}
+        </p>
+        @if (skills().length === 0) {
+          <p class="empty">No skills loaded</p>
         }
-        @for (inv of invocations(); track inv.id) {
-          <div class="skill-card">
-            <div>
-              <span class="badge">
-                {{ inv.name }}
-              </span>
-            </div>
-            <div class="kv">
-              <p class="kv__label">Input</p>
-              <pre class="code">{{ formatArgs(inv.args) }}</pre>
-            </div>
-            @if (inv.result !== undefined) {
-              <div class="kv">
-                <p class="kv__label kv__label--success">Output</p>
-                <pre class="code">{{ inv.result }}</pre>
-              </div>
+        @for (skill of skills(); track skill.name) {
+          <div
+            class="skill"
+            data-testid="skill"
+            [attr.data-name]="skill.name"
+            [attr.data-opened]="skill.opened.length > 0 ? 'true' : 'false'"
+          >
+            <span class="skill__name">{{ skill.name }}</span>
+            <p class="skill__description">{{ skill.description }}</p>
+            @if (skill.opened.length === 0) {
+              <p class="skill__files skill__files--idle">body not read</p>
+            } @else {
+              @for (file of skill.opened; track file) {
+                <p class="skill__files" data-testid="skill-open">{{ file }}</p>
+              }
             }
           </div>
         }
+        <p class="hint">
+          Only the name and description above were put in the model's prompt. Everything else was
+          read on demand.
+        </p>
       </div>
     </example-chat-layout>
   `,
-  styles: [`
-    .panel {
-      display: flex;
-      flex-direction: column;
-      gap: 0.75rem;
-      padding: 1rem;
-      background: var(--tplane-chat-bg);
-      color: var(--tplane-chat-text);
-    }
+  styles: [
+    `
+      .panel {
+        display: flex;
+        flex-direction: column;
+        gap: 0.75rem;
+        padding: 1rem;
+        background: var(--tplane-chat-bg);
+        color: var(--tplane-chat-text);
+      }
 
-    .cap {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-      text-transform: uppercase;
-      letter-spacing: 0.12em;
-      color: var(--tplane-chat-text-muted);
-    }
+      .cap {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        font-weight: 700;
+        letter-spacing: 0.12em;
+        text-transform: uppercase;
+      }
 
-    .empty {
-      margin: 0;
-      font-size: var(--tplane-chat-font-size-sm);
-      font-style: italic;
-      color: var(--tplane-chat-text-muted);
-    }
+      .source {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
 
-    .skill-card {
-      display: flex;
-      flex-direction: column;
-      gap: 0.5rem;
-      padding: 0.75rem;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-    }
+      .empty {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-sm);
+        font-style: italic;
+      }
 
-    .badge {
-      display: inline-flex;
-      align-items: center;
-      width: fit-content;
-      border-radius: var(--tplane-chat-radius-launcher);
-      padding: 0.125rem 0.5rem;
-      background: var(--tplane-chat-primary);
-      color: var(--tplane-chat-on-primary);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-    }
+      .skill {
+        display: flex;
+        flex-direction: column;
+        gap: 0.25rem;
+        padding: 0.5rem;
+        border-radius: var(--tplane-chat-radius-card);
+        background: var(--tplane-chat-surface-alt);
+      }
 
-    .kv {
-      display: flex;
-      flex-direction: column;
-      gap: 0.25rem;
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-xs);
-    }
+      .skill[data-opened='true'] {
+        outline: 1px solid var(--tplane-chat-primary);
+      }
 
-    .kv__label {
-      margin: 0;
-      color: var(--tplane-chat-text);
-      font-weight: 700;
-    }
+      .skill__name {
+        color: var(--tplane-chat-text);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-sm);
+      }
 
-    .kv__label--success {
-      color: var(--tplane-chat-success);
-    }
+      .skill__description {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        line-height: var(--tplane-chat-line-height);
+      }
 
-    .code {
-      margin: 0;
-      padding: 0.5rem;
-      overflow-x: auto;
-      white-space: pre-wrap;
-      overflow-wrap: anywhere;
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface);
-      color: var(--tplane-chat-text);
-      font: var(--tplane-chat-font-size-xs) / 1.5 var(--tplane-chat-font-mono);
-    }
-  `],
+      .skill__files {
+        margin: 0;
+        color: var(--tplane-chat-text);
+        font-family: var(--tplane-chat-font-mono);
+        font-size: var(--tplane-chat-font-size-xs);
+      }
+
+      .skill__files--idle {
+        color: var(--tplane-chat-text-muted);
+        font-style: italic;
+      }
+
+      .hint {
+        margin: 0;
+        color: var(--tplane-chat-text-muted);
+        font-size: var(--tplane-chat-font-size-xs);
+        line-height: var(--tplane-chat-line-height);
+      }
+    `,
+  ],
 })
 export class SkillsComponent {
-  readonly ui = views({ 'calculator-result': CalculatorResultComponent, 'word-count-result': WordCountResultComponent });
-  readonly uiStore = signalStateStore({});
-
   protected readonly agent = injectAgent();
 
-  private readonly SKILL_NAMES = new Set(['calculator', 'word_count', 'summarize']);
-
-  /**
-   * Derived signal: extracts skill invocations from the message stream.
-   *
-   * Scans AI messages for tool_calls matching known skill names, then pairs
-   * each with its corresponding tool result message via tool_call_id.
-   */
-  protected readonly invocations = computed<SkillInvocation[]>(() => {
-    const msgs = this.agent.langGraphMessages();
-    const resultMap = new Map<string, string>();
-
-    // Build a lookup of tool_call_id -> result content from tool messages
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function'
-        ? msg._getType()
-        : (msg as unknown as Record<string, string>)['type'] ?? '';
-      if (type === 'tool') {
-        const toolCallId = (msg as unknown as Record<string, string>)['tool_call_id'];
-        const content = typeof msg.content === 'string'
-          ? msg.content
-          : JSON.stringify(msg.content);
-        if (toolCallId) {
-          resultMap.set(toolCallId, content);
-        }
+  protected readonly suggestions = SUGGESTIONS;
+
+  private readonly liveSkills = computed<Record<string, unknown>[] | null>(() => {
+    for (const event of [...this.agent.customEvents()].reverse()) {
+      if (event.name !== SKILLS_EVENT) continue;
+      const metadata = (event.data as { skills_metadata?: unknown } | undefined)?.[
+        'skills_metadata'
+      ];
+      if (Array.isArray(metadata) && metadata.length > 0) {
+        return metadata as Record<string, unknown>[];
       }
     }
+    return null;
+  });
 
-    // Extract tool_calls from AI messages that match known skill names
-    const invocations: SkillInvocation[] = [];
-    for (const msg of msgs) {
-      const type = typeof msg._getType === 'function'
-        ? msg._getType()
-        : (msg as unknown as Record<string, string>)['type'] ?? '';
-      if (type === 'ai') {
-        const toolCalls = (msg as unknown as Record<string, unknown[]>)['tool_calls'];
-        if (Array.isArray(toolCalls)) {
-          for (const tc of toolCalls) {
-            const call = tc as { id: string; name: string; args: Record<string, unknown> };
-            if (this.SKILL_NAMES.has(call.name)) {
-              invocations.push({
-                id: call.id,
-                name: call.name,
-                args: call.args,
-                result: resultMap.get(call.id),
-              });
-            }
-          }
-        }
-      }
+  private readonly settledSkills = computed<Record<string, unknown>[] | null>(() => {
+    const metadata = (this.agent.value() as Record<string, unknown> | undefined)?.[
+      'skills_metadata'
+    ];
+    return Array.isArray(metadata) && metadata.length > 0
+      ? (metadata as Record<string, unknown>[])
+      : null;
+  });
+
+  protected readonly skillsSource = computed<'live' | 'checkpoint' | 'none'>(() => {
+    if (this.liveSkills()) return 'live';
+    return this.settledSkills() ? 'checkpoint' : 'none';
+  });
+
+  /** Absolute paths the agent has read with `read_file` during this run. */
+  private readonly openedPaths = computed<string[]>(() => {
+    const paths: string[] = [];
+    for (const call of this.agent.toolCalls()) {
+      if (call.name !== 'read_file') continue;
+      const path = (call.args as Record<string, unknown> | undefined)?.['file_path'];
+      if (typeof path === 'string' && !paths.includes(path)) paths.push(path);
     }
+    return paths;
+  });
 
-    return invocations;
+  protected readonly skills = computed<SkillEntry[]>(() => {
+    const source = this.liveSkills() ?? this.settledSkills() ?? [];
+    const opened = this.openedPaths();
+    return source.map((entry) => {
+      const path = String(entry['path'] ?? '');
+      const root = path.slice(0, path.lastIndexOf('/') + 1);
+      return {
+        name: String(entry['name'] ?? ''),
+        description: String(entry['description'] ?? ''),
+        path,
+        root,
+        opened: root ? opened.filter((candidate) => candidate.startsWith(root)) : [],
+      };
+    });
   });
 
-  protected formatArgs(args: Record<string, unknown>): string {
-    return JSON.stringify(args, null, 2);
+  protected send(text: string): void {
+    void this.agent.submit({ message: text });
   }
 }
diff --git a/cockpit/deep-agents/skills/angular/src/app/views/calculator-result.component.ts b/cockpit/deep-agents/skills/angular/src/app/views/calculator-result.component.ts
deleted file mode 100644
index 1fee572fd..000000000
--- a/cockpit/deep-agents/skills/angular/src/app/views/calculator-result.component.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'calculator-result',
-  standalone: true,
-  template: `
-    <div class="result-pill">
-      <span class="result-badge">
-        calculator
-      </span>
-      <span class="result-expression">
-        {{ expression() }}
-      </span>
-      <span class="result-value">
-        = {{ result() }}
-      </span>
-    </div>
-  `,
-  styles: [`
-    .result-pill {
-      display: inline-flex;
-      align-items: center;
-      gap: 0.5rem;
-      margin: 0.25rem 0;
-      padding: 0.375rem 0.75rem;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-      color: var(--tplane-chat-text);
-    }
-
-    .result-badge {
-      display: inline-flex;
-      align-items: center;
-      border-radius: var(--tplane-chat-radius-launcher);
-      padding: 0.125rem 0.5rem;
-      background: color-mix(in srgb, var(--tplane-chat-success) 16%, transparent);
-      color: var(--tplane-chat-success);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-    }
-
-    .result-expression {
-      font: var(--tplane-chat-font-size-sm) / 1.5 var(--tplane-chat-font-mono);
-    }
-
-    .result-value {
-      font-size: var(--tplane-chat-font-size-sm);
-      font-weight: 700;
-    }
-  `],
-})
-export class CalculatorResultComponent {
-  readonly expression = input<string>('');
-  readonly result = input<string>('');
-}
diff --git a/cockpit/deep-agents/skills/angular/src/app/views/word-count-result.component.ts b/cockpit/deep-agents/skills/angular/src/app/views/word-count-result.component.ts
deleted file mode 100644
index 2adc88ca8..000000000
--- a/cockpit/deep-agents/skills/angular/src/app/views/word-count-result.component.ts
+++ /dev/null
@@ -1,56 +0,0 @@
-import { Component, input } from '@angular/core';
-
-@Component({
-  selector: 'word-count-result',
-  standalone: true,
-  template: `
-    <div class="result-pill">
-      <span class="result-badge">
-        word_count
-      </span>
-      <span class="result-expression">
-        {{ count() }}
-      </span>
-      <span class="result-value">
-        words
-      </span>
-    </div>
-  `,
-  styles: [`
-    .result-pill {
-      display: inline-flex;
-      align-items: center;
-      gap: 0.5rem;
-      margin: 0.25rem 0;
-      padding: 0.375rem 0.75rem;
-      border: 1px solid var(--tplane-chat-separator);
-      border-radius: var(--tplane-chat-radius-card);
-      background: var(--tplane-chat-surface-alt);
-      color: var(--tplane-chat-text);
-    }
-
-    .result-badge {
-      display: inline-flex;
-      align-items: center;
-      border-radius: var(--tplane-chat-radius-launcher);
-      padding: 0.125rem 0.5rem;
-      background: var(--tplane-chat-warning-bg);
-      color: var(--tplane-chat-warning-text);
-      font-size: var(--tplane-chat-font-size-xs);
-      font-weight: 700;
-    }
-
-    .result-expression {
-      font: 700 var(--tplane-chat-font-size-sm) / 1.5 var(--tplane-chat-font-mono);
-    }
-
-    .result-value {
-      color: var(--tplane-chat-text-muted);
-      font-size: var(--tplane-chat-font-size-sm);
-    }
-  `],
-})
-export class WordCountResultComponent {
-  readonly count = input<string>('');
-  readonly text = input<string>('');
-}
diff --git a/cockpit/deep-agents/skills/angular/src/index.ts b/cockpit/deep-agents/skills/angular/src/index.ts
index fe4611ada..0498c15d9 100644
--- a/cockpit/deep-agents/skills/angular/src/index.ts
+++ b/cockpit/deep-agents/skills/angular/src/index.ts
@@ -30,6 +30,7 @@ export const deepAgentsSkillsAngularModule: CockpitCapabilityModule = {
     'cockpit/deep-agents/skills/angular/prompts/skills.md',
   ],
   codeAssetPaths: [
-    'cockpit/deep-agents/skills/angular/src/app.component.ts',
+    'cockpit/deep-agents/skills/angular/src/app/skills.component.ts',
+    'cockpit/deep-agents/skills/angular/src/app/app.config.ts',
   ],
 };
diff --git a/cockpit/deep-agents/skills/python/docs/guide.md b/cockpit/deep-agents/skills/python/docs/guide.md
index 1b9d8340a..d948bf422 100644
--- a/cockpit/deep-agents/skills/python/docs/guide.md
+++ b/cockpit/deep-agents/skills/python/docs/guide.md
@@ -1,163 +1,128 @@
-# Multi-Skill Agent with Angular
+# Skills with Deep Agents
 
 <Summary>
-Build a chat interface that shows real-time skill invocations using `provideAgent()` and
-`injectAgent()` from `@threadplane/langgraph`. The agent selects from specialized tools (calculator, word counter,
-summarizer) based on the user's request, and the sidebar displays each skill invocation as a card.
+Give an agent procedures it loads on demand. A skill is a folder with a `SKILL.md`;
+`SkillsMiddleware` puts only the frontmatter — a name and a description — in the system prompt
+and leaves the body on the filesystem until a request matches. Rendering both halves is what
+makes progressive disclosure visible rather than theoretical.
 </Summary>
 
 <Prompt>
-Add a skill invocation sidebar to this Angular component using `provideAgent()` and `injectAgent()` from `@threadplane/langgraph`. Use `stream.messages()` to access tool call data, derive `skillInvocations` with `computed()`, and bind them to the sidebar beside the `<chat>` component from `@threadplane/chat`.
+Add a skill-index panel beside the `<chat>` component. Read `skills_metadata` from
+`agent.customEvents()`, and mark each skill as opened when `agent.toolCalls()` shows a
+`read_file` under that skill's directory.
 </Prompt>
 
-<Steps>
-<Step title="Configure the provider">
+<Callout type="info" title="Provider setup lives in the LangGraph quickstart">
+This guide assumes `provideAgent()` is already configured. If it is not, work through the
+[LangGraph quickstart](/docs/langgraph/getting-started/quickstart) first.
+</Callout>
 
-Set up `provideAgent()` in your app config with the LangGraph API URL:
+<Steps>
+<Step title="Write a SKILL.md">
 
-```typescript
-// app.config.ts
-import { ApplicationConfig } from '@angular/core';
-import { provideAgent } from '@threadplane/langgraph';
-
-export const appConfig: ApplicationConfig = {
-  providers: [
-    provideAgent({
-      apiUrl: 'https://your-deployment.langgraph.app',
-      assistantId: 'da-skills',
-    }),
-  ],
-};
-```
+The frontmatter is the part the model sees first, so the `description` is doing the routing.
+Write it as a matching rule, not as a summary:
 
-This makes the configured agent available to all `injectAgent()` calls in your app.
+```markdown
+---
+name: runway-analysis
+description: Decide whether a runway is long enough for a given aircraft at a given field elevation. Use when the user asks about runway suitability, takeoff or landing distance, or operating out of a high-elevation field.
+license: MIT
+---
 
-</Step>
-<Step title="Create the streaming resource">
+# Runway Analysis
 
-In your component, call `injectAgent()` to retrieve the configured skills agent:
+## Procedure
 
-```typescript
-// skills.component.ts
-import { injectAgent } from '@threadplane/langgraph';
-
-export class SkillsComponent {
-  protected readonly stream = injectAgent();
-}
+1. Get the field elevation and the longest runway length.
+2. Read `/skills/runway-analysis/reference/margins.md` for the margin table.
+   Do not work from memory — the table is the authority.
+3. Compare, then state the verdict and the two numbers you compared.
 ```
 
-The resource manages the connection, message history, loading state, and errors automatically.
+Step 2 is the second stage of the disclosure. The reference file costs nothing until the
+`SKILL.md` sends the agent to it.
 
 </Step>
-<Step title="Derive skill invocations with computed()">
+<Step title="Mount the skills somewhere a server can serve">
 
-Use Angular's `computed()` to reactively extract skill invocations from tool calls in `stream.messages()`:
+`SkillsMiddleware` reads through a backend, and the choice of backend is a deployment decision.
+`FilesystemBackend` documents itself as inappropriate for servers and HTTP APIs, which rules it
+out for anything with a public URL. Seeding a process-local store and mounting it read-only
+keeps the content in the repo without giving the agent the host:
 
-```typescript
-import { computed } from '@angular/core';
-import { injectAgent } from '@threadplane/langgraph';
-
-interface SkillInvocation {
-  skillName: string;
-  args: string;
-  result?: string;
-}
-
-export class SkillsComponent {
-  protected readonly stream = injectAgent();
-
-  skillInvocations = computed(() => {
-    const msgs = this.stream.messages();
-    const invocations: SkillInvocation[] = [];
-    for (const m of msgs) {
-      if ((m as any).tool_calls) {
-        for (const tc of (m as any).tool_calls) {
-          invocations.push({
-            skillName: tc.name,
-            args: JSON.stringify(tc.args),
-            result: tc.output,
-          });
-        }
-      }
-    }
-    return invocations;
-  });
-}
+```python
+def _seed_skills_store() -> InMemoryStore:
+    store = InMemoryStore()
+    backend = StoreBackend(namespace=lambda _runtime: SKILLS_NAMESPACE, store=store)
+    for path in sorted(SKILLS_DIR.rglob("*.md")):
+        backend.write(f"/{path.relative_to(SKILLS_DIR).as_posix()}", path.read_text())
+    return store
+
+graph = create_deep_agent(
+    ...,
+    backend=CompositeBackend(
+        default=StateBackend(),
+        routes={"/skills/": StoreBackend(namespace=..., store=SKILLS_STORE)},
+    ),
+    skills=["/skills/"],
+)
 ```
 
-Each tool call in an AI message maps to a skill invocation card. The `result` field is populated once the tool returns.
-
-<Tip>
-`stream.messages()` is a signal. `computed()` recalculates `skillInvocations` automatically whenever new messages arrive — no manual subscription needed.
-</Tip>
+<Callout type="warning" title="CompositeBackend strips the route prefix">
+Seed the store at `/runway-analysis/SKILL.md`, not `/skills/runway-analysis/SKILL.md`. The
+composite removes the matched prefix before delegating and re-adds it to the result, so a store
+seeded with the prefix surfaces to the agent as `/skills/skills/runway-analysis/...` and the
+skill scan finds nothing.
+</Callout>
 
 </Step>
-<Step title="Build the template with skill invocation sidebar">
-
-Use the `<chat>` component from `@threadplane/chat` and render a sibling sidebar:
-
-```html
-<chat [agent]="stream" />
-
-<aside>
-    <h3>Skill Invocations</h3>
-    @for (inv of skillInvocations(); track $index) {
-      <div>
-        <span>{{ inv.skillName }}</span>
-        <span>{{ inv.result ? 'done' : 'running…' }}</span>
-        <div>{{ inv.args }}</div>
-        @if (inv.result) {
-          <div>{{ inv.result }}</div>
-        }
-      </div>
-    }
-    @empty {
-      <p>Ask the agent to calculate, count words, or summarize text.</p>
-    }
-</aside>
-```
+<Step title="Tell the model the index is an index">
 
-Each invocation card shows the skill name, input args, and result once available.
+The model has the names and descriptions and nothing else, so the prompt has to say what to do
+with them:
+
+```markdown
+Your procedures are not in this prompt — they are skills under `/skills/`.
+When a request matches a skill, read its `SKILL.md` before you start, and
+follow the procedure it gives you. If the `SKILL.md` points at another file,
+read that too — the numbers in a reference file are the authority, and your
+recollection is not.
+```
 
 </Step>
-<Step title="The LangGraph skills backend">
+<Step title="Render both halves">
 
-The backend defines three tool skills and uses a tool-calling agent loop with `ToolNode`:
+`skills_metadata` is annotated `PrivateStateAttr`, so it is absent from the `values` stream and
+needs the same custom-event shim as the memory capability. What the agent actually opened needs
+no shim at all — `read_file` is an ordinary tool call:
 
-```python
-# graph.py
-from langchain_core.tools import tool
-from langgraph.prebuilt import ToolNode
-
-@tool
-def calculator(expression: str) -> str:
-    """Evaluate a mathematical expression."""
-    return str(eval(expression, {"__builtins__": {}}, {}))
-
-@tool
-def word_count(text: str) -> str:
-    """Count the number of words in text."""
-    return f"{len(text.split())} words"
-
-@tool
-def summarize(text: str) -> str:
-    """Summarize text in one sentence."""
-    sentences = [s.strip() for s in text.split(".") if s.strip()]
-    return sentences[0] + "." if sentences else "No content."
-
-# Bind all tools to the LLM
-llm = ChatOpenAI(model="gpt-5-mini").bind_tools([calculator, word_count, summarize])
+```typescript
+private readonly openedPaths = computed<string[]>(() => {
+  const paths: string[] = [];
+  for (const call of this.agent.toolCalls()) {
+    if (call.name !== 'read_file') continue;
+    const path = (call.args as Record<string, unknown> | undefined)?.['file_path'];
+    if (typeof path === 'string' && !paths.includes(path)) paths.push(path);
+  }
+  return paths;
+});
 ```
 
-The agent selects which skill to call based on the user's request. `ToolNode` dispatches the call and returns the result as a `ToolMessage`.
-
-<Tip>
-The agent loops back after each tool call, allowing it to call multiple skills in a single turn if the user's request requires it (e.g., "count the words in this summary first, then calculate 10% of that number").
-</Tip>
+Match those against each skill's directory and the panel shows exactly what progressive
+disclosure bought: one skill opened, the rest still on disk.
 
 </Step>
 </Steps>
 
 <Tip>
-The `@empty` block in `@for` renders when no skill invocations have occurred yet — a clean way to show a placeholder before the user submits their first request.
+The strongest test of a skills setup is the skill that does NOT get opened. If every skill's
+files are read on every request, the index is not routing anything and the descriptions need
+work.
 </Tip>
+
+<Related>
+- [Deep Agents Memory](/deep-agents/core-capabilities/memory/overview/python) — the same private-state visibility problem, for `memory_contents`
+- [Deep Agents Filesystem](/deep-agents/core-capabilities/filesystem/overview/python) — the backends the skill mount is built from
+</Related>
diff --git a/cockpit/deep-agents/skills/python/prompts/skills.md b/cockpit/deep-agents/skills/python/prompts/skills.md
index 5b119d456..2bc405fdc 100644
--- a/cockpit/deep-agents/skills/python/prompts/skills.md
+++ b/cockpit/deep-agents/skills/python/prompts/skills.md
@@ -1,10 +1,15 @@
-# Multi-Skill Agent
+# Dispatch Desk with Skills
 
-You are a multi-skill agent that can calculate math expressions, count words in text, and summarize content.
+You are a dispatcher. Your procedures are not in this prompt — they are skills on
+your filesystem under `/skills/`, and each one is a folder with a `SKILL.md`.
 
-Choose the appropriate skill tool based on the user's request:
-- Use `calculator` when the user wants to evaluate a math expression or perform arithmetic.
-- Use `word_count` when the user wants to count words in a piece of text.
-- Use `summarize` when the user wants a brief summary of a longer text.
+You have been given the name and description of every skill up front. That index
+is deliberately short. When a request matches a skill, read its `SKILL.md` with
+`read_file` before you start, and follow the procedure it gives you. If the
+`SKILL.md` points at another file, read that too — the numbers in a reference
+file are the authority, and your recollection is not.
 
-Always use the most appropriate skill. If the request doesn't match any skill, respond conversationally.
+Use `lookup_field_elevation`, `lookup_runway_length`, and `lookup_weather` for
+airport data.
+
+Say which skill you used in your answer, and keep the answer to a few sentences.
diff --git a/cockpit/deep-agents/skills/python/pyproject.toml b/cockpit/deep-agents/skills/python/pyproject.toml
index fe29a7a4a..fd75fa5d8 100644
--- a/cockpit/deep-agents/skills/python/pyproject.toml
+++ b/cockpit/deep-agents/skills/python/pyproject.toml
@@ -3,14 +3,15 @@ name = "cockpit-deep-agents-skills"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = [
-    "langgraph>=0.3",
+    "deepagents==0.7.11",
     "langchain-openai>=0.3",
+    "langgraph>=0.3",
     "langsmith>=0.2",
 ]
 
 [tool.uv]
 dev-dependencies = [
-    "langgraph-cli[inmem]>=0.1",
+    "langgraph-cli[inmem]>=0.4",
 ]
 
 [build-system]
diff --git a/cockpit/deep-agents/skills/python/skills/runway-analysis/SKILL.md b/cockpit/deep-agents/skills/python/skills/runway-analysis/SKILL.md
new file mode 100644
index 000000000..c8793a745
--- /dev/null
+++ b/cockpit/deep-agents/skills/python/skills/runway-analysis/SKILL.md
@@ -0,0 +1,27 @@
+---
+name: runway-analysis
+description: Decide whether a runway is long enough for a given aircraft at a given field elevation. Use when the user asks about runway suitability, takeoff or landing distance, or operating out of a high-elevation field.
+license: MIT
+---
+
+# Runway Analysis
+
+## When to use
+
+The user is asking whether an aircraft can safely operate from a specific
+runway, or is comparing two airports for a trip.
+
+## Procedure
+
+1. Get the field elevation and the longest runway length with
+   `lookup_field_elevation` and `lookup_runway_length`.
+2. Read `/skills/runway-analysis/reference/margins.md` for the required margin
+   table. Do not work from memory — the table is the authority.
+3. Compare the runway length against the required distance for the aircraft
+   class at that elevation.
+4. State the verdict in one sentence, then give the two numbers you compared.
+
+## Reporting
+
+Always name the margin you applied. A verdict without the margin is not
+reviewable.
diff --git a/cockpit/deep-agents/skills/python/skills/runway-analysis/reference/margins.md b/cockpit/deep-agents/skills/python/skills/runway-analysis/reference/margins.md
new file mode 100644
index 000000000..8aaa074d5
--- /dev/null
+++ b/cockpit/deep-agents/skills/python/skills/runway-analysis/reference/margins.md
@@ -0,0 +1,16 @@
+# Required Runway Margins
+
+Balanced-field length required at sea level, and the elevation penalty applied
+per 1,000 ft of field elevation.
+
+| Aircraft class      | Sea-level balanced field | Penalty per 1,000 ft |
+| ------------------- | ------------------------ | -------------------- |
+| Light jet           | 3,500 ft                 | 12%                  |
+| Mid-size jet        | 4,800 ft                 | 14%                  |
+| Super-mid jet       | 5,600 ft                 | 15%                  |
+| Regional turboprop  | 4,200 ft                 | 10%                  |
+
+Apply the penalty linearly: a mid-size jet at 7,820 ft needs
+4,800 x (1 + 0.14 x 7.82) = 10,057 ft.
+
+Add a further 15% for a wet runway.
diff --git a/cockpit/deep-agents/skills/python/skills/weather-brief/SKILL.md b/cockpit/deep-agents/skills/python/skills/weather-brief/SKILL.md
new file mode 100644
index 000000000..8d011c767
--- /dev/null
+++ b/cockpit/deep-agents/skills/python/skills/weather-brief/SKILL.md
@@ -0,0 +1,24 @@
+---
+name: weather-brief
+description: Turn a raw field observation into a short operational weather brief. Use when the user asks about conditions, ceilings, visibility, or whether the weather is a problem for a departure or arrival.
+license: MIT
+---
+
+# Weather Brief
+
+## When to use
+
+The user wants to know what the current conditions mean, not just what they are.
+
+## Procedure
+
+1. Get the observation with `lookup_weather`.
+2. Classify the ceiling: below 1,000 ft is low, 1,000 to 3,000 ft is marginal,
+   above 3,000 ft is unrestricted.
+3. Call out any gust spread greater than 8 knots and any advisory in the
+   observation text.
+
+## Reporting
+
+Two sentences: the conditions, then the operational consequence. Never repeat
+the raw observation verbatim.
diff --git a/cockpit/deep-agents/skills/python/src/graph.py b/cockpit/deep-agents/skills/python/src/graph.py
index 32274232b..d76740dec 100644
--- a/cockpit/deep-agents/skills/python/src/graph.py
+++ b/cockpit/deep-agents/skills/python/src/graph.py
@@ -1,96 +1,186 @@
-"""
-Deep Agents Skills Graph
-
-Demonstrates a multi-skill agent that selects specialized tools based on
-the user's request. The agent can calculate math expressions, count words
-in text, and summarize content. Tool calls are visible to the frontend
-via stream.messages(), powering the skill invocation sidebar.
+"""Deep Agents skills capability: SKILL.md progressive disclosure.
+
+A skill is a folder with a `SKILL.md` whose YAML frontmatter carries a `name`
+and a `description`. `SkillsMiddleware` loads only that frontmatter into the
+system prompt — a short index the model can scan — and leaves the body on the
+filesystem for the agent to `read_file` when a request actually matches. That
+two-stage load is what "progressive disclosure" means: the instructions cost
+nothing until they are needed, and a reference file inside the skill costs
+nothing until the SKILL.md sends the agent to it.
+
+Where the skills live matters. They must be readable through a backend, and this
+demo is deployed on a shared public LangGraph deployment, so a host-filesystem
+backend is out — `FilesystemBackend` documents itself as inappropriate for
+servers, and the same reasoning retired the sandboxes topic. Instead the bundled
+skills are seeded into a process-local `InMemoryStore` at import and mounted
+read-only at `/skills/` through a `CompositeBackend`; everything else the agent
+writes goes to the thread's own `StateBackend`.
+
+Visibility: `skills_metadata` is annotated `PrivateStateAttr`, so it never
+reaches the `values` stream. `SkillsVisibilityMiddleware` republishes it as a
+`custom` stream event.
 """
 
 from pathlib import Path
-from typing import Annotated
-import operator
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import ToolNode
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, BaseMessage, AIMessage
+from typing import Any
+
+from deepagents import create_deep_agent
+from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
+from langchain.agents.middleware import AgentMiddleware
 from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.config import get_stream_writer
+from langgraph.store.memory import InMemoryStore
 
 PROMPTS_DIR = Path(__file__).parent.parent / "prompts"
+SKILLS_DIR = Path(__file__).parent.parent / "skills"
+
+#: Mount point the agent sees. `SkillsMiddleware` scans one level below it.
+SKILLS_ROOT = "/skills/"
+
+SKILLS_NAMESPACE = ("cockpit", "deep-agents-skills")
+
+#: Custom stream event name the Angular panel listens for.
+SKILLS_EVENT = "deep_agents.skills"
+
+FIELD_ELEVATION_FT = {
+    "KSFO": 13,
+    "KDEN": 5434,
+    "KJFK": 13,
+    "KASE": 7820,
+    "KLAX": 125,
+    "KBOS": 20,
+}
+
+RUNWAY_LENGTH_FT = {
+    "KSFO": 11870,
+    "KDEN": 16000,
+    "KJFK": 14511,
+    "KASE": 8006,
+    "KLAX": 12091,
+    "KBOS": 10083,
+}
+
+WEATHER = {
+    "KSFO": "Marine layer, 800 ft overcast, visibility 4 SM, wind 280 at 14.",
+    "KDEN": "Clear, visibility 10 SM, wind 350 at 22 gusting 31.",
+    "KJFK": "Broken 3500 ft, visibility 10 SM, wind 040 at 9.",
+    "KASE": "Few 12000 ft, visibility 10 SM, wind 300 at 6, mountain wave advisory.",
+    "KLAX": "Clear, visibility 10 SM, wind 260 at 8.",
+    "KBOS": "Overcast 1200 ft, visibility 6 SM, wind 120 at 17.",
+}
 
 
-class SkillsState(dict):
-    messages: Annotated[list[BaseMessage], operator.add]
+@tool
+def lookup_field_elevation(airport: str) -> str:
+    """Return the field elevation in feet for a four-letter ICAO airport code."""
+    elevation = FIELD_ELEVATION_FT.get(airport.upper())
+    if elevation is None:
+        return f"No field elevation on file for {airport.upper()}."
+    return f"{airport.upper()} field elevation is {elevation} ft."
 
 
 @tool
-def calculator(expression: str) -> str:
-    """Evaluate a mathematical expression and return the result.
-
-    Args:
-        expression: A mathematical expression to evaluate (e.g. '42 * 7').
-    """
-    try:
-        result = eval(expression, {"__builtins__": {}}, {})  # noqa: S307
-        return str(result)
-    except Exception as e:
-        return f"Error evaluating expression: {e}"
+def lookup_runway_length(airport: str) -> str:
+    """Return the longest runway length in feet for a four-letter ICAO airport code."""
+    length = RUNWAY_LENGTH_FT.get(airport.upper())
+    if length is None:
+        return f"No runway data on file for {airport.upper()}."
+    return f"{airport.upper()} longest runway is {length} ft."
 
 
 @tool
-def word_count(text: str) -> str:
-    """Count the number of words in a piece of text.
+def lookup_weather(airport: str) -> str:
+    """Return the current field conditions for a four-letter ICAO airport code."""
+    conditions = WEATHER.get(airport.upper())
+    if conditions is None:
+        return f"No observation on file for {airport.upper()}."
+    return f"{airport.upper()}: {conditions}"
 
-    Args:
-        text: The text to count words in.
+
+def _seed_skills_store() -> InMemoryStore:
+    """Load the bundled skill folders into a process-local store.
+
+    Read once at import. The store is never written to at runtime, so the agent
+    sees `/skills/` as a stable read-only mount even though it is served by the
+    same `StoreBackend` machinery as a writable one.
     """
-    words = text.split()
-    return f"{len(words)} words"
+    store = InMemoryStore()
+    backend = StoreBackend(namespace=lambda _runtime: SKILLS_NAMESPACE, store=store)
+    for path in sorted(SKILLS_DIR.rglob("*.md")):
+        # Paths are stored WITHOUT the mount prefix. `CompositeBackend` strips
+        # the route prefix before delegating, so a store seeded at
+        # `/skills/runway-analysis/...` would surface as
+        # `/skills/skills/runway-analysis/...` to the agent.
+        backend.write(f"/{path.relative_to(SKILLS_DIR).as_posix()}", path.read_text())
+    return store
 
 
-@tool
-def summarize(text: str) -> str:
-    """Summarize a piece of text in one sentence.
+SKILLS_STORE = _seed_skills_store()
+
+
+class SkillsVisibilityMiddleware(AgentMiddleware):
+    """Republish `skills_metadata` as a `custom` stream event.
+
+    Same shim as the memory capability's: the key stays private on the state and
+    is announced alongside it, so a panel can render the loaded skill index
+    while the agent works rather than only once the run settles.
+    """
 
-    Args:
-        text: The text to summarize.
+    @property
+    def name(self) -> str:
+        return "SkillsVisibilityMiddleware"
+
+    def _emit(self, state: dict[str, Any]) -> None:
+        metadata = state.get("skills_metadata")
+        if metadata is None:
+            return
+        try:
+            writer = get_stream_writer()
+        except (RuntimeError, KeyError):
+            return
+        writer(
+            {
+                "name": SKILLS_EVENT,
+                "data": {
+                    "skills_metadata": metadata,
+                    "skills_load_errors": state.get("skills_load_errors") or [],
+                },
+            }
+        )
+
+    def before_model(self, state: dict[str, Any], runtime: Any) -> None:  # noqa: ANN401, ARG002
+        self._emit(state)
+        return None
+
+    def after_agent(self, state: dict[str, Any], runtime: Any) -> None:  # noqa: ANN401, ARG002
+        self._emit(state)
+        return None
+
+
+def build_skills_agent():
+    """Build the skills agent.
+
+    `CompositeBackend` routes by path prefix, longest first. `/skills/` resolves
+    to the seeded store; everything else falls through to `StateBackend`, so any
+    notes the agent writes stay on the thread and never touch the skill mount.
     """
-    sentences = [s.strip() for s in text.split(".") if s.strip()]
-    if not sentences:
-        return "No content to summarize."
-    first = sentences[0]
-    total = len(sentences)
-    return f"{first}. [{total} sentence(s) total]"
-
-
-def build_skills_graph():
-    tools = [calculator, word_count, summarize]
-    llm = ChatOpenAI(model="gpt-5-mini", streaming=True).bind_tools(tools)
-    tool_node = ToolNode(tools)
-
-    async def agent(state: SkillsState) -> dict:
-        """Choose and call the appropriate skill tool."""
-        system_prompt = (PROMPTS_DIR / "skills.md").read_text()
-        messages = [SystemMessage(content=system_prompt)] + state["messages"]
-        response = await llm.ainvoke(messages)
-        return {"messages": [response]}
-
-    def should_continue(state: SkillsState) -> str:
-        last = state["messages"][-1]
-        if isinstance(last, AIMessage) and last.tool_calls:
-            return "tools"
-        return END
-
-    graph = StateGraph(SkillsState)
-    graph.add_node("agent", agent)
-    graph.add_node("tools", tool_node)
-    graph.set_entry_point("agent")
-    graph.add_conditional_edges("agent", should_continue, {
-        "tools": "tools",
-        END: END,
-    })
-    graph.add_edge("tools", "agent")
-    return graph.compile()
-
-
-graph = build_skills_graph()
+    return create_deep_agent(
+        model=ChatOpenAI(model="gpt-4.1", temperature=0),
+        tools=[lookup_field_elevation, lookup_runway_length, lookup_weather],
+        system_prompt=(PROMPTS_DIR / "skills.md").read_text(),
+        backend=CompositeBackend(
+            default=StateBackend(),
+            routes={
+                SKILLS_ROOT: StoreBackend(
+                    namespace=lambda _runtime: SKILLS_NAMESPACE,
+                    store=SKILLS_STORE,
+                ),
+            },
+        ),
+        skills=[SKILLS_ROOT],
+        middleware=[SkillsVisibilityMiddleware()],
+    )
+
+
+graph = build_skills_agent()
diff --git a/cockpit/deep-agents/skills/python/uv.lock b/cockpit/deep-agents/skills/python/uv.lock
index 5e7586f12..3c0912e6a 100644
--- a/cockpit/deep-agents/skills/python/uv.lock
+++ b/cockpit/deep-agents/skills/python/uv.lock
@@ -1,190 +1,304 @@
 version = 1
 revision = 3
 requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
 
 [[package]]
 name = "annotated-types"
-version = "0.7.0"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
+[[package]]
+name = "anthropic"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "docstring-parser" },
+    { name = "httpx2" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b4/50/463166f02179ab279edb61de1589a6f69cb3838d6a2fb6f2c92a3f8042f1/anthropic-1.3.0.tar.gz", hash = "sha256:6873492a77ede8849a161ab1bc78bc9a1e492a006d0b5bb4c57ac77845df838a", size = 1148177, upload-time = "2026-09-01T17:37:10.392Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
+    { url = "https://files.pythonhosted.org/packages/2c/5d/7863a9961d320c23787c7b594956afe4e878f9c0ae2376b11a20e416791d/anthropic-1.3.0-py3-none-any.whl", hash = "sha256:e7e7dbebf9f3c84a23954ab989378af6ae10a4d1804c81e9fea4b5ced695ce75", size = 1296959, upload-time = "2026-09-01T17:37:08.525Z" },
 ]
 
 [[package]]
 name = "anyio"
-version = "4.13.0"
+version = "4.14.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
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 source = { editable = "." }
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 [[package]]

From 5780e5a79ce36e46c623b5aca13ddcb9b62a5b64 Mon Sep 17 00:00:00 2001
From: Brian Love <brian@liveloveapp.com>
Date: Tue, 1 Sep 2026 12:45:03 -0700
Subject: [PATCH 7/7] chore(cockpit): guard the deepagents dependency set
 through the shared manifest
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Regenerating `deployments/shared-dev/langgraph.json` after the rebuild is a
no-op: the five deep-agents graph names and staged dep aliases are unchanged,
and the sandboxes entry was already removed in the first commit of this
branch. So the drift check has nothing to report, which is the correct
outcome and also the least informative one.

What DID change is what those staged deps install. Each topic's pyproject now
pins `deepagents==0.7.11`, and the shared deployment builds every dep from
its own pyproject. If a pin goes missing, nothing fails until the deploy
does — the graphs import `deepagents` at module scope, so the revision dies
at startup rather than at build, well after the merge that caused it.

Adds two assertions to the generator's spec:

- every deep-agents staged dep pins deepagents 0.7.11, and its lock resolves
  `langchain-anthropic` and `langchain-google-genai`. Those two are mandatory
  transitive imports even for an OpenAI-only graph; they are hard
  dependencies of `deepagents` itself, so the pin is what brings them, and
  this checks the lock agrees rather than trusting that.
- the manifest still carries all five graphs and no longer carries
  `da-sandboxes`. `subagents` is the odd name out — it predates the `da-`
  prefix and the Angular environment's assistantId matches it, so a rename
  would break that demo in production only.

Loosening one pin to `deepagents>=0.7` turns `nx test scripts` red.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---
 .../generate-shared-deployment-config.spec.ts | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/scripts/generate-shared-deployment-config.spec.ts b/scripts/generate-shared-deployment-config.spec.ts
index a3d6b14be..b0de0a132 100644
--- a/scripts/generate-shared-deployment-config.spec.ts
+++ b/scripts/generate-shared-deployment-config.spec.ts
@@ -38,4 +38,50 @@ describe('generate-shared-deployment-config', () => {
     };
     expect(manifest.dependencies.some((d) => d.includes('ag-ui'))).toBe(false);
   });
+
+  it('carries the deepagents dependency set into every deep-agents staged dep', () => {
+    // The deep-agents topics run on the real `deepagents` package, and the
+    // shared deployment installs each staged dep from its own pyproject. If a
+    // topic's pyproject loses the pin, nothing here fails until the deploy
+    // does — the graph imports `deepagents` at module scope, so the revision
+    // dies at startup rather than at build.
+    //
+    // `langchain-anthropic` and `langchain-google-genai` are mandatory
+    // transitive imports of `deepagents` even for an OpenAI-only graph. They
+    // are hard dependencies of the package itself, so pinning `deepagents`
+    // is what brings them; this asserts the lock agrees rather than trusting
+    // that.
+    const root = resolve(__dirname, '..');
+    execSync('npx tsx scripts/generate-shared-deployment-config.ts', {
+      cwd: root,
+      stdio: 'pipe',
+    });
+
+    const deepAgentAliases = ['da-planning', 'da-filesystem', 'da-subagents', 'da-memory', 'da-skills'];
+    for (const alias of deepAgentAliases) {
+      const depRoot = resolve(root, 'deployments/shared-dev/deps', alias);
+      const pyproject = readFileSync(resolve(depRoot, 'pyproject.toml'), 'utf8');
+      expect(pyproject).toContain('deepagents==0.7.11');
+
+      const lock = readFileSync(resolve(depRoot, 'uv.lock'), 'utf8');
+      for (const transitive of ['langchain-anthropic', 'langchain-google-genai']) {
+        expect(lock).toContain(`name = "${transitive}"`);
+      }
+    }
+  });
+
+  it('keeps every deep-agents graph in the aggregated manifest', () => {
+    const root = resolve(__dirname, '..');
+    const manifest = JSON.parse(
+      readFileSync(resolve(root, 'deployments/shared-dev/langgraph.json'), 'utf8'),
+    ) as { graphs: Record<string, string> };
+
+    // `subagents` is the odd one out: its graph name predates the `da-` prefix
+    // and the Angular environment's assistantId matches it, so renaming it
+    // would silently break that demo in production.
+    expect(Object.keys(manifest.graphs)).toEqual(
+      expect.arrayContaining(['da-planning', 'da-filesystem', 'subagents', 'da-memory', 'da-skills']),
+    );
+    expect(manifest.graphs).not.toHaveProperty('da-sandboxes');
+  });
 });