execution-provenance

TypeScript workflow engine with named stages, tracked state and recorded decision evidence for execution debugging.

TypeScript AI agent framework with recorded context, tool calls and decisions, plus configurable answer checks.

TypeScript action and journey graphs for agent-accessible web apps, with live availability and recorded interaction outcomes.

R-LAM is a reproducibility-constrained execution framework for Large Action Models in scientific workflow automation. It enables adaptive, agent-driven workflow execution while enforcing strict guarantees on auditability, determinism, and replayability.

Zero-install saved-call inspector and native regression tests for n8n. Trace recorded item links in your browser; validate workflows with Docker, CLI or GitHub Actions.

Execution records, replay, time travel and honest attribution — independent of the engine that produced them

Experimental Beta: coordinated visualization and analysis with selection provenance, recorded interactions and evidence inspection. Source checkout only; no npm release.

Independent AI systems research on execution provenance, causal failure attribution, and verified recovery for multi-agent systems.

TypeScript framework for configurable multi-tenant platforms: scoped profiles, feature modules, adapter routing, actions and retained execution evidence. See README for source-candidate status.

TypeScript commerce workflows and business profiles on Blueprint: scoped actions, shipping, after-sales and retained operation evidence. See README for source-candidate status.

Home of footprintjs: execution provenance for AI agents and software. A film, the library list and the thinking behind it.

Footprint ecosystem profile: workflow libraries, agent tooling, recorded evidence and project navigation.

Beta ML evidence library for Python and TypeScript: model and dataset references, evaluation receipts, release guards and saved-workflow investigation.

Why Lens: React components for inspecting recorded agentfootprint prompts, tool calls, context and decisions.

Execution provenance protocol for AI agents — tamper-evident, third-party verifiable behavioral records. Hash chains (SHA-256) + Ed25519 signatures + JCS (RFC 8785). Zero dependencies, 202 tests.

React components for inspecting FootPrint workflow execution, recorded state changes and time-travel debugging.

OpenExecution Provenance Specification — implements AEGIS (Agent Execution Governance and Integrity Standard) for auditable, tamper-evident AI agent behavioral records. Apache 2.0.