Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.

AI observability platform for production LLM and agent systems.

The Agent Memory Stack

Reference architecture for fail-open, witness-only evidence systems with independent verification and public settlement.

Open-source observability and audit trail platform for AI agents. MCP-native, tamper-evident event logging, real-time dashboard.

A schema-driven module development framework for Python.

The flight recorder for AI agents. Tamper-proof, ISO 42001 + HIPAA-compliant audit trails for everything AI agents do on your system. File changes + shell commands + PHI detection + agent identity.

Make AI work reproducible, debuggable, and contestable — like real software.

One line to instrument your agent and capture every event in an immutable, queryable audit trail.

ChainWatch is a flight data recorder for multi-step AI systems. It's a CLI-based tool that records every step in an AI decision chain, links them together in order, prevents tampering, and allows you to verify the chain's integrity and replay the full decision flow.

Teams and Solo Devs Claude Code hooks setup for observability and guardrails. Understand how skill, subagents, prompts are working and where is claude struggling to improve systematically

The node-level tracing library for agentic software.

A hands-on course repository for Evaluating AI Agents, created with Arize AI, that teaches you how to systematically evaluate, debug, and improve AI agents using observability tools, structured experiments, and reliable metrics. Learn production-grade techniques to enhance agent performance during development and after deployment.

ai-agents llm-ai gpt-workflows ai-audit execution-logging ai-research reproducibility observability provenance ai-operations research-infrastructure operational-memory

Lightweight notes and tools for detecting early instability in multi-turn agents using only traces, latency patterns, and simple structural metrics. Framework-agnostic.

OpenTelemetry instrumentation package for the Dust SDK. Automatically instruments agent interactions using OTel GenAI Semantic Conventions.

🦞 OpenClaw Flight Recorder PoC · local-only sidecar · RFC-001 JSONL → verifiable badge + hash-chained receipts (CI tests). Optional Canon-52/Law52 anchors.

🤖 Build context-aware, autonomous AI agents with this production-ready starter template for seamless development and deployment.

A TypeScript framework for building intelligent AI agents with Effect-TS. Features LLM providers (Anthropic, OpenAI, Google Gemini, Ollama), multi-layer memory, reasoning strategies (ReAct, Plan-Execute, ToT, Reflexion), tool execution, guardrails, verification, cost tracking, and multi-agent orchestration.