Agent Traces

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

Autonomous coding workflow that generates implementations, runs unit test executions, and self-corrects runtime stack traces.

Observability and guardrails for your ai agents. AgentTrail Guard traces your agent's execution and automatically compiles repeat failures into permanent guardrails.

Curated list of AutoResearch use cases with optimization traces and open source implementations

A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability

An agent skill for finding papers worth reading—trace research lineages, map evidence networks, and weigh supporting and conflicting findings to guide your next research decision.

Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters

🤖 A full-stack Agent Chat Runtime for pre-production validation, built with act, TypeScript, and LangGraph JS combining streaming, multimodal, HITL, and tools with durable Task/Step execution, retry budgets, idempotency, compensation, distributed locks, context governance, OpenTelemetry tracing, model fallback, cost tracking, and Opik evaluation.

Find which script, notebook, data, command, or AI agent produced a file — locally, with evidence and honest uncertainty.

Threat modeling for AI coding agents: point Claude Code, Codex or any agent at your repo and get an evidence-traced threat model. 36 architectures, 29 methods, MIT.

Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.

LightAgent: Lightweight Python framework for OpenAI-compatible agents with tools, memory, guardrails, tracing, lifecycle hooks, multi-agent collaboration, and workflows.

Complete ETCLOVG framework for AI Agent workflows - DAG+FSM orchestration, Ebbinghaus memory, discipline routing, skill evolution, trace system, governance. 80+ tests, zero deps, 7/7 layers.

🤖 A full-stack Agent Chat Runtime for pre-production validation, built with act, TypeScript, and LangGraph JS combining streaming, multimodal, HITL, and tools with durable Task/Step execution, retry budgets, idempotency, compensation, distributed locks, context governance, OpenTelemetry tracing, model fallback, cost tracking, and Opik evaluation.

Open-source, license-free MCP server for RTL waveform debug: reads FST waveforms (VCD/FSDB auto-convert) plus a SystemVerilog netlist, with 34 tools covering driver analysis, value/X tracing, pass-fail waveform diff, a browser wave viewer the agent drives, and pre-simulation static analysis.

Evidence-chain whiteboard + MCP server for AI research agents: every claim traces back to the run and code that produced it. 讓 AI agent 做研究時，每個結論都能追到產生它的程式碼。Claude Code · Codex · Cursor · Gemini CLI

Intercept and inspect Coding Agent API traffic from Claude Code, Codex CLI, Gemini CLI, Cursor CLI, OpenCode, Kimi/Kimi Code, Pi, and Hermes in a local trace viewer.

Local-first AI agent trace workbench. Inspect Claude Code, Codex and OTLP logs, compare runs, and export privacy-conscious reports. No backend or API keys.

Autonomous AI-agent orchestration engine for job discovery with decision traces, tool adapters, and production-grade run control.

Tamper-evident tracing, policy gates and cross-checks for AI agents: see what the agent did, and why.

The cross-harness, self-improving memory layer for AI agents. Join our community: https://discord.com/invite/zdNChS2fBu