Agent Tinman – Autonomous failure discovery for LLM systems

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Hey HN, I built Tinman because finding LLM failures in production is a pain in the ass. Traditional testing checks what you've already thought of. Tinman tries to find what you haven't. It's an autonomous research agent that: - Generates hypotheses about potential failure modes - Designs and runs experiments to test them - Classifies failures (reasoning errors, tool use, context issues, etc.) - Proposes interventions and validates them via simulation The core loop runs continuously. Each cycle informs the next. Why now: With tools like OpenClaw/ClawdBot giving agents real system access, the failure surface is way bigger than "bad chatbot response." Tinman has a gateway adapter that connects to OpenClaw's WebSocket stream for real-time analysis as requests flow through. Three modes: - LAB: unrestricted research against dev - SHADOW: observe production, flag issues - PRODUCTION: human approval required Tech: - Python, async throughout - Extensible GatewayAdapter ABC for any proxy/gateway - Memory graph for tracking what was known when - Works with OpenAI, Anthropic, Ollama, Groq, OpenRouter, Together pip install AgentTinman tinman init && tinman tui GitHub: https://github.com/oliveskin/Agent-Tinman Docs: https://oliveskin.github.io/Agent-Tinman/ OpenClaw adapter: https://github.com/oliveskin/tinman-openclaw-eval Apache 2.0. No telemetry, no paid tier. Feedback and contributions welcome.

Visit product View on Hacker NewsJan 31, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Hacker NewsMay not resonate with HN audience · Strong signals: llama, io · Missing: https docs, excited, just released

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