iamanandsingh

iamanandsingh / agent-history-protocol

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Agent History Protocol — tamper-evident recording for AI agents

17
3
100% credibility
Found Mar 22, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Agent History Protocol is an open standard with SDKs for creating tamper-evident, verifiable logs of AI agent actions like tool calls, inferences, and authorizations.

How It Works

1
📖 Discover safe AI logging

You hear about a simple way to keep a perfect, unchangeable diary of everything your AI assistant does, like a black box for smart helpers.

2
🛠️ Get the diary tool

Download the easy tool that watches your AI and writes down every step it takes.

3
🔗 Connect your AI helper

Tell the diary to follow your AI assistant, and it starts recording actions quietly in the background.

4
🤖 Use your AI as usual

Chat with your AI, ask it to search or decide things, and it works normally while the diary captures every move safely.

5
📱 Peek at the diary

Open the diary anytime to read a clear list of what your AI did, when, and why.

6
✅ Prove it's real

Check the unbreakable seal to confirm no one tampered with the records—everything is trustworthy.

🏆 Perfect AI history

You now have a reliable story of your AI's actions, ready to review, share, or audit with full confidence.

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AI-Generated Review

What is agent-history-protocol?

Agent History Protocol builds tamper-evident logs for AI agents, capturing every tool call, LLM inference, and delegation in a verifiable hash chain—like a flight recorder for agent chat history. Developers get Python and TypeScript SDKs to record actions with one call, plus auto-instrumentation for HTTP requests in frameworks like LangChain. Use the CLI to inspect logs (`ahp log`), verify integrity (`ahp verify`), or export to JSONL/OTLP for audits.

Why is it gaining traction?

It stands out by making agent history tamper-proof without slowing agents—fail-open design ensures crashes don't break your flow, while levels add signing or third-party witnesses for compliance. Unlike basic logging, anyone can independently verify chains, perfect for debugging LangChain agent history or GitHub Copilot actions in production. The open spec invites multi-vendor support, from agent GitHub actions to Claude integrations.

Who should use this?

AI agent builders auditing LangChain runs, teams deploying GitHub Copilot or OpenAI agents needing provable traces, and compliance engineers tracking agent GitHub code changes or browser use. Ideal for devs instrumenting multi-agent systems with real LLMs, where you need to reconstruct decisions post-incident.

Verdict

Early alpha (17 stars, 1.0% credibility) with solid docs and demos, but light on tests—grab it if you're pioneering agent audits, otherwise watch for maturity. Worth a spin for tamper-proof LangChain agent history today.

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