Hey HN! In 2025, I've spent more time than ever conversing with AI coding agents, particularly Claude Code. These conversations are an intimate look into how we think and solve problems. Every chat with the agent contains valuable solutions, patterns, decisions, and mistakes. So being able to search, analyze, and learn from those interactions isn't just convenient, it's becoming essential.
To help me do this, I built a tool to process Claude Code conversations:
* Import and search your entire conversation history across projects
* Analyze sessions, choosing from over 300 LLM models, via OpenRouter, to extract insight and patterns (decisions made, error patterns, how you use AI agents)
* Share insights as GitHub Gists (as long as the text passes a security scan)
* View basic aggregate statistics on Claude Code usage
The tool is built with Python, Streamlit, SQLite with FTS5, OpenRouter, and Gitleaks.
I made this for myself, and sharing it in case it helps you too. Once your conversations are in a database, you can start asking questions like “What were the key technical decisions on this project?”, “How did the agent help to research and prototype this feature?”, "What steps did I take to implement this?" and “What errors does the agent commonly make?”
It’s a work in progress, and I'm planning on adding more features. Currently only tested on macOS 14.7 with Claude Code 2.0.21. If you’re curious what your Claude Code sessions may reveal, take it for a spin!
Hey HN! In 2025, I've spent more time than ever conversing with AI coding agents, particularly Claude Code. These conversations are an intimate look into how we think and solve problems. Every chat with the agent contains valuable solutions, patterns, decisions, and mistakes. So being able to search, analyze, and learn from those interactions isn't just convenient, it's becoming essential.
To help me do this, I built a tool to process Claude Code conversations:
https://github.com/sujankapadia/claude-code-analytics
* Import and search your entire conversation history across projects
* Analyze sessions, choosing from over 300 LLM models, via OpenRouter, to extract insight and patterns (decisions made, error patterns, how you use AI agents)
* Share insights as GitHub Gists (as long as the text passes a security scan)
* View basic aggregate statistics on Claude Code usage
The tool is built with Python, Streamlit, SQLite with FTS5, OpenRouter, and Gitleaks.
I made this for myself, and sharing it in case it helps you too. Once your conversations are in a database, you can start asking questions like “What were the key technical decisions on this project?”, “How did the agent help to research and prototype this feature?”, "What steps did I take to implement this?" and “What errors does the agent commonly make?”
It’s a work in progress, and I'm planning on adding more features. Currently only tested on macOS 14.7 with Claude Code 2.0.21. If you’re curious what your Claude Code sessions may reveal, take it for a spin!