Hermeneutic turns your AI corrections into context for the next task

Hermes Labs has released Hermeneutic, an open-source tool that mines user corrections from coding-agent conversations and surfaces relevant guidance when similar tasks arise. Hermeneutic reads Claude Code, Codex, and OpenAI-format logs, records the exchange around each correction into a local corpus, and uses Ollama embeddings to match new prompts against earlier ones, emitting a compact guidance preamble with citations to supporting correction records. It requires Python 3.10+ and Ollama, and offers an optional Claude Code hook to inject retrieved guidance as prompt context.

You ask a coding agent to review a function. It starts refactoring. You steer it back to the review you requested. That exchange contains useful information about your intended scope. Hermeneutic https://github.com/hermes-labs-ai/hermeneutic , built by Hermes Labs, mines corrections from agent conversations and surfaces relevant guidance when similar tasks come up. In this example, a later code-review request could receive guidance about staying within the requested scope—drawn from your earlier corrections. Your conversation history becomes a source of context for new work. Hermeneutic reads supported Claude Code, Codex, and OpenAI-format logs. It recognizes correction-shaped user turns and records the surrounding exchange when available: your request → agent response → your correction → repaired response The resulting local corpus preserves the episode around each correction. The bucket command lets you inspect recurring categories, including missed constraints, wrong targets, unnecessary confirmation, and scope expansion. Retrieval makes those records useful during another task. Using Ollama embeddings, Hermeneutic matches a new prompt against earlier prompts in the corpus. Relevant matches supply correction categories, which become a compact guidance preamble through category templates. Each advice bullet cites its supporting correction records. For a release task, that might surface your prior guidance to cite command output when reporting completion. For a similar review task, it might surface guidance about respecting the requested scope. With Python 3.10+ and Ollama available: python -m pip install hermeneutic==0.1.12 hermeneutic mine ~/.claude/projects \ --format claude-code \ --glob ' / .jsonl' \ --out ~/.hermeneutic/triples.jsonl hermeneutic bucket ~/.hermeneutic/triples.jsonl ollama pull nomic-embed-text hermeneutic compile-index --triples ~/.hermeneutic/triples.jsonl hermeneutic compile 'Review this function and report your findings.' When matching corrections meet the relevance threshold, the final command emits guidance for that prompt. Use it directly in your workflow, or install the optional Claude Code hook to supply retrieved guidance as prompt context: hermeneutic install-compile-hook Start with the corrections you already made. Try Hermeneutic on GitHub https://github.com/hermes-labs-ai/hermeneutic , follow the memory setup https://github.com/hermes-labs-ai/hermeneutic hermeneutic-memory , and star the repository to follow its development. What correction do you keep repeating to your coding agent? Built by Hermes Labs https://hermes-labs.ai . Prepared by our agent infrastructure from the project README and documentation.