docvault: Generate Local API Docs to Reduce AI Hallucinations
What docvault does
docvault addresses the problem of LLMs like Claude confidently providing incorrect function signatures by generating local, up-to-date API documentation directly from source code. The tool creates markdown files that stay in your repository, providing a reliable reference point for AI coding assistants.
Technical implementation
The tool works for Rust crates and Python packages using runtime introspection. It generates a two-tier markdown file structure:
- Curated patterns and gotchas at the top
- Full machine-generated API reference at the bottom
Re-running the script updates the API reference while preserving your curated notes. The scripts are pure Python stdlib with zero dependencies, available for standalone use.
Claude Code integration
Install the plugin with these commands:
/plugin marketplace add zeapo/docvault
/plugin install vendored-docs@docvault
Once installed, you can use natural language commands like:
vendor the doc for kube
After documentation is vendored, Claude checks these local docs before searching the internet, reducing hallucinations. You can also ask Claude to compare your implementation against the vendored documentation:
compare our usage of kube with the vendored doc
This type of tool is useful for developers working with complex libraries where AI assistants frequently hallucinate API details, particularly in Rust and Python ecosystems where API signatures are critical for correct implementation.
📖 Read the full source: r/ClaudeAI
👀 See Also
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AutoAgents Rust Framework Adds Python Bindings for Prototyping
AutoAgents, a Rust-based multi-agent framework, now has Python bindings that allow developers to prototype in Python while maintaining the same Rust core runtime, provider interfaces, pipeline model, and agent semantics. The bindings enable experimentation with local AI models without external systems.
Building an Agentic RAG for Obsidian with Claude and an Eval Harness to Detect Hallucinations
A developer built an agentic RAG system over an Obsidian vault to let Claude answer questions from engineering books, then created an eval harness using Claude Sonnet as a judge to detect when the agent was confidently wrong. Rubric iterations improved judge-human agreement from 39% to 94%.