Ikalus1988/MisakaNet

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | https://misakanet.org

What it solves

MisakaNet provides a shared "failure-memory" for AI coding agents to prevent them from repeating the same debugging errors. Instead of an agent spending time re-diagnosing a known bug or environment issue, it can search a database of verified failure-recovery lessons to find a documented fix path immediately.

How it works

The project operates as a git-backed knowledge base of Markdown files containing problem descriptions, root causes, fixes, and verification steps. It uses a zero-dependency BM25 keyword search engine to retrieve relevant lessons. Agents can access this knowledge through multiple interfaces, including a Model Context Protocol (MCP) server (both local stdio and remote HTTP), a CLI, and a Python library. When an agent encounters an error it cannot solve, it can submit a redacted failure report via an intake process that maintainers review to create new lessons.

Who it’s for

It is primarily designed for AI agents (such as Claude Code, Cursor, and Windsurf) and developers who want to reduce the time agents spend stuck on repetitive technical failures.

Highlights

  • Agent-Native Interfaces: Supports MCP, WebMCP, and specialized adapters for tools like the DeepSeek Harness.
  • Verified Evidence Levels: Lessons are categorized by evidence levels (E0-E4), ranging from community reports to production-proven fixes.
  • Zero-Dependency Search: Uses a lightweight BM25 engine that requires only the Python standard library for local searches.
  • Broad Compatibility: Integrates with major AI coding tools including Claude Code, Cursor, VS Code, and Copilot.

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