Context Harness is a single Rust binary that gives AI tools like Cursor and Claude project-specific memory. It ingests docs, code, Jira tickets, Slack threads, and anything else into a local SQLite database, indexes them with FTS5 and optional vector embeddings, and exposes hybrid search via CLI and an MCP-compatible HTTP server.
I built this because I kept hitting the same problem: AI tools are powerful but have no memory of my complex multi-repo project. They can't search our internal docs, past incidents, or architecture decisions. Cloud RAG services exist, but they're complex, expensive, and your data leaves your machine. I wanted something I could point at my sources and just run `ctx sync all`.
Quick start:
# Install (pre-built binaries available for macOS/Linux/Windows)
cargo install --git https://github.com/parallax-labs/context-harness.git
# Create config and initialize
ctx init
# Sync your data sources (filesystem, Git, S3, or Lua scripts)
ctx sync all
# Search from CLI
ctx search "how does the auth service validate tokens"
# Or start the MCP server for Cursor/Claude Desktop
ctx serve mcp
What it does differently from other RAG tools:
- *Truly local*: SQLite + single binary. No Docker, no Postgres, no cloud. Local embeddings (bundled or pure-Rust) so semantic and hybrid search work with zero API keys. Back up your entire knowledge base with `cp ctx.sqlite ctx.sqlite.bak`.
- *Hybrid search*: FTS5 keyword scoring + cosine vector similarity with configurable blending. Works without embeddings too (keyword-only mode); with local embeddings you get full hybrid search offline.
- *Lua extensibility*: Write custom connectors, tools, and agents in Lua without recompiling anything. The Lua VM has HTTP, JSON, crypto, and filesystem APIs built in.
- *Extension registry*: `ctx registry init` installs a Git-backed community registry with 10 connectors (Jira, Confluence, Slack, Notion, RSS, Stack Overflow, Linear, etc.), 4 MCP tools, and 2 agent personas.
- *MCP protocol*: Cursor, Claude Desktop, Continue.dev, and any MCP-compatible client can connect and search your knowledge base directly.
Embeddings: you can run *fully offline* — the default build uses local embeddings (fastembed with bundled ONNX on most platforms, or a pure-Rust tract path on Linux musl and Intel Mac). No API key required. Optional: Ollama (local LLM stack) or OpenAI if you prefer. Keyword-only mode needs zero deps. There's no built-in auth layer; it's designed for local or trusted network use.
Stack: Rust, SQLite (WAL mode), FTS5, mlua (Lua 5.4), axum, MCP Streamable HTTP. MIT licensed.
GitHub: https://github.com/parallax-labs/context-harness
Docs: https://parallax-labs.github.io/context-harness/
Community Registry: https://github.com/parallax-labs/ctx-registry
If you find it useful, a star on GitHub is always appreciated.
Would love feedback on the search quality tuning (hybrid alpha, candidate counts) and the Lua extension model.
I built this because I kept hitting the same problem: AI tools are powerful but have no memory of my complex multi-repo project. They can't search our internal docs, past incidents, or architecture decisions. Cloud RAG services exist, but they're complex, expensive, and your data leaves your machine. I wanted something I could point at my sources and just run `ctx sync all`.
Quick start:
What it does differently from other RAG tools:- *Truly local*: SQLite + single binary. No Docker, no Postgres, no cloud. Local embeddings (bundled or pure-Rust) so semantic and hybrid search work with zero API keys. Back up your entire knowledge base with `cp ctx.sqlite ctx.sqlite.bak`.
- *Hybrid search*: FTS5 keyword scoring + cosine vector similarity with configurable blending. Works without embeddings too (keyword-only mode); with local embeddings you get full hybrid search offline.
- *Lua extensibility*: Write custom connectors, tools, and agents in Lua without recompiling anything. The Lua VM has HTTP, JSON, crypto, and filesystem APIs built in.
- *Extension registry*: `ctx registry init` installs a Git-backed community registry with 10 connectors (Jira, Confluence, Slack, Notion, RSS, Stack Overflow, Linear, etc.), 4 MCP tools, and 2 agent personas.
- *MCP protocol*: Cursor, Claude Desktop, Continue.dev, and any MCP-compatible client can connect and search your knowledge base directly.
Embeddings: you can run *fully offline* — the default build uses local embeddings (fastembed with bundled ONNX on most platforms, or a pure-Rust tract path on Linux musl and Intel Mac). No API key required. Optional: Ollama (local LLM stack) or OpenAI if you prefer. Keyword-only mode needs zero deps. There's no built-in auth layer; it's designed for local or trusted network use.
Stack: Rust, SQLite (WAL mode), FTS5, mlua (Lua 5.4), axum, MCP Streamable HTTP. MIT licensed.
GitHub: https://github.com/parallax-labs/context-harness
Docs: https://parallax-labs.github.io/context-harness/
Community Registry: https://github.com/parallax-labs/ctx-registry
If you find it useful, a star on GitHub is always appreciated.
Would love feedback on the search quality tuning (hybrid alpha, candidate counts) and the Lua extension model.