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Callimachus

by BetaBots-LLC

Searches local AI coding-agent history with hybrid keyword and on-device semantic retrieval.

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Install Callimachus

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "betabots-llc-callimachus": {
      "command": "npx",
      "args": [
        "-y",
        "callimachus-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y callimachus-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

BetaBots-LLC/callimachus MCP server exposes a local index of AI coding-agent conversations through tools for thread search, project-scoped search, recent-thread lookup, and full-thread retrieval. It combines SQLite FTS5 keyword ranking with on-device vector KNN, keeping indexed conversation data on the local machine. Use it when an agent needs to recall previous implementation work, decisions, or discussions across supported coding tools. The broader Callimachus application also provides memory, snapshots, decision checks, and other local history features that the MCP server can expose.

Use cases

  • Search previous coding-agent threads for implementation details
  • Find conversations that touched a specific file path
  • Retrieve project-specific decisions and gotchas
  • Resume work from a saved agent-session snapshot

Key features

  • Hybrid SQLite FTS5 and on-device vector search
  • Indexes conversations from 11 coding-agent tools
  • Project, source, tag, and file-mention filtering
  • Thread retrieval and recent-history lookup
  • Local session snapshots and decision checks
  • Shared local index for desktop app and MCP sidecar

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

17/18 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 9d ago via OSV.dev · callimachus-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars46Stargazers on the source repository.
npm downloads1.2kPackage downloads in the last 30 days.
Last commit3mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How Callimachus works

What BetaBots-LLC/callimachus MCP server does

BetaBots-LLC/callimachus MCP server gives coding agents access to a local catalogue of conversation history. Callimachus indexes threads from 11 supported coding-agent tools: Claude Code, Codex, Cursor, Gemini CLI, Qwen Code, Goose, OpenCode, Continue, Cline, Roo Code, and Kilo Code. The resulting data is stored in SQLite and can be queried without sending the indexed history to a hosted search service.

The main use is historical recall. An agent can look for prior work across all indexed threads, narrow results to the current project, inspect recent threads, or retrieve a selected thread. Search can also use file mentions, allowing a path such as file:embed/mod.rs to find conversations that discussed a particular file. Filters include source, project, subagents, starred threads, and tags.

How it works

Keyword retrieval uses SQLite FTS5 with BM25 ranking. Semantic retrieval uses embeddings generated on the device with fastembed, then performs vector KNN queries through sqlite-vec. Callimachus combines the keyword and semantic result lists using Reciprocal Rank Fusion. Semantic search is not available immediately after installation: the conversation index must be populated first, followed by a separate semantic-index build.

The MCP sidecar and the desktop application use the same local index.db and share the desktop core library. A background file watcher keeps the index current after sources have been configured. The server can also write selected information back to Callimachus memory, including completed TODOs, decisions, gotchas, and session snapshots, rather than modifying project files directly.

Setup and configuration

The project publishes signed desktop builds for macOS, Windows, and Linux through GitHub Releases. It can also be built from the repository with pnpm and the Tauri development workflow. The repository is a pnpm and Turborepo monorepo; development setup includes pnpm install followed by pnpm desktop:dev.

After the first launch, configure the conversation sources in Settings or trigger Reindex. Build the semantic index separately when vector search is needed. Reindexing and semantic-index construction run as distinct background jobs and do not hold the database write lock at the same time.

No cloud search credential is required for the local index or MCP history queries. Optional LLM features can use local Ollama, an existing Claude Code or Codex CLI subscription, or a configured cloud provider. Stored provider credentials use the operating system credential store.

Tools and capabilities

The documented MCP-facing capabilities include:

  • searchthreads for searching indexed conversations.
  • searchcurrentproject for restricting retrieval to the active project.
  • recentthreads for finding recently indexed conversations.
  • getthread for reading a selected conversation.
  • linked_commits for retrieving inferred relationships between threads and Git commits.
  • snapshot_session, list_snapshots, and load_snapshot for creating and resuming session checkpoints.
  • check_decision for surfacing an existing decision before an agent revisits a proposal.

The exact availability of memory-writing and session features depends on the bundled application version and its local data.

Limitations and notes

Results depend on which sources have been configured and indexed. A new installation starts with an empty index, and semantic retrieval requires the additional semantic-index build. The semantic model and vector search run locally, but optional distillation, RAG answers, and chat can send prompts to the LLM provider selected by the user.

This project is licensed under AGPL-3.0.

Read the full README View source on GitHub

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Frequently Asked Questions about Callimachus

The MCP server is bundled as a sidecar with the Callimachus desktop application. The material points to signed macOS, Windows, and Linux builds, but does not provide a standalone package-install command.

It combines SQLite FTS5/BM25 keyword search with on-device semantic vector KNN through sqlite-vec, then fuses the rankings.

No cloud key is required for local indexing and history search. Optional LLM features can use local Ollama, logged-in Claude Code or Codex CLI access, or a configured cloud provider.

It supports Claude Code, Codex, Cursor, Gemini CLI, Qwen Code, Goose, OpenCode, Continue, Cline, Roo Code, and Kilo Code.

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