Muninn

Code search and repository knowledge for AI coding agents through MCP.

Muninn maintains a searchable code index for Codex, Claude Code and other compatible MCP clients. Ask where a behavior is implemented, retrieve relevant source ranges, and inspect the code before changing it.

Git worktrees share unchanged content and embeddings while keeping separate searchable views. A search selects the checkout named by repo or cwd, so a feature branch and the primary checkout can return their own implementations.

• Combine semantic and full-text retrieval with hybrid search.

• Follow recorded structural relationships and verify them against source.

• Reuse unchanged indexed content across linked Git worktrees.

• Store and search curated repository notes.

Muninn needs PostgreSQL with pgvector, Apache AGE and pgcrypto, an indexing watcher, and an MCP connection. Local and hosted embedding backends are available; hosted embeddings send selected source chunks to the provider.

Start with Getting started. Agents performing installation use AGENTS.md.

Article: Muninn code search across Git worktrees.

Install the binaries with Nix

With Nix installed and its nix-command and flakes features enabled, build the CLI, watcher and MCP server without installing them:

nix build github:kamysh/muninn#muninn
./result/bin/muninn --version
./result/bin/muninn-index --version
./result/bin/muninn-mcp --version

To add these programs to your user profile instead, use nix profile add github:kamysh/muninn#muninn. This installs binaries only. Follow Getting started to prepare PostgreSQL and its extensions, configure embeddings, start the watcher and connect your MCP client. No database or service is created by this package. Use the installed absolute executable paths when configuring the watcher and MCP client; command -v muninn-index and command -v muninn-mcp show them after installation.

The commands follow the repository's default branch. Append a commit to the repository address, as in github:kamysh/muninn/COMMIT#muninn, to pin a version.

Related MCP server: grepsense

Why Muninn exists

An agent repeatedly exploring a large codebase spends context and time finding the same implementations. Plain text search is useful when the name is known; questions about behavior, relationships and prior repository decisions need other entry points. Muninn maintains a searchable index and durable notes so an agent can find relevant source, then read and verify it.

Git worktrees add a second problem: each checkout has its own files, but copying a full chunk/vector index for every branch wastes space and embedding work. Muninn groups linked worktrees into a repository family. It shares unchanged file versions and embeddings, stores file differences per checkout, and searches the checkout the caller selected. Updating the primary checkout adjusts those differences without changing another checkout's published view.

Registration is explicit. A cheap inventory helps a human or agent choose which files matter before the first index starts. An enabled family discovers linked worktrees automatically and shares its configuration across them. Independent non-Git directories can also be registered.

What runs

Use postgres-ai for installation. The underlying prerequisites are PostgreSQL with pgvector, Apache AGE and pgcrypto; postgres-ai supplies them together. Assembling a compatible stack yourself is an advanced, non-recommended alternative, documented separately in the custom database guide.

MCP provides search_hybrid, search_fulltext, search_semantic, search_structural, and record_knowledge, search_knowledge, list_knowledge, delete_knowledge.

Search results are evidence to inspect, not compiler-verified answers. Full-text search is tokenized; structural edges are name-based candidates. During a merge, rebase or unresolved conflict, Muninn retains the last completed index instead of indexing transient conflict contents. See behavior and limits.

The Codex and Claude Code guidance asks agents to explain, in plain language, how useful a Muninn retrieval was for the current task: a 0–5 score, a reason and the next step. This is the agent’s judgment, not a search rank. The agent setup guide installs that guidance.

Data and privacy

The database and processes can run on your machine. Local embeddings run there after the model is downloaded. Hosted Voyage/OpenAI embeddings send selected source chunks and query text to the provider; those chunks are source code. A remote PostgreSQL deployment also stores the index off-machine. MCP returns source excerpts to the chosen agent client, whose model/data settings apply. Local indexing alone does not make a cloud agent session offline.

Start here

These docs describe the 1.2.2 source tree. Install matching binaries from v1.2.2 or build from this source tree.

Everyday commands

muninn add /path/to/repo                 # Human configuration TUI; separate Start confirmation
muninn config /path/to/checkout          # Review/edit shared family policy
muninn preflight /path/to/checkout --json
muninn config /path/to/checkout --json    # Agent inspection
muninn start /path/to/checkout --json     # Explicitly enable and run indexing
muninn status                           # Registered-family overview
muninn usage --days 30

Agents should check a complete JSON policy before saving it and guard saves and start requests with the returned family ID/revision. See the shared skill. New repositories snapshot global family settings at creation; review the visible defaults and select the backend before registering. Saved settings do not follow later global edits. status PATH, pause, resume and remove still use the registered locator shown by status; they are not arbitrary-worktree commands. In particular, remove deletes an entire registered family's code index.

Related projects

Muninn and Mimir can run independently. Their documented setup can share one compatible PostgreSQL instance with separate roles and databases.

License

Apache License 2.0 — see LICENSE. Contributions are subject to the Contributor License Agreement.