agentRamen MCP server

agentRamen

<!-- mcp-name: io.github.palrajjp/agentramen -->

Apache-2.0 licensed. See the security policy and community code of conduct.

agentRamen indexes source structure and Git history, then retrieves task-relevant files and excerpts. It is local-first by default: each developer keeps a private SQLite index. Teams can optionally publish sanitized, commit-pinned snapshots to a centrally hosted, OIDC-protected read-only MCP service.

Quick start

Install agentRamen, then run it from the repository you want to explore:

python -m pip install agentramen
cd /path/to/your/repository
agentramen init
agentramen context "Add authentication" --budget 2000
agentramen impact src/auth.py

agentramen init creates the configuration, indexes the repository, and offers an optional GitHub Actions caller workflow. The index is stored locally in .agentramen/graph.db.

Try it in 30 seconds with examples/demo.sh. See the roadmap and contributing guide.

A minimal VS Code extension is also available.

What it can do

• Find task context: rank files using paths, symbols, imports, Git history, and optional local semantic similarity.
• Explain change impact: inspect dependencies, dependents, co-changes, hotspots, and file history.
• Map repository structure: explore architecture, export the graph, and query cached commit snapshots.
• Connect to coding agents: use local MCP stdio, the browser UI, or an optional authenticated centralized MCP service.
• Keep local indexes private: in local mode, the database and optional embeddings stay in each checkout; central mode transfers only an explicit sanitized snapshot to infrastructure you operate.

How it works

flowchart LR
  repo[Working tree and Git history] --> indexer[Indexer and language analyzers]
  indexer --> db[(Local SQLite graph)]
  optional[Optional local embeddings] --> db
  task[Task or query] --> clients[CLI, MCP, or local HTTP]
  clients --> retrieve[Retrieval and graph queries]
  retrieve <--> db
  retrieve --> excerpts[Ranked files and source excerpts]

The default install uses the Python standard library and conservative analysis for other languages. Optional extras add Tree-sitter parsing, local semantic retrieval, and model-aware token counting.

Optional extras

python -m pip install 'agentramen[treesitter]'
python -m pip install 'agentramen[semantic]'
python -m pip install 'agentramen[tokenizer]'
python -m pip install 'agentramen[central]'

The default install has no runtime dependencies. Tree-sitter, semantic embeddings, tokenizers, and the remote MCP service are opt-in. Semantic model setup may download weights on first use; inference runs locally.

Shortened. The full README is on GitHub.