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The primary manifest — the file an agent reads to learn what this artifact does.
CodeGraph Memory MCP Lite
Local-first MCP-style code memory for research repositories: file search, Python symbols, import graph, and repo context packs.
Research codebases are often small enough that a heavyweight code intelligence stack is overkill, but large enough that agents lose track of modules, configs, training scripts, and README claims. This project provides a tiny local codegraph memory layer for AI agents. It indexes a repository, extracts Python classes/functions/imports with the standard ast module, stores file text in SQLite FTS, and exposes MCP-style tools over stdio.
If this helps your agent understand a research repo faster, a star helps other builders discover it.
Features
- Local-only SQLite database.
- File full-text search over code, Markdown, configs, and scripts.
- Python symbol extraction: classes, functions, async functions, and imports.
- Lightweight import graph and symbol lookup.
- MCP-style tools:
index_codebasesearch_code_memoryfind_symbolget_dependency_graphsummarize_repo_context
Quick Start
git clone https://github.com/StaryMoon/codegraph-memory-mcp-lite.git
cd codegraph-memory-mcp-lite
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
codegraph-memory index examples/sample_repo
codegraph-memory symbol Trainer
codegraph-memory graph --limit 20
codegraph-memory contextMCP Config
{
"mcpServers": {
"codegraph-memory-lite": {
"command": "python3",
"args": ["-m", "codegraph_memory_mcp_lite.server", "serve"],
"env": {
"CODEGRAPH_MEMORY_DB": "/absolute/path/to/codegraph-memory.sqlite"
}
}
}
}Why This Exists
Most code assistants can read files, but they often lack a persistent local map of:
- where classes and functions are defined;
- which modules import which modules;
- what README claims the repo makes;
- what training/evaluation scripts exist;
- which files are relevant to a user query.
This project gives an agent a small memory substrate before it starts editing.
Related Projects
License
MIT.