memos — Structural code index for AI agents

GitHub: TAskMAster339/memos-engine

memos builds a structural index (symbols, call edges, imports) of a TypeScript/TSX / Go codebase using tree-sitter and stores it in SQLite. It is the first layer of a larger Memory OS for AI coding agents — instead of grepping text, agents query structure (definitions, callers, callees).

Integrating memos into your project

To give your AI coding agent structural code understanding and persistent memory, add memos to your project in three steps:

1. Install memos

pip install memos-engine
# or via uv:
uv tool install memos-engine

Or clone and install from source:

git clone https://github.com/TAskMAster339/memos-engine.git
cd memos-engine
uv tool install -e .

Or install from PyPI (no clone needed):

pip install memos-engine
# or via uv:
uv tool install memos-engine

2. Index your project

cd /path/to/your/project
memos index --path .

This creates {project}/.memos/memory.db with all symbols, call edges, and imports. Subsequent runs skip unchanged files via content hash.

3. Add AGENTS_EXAMPLE.md to your project

Copy the file AGENTS_EXAMPLE.md from the memos repo into the root of your project as AGENTS.md (or CLAUDE.md, or .opencode/instructions, depending on your agent). It tells the agent:

• To call open_project at the start of every session
• To use find_symbol_tool / find_calls_tool instead of grep
• To call get_context_tool before editing a function
• To save decisions via memory_add_note so they survive across sessions

Inside AGENTS_EXAMPLE.md you only need to change the repo path if your agent doesn't resolve relative paths automatically. Everything else is ready to use.

Optional: configure MCP for your AI client — see MCP Server below.

Quick start

# Clone and install globally
git clone https://github.com/TAskMAster339/memos-engine.git
cd memos-engine
uv tool install -e .

# Index a project
memos index --path /path/to/your/project

# Start the MCP server for AI agents
memos serve-mcp

Or using uv run without global install:

uv sync                            # install deps
uv run memos index --path .        # index current project
uv run memos index --path . --full # force reindex (ignore hashes)

Query

# Find a symbol by name
uv run memos query symbol greet

# Filter by kind
uv run memos query symbol greet --kind function

# Find who calls a symbol (callers)
uv run memos query calls greet --direction callers

# Find what a symbol calls (callees)
uv run memos query calls main --direction callees

# Show everything for a file (symbols + calls + imports)
uv run memos query module src/index.ts

All query commands output JSON and accept --path <project_root> to point at an indexed project (defaults to current directory).

HTTP API

Start the FastAPI server on an indexed project:

# via CLI
uv run memos serve --path /project --port 8000

# or via uvicorn directly
MEMOS_PROJECT_PATH=/project uv run uvicorn memos.api.main:app

Endpoints:

Method	Path	Description
GET	/symbols?name=greet&kind=function	Find symbols by name
GET	/symbols/{id}/calls?direction=callers|callees	Find callers/callees of a symbol
GET	/symbols/{id}/context	Full context (symbol + callers + callees + memories + summary)
GET	/symbols/{id}/rename-impact	Analyse rename blast radius
GET	/modules/{path}	Show everything for a file
GET	/modules/{path}/diff-impact	Analyse diff blast radius for exported symbols
GET	/unused-symbols	Find private functions never called
GET	/dead-imports	Find unresolved imports
GET	/dependency-graph	File-level dependency graph
GET	/import-cycles	Find import cycles
POST	/search/semantic	Semantic search by natural language
POST	/memories	Add memory entry
GET	/memories	List memory entries
GET	/memories/search?query=...	Full-text search over memory entries
POST	/memories/prune	Delete stale memory entries (dry-run by default)

All endpoints return JSON. Set MEMOS_PROJECT_PATH (defaults to .).

Semantic Search

# Via HTTP API
curl -X POST http://localhost:8000/search/semantic \
  -H "Content-Type: application/json" \
  -d '{"query": "user authentication", "top_k": 5}'

Uses all-MiniLM-L6-v2 embeddings via fastembed (ONNX, no GPU required).

MCP Server

The MCP server exposes the indexed codebase to AI agents. Install globally:

pip install memos-engine
memos serve-mcp

Or via uv:

uv tool install memos-engine
memos serve-mcp

Or without global install (from source checkout):

uv run memos serve-mcp

The server starts without a project. Use the open_project tool to select a project:

{
  "tool": "open_project",
  "arguments": {
    "path": "/path/to/your/project"
  }
}

The server auto-indexes the project if it hasn't been indexed yet. Multiple projects can be opened and queried in the same session without restarting the server.

Available tools:

Tool	Description
open_project	Open a project by path (auto-indexes if needed)
find_symbol_tool	Search symbols by name (+ kind, file filter)
find_calls_tool	Find callers or callees of a symbol
get_module_tool	Full file info (symbols, calls, imports)
get_context_tool	Full context before editing (symbol + callers + callees + memories + summary)
semantic_search_tool	Natural language search over code
list_files_tool	List all indexed files
list_symbols_tool	List all indexed symbols
list_projects_tool	Current project info with stats
memory_add_note	Add a note to episodic memory
get_memories	Retrieve memory entries
rename_impact_tool	Analyse what breaks if a symbol is renamed
diff_impact_tool	Analyse blast radius for a file's exported symbols
find_unused_symbols_tool	Find private functions never called
find_dead_imports_tool	Find unresolved imports
get_dependency_graph_tool	File-level dependency graph
find_import_cycles_tool	Find import cycles
memory_search_tool	Full-text search over memory entries
memory_prune_tool	Delete stale memory entries (dry-run by default)
reindex_file_tool	Re-index a single file after editing

Each query tool accepts an optional project parameter to target a specific opened project (defaults to the most recently opened one).

Integration with OpenCode

Add to your OpenCode MCP configuration:

{
  "mcpServers": {
    "memos": {
      "command": [
        "memos",
        "serve-mcp"
      ]
    }
  }
}

Then use open_project within OpenCode to select your project.

Integration with Claude Desktop

Configure in claude_desktop_config.json:

{
  "mcpServers": {
    "memos": {
      "command": "memos",
      "args": ["serve-mcp"]
    }
  }
}

Tests

uv run pytest -v

Architecture notes

• Two memory types: derived (AST, summaries — reproducible, keyed by content hash) and episodic (agent notes — append-only, survives refactors).
• Indexer produces plain dataclasses (ParseResult); CLI converts them to pydantic models for DB insertion.
• Call edges and imports are stored unresolved (FK = NULL) on first pass; second-pass resolution is a separate task.
• The CLI (memos index) is a thin adapter over indexer/ + core/db.py — no business logic.
• Semantic search: sqlite-vec vec0 table, lazy-loaded fastembed model, cascade cleanup on reindex (--no-embed flag to skip).
• MCP server (memos serve-mcp) is a thin FastMCP adapter over query/core.py — same pattern as the FastAPI adapter.