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blueman82

Recall

by blueman82
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Python
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Recall

Long-term memory system for MCP-compatible AI assistants with semantic search and relationship tracking.

Features

  • Persistent Memory Storage: Store preferences, decisions, patterns, and session context

  • Semantic Search: Find relevant memories using natural language queries via ChromaDB vectors

  • Memory Relationships: Create edges between memories (supersedes, relates_to, caused_by, contradicts)

  • Namespace Isolation: Global memories vs project-scoped memories

  • Context Generation: Auto-format memories for session context injection

  • Deduplication: Content-hash based duplicate detection

Installation

# Clone the repository git clone https://github.com/yourorg/recall.git cd recall # Install with uv uv sync # Ensure Ollama is running with required models ollama pull mxbai-embed-large # Required: embeddings for semantic search ollama pull llama3.2 # Optional: session summarization for auto-capture hook ollama serve

Usage

Run as MCP Server

uv run python -m recall

CLI Options

uv run python -m recall --help Options: --sqlite-path PATH SQLite database path (default: ~/.recall/recall.db) --chroma-path PATH ChromaDB storage path (default: ~/.recall/chroma_db) --collection NAME ChromaDB collection name (default: memories) --ollama-host HOST Ollama server URL (default: http://localhost:11434) --ollama-model MODEL Embedding model (default: mxbai-embed-large) --ollama-timeout SECS Request timeout (default: 30) --log-level LEVEL DEBUG, INFO, WARNING, ERROR, CRITICAL (default: INFO)

meta-mcp Configuration

Add Recall to your meta-mcp servers.json:

{ "recall": { "command": "uv", "args": [ "run", "--directory", "/path/to/recall", "python", "-m", "recall" ], "env": { "RECALL_LOG_LEVEL": "INFO", "RECALL_OLLAMA_HOST": "http://localhost:11434", "RECALL_OLLAMA_MODEL": "mxbai-embed-large" }, "description": "Long-term memory system with semantic search", "tags": ["memory", "context", "semantic-search"] } }

Or for Claude Code / other MCP clients (claude.json):

{ "mcpServers": { "recall": { "command": "uv", "args": [ "run", "--directory", "/path/to/recall", "python", "-m", "recall" ], "env": { "RECALL_LOG_LEVEL": "INFO" } } } }

Environment Variables

Variable

Default

Description

RECALL_SQLITE_PATH

~/.recall/recall.db

SQLite database file path

RECALL_CHROMA_PATH

~/.recall/chroma_db

ChromaDB persistent storage directory

RECALL_COLLECTION_NAME

memories

ChromaDB collection name

RECALL_OLLAMA_HOST

http://localhost:11434

Ollama server URL

RECALL_OLLAMA_MODEL

mxbai-embed-large

Embedding model name

RECALL_OLLAMA_TIMEOUT

30

Ollama request timeout in seconds

RECALL_LOG_LEVEL

INFO

Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)

RECALL_DEFAULT_NAMESPACE

global

Default namespace for memories

RECALL_DEFAULT_IMPORTANCE

0.5

Default importance score (0.0-1.0)

RECALL_DEFAULT_TOKEN_BUDGET

4000

Default token budget for context

MCP Tool Examples

memory_store_tool

Store a new memory with semantic indexing:

{ "content": "User prefers dark mode in all applications", "memory_type": "preference", "namespace": "global", "importance": 0.8, "metadata": {"source": "explicit_request"} }

Response:

{ "success": true, "id": "550e8400-e29b-41d4-a716-446655440000", "content_hash": "a1b2c3d4e5f67890" }

memory_recall_tool

Search memories by semantic similarity:

{ "query": "user interface preferences", "n_results": 5, "namespace": "global", "memory_type": "preference", "min_importance": 0.5, "include_related": true }

Response:

{ "success": true, "memories": [ { "id": "550e8400-e29b-41d4-a716-446655440000", "content": "User prefers dark mode in all applications", "type": "preference", "namespace": "global", "importance": 0.8, "created_at": "2024-01-15T10:30:00", "accessed_at": "2024-01-15T14:22:00", "access_count": 3 } ], "total": 1, "score": 0.92 }

memory_relate_tool

Create a relationship between memories:

{ "source_id": "mem_new_123", "target_id": "mem_old_456", "relation": "supersedes", "weight": 1.0 }

Response:

{ "success": true, "edge_id": 42 }

memory_context_tool

Generate formatted context for session injection:

{ "query": "coding style preferences", "project": "myproject", "token_budget": 4000 }

Response:

{ "success": true, "context": "# Memory Context\n\n## Preferences\n\n- User prefers dark mode [global]\n- Use 2-space indentation [project:myproject]\n\n## Recent Decisions\n\n- Decided to use FastAPI for the backend [project:myproject]\n", "token_estimate": 125 }

memory_forget_tool

Delete memories by ID or semantic search:

{ "memory_id": "550e8400-e29b-41d4-a716-446655440000", "confirm": true }

Or delete by search:

{ "query": "outdated preferences", "namespace": "project:oldproject", "n_results": 10, "confirm": true }

Response:

{ "success": true, "deleted_ids": ["550e8400-e29b-41d4-a716-446655440000"], "deleted_count": 1 }

Architecture

┌─────────────────────────────────────────────────────────────┐ │ MCP Server (FastMCP) │ │ memory_store │ memory_recall │ memory_relate │ memory_forget │ └───────────────────────────┬─────────────────────────────────┘ │ ┌───────────▼───────────┐ │ HybridStore │ │ (Coordinated Layer) │ └───────────┬───────────┘ │ ┌───────────────────┼───────────────────┐ │ │ │ ┌───────▼───────┐ ┌───────▼───────┐ ┌───────▼───────┐ │ SQLiteStore │ │ ChromaStore │ │ OllamaClient │ │ (Metadata & │ │ (Vector │ │ (Embedding │ │ Relations) │ │ Search) │ │ Generation) │ └───────────────┘ └───────────────┘ └───────────────┘

Development

# Install dev dependencies uv sync --dev # Run tests uv run pytest tests/ # Run tests with coverage uv run pytest tests/ --cov=recall --cov-report=html # Type checking uv run mypy src/recall # Run specific integration tests uv run pytest tests/integration/test_mcp_server.py -v

Requirements

  • Python 3.13+

  • Ollama with:

    • mxbai-embed-large model (required for semantic search)

    • llama3.2 model (optional, for session auto-capture hook)

  • ~500MB disk space for ChromaDB indices

License

MIT

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