Skip to main content

Universal AI memory layer - cross-client, cross-repo context management with RAG

Project description

ContextFS

Universal AI Memory Layer - Cross-client, cross-repo context management with RAG.

Works with Claude Code, Claude Desktop, Gemini CLI, Codex CLI, and any MCP client.

PyPI License: MIT Python 3.10+

Documentation | Developer Memory Workflow Guide | GitHub

Features

  • Semantic Search - ChromaDB + sentence-transformers for intelligent retrieval
  • Auto Code Indexing - Automatically index repositories for semantic code search
  • Dual Storage - Smart routing between FTS (keywords) and RAG (semantic)
  • Cross-Repo Memory - Memories track source repository automatically
  • Session Management - Automatic capture and replay of conversation context
  • MCP Server - Standard protocol for universal client support
  • Plugins - Native integrations for Claude Code, Gemini CLI, Codex CLI
  • Web UI - Browse and search memories with side-by-side FTS/RAG comparison

Quick Start

# Run with uvx (no install needed)
uvx contextfs --help
uvx contextfs-mcp  # Start MCP server

# Or install with pip
pip install contextfs

# Or install with uv
uv pip install contextfs

# Or install from source
git clone https://github.com/MagnetonIO/contextfs.git
cd contextfs
pip install -e .

Upgrading

# Upgrade with pip
pip install --upgrade contextfs

# Upgrade with uv
uv pip install --upgrade contextfs

# Upgrade with uvx (automatic on next run)
uvx --upgrade contextfs --help

Usage

CLI

# Save memories
contextfs save "Use PostgreSQL for the database" --type decision --tags db,architecture
contextfs save "API uses snake_case keys" --type fact --tags api,style

# Search
contextfs search "database decisions"
contextfs search "api conventions" --type fact

# Recall specific memory
contextfs recall abc123

# List recent
contextfs list --limit 20 --type decision

# Sessions
contextfs sessions

Python API

from contextfs import ContextFS, MemoryType

ctx = ContextFS()

# Save
ctx.save(
    "Use JWT for authentication",
    type=MemoryType.DECISION,
    tags=["auth", "security"],
)

# Search
results = ctx.search("authentication")
for r in results:
    print(f"[{r.score:.2f}] {r.memory.content}")

# Get context for a task
context = ctx.get_context_for_task("implement login")
# Returns formatted strings ready for prompt injection

MCP Server

Add to your MCP client config (Claude Code, Claude Desktop):

{
  "mcpServers": {
    "contextfs": {
      "command": "uvx",
      "args": ["contextfs-mcp"]
    }
  }
}

Or with Python directly:

{
  "mcpServers": {
    "contextfs": {
      "command": "python",
      "args": ["-m", "contextfs.mcp_server"]
    }
  }
}

MCP Tools:

Tool Description
contextfs_save Save memory (auto-indexes repo, logs to session)
contextfs_search Semantic search with cross-repo support
contextfs_recall Get specific memory by ID
contextfs_list List recent memories
contextfs_update Update existing memory content, type, tags, or project
contextfs_delete Delete a memory by ID
contextfs_index Index current repository for code search
contextfs_index_status Check or cancel background indexing progress
contextfs_list_repos List all repositories with memories
contextfs_list_tools List source tools (claude-code, claude-desktop, etc.)
contextfs_list_projects List all projects
contextfs_sessions List sessions
contextfs_load_session Load session messages
contextfs_message Add message to current session
contextfs_update_session Update session label or summary
contextfs_delete_session Delete a session and its messages
contextfs_import_conversation Import JSON conversation as episodic memory

MCP Prompts:

Prompt Description
contextfs-save-memory Guided memory save with type selection
contextfs-index Index repository for semantic search
contextfs-session-guide Instructions for session capture
contextfs-save-session Save current session

Plugins

Claude Code

# Install hooks for automatic context capture
python -c "from contextfs.plugins.claude_code import install_claude_code; install_claude_code()"

Gemini CLI / Codex CLI

from contextfs.plugins.gemini import install_gemini
from contextfs.plugins.codex import install_codex

install_gemini()  # For Gemini CLI
install_codex()   # For Codex CLI

Cross-Repo Namespaces

ContextFS automatically detects your git repository and isolates memories:

# In repo A
ctx = ContextFS()  # namespace = "repo-<hash-of-repo-a>"
ctx.save("Repo A specific fact")

# In repo B
ctx = ContextFS()  # namespace = "repo-<hash-of-repo-b>"
# Won't see Repo A's memories

# Global namespace (shared across repos)
ctx = ContextFS(namespace_id="global")
ctx.save("Shared across all repos")

Configuration

Environment variables:

CONTEXTFS_DATA_DIR=~/.contextfs
CONTEXTFS_EMBEDDING_MODEL=all-MiniLM-L6-v2
CONTEXTFS_CHUNK_SIZE=1000
CONTEXTFS_DEFAULT_SEARCH_LIMIT=10
CONTEXTFS_AUTO_SAVE_SESSIONS=true
CONTEXTFS_AUTO_LOAD_ON_STARTUP=true

Supported Languages

ContextFS supports 50+ file types including Python, JavaScript, TypeScript, Go, Rust, Java, C++, and more. See full list in docs.

Developer Memory Workflow (DMW)

ContextFS enables persistent developer memory across sessions with typed memories:

Type Use Case
fact Project configurations, conventions
decision Architectural choices with rationale
code Algorithms, patterns, important snippets
error Bug fixes, error patterns, solutions
procedural Setup guides, deployment steps
episodic Session transcripts, conversations

See the full Developer Memory Workflow Guide for patterns and examples.

Memory Lineage & Graph Operations

ContextFS tracks memory evolution and relationships with graph-backed lineage:

# Evolve memory (update with history tracking)
contextfs evolve <id> "Updated content" --summary "Why it changed"

# View lineage (ancestors/descendants)
contextfs lineage <id> --direction both

# Merge multiple memories
contextfs merge <id1> <id2> --summary "Combined knowledge" --strategy union

# Split memory into parts
contextfs split <id> "Part 1" "Part 2" --summaries "First|Second"

# Link related memories
contextfs link <id1> <id2> references --bidirectional

# Find connected memories
contextfs related <id> --depth 2

MCP Tools for Graph Operations:

Tool Description
contextfs_evolve Update memory with history tracking
contextfs_merge Combine multiple memories into one
contextfs_split Divide memory into separate parts
contextfs_link Create relationships between memories
contextfs_related Find connected memories via graph traversal
contextfs_lineage View memory evolution history

Relationship Types: references, depends_on, contradicts, supports, supersedes, related_to, derived_from, part_of, implements

Session Management

# List sessions
contextfs sessions

# Save current session
contextfs save --save-session current --label "feature-auth"

# Load session context
contextfs load-session <session_id>

Source tool auto-detected (claude-code, claude-desktop) or set via CONTEXTFS_SOURCE_TOOL.

Web UI

Start the web server to browse and search memories:

contextfs web
# Opens at http://localhost:8000

contextfs web --port 3000  # Custom port

Features:

  • Browse all memories with filtering by type, repo, and project
  • Side-by-side FTS vs RAG search comparison
  • Session browser and message viewer
  • Real-time memory statistics

Architecture

┌──────────────────────────────────────────────────────────────────┐
│                        ContextFS Core                             │
├──────────────────────────────────────────────────────────────────┤
│   ┌───────┐   ┌───────┐   ┌───────┐   ┌───────┐                  │
│   │  CLI  │   │  MCP  │   │ Web UI│   │Python │                  │
│   │       │   │Server │   │       │   │  API  │                  │
│   └───┬───┘   └───┬───┘   └───┬───┘   └───┬───┘                  │
│       └───────────┴─────┬─────┴───────────┘                      │
│                         │                                         │
│                 ┌───────▼───────┐                                 │
│                 │  ContextFS()  │                                 │
│                 │   core.py     │                                 │
│                 └───────┬───────┘                                 │
│                         │                                         │
│         ┌───────────────┼───────────────┐                         │
│         │               │               │                         │
│ ┌───────▼───────┐ ┌─────▼─────┐ ┌───────▼───────┐                │
│ │MemoryLineage  │ │StorageRouter│ │ AutoIndexer │                │
│ │(Graph Ops)    │ │           │ │ (Code Index) │                 │
│ └───────┬───────┘ └─────┬─────┘ └───────────────┘                │
│         │               │                                         │
│         │       ┌───────▼───────┐                                 │
│         │       │TypedStorage   │ ← EdgeRelation, MemoryEdge     │
│         │       │   Protocol    │   GraphPath, GraphTraversal    │
│         │       └───────┬───────┘                                 │
│         │               │                                         │
│         └───────┬───────┼───────┬───────────────┐                │
│                 │       │       │               │                 │
│         ┌───────▼──┐ ┌──▼───┐ ┌─▼────────┐ ┌────▼─────┐          │
│         │ SQLite   │ │Chroma│ │PostgreSQL│ │ FalkorDB │          │
│         │ + FTS5   │ │  DB  │ │ +pgvector│ │ (Cypher) │          │
│         │(default) │ │(RAG) │ │ (hosted) │ │ (graph)  │          │
│         └──────────┘ └──────┘ └──────────┘ └──────────┘          │
└──────────────────────────────────────────────────────────────────┘

Storage Backends

Backend Purpose
SQLite + FTS5 Default local storage, keyword search, sessions
ChromaDB Vector embeddings, semantic/RAG search
PostgreSQL + pgvector Hosted deployments, team sharing
FalkorDB Advanced graph queries via Cypher

Typed Storage Protocol

The StorageProtocol provides a unified interface across backends with typed models:

  • EdgeRelation - 20+ relationship types (references, depends_on, contradicts, etc.)
  • MemoryEdge - Typed edges with weights and metadata
  • GraphPath / GraphTraversal - Path finding and subgraph queries

License

MIT

Authors

Matthew Long and The YonedaAI Collaboration

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

contextfs-0.1.16.tar.gz (2.1 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

contextfs-0.1.16-py3-none-any.whl (249.5 kB view details)

Uploaded Python 3

File details

Details for the file contextfs-0.1.16.tar.gz.

File metadata

  • Download URL: contextfs-0.1.16.tar.gz
  • Upload date:
  • Size: 2.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for contextfs-0.1.16.tar.gz
Algorithm Hash digest
SHA256 3025def40547628f864f2d0e0385d1903ddc61266d769bad3add9a99e56968e3
MD5 47601f9eb68460ed7b4f6237d4b8b2ae
BLAKE2b-256 2ac38367e2d93f80e11ae722dc1a6963307350ccc45aa59514cbbf34db2a9234

See more details on using hashes here.

Provenance

The following attestation bundles were made for contextfs-0.1.16.tar.gz:

Publisher: release.yml on MagnetonIO/contextfs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file contextfs-0.1.16-py3-none-any.whl.

File metadata

  • Download URL: contextfs-0.1.16-py3-none-any.whl
  • Upload date:
  • Size: 249.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for contextfs-0.1.16-py3-none-any.whl
Algorithm Hash digest
SHA256 6e626916656305fad234ed31c6617b7a5f8f3edd466934c34ffa5009a1215e7e
MD5 4108964de51e1d7eff0abe4ef9f7d216
BLAKE2b-256 529267d57ab87f0c7ed5f0960cb260d8692a2353f4a2e8f9e8da72a23de50604

See more details on using hashes here.

Provenance

The following attestation bundles were made for contextfs-0.1.16-py3-none-any.whl:

Publisher: release.yml on MagnetonIO/contextfs

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page