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.
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 metadataGraphPath/GraphTraversal- Path finding and subgraph queries
License
MIT
Authors
Matthew Long and The YonedaAI Collaboration
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