ContextFS¶
Universal AI Memory Layer — Cross-client, cross-repo context management with semantic search
Features¶
| Feature | Description | |
|---|---|---|
| Semantic Memory | Store and retrieve context using semantic search powered by ChromaDB and sentence transformers. Learn more → | |
| Cross-Repo | Memories are automatically namespaced by repository, with support for cross-repo search and project grouping. Learn more → | |
| Multi-Client | Works with Claude Desktop, Claude Code, Gemini, ChatGPT, and any MCP-compatible client. Learn more → | |
| CLI & MCP | Full-featured CLI for memory management plus MCP server for AI tool integration. Learn more → |
Installation¶
Quick Example¶
from contextfs import ContextFS
# Initialize (auto-detects current repo)
ctx = ContextFS()
# Save a memory
ctx.save(
content="Authentication uses JWT tokens with 24h expiry",
type="decision",
tags=["auth", "security"]
)
# Search memories
results = ctx.search("how does authentication work?")
for r in results:
print(f"{r.score:.2f}: {r.memory.content}")
CLI Usage¶
# Save a memory
contextfs save "API uses REST with JSON responses" --type decision --tags api,design
# Search memories
contextfs search "API design patterns"
# Index a repository
contextfs index
# List recent memories
contextfs list
Why ContextFS?¶
Modern AI development involves multiple tools, repositories, and long-running projects. ContextFS solves the context fragmentation problem:
- Memory across sessions — Don't repeat yourself to AI tools
- Memory across tools — Share context between Claude, Gemini, and others
- Memory across repos — Find related decisions from other projects
- Semantic search — Natural language queries over your entire context history
Theoretical Foundation¶
ContextFS is built on principles from Type-Safe Context Engineering, applying insights from protein folding and type theory to AI memory systems.