Graphiti
ActiveDescription
Graphiti is a temporal knowledge-graph engine for agent memory, helping systems continuously accumulate long-term context.
Key Features
- Temporal fact management — Each fact has a validity window; old facts are invalidated, not deleted. Query what's true now or at any point in time
- Provenance & lineage tracking — Every entity and relationship traces back to raw data episodes, full lineage from derived fact to source
- Hybrid retrieval — Combines semantic embeddings, keyword BM25, and graph traversal for low-latency high-precision queries without LLM summarization
- Incremental graph construction — New data integrates immediately without batch recomputation; graph evolves in real-time as episodes are ingested
- Prescribed & learned ontology — Define entity/edge types via Pydantic models or let structure emerge from data automatically
- MCP Server integration — Provides MCP Server for Claude, Cursor and other clients to use temporal context graph memory
Use Cases
💡 AI Agent long-term memory — Provide evolving structured context for conversational agents, replacing flat document chunks
💡 Customer relationship tracking — Auto-build and update temporal knowledge graphs of customer preferences and interaction history
💡 Enterprise knowledge management — Integrate structured and unstructured enterprise data into a unified queryable graph
💡 Real-time decision support — Provide agents with decision basis from latest facts and historical change trends
💡 Compliance auditing — Track information changes through complete fact timelines to support compliance audits
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (30.0k stars)
- • Permissive open-source license (Apache-2.0)
- • Established track record (2 years in production)
Tags
Categories
Quick Start
pip install graphiti-core
from graphiti_core import Graphiti
import asyncio
async def main():
# Connect to Neo4j
client = Graphiti(
"bolt://localhost:7687",
"neo4j",
"password"
)
await client.build_indices_and_constraints()
# Add data
await client.add_episode(
name="conversation",
body="Kendra loves Adidas shoes",
source_description="user conversation"
)
# Search
results = await client.search("What brand does Kendra like?")
print(results)
asyncio.run(main())