graphiti
Graphiti is an open-source Python framework that builds temporal knowledge graphs for AI agents, tracking how facts change over time with full provenance to source data. It enables incremental updates and hybrid retrieval (semantic, keyword, and graph-based) without batch recomputation, making it suitable for dynamic, context-aware agent applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | getzep/graphiti |
| Owner | getzep |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 28.5k |
| Forks | 2.9k |
| Open issues | 415 |
| Latest release | v0.29.2 (2026-06-08) |
| Last updated | 2026-07-08 |
| Source | https://github.com/getzep/graphiti |
What graphiti is
Graphiti constructs bi-temporal context graphs with entities, relationships (facts with validity windows), episodes (raw data provenance), and pluggable graph backends. It supports prescribed ontology via Pydantic models and learned structure, offering sub-second hybrid retrieval combining embeddings, BM25, and graph traversal for evolving, real-world data streams.
Get the graphiti source
Clone the repository and explore it locally.
git clone https://github.com/getzep/graphiti.gitcd graphiti# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires selection and integration of a graph backend (database, embedding store, vector index); no default single deployment artifact provided.
- Custom ontology definition via Pydantic models is optional but recommended for structured data; learned ontology may require iteration and validation.
- Episode ingestion pipeline must be designed to feed raw data reliably; schema evolution and data quality considerations fall on the implementer.
- Hybrid retrieval tuning (semantic weight vs. keyword vs. graph traversal) is custom per use-case; no pre-optimized defaults documented.
- Temporal query semantics (validity windows, fact invalidation logic) must be understood and correctly applied in business logic.
When to avoid it — and what to weigh
- Static Document Summarization — If your primary need is batch-oriented knowledge extraction from fixed document sets, consider GraphRAG or traditional RAG instead. Graphiti is optimized for continuous evolution, not one-shot processing.
- Turnkey Production Deployment — If you need managed infrastructure, governance, sub-200ms retrieval guarantees, or enterprise support out-of-the-box, use Zep (the commercial platform). Graphiti requires self-hosting and custom operational setup.
- Simple Chatbot Memory — For basic conversation history or session-level context, the complexity and overhead of a temporal knowledge graph may be unjustified. Consider simpler vector stores or traditional conversation buffers.
- Graph-Backend Agnostic Requirements — Graphiti requires a pluggable graph backend. If your infrastructure lacks a suitable graph database or embedding store, integration work is required before deployment can begin.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under Apache terms.
Apache-2.0 permits commercial use, but you must retain license notices and copyright attribution in source and distributed code. No warranty or liability provided by the licensor. For production AI agent services, review liability, indemnification, and support implications with legal counsel. This is the open-source engine only; enterprise features and support are available through Zep's managed platform.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Graphiti itself is an open-source Python library; security posture depends on your graph backend, embedding store, and agent integration. No encryption, authentication, or multi-tenancy baked in. Provenance tracking to episodes helps audit data lineage. Self-hosting requires you to manage secrets, access control, and compliance; no managed security guarantees provided.
Alternatives to consider
Zep (Managed Platform)
Commercial platform offering managed temporal context graphs with enterprise governance, sub-200ms retrieval, dashboards, and support. Choose if you want turnkey production infrastructure and can accept vendor lock-in.
GraphRAG
Microsoft's batch-oriented knowledge graph extraction for static document sets. Better for one-shot summarization; not optimized for continuous, evolving agent context.
Traditional Vector RAG + Conversation State
Simpler alternative using embeddings + vector search + chat history. Sufficient for many use-cases; avoids graph complexity if temporal fact management and provenance are not required.
Build on graphiti with DEV.co software developers
Graphiti is a powerful fit for AI agents requiring evolving context and temporal fact management. Devco engineers can help you assess backend integration, ontology design, and production deployment. Contact us to explore fit and implementation effort.
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graphiti FAQ
Can I use Graphiti in production?
Do I have to define an ontology upfront?
What graph databases are supported?
How does Graphiti handle contradictory facts?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like graphiti into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your rag frameworks stack.
Evaluate Graphiti for Your Agent Architecture
Graphiti is a powerful fit for AI agents requiring evolving context and temporal fact management. Devco engineers can help you assess backend integration, ontology design, and production deployment. Contact us to explore fit and implementation effort.