Graphiti
Graphiti is an open-source Python library that enables AI agents to build and maintain real-time knowledge graphs. It ingests unstructured and structured data, extracts entities and relationships using language models, and incrementally updates a graph structure to reflect evolving information. The library is designed to integrate seamlessly with AI agent frameworks, providing persistent, queryable memory that enhances reasoning and decision-making. At its core, Graphiti processes incoming data streams—such as chat conversations, documents, or API responses—and converts them into nodes and edges in a graph database. It supports incremental updates, meaning new information is merged without requiring complete reprocessing of existing data. This real-time capability allows agents to stay up-to-date with minimal latency. Graphiti offers a developer-friendly API, likely installable via pip, and includes examples for common use cases. It abstracts away the complexity of entity extraction, relation classification, and graph management, letting developers focus on agent logic. The library is backed by a strong open-source community, evidenced by over 26,000 GitHub stars and frequent contributions. Given its focus on real-time knowledge graph construction, Graphiti is particularly suited for applications requiring dynamic memory, such as conversational AI, research assistants, and enterprise knowledge management. It aims to bridge the gap between raw data and structured knowledge, empowering AI agents with context-rich, interconnected information.
- Category
- AI Agents
- Pricing
- Open Source
Problem solved
AI agents often struggle with maintaining a coherent, up-to-date understanding of context over time, leading to inconsistent responses and limited reasoning. Graphiti solves this by providing a real-time knowledge graph layer that agents can use to store and retrieve structured facts, relationships, and historical context as they interact with data, improving accuracy and personalization.
Use cases
- A customer support chatbot builds a knowledge graph of product issues and resolutions, enabling faster and more accurate answers.
- An AI research assistant connects academic papers, authors, and concepts to help researchers discover relevant work.
- A personalized recommendation agent maintains user preferences and interaction history in a graph for tailored suggestions.
- An enterprise knowledge agent maps internal documents, teams, and processes to answer employee questions.
- A game NPC remembers player actions and adapts its behavior based on a dynamically updated relationship graph.
- A financial analysis agent tracks market events, company relationships, and news to generate insights.
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Real-time incremental updates to knowledge graphs
- Seamless integration with AI agents and LLMs
- Active open-source community with high adoption
- Python-native and easy to install
- Abstracts complex graph management logic
Cons
- Reliance on external language models for extraction may introduce latency or cost
- Documentation and tutorials may be limited for newcomers
- Potential scalability concerns with very large or highly dynamic graphs
Official website: https://github.com/getzep/graphiti
Canonical LambdaBase page: https://www.lambdabase.com/tools/graphiti