Show HN: Graphiti – LLM-Powered Temporal Knowledge Graphs
Graphiti is a framework for creating dynamic Knowledge Graphs that process structured and unstructured data. It features temporal awareness, episodic processing, and requires Python, Neo4j, and an OpenAI API key.
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Graphiti is a framework designed for creating dynamic, temporally aware Knowledge Graphs that represent complex relationships between entities over time. It can process both unstructured and structured data, making it suitable for applications such as Large Language Model (LLM) integrations, user-interactive assistants, and autonomous agents. Key features include temporal awareness for tracking changes, episodic processing for maintaining data provenance, hybrid search capabilities, and scalability for handling large datasets. To install Graphiti, users need Python 3.10 or higher, Neo4j 5.21 or higher, and an OpenAI API key for LLM inference, with installation possible via pip. A quick start example demonstrates how to initialize Graphiti, build indices, add episodes, and perform searches. Comprehensive documentation and support are available through the project's website and Discord server. Graphiti is under active development, with plans for future enhancements.
- Graphiti enables the creation of temporally aware Knowledge Graphs.
- It supports both unstructured and structured data for advanced querying.
- Key features include temporal awareness, episodic processing, and hybrid search.
- Installation requires Python, Neo4j, and an OpenAI API key.
- Active development is ongoing, with future improvements planned.
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7 commentsWould it make most sense to capture this with multiple Graphiti graphs? Or would it be possible to do this in one graph?
At the end of the day the analysis would be finding insights across all interviewees and you want the cumulative knowledge…
You are definitely onto something here.
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