graphiti – Temporal Knowledge Graphs for Agentic Applications
Hey HN - Paul, Preston, and Daniel from Zep here. We’re excited to show you graphiti, a library for building and searching dynamic, temporally aware knowledge graphs. https://git.new/graphiti With graphiti, you can model complex, evolving relationships between entities over time. graphiti ingests both unstructured and structured data and the resulting graph may be queried using a fusion of time, full-text, semantic, and graph algorithm approaches. With graphiti, you can build LLM applications such as: - Assistants that learn from user interactions, fusing personal knowledge…
In plain words
Graphiti is a library for building knowledge graphs that track how relationships between entities change over time. It processes both unstructured and structured data, allowing queries through time-based, full-text, semantic, and graph algorithm approaches. The tool is designed for developers creating AI applications such as assistants that learn from user interactions or autonomous agents that reason with real-time data from multiple sources like CRM systems and streaming inputs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN - Paul, Preston, and Daniel from Zep here. We’re excited to show you graphiti, a library for building and searching dynamic, temporally aware knowledge graphs. https://git.new/graphiti With graphiti, you can model complex, evolving relationships between entities over time. graphiti ingests both unstructured and structured data and the resulting graph may be queried using a fusion of time, full-text, semantic, and graph algorithm approaches. With graphiti, you can build LLM applications such as: - Assistants that learn from user interactions, fusing personal knowledge with dynamic data from business systems like CRMs and billing platforms. - Agents that autonomously execute complex tasks, reasoning with state changes from multiple dynamic sources as varied as traffic conditions or streaming voice transcriptions. graphiti differs from GraphRAG and other graph libraries. It’s purpose-built for dynamic data and agentic use: - Smart Graph Updates: Automatically evaluates new entities against the current graph, revising both to reflect the latest context. - Rich Edge Semantics: Generates human-readable, semantic, and full-text searchable representations for edges during graph construction, enabling search and enhancing interpretability. - Temporal Awareness: Extracts and updates time-based edge metadata from input data, enabling reasoning over changing relationships. - Hybrid Search: Offers semantic, BM25, and graph-based search with the ability to fuse results. - Fast: Search results in < 100ms, with latency primarily determined by the 3rd-party embedding API call. - Schema Consistency: Maintains a coherent graph structure by reusing existing schema, preventing unnecessary proliferation of node and edge types. We built graphiti to power Zep Memory, a long-term memory layer for building personalized and accurate LLM apps. We believe graphiti’s potential extends beyond memory applications and have open sourced it to support and grow these use cases. github: https://git.new/graphiti documentation: https://help.getzep.com/graphiti We’d appreciate your feedback, contributions, or just to hear about the awesome projects you’ve built with graphiti! - Paul, Preston, & Daniel
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2024
the whole month →
- IY
Life & fun · 2024 · ytch.xyz
- IA
Hey there HN! We’re Joe and Stopa, and today we’re open sourcing InstantDB, a client-side database that makes it easy to build real-time and collaborative apps like Notion and Figma. Building modern apps these days involves a lot of schleps. For a basic CRUD app you need to spin up servers, wire up endpoints, integrate auth, add permissions, and then marshal data from the backend to the frontend and back again. If you want to deliver a buttery smooth user experience, you’ll need to add optimistic updates and rollbacks. We do these steps over and over for every feature we build, which can…
Dev tools · 2024 · github.com