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graphiti – Temporal Knowledge Graphs for Agentic Applications

Hacker News

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 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

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, lua · Missing: https docs, just released, filesystem
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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