--- title: "graphiti vs mem0" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/getzep-graphiti-vs-mem0ai-mem0" tools: ["getzep-graphiti", "mem0ai-mem0"] --- # graphiti vs mem0 Neutral, constraint-first comparison with live GitHub stats. | | [graphiti](/tools/getzep-graphiti.md) | [mem0](/tools/mem0ai-mem0.md) | | --- | --- | --- | | Tagline | Build Real-Time Knowledge Graphs for AI Agents | Universal memory layer for AI Agents | | Stars | 28,498 | 60,369 | | Forks | 2,869 | 7,008 | | Open issues | 415 | 504 | | Language | Python | Python | | Adopt for | Graphiti is a framework for building temporal context graphs, essential for AI agents that operate in environments with rapidly evolving or frequently changing data. | Mem0 is a comprehensive tool that optimizes token usage and reduces latency for efficient long-term memory management in AI agents. It has recently introduced significant improvements in its algorithm, boosting benchmark | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphiti](/tools/getzep-graphiti.md) | [mem0](/tools/mem0ai-mem0.md) | | --- | --- | --- | | Open issues (now) | 415 | 504 | | Full report | [trust report](/tools/getzep-graphiti/trust.md) | [trust report](/tools/mem0ai-mem0/trust.md) | **Typed relationship:** graphiti _(alternative)_ mem0 Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context. ## Decision facts: graphiti - **Requirements:** Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development. - **Adopt for:** Graphiti is a framework for building temporal context graphs, essential for AI agents that operate in environments with rapidly evolving or frequently changing data. ## Decision facts: mem0 - **Pricing:** unknown - The repository mentions an Apache-2.0 license but pricing information is not provided. - **Requirements:** While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations. - **Adopt for:** Mem0 is a comprehensive tool that optimizes token usage and reduces latency for efficient long-term memory management in AI agents. It has recently introduced significant improvements in its algorithm, boosting benchmark ## Choose when ### Choose graphiti if… - Requirements: Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development.. - Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context. - Tags unique to graphiti: llms, rag, graph. - graphiti ships Docker support for self-hosted deployment. - - When developing interactive applications where the context graph needs to evolve dynamically based on user interactions and external information. ### Choose mem0 if… - Pricing: The repository mentions an Apache-2.0 license but pricing information is not provided.. - Requirements: While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations.. - Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context. - Tags unique to mem0: genai, llm, python, memory-management. - - When developing AI applications where enhancing the efficiency of memory retention is crucial. - If your project requires state-of-the-art performance across various benchmarks like LoCoMo and Long ## When NOT to use graphiti - - If your application requires only a static snapshot of the knowledge graph at a given point in time. - - For use cases where data does not change rapidly or significantly over time, making the tracking of temporal validity windows unnecessary. - - When you prefer traditional retrieval-augmented generation (RAG) methods without support for incremental updates and efficient historical queries. ## When NOT to use mem0 - - If your project does not require long-term memory management or advanced state management techniques. - - In scenarios where the application's performance is already optimized for token usage and latency without needing external enhancements. - - For applications that do not benefit from new features like entity linking, temporal reasoning, and multi-signal retrieval. ## Common questions ### What is the difference between graphiti and mem0? graphiti: Build Real-Time Knowledge Graphs for AI Agents. mem0: Universal memory layer for AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose graphiti over mem0? Choose graphiti over mem0 when Requirements: Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development.; Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context; Tags unique to graphiti: llms, rag, graph; graphiti ships Docker support for self-hosted deployment; - When developing interactive applications where the context graph needs to evolve dynamically based on user interactions and external information. ### When should I choose mem0 over graphiti? Choose mem0 over graphiti when Pricing: The repository mentions an Apache-2.0 license but pricing information is not provided.; Requirements: While Docker is suggested in the repository description for deployment purposes, it’s noted that Mem0 itself does not explicitly require Docker to function. Use; Ensure that your environment meets Python requirements and has access to dependencies necessary for advanced memory operations.; Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context; Tags unique to mem0: genai, llm, python, memory-management; - When developing AI applications where enhancing the efficiency of memory retention is crucial. - If your project requires state-of-the-art performance across various benchmarks like LoCoMo and Long. ### When should I avoid graphiti? - If your application requires only a static snapshot of the knowledge graph at a given point in time. - For use cases where data does not change rapidly or significantly over time, making the tracking of temporal validity windows unnecessary. - When you prefer traditional retrieval-augmented generation (RAG) methods without support for incremental updates and efficient historical queries. ### When should I avoid mem0? - If your project does not require long-term memory management or advanced state management techniques. - In scenarios where the application's performance is already optimized for token usage and latency without needing external enhancements. - For applications that do not benefit from new features like entity linking, temporal reasoning, and multi-signal retrieval. ### Is graphiti or mem0 more popular on GitHub? mem0 has more GitHub stars (60,369 vs 28,498). Stars measure visibility, not whether either tool fits your constraints. ### Are graphiti and mem0 open source? Yes - both are open-source projects on GitHub (graphiti: Apache-2.0, mem0: Apache-2.0). ### Where can I find alternatives to graphiti or mem0? GraphCanon lists graph-backed alternatives at /tools/getzep-graphiti/alternatives and /tools/mem0ai-mem0/alternatives (/tools/getzep-graphiti/alternatives.md, /tools/mem0ai-mem0/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/getzep-graphiti-vs-mem0ai-mem0.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graphiti or mem0? graphiti: Very active. mem0: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for graphiti and mem0? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: graphiti: /tools/getzep-graphiti/trust; mem0: /tools/mem0ai-mem0/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=getzep-graphiti`](/api/graphcanon/graph?tool=getzep-graphiti) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_