OpenMemory VS Graphiti

Graphiti

Build personalized AI agents that learn from dynamic data

Not present

OpenMemory features and specs

Open Source
OpenMemory is an open-source project, allowing developers to freely use, modify, and distribute the software according to their needs.
Community Support
Being hosted on GitHub, OpenMemory benefits from a community of contributors who can provide support, improvements, and bug fixes.
Free Access
The project is available for free, lowering the barrier to entry for individuals and organizations looking to incorporate memory management solutions.
Transparency
The open-source nature ensures transparency in how memory is managed, which can help in security reviews and performance optimization.
Customizability
Users and developers can tailor the system to better fit their specific requirements due to the customizable nature of open-source software.

Possible disadvantages of OpenMemory

Lack of Official Support
As an open-source project, there may be no official customer support, making it potentially challenging for users to resolve issues without community help.
Variable Quality
Contributions from multiple sources can lead to inconsistencies in code quality and documentation, which might affect reliability.
Potential Security Risks
Open-source projects can be subject to security vulnerabilities if not regularly monitored and updated by the community.
Complexity
The system might require a level of technical expertise to implement, customize, and maintain, which can be a barrier for less-experienced users.
Limited Documentation
Open source projects sometimes suffer from sparse or outdated documentation, which can hinder user understanding and implementation.

Graphiti features and specs

No features have been listed yet.

Analysis of OpenMemory

Overall verdict

OpenMemory is a solid open-source memory layer for AI applications, offering a self-hostable, privacy-focused way to give LLMs persistent, portable memory across sessions and tools.

Why this product is good

Open-source and self-hostable, giving you full control over your data and avoiding vendor lock-in
Provides persistent, portable memory that can be shared across different AI apps and LLM clients
Privacy-focused design keeps sensitive memory data local rather than sending it to third-party services
Integrates with popular protocols like MCP (Model Context Protocol), making it compatible with many AI tools
Active community and transparent development typical of open-source projects allow for customization and contributions

Recommended for

Developers building AI applications that need long-term or cross-session memory
Privacy-conscious users who want to keep AI memory data on their own infrastructure
Teams wanting a vendor-neutral, portable memory layer shared across multiple LLM clients
Hobbyists and tinkerers comfortable with self-hosting and open-source tooling
Projects using MCP-compatible AI assistants that require persistent context

Analysis of Graphiti

Overall verdict

Graphiti is a well-regarded open-source framework for building real-time, temporally-aware knowledge graphs, particularly useful for AI agents and applications that need persistent, evolving memory. It's actively maintained by Zep and has gained solid traction in the LLM and agent development community.

Why this product is good

Purpose-built for real-time knowledge graphs that update incrementally without full recomputation
Temporal awareness lets it track how facts and relationships change over time
Designed specifically for AI agent memory, enabling more context-aware and persistent applications
Integrates with LLMs and supports hybrid retrieval (semantic, keyword, and graph-based search)
Open-source with active development and backing from Zep, plus growing community adoption
Scalable architecture suitable for production use cases involving dynamic data

Recommended for

Developers building AI agents that need long-term, evolving memory
Teams creating LLM-powered applications requiring context-aware retrieval
Projects needing temporally-aware knowledge graphs that track changes over time
Use cases involving dynamic, frequently-updated data rather than static datasets
Engineers exploring alternatives to traditional RAG for more structured, relational context

OpenMemory videos

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Graphiti videos

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What is Graphiti Temporal Knowledge Graph?

Category Popularity

0-100% (relative to OpenMemory and Graphiti)

Graphiti

65 65%

35% 35

Developer Tools

52 52%

Developer Tools

48% 48

Productivity

100 100%

Productivity

0% 0

AI Tools

62 62%

AI Tools

38% 38

User comments

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Social recommendations and mentions

Based on our record, Graphiti seems to be more popular. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OpenMemory mentions (0)

We have not tracked any mentions of OpenMemory yet. Tracking of OpenMemory recommendations started around Mar 2026.

Graphiti mentions (9)

Your AI Agent Forgets Everything After Every Session. Graphiti Fixes That.
Graphiti is an open-source framework by Zep for building and querying temporal context graphs for AI agents. It's the engine behind Zep's managed memory platform, but it's fully usable standalone. - Source: dev.to / about 1 month ago
I Tested 33 AI Memory Engines — Here's What Actually Works
Graphiti by Zep is the temporal knowledge graph. Its core insight: knowing the current state isn't enough. You need to know when things changed and what was true before. - Source: dev.to / 2 months ago
I Built Two Ollama Tools I Don't Actually Need Yet
Several services share the same Ollama instance on a dedicated host that homelab-agent is provisioning: LibreChat for interactive chat, a SearXNG MCP server for ML-reranked search, and three background embedding jobs — graphiti, jobsearch-mcp, and memsearch-watch. - Source: dev.to / 3 months ago
I Benchmarked Graphiti vs Mem0: The Hidden Cost of Context Blindness in AI Memory
It started with a 35,000-token "Master Prompt" that she maintained manually in Notion. Every time something changed in her life, she updated it by hand. That obviously didn't scale. So I moved to Graphiti, a knowledge graph framework that extracts entities and relationships from conversations automatically. - Source: dev.to / 4 months ago
Show HN: A file-based agent memory framework that works like skill
- *[Zep](https://github.com/getzep/graphiti)* uses graphs, which handle structure well but add complexity and maintenance overhead. - Source: Hacker News / 7 months ago