Comparison
graphiti vs mem0
Verdict
Pick graphiti if graphiti is a Python-based toolkit for building real-time knowledge graphs utilized by AI agents, supporting Neo4j, FalkorDB, Amazon Neptune, and Kuzu as database options along with OpenAI, Anthropic, Groq, and Gemini LМ; pick mem0 if mem0 is a Python-based memory infrastructure for AI agents and applications, offering a persistent context layer suitable for production environments.
Markdown twin · graphiti alternatives · mem0 alternatives
GraphCanon updated Sep 20, 2026
25views this month
Trust & integrity
| Signal | graphiti | mem0 |
|---|---|---|
| Maintenance | Very active (0d since push) As of Aug 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- graphiti
- Build Real-Time Knowledge Graphs for AI Agents
- mem0
- Drop-in memory infrastructure for AI agents and apps
Stars
- graphiti
- 30k
- mem0
- 66k
Forks
- graphiti
- 3.0k
- mem0
- 7.7k
Open issues
- graphiti
- 488
- mem0
- 754
Language
- graphiti
- Python
- mem0
- Python
Adopt for
- graphiti
- Graphiti is a Python-based toolkit for building real-time knowledge graphs utilized by AI agents, supporting Neo4j, FalkorDB, Amazon Neptune, and Kuzu as database options along with OpenAI, Anthropic, Groq, and Gemini LМ
- mem0
- mem0 is a Python-based memory infrastructure for AI agents and applications, offering a persistent context layer suitable for production environments.
Persona
- graphiti
- -
- mem0
- -
Runtime
- graphiti
- -
- mem0
- -
License
- graphiti
- Apache-2.0
- mem0
- mem0 is distributed under the Apache-2.0 license, allowing for broad usage and modification with attribution.
Last pushed
- graphiti
- Aug 17, 2026
- mem0
- Sep 18, 2026
Categories
- graphiti
- AI Agents, LLM Frameworks
- mem0
- AI Agents, Data & Retrieval
Trust and health
Open issues (now)
- graphiti
- 488
- mem0
- 754
Stars delta
- graphiti
- +1.1k (30d)
- mem0
- +2.8k (30d)
Open issues delta
- graphiti
- +45 (30d)
- mem0
- +63 (30d)
Full report
- graphiti
- Trust report
- mem0
- Trust report
Typed relationship
graphiti alternative mem0Mem0 is another universal memory layer for AI Agents, which competes with Graphiti's temporal context graphs in managing agent memory and context.
Shared compatibility
- Python · graphiti: Python runtime · mem0: Python runtime
Choose graphiti if…
- 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: agents, amazon-neptune, falkordb, graph.
- Also covers LLM Frameworks.
- graphiti ships Docker support for self-hosted deployment.
- If you require seamless integration with LLM services that support structured output like OpenAI, Anthropic, or Google Gemini.
When NOT to use graphiti
- Consider alternative tools if your project primarily uses non-compliant LLM providers without structured output support.
- If you prefer not to use Python or do not need the capability to build real-time knowledge graphs for AI applications.
Choose mem0 if…
- Pricing: The core functionality of mem0 is free to use under the Apache-2.0 license, but additional features or support might come at a cost..
- Requirements: Min 2 GB RAM; Python environment is required for installation and use..
- 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: agentic-memory, ai-agents, genai, long-term-memory.
- Also covers Data & Retrieval.
- When you need a persistent memory layer for AI agents that can be easily integrated into existing applications without significant changes.
When NOT to use mem0
- If your project is not in Python, as mem0 is specifically designed for Python environments.
- When you require a more customizable memory management system that allows for extensive configuration beyond what mem0 offers.
- If your application does not need a persistent memory layer or if the existing memory management solutions are sufficient.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (getzep/graphiti) · observed Sep 20, 2026
- GitHub forks (getzep/graphiti) · observed Sep 20, 2026
- Last push (getzep/graphiti) · observed Aug 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (mem0ai/mem0) · observed Sep 20, 2026
- GitHub forks (mem0ai/mem0) · observed Sep 20, 2026
- Last push (mem0ai/mem0) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: graphiti 30k · mem0 66k (synced Sep 20, 2026).
Common questions
- What is the difference between graphiti and mem0?
- graphiti: Build Real-Time Knowledge Graphs for AI Agents. mem0: Drop-in memory infrastructure for AI agents and apps. See the comparison table for live GitHub stats and shared categories.
- When should I choose graphiti over mem0?
- Choose graphiti over mem0 when 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: agents, amazon-neptune, falkordb, graph; Also covers LLM Frameworks; graphiti ships Docker support for self-hosted deployment; If you require seamless integration with LLM services that support structured output like OpenAI, Anthropic, or Google Gemini.
- When should I choose mem0 over graphiti?
- Choose mem0 over graphiti when Pricing: The core functionality of mem0 is free to use under the Apache-2.0 license, but additional features or support might come at a cost.; Requirements: Min 2 GB RAM; Python environment is required for installation and use.; 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: agentic-memory, ai-agents, genai, long-term-memory; Also covers Data & Retrieval; When you need a persistent memory layer for AI agents that can be easily integrated into existing applications without significant changes.
- When should I avoid graphiti?
- Consider alternative tools if your project primarily uses non-compliant LLM providers without structured output support. If you prefer not to use Python or do not need the capability to build real-time knowledge graphs for AI applications.
- When should I avoid mem0?
- If your project is not in Python, as mem0 is specifically designed for Python environments. When you require a more customizable memory management system that allows for extensive configuration beyond what mem0 offers. If your application does not need a persistent memory layer or if the existing memory management solutions are sufficient.
- Is graphiti or mem0 more popular on GitHub?
- mem0 has more GitHub stars (65,557 vs 30,018). 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 graphiti alternatives and mem0 alternatives (graphiti markdown twin, mem0 markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison 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 trust report; mem0 trust report.