8 Best Graphiti Alternatives in 2026 (Open Source)

Graphiti — Build Real-Time Knowledge Graphs for AI Agents. Builds temporal graphs that track what's true now and what was true before, unlike static knowledge graphs.

These 8 open-source tools do the same job. They are ordered by how closely they match Graphiti, with live GitHub data so you can see which projects are actively maintained.

By package downloads Graphify is the most used here (1.6M in the last 30 days), and it also has the most GitHub stars. See all agent tools by downloads.

1. 1. Memary

   The Open Source Memory Layer For Autonomous Agents

   What sets it apart: vs LangChain Memory / Mem0: graph-database-backed memory system emulating human memory (breadth + depth tracking) — agents automatically build and query knowledge graphs rather than flat conversation history

   Best for: Building persistent, context-aware AI agents with evolving memory; User preference tracking and personalization across sessions; Multi-user agent management with separate knowledge contexts

   ⭐ 2.7kGraphiti vs Memary →Full profile →

2. 2. Cognee

   Knowledge Engine for AI Agent Memory in 6 lines of code

   What sets it apart: Unlike Mem0 (conversation memory) or Chroma (pure vector search), Cognee builds an evolving knowledge graph from documents, combining vector + graph search with cognitive science approaches, ontology grounding, and cross-agent knowledge sharing — making it AI memory infrastructure rather than just a vector database.

   Best for: AI agent developers who need persistent, learning memory that combines vector search with knowledge graph relationships; Enterprise use cases requiring tenant isolation, audit trails, and cross-agent knowledge sharing

   ⭐ 31.3kGraphiti vs Cognee →Full profile →

3. 3. MemOS

   Memory OS for LLMs and AI agents with graph-structured, multimodal storage and retrieval

   What sets it apart: Provides a unified memory operating system with graph-structured memory that's inspectable and editable, not just a black-box embedding store.

   Best for: AI agents needing long-term memory; multi-agent collaboration systems; developers building context-aware agents

   ⭐ 11.7kGraphiti vs MemOS →Full profile →

4. 4. OpenViking

   Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.

   What sets it apart: Organizes agent context as an inspectable virtual filesystem with URI addresses instead of a black-box embedding store.

   Best for: Developers needing structured agent memory; Projects requiring inspectable and editable agent knowledge; Teams wanting a unified filesystem for agent context

   ⭐ 39.2kGraphiti vs OpenViking →Full profile →

5. 5. GraphRAG

   A modular graph-based Retrieval-Augmented Generation (RAG) system

   What sets it apart: Uses knowledge graph memory structures rather than traditional vector search for enhanced LLM context retrieval.

   Best for: enhancing LLM reasoning with private data; creating structured knowledge graphs from documents; research projects exploring graph-based RAG

   ⭐ 36.2kGraphiti vs GraphRAG →Full profile →

6. 6. LightRAG

   [EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

   What sets it apart: Combines simple RAG implementation with advanced features like knowledge graphs, multimodal processing, and comprehensive evaluation tooling.

   Best for: developers building RAG-powered agents; multimodal document processing; knowledge-intensive agent applications

   ⭐ 40.0kGraphiti vs LightRAG →Full profile →

7. 7. Supermemory

   Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.

   What sets it apart: Claims #1 performance on three major AI memory benchmarks with 95% recall and 99.4% context reduction.

   Best for: Adding persistent memory to AI agents; Building AI products with memory capabilities; Running memory systems locally

   ⭐ 31.1kGraphiti vs Supermemory →Full profile →

8. 8. Graphify

   Local tool that parses code, docs, SQL schemas, configs, and PDFs into a queryable knowledge graph

   What sets it apart: Uses deterministic AST parsing to create an explainable knowledge graph locally, without vector stores or embeddings, specifically for AI coding workflows.

   Best for: Developers using AI coding assistants; Understanding complex codebases; Navigating project documentation and relationships

   ⭐ 123.4kGraphiti vs Graphify →Full profile →