Graphiti vs ModuleX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Graphiti and ModuleX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Graphiti
getzep (GitHub)
Open-source project to build real-time knowledge graphs and persistent memory stores for AI agents.
Key features
- Real-time Graph Construction: Extracts entities and relationships from incoming text and builds a dynamic graph representation so agent context is stored as structured nodes and edges for fast retrieval.
- MCP-Compatible Server API: Exposes endpoints and protocols aligned with Model Context Protocol patterns to let AI agents query episodes, entities, and contextual graph data as persistent memory.
- Document Ingestion and Registration: Registers documents across multiple formats into the graph store, enabling documents to be linked, searched, and referenced by agents in retrieval workflows.
- Graph Database Integration: Supports integration with graph-backed storage (examples and forks reference Neo4j and FalkorDB) to persist entities, relationships, vectors, and perform graph queries.
- Episode-Based Memory Management: Groups interactions into episodes with metadata (UUIDs, timestamps) to enable chronological context, session tracking, and selective retrieval of past interactions.
- Multi‑Project & Docker Deployment: Community forks and examples provide CLI and Docker Compose setups to run root and project-specific MCP servers, enabling multi-project sharing of a single graph database.
- Developer Tooling & Extensibility: Source-code-first, open repository structure allows customization, extension, and integration into agent stacks and RAG pipelines.
- Extract entities and relationships from text to build knowledge graphs
- Persist graph data in Neo4j graph database
- Model Context Protocol (MCP) server implementation for context serving
- Docker Compose and CLI tooling for quick local deployment and multi-project setups
- Support for project-specific MCP servers sharing a common database (multi-tenant graphs)
- Document registration and ingestion across multiple file formats for RAG workflows
- Integrations/examples showing usage with Cursor and agent systems to store prompts as graph memory
Best for
- Persistent Conversational Memory: Provide chatbots and assistants with long-term memory by storing and retrieving entities and relationships learned across sessions.
- RAG Backend for Document Search: Index and link documents into a knowledge graph so retrieval-augmented generation pipelines can find relevant passages via graph relationships and metadata.
- Agent Context Sharing Across Projects: Run multi-project MCP servers so multiple agents or teams can share and query a centralized knowledge graph for consistent context.
- Debugging and Traceability: Use episode grouping and entity links to trace agent decisions back to source documents and previous interactions for audit and improvement.
- Entity Relationship Discovery: Extract and visualize relationships across ingested content to discover connected concepts, people, locations, or events for analytics or recommendation systems.
- Providing persistent structured memory for conversational AI agents
- Backend for retrieval-augmented generation (RAG) systems using graph storage
- Indexing and searching entities/relations from ingested documents
- Multi-project knowledge graph deployments that share a central Neo4j instance
- Developer experimentation and prototyping of graph-based context for models
M
ModuleX
ModuleX
An AI workflow orchestration platform to build with natural language or a visual canvas, connect 600+ tools, and run any major AI model.
Key features
- Natural-Language & Visual Builder: Build workflows by describing them in plain language or using a visual canvas.
- 600+ Tool Integrations: Connect CRMs, databases, communication tools, and more across your stack.
- Any Major AI Model: Run workflows with every major AI model using your own keys at provider rates.
- Deep Agentic Assistant: Describe a goal and a deep agent reasons, picks the right tools, and executes across integrations.
- Multiple Execution Modes: Trigger workflows via chat, SDK, or REST API.
- Real-Time Cost Visibility: See every step and its cost in real time as workflows run.
- Developer SDKs: Native JavaScript and Python SDKs plus curl/REST endpoints for embedding automation.
Best for
- Business Automation: Orchestrate multi-step workflows across CRM, database, and communication tools.
- Agentic Task Execution: Hand a goal to the deep agent and let it select tools and complete it.