Causal vs Graphiti: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Causal and Graphiti — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Causal
Causal Software Limited
An infinite AI canvas for creative planning, where notes, files, images and links sit in one spatial workspace an agent can read and build on.
Key features
- Infinite Spatial Canvas: A freeform, unbounded board where notes, images, links and files are arranged by meaning, so layout itself becomes the organisation rather than a folder hierarchy.
- Context-Aware Agent: The AI reads the whole canvas and understands how ideas connect, then answers questions and researches topics with the surrounding board as context.
- Native Output Generation: Prompts are turned into canvas content directly, with the agent creating notes, files and web-link cards and placing them where they belong instead of returning plain text.
- Rich File Previews: PDFs, Word and Adobe documents, markdown, spreadsheets, images and video up to 20 MB open fullscreen in-app, and markdown and CSV files can be edited in place and saved back to the file.
- Dual Text Editing: Quick notes live directly on the canvas while longer pieces open into a full-page editor, both sharing headings, lists, checkboxes, quotes, code blocks, highlights, images and links.
- Structure Tools: Collections pack related nodes into tidy columns, nested canvases give a sub-topic its own space, and an unsorted tray parks anything not ready to be placed.
- One-Click Sharing: Any canvas becomes a read-only link that recipients open without an account, covering nested canvases too, and sharing can be revoked at any time.
- Template Library: Ready-made boards for app flows, app plans, brand research, branding boards, competitor research, onboarding, storyboards, video briefs and plans, website moodboards and website plans.
Best for
- Product Planning: Map every screen in an app and the routes between them, then keep features, screens and shipping order in one view instead of three separate documents.
- Brand Development: Collect the brands, palettes and voices you are borrowing from, then settle type, colour and marks in one place the whole team works from.
- Competitive Research: Put rival products side by side with your own on a single board and find the gap you can actually take.
- Video and Film Pre-Production: Block out a shoot frame by frame, hand an editor references, tone and deliverables on one canvas, and follow a video from script to final cut with every asset attached to its step.
- Website Design Prep: Gather reference sites, type and colour a build should feel like, then lay out every page and its contents before the first component is built.
- Team Onboarding: Walk a new starter through the tools, files and people one frame at a time on a shareable board.
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