Assistly vs Graphiti: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Assistly and Graphiti — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Assistly
Assistly
A live meeting assistant for Mac and Windows that reads call audio locally and shows guidance in an overlay excluded from screen shares.
Key features
- Bot-Free System Audio Capture: Works from your computer's audio rather than joining the meeting, so nothing appears in the participant list and there is nothing to integrate with the call app.
- Screen-Capture-Excluded Overlay: The assistant window is excluded from screen capture at the OS level, so it stays visible to you and invisible in shares and recordings.
- Auto-Assist Without Prompting: Detects when a question lands or when you think out loud and streams structured talking points into your thread automatically, with no hotkey and no break in eye contact.
- Multi-Speaker Language Tracking: Separates your voice from other participants and follows who said what across dozens of auto-detected languages, even when the call switches language mid-sentence.
- Two-Way MCP Context: Pulls context from Google Calendar, Notion, Linear or any MCP server during the call, and exposes your meeting history back over MCP so Claude, ChatGPT or Cursor can query it later.
- Personas from Your Material: Builds a persona from your CV, docs and notes and switches modes for a sales call, client review or interview so responses match your background and phrasing.
- Automatic Recap and Action Items: Turns the transcript into a summary with owners and deadlines the moment the call ends, auto-saved and searchable across sessions.
- Per-Client Projects: Files each session to a project based on the calendar, and scopes answers and mid-call lookups to that client's history so context never crosses between accounts.
Best for
- Live Sales Calls: Surfacing objection handling and product detail the instant a prospect asks, without breaking eye contact to search a doc.
- Client Account Reviews: Recalling what was committed to a specific client in a previous session, with the source call cited, while the review is still running.
- Non-Native Language Meetings: Following a call that switches language mid-sentence and receiving guidance in clear English.
- Customer Success Handoffs: Leaving every call with a written summary and assigned action items instead of reconstructing notes afterwards.
- Meetings Where Bots Are Unwelcome: Getting live assistance on calls with clients or legal teams who object to a recording bot joining the room.
- Querying Past Meetings from Your Editor: Asking Claude, ChatGPT or Cursor what was agreed in a past session over MCP without opening the app.
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