Assistly vs Zep: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Assistly and Zep — 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.
Zep
Zep Software, Inc.
Context engineering platform providing long-term memory, temporal knowledge graphs, Graph RAG, and automated context assembly for AI agents.
Key features
- Persistent Long-Term Memory: Stores full chat histories and conversation artifacts persistently to enable recall across long time spans, improving continuity in conversational experiences.
- Temporal Knowledge Graph (Graphiti): Builds a temporal knowledge graph with valid_at and invalid_at timestamps to track changing user state, preferences, and relationships over time for accurate contextual reasoning.
- Asynchronous Summaries & Artifacts: Automatically generates summaries, classifications, and structured artifacts from messages asynchronously to avoid adding latency to the user chat experience.
- Embeddings & Vector Search: Embeds messages and summaries to enable fast semantic search and retrieval of relevant past conversation snippets and business data.
- Document Collections: Provides a simple document-collection abstraction for vector search to complement memory features without being a general-purpose vector database.
- SDKs & Integrations: Official SDKs for Python, TypeScript/JavaScript, and Go with integrations for LangChain and LlamaIndex to simplify adoption in existing agent stacks.
- Managed Cloud Service (Zep Cloud): Offers a managed deployment with low latency, high availability, and additional capabilities like dialog classification and structured data extraction.
- Graph RAG & Automated Context Assembly: Combines graph-aware retrieval augmented generation with automated assembly of context from chat history and business data to reduce hallucinations and improve relevance.
- Persistent chat history storage and retrieval for AI assistants
- Automated generation of summaries and other conversation artifacts
- Message and summary embeddings to enable semantic search
- Document Collections abstraction for vector/document search
- Temporal knowledge graph (Graphiti) with valid_at/invalid_at to track state changes
- Automated context assembly for prompt construction (agent memory)
- Cloud managed offering (Zep Cloud) with low latency, HA, scalability, dialog classification, and structured data extraction
- Official SDKs: Python (zep-cloud / zep-python), TypeScript/JavaScript (@getzep/zep-cloud / zep-js), Go (zep-go)
- Asynchronous processing pipeline to avoid blocking user chat experience
- Client libraries with features like automatic retries and exponential backoff
Best for
- Personalized Conversational Assistants: Maintain long-term user memory so assistants remember user preferences, prior conversations, and context across sessions to deliver personalized responses.
- Customer Support with Historical Context: Provide support agents or bots immediate access to past conversation threads, summaries, and structured artifacts to resolve recurring or complex issues faster.
- Reducing Hallucinations in LLMs: Use embeddings, graph-aware retrieval, and structured context assembly to ground model responses in verifiable past interactions and business data.
- Temporal User Profiling: Track changing user attributes and preferences over time using the temporal knowledge graph to drive targeted recommendations and dynamic personalization.
- Agent State Tracking and Change History: Record state transitions with valid/invalid timestamps so agents can reason about when facts were true and how user situations evolved.
- Augmenting RAG Workflows: Improve retrieval-augmented generation by assembling relevant chat-derived context and document collections to include only what matters in prompts.
- Scaling Memory for Production: Persist conversation data to databases and use Zep Cloud for low-latency, scalable memory services in production AI applications.
- Personalized conversational agents that recall historical user interactions
- Reducing hallucinations by providing relevant past-context to LLM prompts
- RAG workflows combining chat memory and document vectors
- Customer support assistants that persist and search prior tickets/conversations
- Stateful agents that need to reason about temporal changes in user data or preferences
- Analytics and insights from long-term conversation archives
- Embedding-based semantic search over conversation content and summaries