Memoria vs Zep: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Memoria and Zep — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Memoria
Anas
On-device photo and video search that indexes the text, speech, objects and faces in your library — no cloud, no account.
Key features
- On-Device OCR: Reads the text inside photos, screenshots, documents, whiteboards and video frames in seven languages, making every written word searchable.
- Local Whisper Transcription: Runs Whisper directly on the device to transcribe the speech in your videos across 99+ languages, so lectures, meetings and voice notes become searchable text.
- Face Detection and Clustering: Detects faces and groups them locally so you can pull up every photo of one person in a single tap, without any cloud face database.
- Object Recognition: Identifies objects in your library so queries like "red bicycle" return matching shots even when nothing was ever tagged.
- Unified Full-Text Index: Combines text, speech, objects and people into one instant search index that lives entirely on the phone.
- Background Indexing: Keeps indexing while the app is closed and prioritises work while the device is charging, so a large library finishes without you babysitting it.
- Zero-Account Privacy Model: No sign-up, no upload and no tracking — analytics are anonymous and opt-out, and the app is GDPR-safe by having nothing to collect.
- One-Time Purchase Unlock: Memoria Plus removes the 250-media indexing cap forever with a single payment processed by Apple or Google, including future on-device models.
Best for
- Finding a Document You Photographed: Recovering an invoice, bill or receipt you snapped months ago by searching the words printed on it rather than scrolling the camera roll.
- Searching Recorded Lectures and Meetings: Locating the moment a specific term was spoken inside a long video by searching the on-device transcript.
- Pulling Every Photo of a Person: Assembling all shots of one friend or family member from a clustered face group for a birthday album or share.
- Recovering Saved Screenshots and Memes: Tracking down a screenshot or meme by the text written on it instead of guessing when you saved it.
- Working Offline or While Travelling: Searching a full media library on a plane or with no signal, since indexing and search never require a network.
- Keeping Sensitive Media Off the Cloud: Making a library of personal, medical or client photos searchable without uploading any of it to a third-party service.
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