5 AI agent memory platforms for persistent context, ranked
Compare Supermemory, Mem0, Zep, Letta and Cognee for persistent agent memory, shared context, changing facts and the business data an agent should query again.
Start with Supermemory when existing assistants need shared memory and source material. Evaluate Mem0 for an add-on memory layer in your own application, Zep for changing relationships, Letta for a durable agent architecture and Cognee for a customizable graph-memory pipeline. Use a managed business-data layer separately when the answer depends on current records and exact totals.
Choose memory for the context that should survive
An assistant should not need the same project conventions, user preferences and prior decisions explained in every conversation. A memory platform can carry that context forward. The buying question is how that context is stored, scoped, corrected and retrieved—and how much of the agent architecture you want to change to use it.
Our default scenario is a builder who already has an agent or framework and wants durable context without replacing the whole application. We prioritize a usable connection to the existing agent; persistent retrieval and correction; explicit user or project scope; inspectable source material; and a manageable operating model. The ranks are Combined's editorial recommendations from documentation, not measured recall scores. Move a specialized option up when its specialization is your requirement.
| Rank | Platform | Best fit in this evaluation | What to test first |
|---|---|---|---|
| 1 | Supermemory | Shared memory and source context across existing assistants | Space selection, source retrieval and correction of an outdated fact |
| 2 | Mem0 | Adding user/agent memory to an existing application | Consistent entity scope and explicit update/delete behavior |
| 3 | Zep | Relationships and facts that evolve over time | The effective time of a changed fact and the graph each user can reach |
| 4 | Letta | Building around a durable agent with its own memory | What is always in context, what is read on demand and how changes persist |
| 5 | Cognee | A configurable graph-memory pipeline under your team's control | Ingestion completion, retrieval scope and the chosen deployment path |
1. Supermemory: shared context for the assistants you already use
Supermemory's current MCP surface has authorized spaces, semantic memory search, stored-document inspection and save/forget operations. An assistant can discover accessible spaces, inspect source documents and recover context without the team having to build a retrieval interface from scratch. That breadth makes it our first evaluation for a team that wants existing assistants to share project context. Supermemory MCP.
Document connectors include systems such as Google Drive, Notion and GitHub. Confirm the actual content type and sync behavior for the source you need; a documentation connector is not automatically a full code-intelligence or business-transaction pipeline. Evaluate spaces with two users and include a changed decision, not just a successful first save. Connectors.
2. Mem0: a focused memory service for your application
Mem0's managed platform extracts memories from supplied conversations and retrieves relevant facts later. It also offers a hosted MCP surface for saving, searching, inspecting, updating and deleting memories. This is a direct fit when your application already has an agent loop and you want to add memory around it. Mem0 Platform, Mem0 MCP.
Treat entity scope as part of every request. A good prototype demonstrates that the right user's preference returns in a new conversation and that another user does not inherit it. Teams that want to run the memory infrastructure themselves can evaluate the separately documented open-source path. Quickstart and scoping, Platform versus open source.
3. Zep: memory for changing relationships
Zep organizes context as temporal graphs of entities and facts, including invalidation when new data supersedes an earlier fact. It can ingest messages, documents, text and structured data and assemble relevant context for an agent. Put it higher when “who owns this now?” and “what changed?” matter more than a static preference lookup. Zep concepts, Working with context.
Zep also has a Memory MCP server for users of existing assistants; it is separate from the server that supplies Zep's public documentation. Check the plan's MCP seats, identity-provider configuration and permissions for standalone graphs. Test the changed relationship and its effective time rather than assuming a graph guarantees the answer. Memory MCP, Governance.
4. Letta: give a durable agent ownership of its memory
Letta's current Agent SDK centers on an agent whose identity and memory persist across conversations and execution environments. Its MemFS model uses an agent-owned git repository: system memory is included in the prompt, while other memory files are available for the agent to read as needed. Optional background consolidation can update that memory from recent work. Agent SDK, Memory.
This is compelling when a lasting engineering teammate or assistant is the product you want to build. It represents a larger architectural choice than adding a search call to a disposable agent loop. Compare the current Agent SDK path; older memory-block tutorials describe the legacy V1 SDK and should not silently define a new implementation.
5. Cognee: a graph-memory pipeline you can shape
Cognee exposes permanent graph and session memory through its current remember, recall and forget interface. Its SDK and REST surfaces provide lower-level control, while the cloud product can ingest documents and build a searchable graph. Move it to the top when customization and pipeline ownership are the priorities. Cognee Python API, Cloud quickstart.
Pay attention to the chosen transport. Current MCP documentation describes owner-scoped dataset-name lookup and directs shared-dataset cases requiring UUID scope toward SDK/REST access. Background ingestion also needs a completion check before a retrieval result establishes coverage. Those are useful questions to resolve in a prototype. MCP tools.
Where Glean and Composio fit
If the central job is finding company documents, code and expertise under an organization's existing knowledge controls, evaluate Glean. Its MCP configuration includes search, code search and user memory tools. That broader company-knowledge deployment is a different starting point from the add-on memory scenario ranked here. Glean MCP configuration.
If the central job is retaining the context of an app-tool workflow, Composio sessions already retain configuration and runtime context across reuse. Session persistence, sandbox-file retention and a long-term memory policy are separate details to evaluate. Composio session lifetime.
A better memory test than “remember my name”
Use a synthetic project with a small, explicit expected history. Store a design decision and its source, then change one fact in a later session. For example: Mira owns a billing exporter on September 2; Ravi takes ownership effective September 10. Another project has a different owner. These are teaching records, not customer results.
Ask the candidate implementation to answer four questions in a new conversation:
- Who owns the exporter now, and which source established the change?
- Who owned it on September 5? If historical recall is unsupported, say so rather than inventing an answer.
- What does a second project's user retrieve when asked the same question?
- After an authorized correction or deletion, what remains in the memory, its source documents and a fresh retrieval?
Record the actual stored objects, returned evidence and retrieval scope. A fact removed from one memory object may still exist in another source or earlier conversation. Evaluate the full path relevant to your application. For example, Zep documents that deleting an episode does not automatically regenerate summaries on shared nodes. Zep deletion behavior.
Query business state when the decision depends on it
Remembering that a customer preferred an annual plan is useful context. Determining which invoices are unpaid or how much open pipeline belongs to that customer requires the relevant records and a defined calculation. A stored summary of last month's answer should not silently become this month's total.
Combined supplies a managed, synced business-data layer that the agent can query with SQL. Keep durable decisions and preferences in the memory system; use the relevant source records and freshness when the question asks about the business now. Combined's text lookup is literal rather than semantic, so use the memory or knowledge system for the retrieval job it is designed to handle. Combined MCP, SQL and vector search.
Give your agent both continuity and useful business evidence. Explore Combined, then use the business-data planner to select the sources for one question. Start with the cardless 5-million-MAR allowance. Pricing.
Sources and further reading
Explore the documentation behind this guide. Product details checked on September 11, 2026.