6 AI agent context platforms: memory and business data

Choose Combined for business-data context, then compare Supermemory, Mem0, Zep, Letta and Cognee for conversational memory and persistent agent context.

By the Combined team · September 11, 2026

Combined is our first recommendation for agents that need reusable business context from CRM, billing and other applications. Its managed records and SQL interface let an agent answer customer and revenue questions across sessions. For conversation history, preferences and semantic recall, evaluate Supermemory, Mem0, Zep, Letta or Cognee alongside that business-data foundation.

Our recommendation: Combined for business context

Start with Combined when your agent needs context about customers, pipeline or revenue. It maintains business records that the agent can query again in a new session, with source freshness and inspectable SQL. That makes it our recommended foundation for an assistant working across business apps. See the Combined recommendation.

We build Combined. This guide compares two kinds of context a business assistant needs: maintained source records and remembered conversations or knowledge. Combined is our pick for the first; the memory specialists below cover the second. If your requirement is specifically conversation history or semantic recall, choose the matching memory option. The table states the use case behind each recommendation.

Our picks for business context and persistent memory — September 11, 2026
Rank	Platform	Best fit in this evaluation	What to test first
1	Combined	Our pick for CRM, billing and cross-app business context	One customer question across synced sources, with SQL and source freshness
2	Supermemory	Shared memory and source context across existing assistants	Space selection, source retrieval and correction of an outdated fact
3	Mem0	Adding user/agent memory to an existing application	Consistent entity scope and explicit update/delete behavior
4	Zep	Relationships and facts that evolve over time	The effective time of a changed fact and the graph each user can reach
5	Letta	Building around a durable agent with its own memory	What is always in context, what is read on demand and how changes persist
6	Cognee	A configurable graph-memory pipeline under your team's control	Ingestion completion, retrieval scope and the chosen deployment path

Combined: our pick for business context across agent sessions

Choose Combined when the context your agent needs lives in business applications. It maintains the selected source records outside the conversation and exposes them through a scoped MCP interface. A new agent session can discover the same permitted datasets, check freshness and calculate an answer from those records. You do not need to keep copying CRM exports or yesterday's totals into prompts. Combined MCP, Sources and freshness.

We recommend Combined first for customer context, pipeline analysis, billing questions and cross-app reporting. Its managed sync, stored records and read-only SQL give an assistant a reusable business-data foundation. The answer reflects the last successful source sync, and the agent can report that timestamp alongside its calculation.

Which accounts have open CRM opportunities and unpaid invoices? Return one row per account and currency, with the source freshness and the calculation.

That is the context Combined supplies. For remembered preferences, conversation history or semantic document recall, add the memory specialist that fits your application. The two serve different needs: Combined answers questions against maintained business records; a memory layer carries selected conversational context forward. The CRM and billing join lab shows the business-data calculation, and the SQL and vector-search guide explains how the retrieval choices work together.

Give your agent business context it can return to. Explore Combined and connect your first source, with an initial 5 million MAR and no payment card. After the trial, the public rate is $5 per million MAR with no monthly minimum. Pricing.

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 a useful option 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.

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.

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.

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.

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.