The 5 best memory and context tools for AI agents in 2026
Every tool in this category says "memory for AI agents" and means something different by it. Some persist facts about a user across sessions. Some persist an agent's own working state. Some maintain a knowledge graph a whole team's agents read. Pick by the label and you'll buy the wrong one.
We build Modem, one of the five below, so weigh our ranking accordingly. The scope column is the one to read first: memory for you, memory for your agent, or memory your team shares are different products.
The short version
| Tool | What it remembers | Scope | Best for |
|---|---|---|---|
| Modem | Customer and product signal, joined into a graph | Team-shared | Agents that need to know what customers said |
| mem0 | Facts extracted from conversations | Per user or agent | Adding user memory to an app you're building |
| Zep | Chat history plus business data in a temporal graph | Org-scale | Enterprises assembling a governed context store |
| Letta | The agent's own state and learned context | Per agent | Long-running agents that must survive restarts |
| Supermemory | Documents and conversations, per user | Per user | Personal memory with fast retrieval |
1. Modem
Modem maintains a customer context graph: topics, people, and companies linked across Slack, Discord, support tickets, email, and sales calls, with the original quotes attached. Nothing gets written to it by your agents; Modem reads the connected channels continuously and keeps the joins current itself. Agents consume it over MCP (claude mcp add --transport http modem https://mcp.modem.dev/mcp in Claude Code), scripts through the @modem-dev/cli package, and people by asking the agent in Slack. Every path reads the same graph.
The distinction from everything else on this list is the domain. Memory tools remember conversations your agent had. Modem's graph knows your customers, whether or not any agent has asked about them yet, because the facts come from where feedback actually lands, not from agent sessions.
Where it fits: teams whose agents keep answering customer questions generically because the evidence lives in channels the agent can't reach. Where it doesn't: it won't store your personal preferences or your agent's working state. Plenty of teams run Modem alongside one of the tools below; they don't overlap.
2. mem0
mem0 is drop-in memory infrastructure for apps and agents: it extracts facts from conversations and retrieves them on later runs, keyed to a user, agent, or session. The open-source core (Apache 2.0) has one of the largest communities in the category, there's a free cloud tier, and the extraction-and-retrieval pipeline is the part they've polished hardest. If you're building a product and want it to remember each user, this is the default choice for a reason.
Where it fits: developers adding per-user memory to their own app. Where it doesn't: memory is scoped to the identifiers you pass it. It won't resolve the same customer across channels or give a whole team's agents one shared picture; that's your application code.
3. Zep
Zep builds agent memory as a temporal knowledge graph and aims squarely at the enterprise. Its open-source engine, Graphiti, versions facts over time and invalidates them when the underlying data changes, and the hosted platform positions itself as a governed "context lake" spanning chat history and business data, with SOC 2 and HIPAA coverage and named enterprise customers.
Zep is the closest conceptual neighbor to Modem on this list: both bet on structure over similarity search. The difference is what arrives built. Zep hands you a strong graph engine and governance to assemble your own context store from your own sources. Modem ships one specific graph, the customer one, already maintained.
Where it fits: enterprises with a platform team building a governed memory layer across many data sources. Where it doesn't: if the context you need is customer feedback, assembling it yourself is the expensive path.
4. Letta
Letta comes out of the MemGPT research line and has grown from a memory API into a runtime for stateful agents: agents that manage their own context window, persist their state, and keep learning between sessions. The core is open source (Apache 2.0), and the ideas, like the agent deciding what to page in and out of context, show up in everyone else's roadmaps a year later.
Where it fits: teams building long-running agents that need durable identity and state, and teams who want the research frontier. Where it doesn't: the memory belongs to the agent you build on Letta. It isn't a store of team knowledge that outside agents, your Claude Code session included, come to for answers.
5. Supermemory
Supermemory is a memory API with genuinely fast hybrid retrieval and a wide set of connectors (Notion, Drive, Gmail, and more), plus an open-source, MIT-licensed, self-hostable core that runs as a single binary. It leans personal: one person's documents and conversations, recallable by their agents, with a consumer app built on the same layer.
Where it fits: individuals and small teams who want their own material recallable across agents, self-hosted if preferred. Where it doesn't: connectors ingest your documents, not your customers. Nothing links a complaint to an account, and per-user stores drift apart across a team.
How to choose
Ask whose memory it is. If the answer is "each user of my app," mem0 or Supermemory. If it's "the agent itself," Letta. If it's "the whole org, across many data sources, with governance," Zep, budget included. And if the memory you're missing is what customers said, who said it, and what they pay you, that's not conversation memory at all; it's a customer context graph, and it's the one on this list that maintains itself. The categories genuinely don't compete, which is why the wrong pick fails quietly: the tool works exactly as designed, remembering things that were never the missing piece.
