Best LLM Memory Solutions for Multi-Agent Teams (2026)
The short answer
For multi-agent teams, the deciding question is how memory is shared, not which product scores highest on a single-agent benchmark. The best LLM memory solutions for multi-agent teams, as of October 30, 2026:
- Letta: best for live shared state between a supervisor and its workers.
- Zep / Graphiti: best for shared facts that change over time.
- Mem0: best for per-agent plus per-user memory at scale with simple scoping.
- Hindsight: best when the team should learn from past runs, not only recall them.
- cognee: best for a shared knowledge graph built from company documents and code.
Compared for teams of agents
| Sharing model | Scoping | Handles changing facts | Licence / stars (Oct 30, 2026) | Hosted price | |
|---|---|---|---|---|---|
| Letta | Memory blocks attached to several agents; one update is seen by all | Per block | The agent edits the block | Apache 2.0 · ~25,300 | Letta Cloud |
| Zep / Graphiti | Shared temporal graph per user or group | Users, graphs, projects | Yes: facts carry validity windows | Graphiti Apache 2.0 · ~32,400 | Free 10,000 credits/mo; Flex $125/mo (50,000 credits); Flex Plus $375/mo |
| Mem0 | Shared store; filter at retrieval | user_id, agent_id, run_id, metadata | Extracts and deduplicates facts | Apache 2.0 · ~68,100 | Free tier; custom plans add graph memory, Dream consolidation, SSO |
| Hindsight | Memory banks several agents read and write | Per bank | Consolidates observations as evidence changes | MIT · ~56,300 (v1.1.0, Oct 21) | Hindsight Cloud |
| cognee | Knowledge graph over documents, tickets, code and agent work | Datasets | Rebuilds the graph as sources change | Apache 2.0 · ~32,900 (v1.6.6, Oct 29) | Cognee Cloud |
Star counts are from GitHub on October 30, 2026; prices from the vendors’ pricing pages on the same day.
How each one fits a team
Letta: live shared state. Letta’s shared memory blocks are the most direct answer to “how do my agents share memory?” You create a block, for example organization or current_plan, and attach it to several agents with block_ids. When one agent updates it, the others see the change immediately, with no agent-to-agent messaging. That suits a supervisor that writes the plan and workers that read it. The cost is coupling: a bad write reaches every agent at once, so keep shared blocks small and let only the supervisor write.
Zep and Graphiti: facts that change. Graphiti builds temporal knowledge graphs that track what is true now and what was true before. That matters when a sales agent, a support agent and a billing agent all reason about the same customer whose plan, owner and status change. Zep is the managed service on top. Its self-serve plans are credit-based: Flex is $125 a month with 50,000 credits, and Flex Plus is $375 a month with 200,000 credits, webhooks and observations. Enterprise adds VPC deployment, SOC 2 Type II and a HIPAA BAA.
Mem0: the simplest scoping. Mem0 sits between your app and the model: send turns to add, call search before the next request. Memories are scoped by user_id, agent_id and run_id, so each agent can keep its own notes while reading what any agent learned about the user. The free plan includes unlimited end users. With your own LLM key you get 50,000 adds and 10,000 retrievals a month, with usage billing past that under a cap you set. Graph memory and the Dream consolidation feature are on custom plans.
Hindsight: a team that improves. Hindsight organises memory into banks and three operations: retain, recall and reflect. It consolidates raw facts into observations and mental models, so a team of agents that runs the same workflow daily gets better instead of piling up duplicate notes. It runs on PostgreSQL via Docker or Helm, or as a hosted cloud.
cognee: the company brain. cognee turns documents, conversations, tickets and code into a self-hosted knowledge graph that agents search and reuse, and it can store agent work alongside human sources. Choose it when the shared memory is mostly organisational knowledge rather than conversation history.
Rules that matter more than the product
- Separate private and shared memory. Each agent keeps working notes; only curated facts go to the shared layer.
- Control who writes. Read-only for most agents, write access for a supervisor or a reviewer agent. One compromised agent should not be able to poison every other agent’s memory. See how to secure AI agent memory.
- Record provenance. Store which agent wrote each memory and when, so you can delete one agent’s bad writes.
- Measure retrieval, not storage. Test whether the right memory comes back for real tasks before you scale the team.
For single-agent memory rankings and benchmarks, see best AI agent memory systems. For the surrounding stack, see infrastructure for multi-agent systems in production.
Last verified: October 30, 2026.