Give Multi-Agent Systems a Shared Memory Backend They Can Trust
Multi-agent systems break down when each agent keeps its own private state. Plans get duplicated, facts conflict, and hand-offs lose context. MemoryLake gives multi-agent systems a shared, structured memory backend with conflict resolution and audit trails — so a crew of agents behaves like one coherent team.
Give Multi-Agent Systems a Shared Memory Backend They Can Trust
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The problem: each agent has its own memory and they don't agree
The planner agent learned the user's budget. The researcher agent has stale numbers. The writer agent never saw either. Hand-offs become re-briefings. Multi-agent systems often regress from "collaboration" to "compounding confusion" without a shared memory backend.
How MemoryLake solves multi-agent memory
One memory namespace per crew
All agents read from and write to the same structured memory. No more parallel realities.
Per-agent access scopes
Fine-grained control over which memory types each agent can read or modify. The planner sees everything; the writer sees only facts marked "ready for output."
Automatic conflict detection
When two agents log contradicting facts, MemoryLake surfaces the conflict and applies your resolution rules.
Hand-off provenance
Every memory entry tracks which agent wrote it, when, and why. Debugging cross-agent failures stops being archaeology.
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How it works for multi-agent systems
- Connect — Each agent in the crew authenticates with its own role-scoped key.
- Structure — As agents work, every fact, event, and reflection lands in shared memory with author metadata.
- Reuse — Each agent retrieves only the memory in scope for its role at inference time.
Before vs. after: multi-agent system memory
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Planner hands off to researcher | Verbal re-brief in the prompt | Researcher reads shared memory directly |
| Two agents log conflicting facts | Both passed downstream | Conflict surfaced and resolved |
| Debugging a bad output | "Which agent dropped the ball?" | Provenance chain in audit log |
| Adding a new agent to the crew | Custom prompt plumbing | Grant memory scope, done |
Who this is for
Teams running multi-agent systems for research, coding, customer ops, or business workflows — where two or more agents need to collaborate on shared context across long horizons.
Try this use case on your own account
A Planner agent records the Halyard 2.0 launch review: Thursday, owned by Ben Ito. Two days later three agents report to the same shared memory in the same minute, each in its own conversation: the Scheduler moves the review to Friday, the QA-gate agent moves it to Monday, the Staffing agent hands it to Ana Ruiz. The owner change lands cleanly. The date ends up as whichever report was processed last, the other day survives only inside "previously …", and no conflict is raised. An audit over fact trace and message times names the blind overwrite, and the lead's decision is written onto the facts.
What you will see
- Three report conversations appended at the same instant; facts are extracted from all three in the background, with
conv cook-statuspolled until done - Over 15 runs the date was settled by arrival order every time (Monday 7, Friday 8) and conflict detection flagged the race 0 times; the single-agent owner change landed cleanly in 14
- The audit: each revision in
fact tracenames itsconversation_id; if every message it came from was stored before the value it replaced existed, its author never saw that value - The lead decides with
fact update, so the trace ends with someone who saw both reports;auditandfactssubcommands, a local web UI, no model calls
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/concurrent-revision-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Can two agents update the same memory at the same time?
Can two agents update the same memory at the same time?
Yes. MemoryLake handles concurrent writes with conflict detection and resolution rules you define.
How are agent identities tracked?
How are agent identities tracked?
Each agent uses its own scoped API key. Every memory entry records the writing agent's ID, role, and timestamp.
Does this work with CrewAI, AutoGen, or LangGraph?
Does this work with CrewAI, AutoGen, or LangGraph?
Yes. MemoryLake exposes a memory backend interface that drops into any multi-agent framework.