[Use cases](https://www.maximem.ai/use-cases/synap)/ Multi-Agent # One memory across every agent. When one agent learns something, every agent knows it. Shared memory, scoped by default. Connect your stack R Research Agentwrites 10-Q filing analyzed. Q3 revenue: $42M confirmed. A Analyst Agentreads Reading Q3 revenue for comp analysis… W Writer Agentreads Drafting summary · citing Q3 revenue… ∑ Final Output Q3 revenue reached $42M, per the 10-Q filing. Shared Memory **3 agents** · 1 source of truth Agent Writes The problem ## Why multi-agent systems drift ### Every agent rebuilds the same context The same facts get retrieved, embedded, and interpreted over and over — by agents that should be sharing work, not duplicating it. Token costs climb; quality doesn't. ### Agents quietly contradict each other Without a shared source of truth, two agents can hold opposite facts. Neither knows. The final output inherits the inconsistency. ### Workflows lose institutional memory What the system learned last week is gone by Monday. Every run starts from scratch, and your stack never gets smarter. How Synap solves it ## Shared state, sensibly scoped ### A single memory surface Every agent writes to and reads from the same memory layer — episodic, semantic, and factual. Stop duplicating retrieval pipelines per agent. ### Scope isolation by default Customer-scoped, session-scoped, tenant-scoped. Agents share what they should and can't see what they shouldn't — enforced at the infrastructure layer, not the prompt. ### Durable institutional memory Insights, patterns, and decisions persist across runs. The stack learns from its own work — week over week, not turn over turn. ### Consistent facts across agents Updates propagate. Stale copies don't linger. Two agents won't disagree on a fact either of them has seen. Your agents stop repeating work. They start building on each other. The stack learns — permanently. Continue exploring [Synap use caseSales & Revenue IntelligenceEvery conversation, objection, and signal carries forward — so your reps never walk into a deal cold.Explore](https://www.maximem.ai/use-cases/synap/sales)[Synap use caseVoice AI AgentsReal-time memory for agents that have to sound human — without the pause, the loop, or the "let me check on that."Explore](https://www.maximem.ai/use-cases/synap/voice-ai)[CompareSynap vs LettaMemory infrastructure vs agent framework.See comparison](https://www.maximem.ai/compare/maximem-synap-vs-letta) Also supports Education · E-Commerce · Legal · Financial Services · HR & Recruiting Whatever your agents do, Synap is the memory layer underneath. [Start with the docs](https://docs.maximem.ai)[Talk to us](https://www.maximem.ai/about#contact) [Synap memory for LangChain →](https://www.maximem.ai/blog/langchain-maximem-synap-memory-integration)[Synap memory for LangGraph →](https://www.maximem.ai/blog/langgraph-synap-memory-integration)[See all 23 integrations →](https://www.maximem.ai/synap/integrations) ## Multi-Agent & Agentic Workflows — FAQ ### How does Synap enable shared memory for multi-agent systems? Synap provides a single memory surface — episodic, semantic, and factual — that every agent in a system writes to and reads from. No more duplicating retrieval pipelines per agent. ### How does Synap handle memory scoping across agents? Synap enforces customer-scoped, session-scoped, and tenant-scoped isolation at the infrastructure layer — not the prompt. Agents share what they should and cannot access what they should not. ### Does Synap support durable institutional memory across workflow runs? Yes. Insights, patterns, and decisions persist across runs — so your agent stack gets smarter week over week, not just turn over turn. ### How does Synap prevent agents from contradicting each other? Updates propagate across the shared memory layer immediately. Stale copies do not linger. Two agents will not hold opposite facts about the same entity. --- Source: [https://www.maximem.ai/use-cases/synap/multi-agent](https://www.maximem.ai/use-cases/synap/multi-agent)