104 lines
3.6 KiB
Plaintext
104 lines
3.6 KiB
Plaintext
---
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title: Multi-Agent Shared Memory
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slug: guides/agents/multi-agent-shared-memory
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---
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Agents can share state via shared memory blocks.
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This allows agents to have a "shared memory".
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You can shared blocks between agents by attaching the same block ID to multiple agents.
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```mermaid
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graph TD
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subgraph Supervisor
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S[Memory Block<br>I am a supervisor]
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SS[Shared Memory Block<br>Organization: Letta]
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end
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subgraph Worker
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W1[Memory Block<br>I am a worker]
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W1S[Shared Memory Block<br>Organization: Letta]
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end
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SS -..- W1S
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```
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In the example code below, we create a shared memory block and attach it to a supervisor agent and a worker agent.
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Because the memory block is shared, when one agent writes to it, the other agent can read the updates immediately.
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<CodeGroup>
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```python title="python" maxLines=50
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# install letta_client with `pip install letta-client`
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from letta_client import Letta
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# create a client to connect to Letta
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client = Letta(token="LETTA_API_KEY")
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# create a shared memory block
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shared_block = client.blocks.create(
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label="organization",
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description="Shared information between all agents within the organization.",
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value="Nothing here yet, we should update this over time."
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)
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# create a supervisor agent
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supervisor_agent = client.agents.create(
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model="anthropic/claude-3-5-sonnet-20241022",
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embedding="openai/text-embedding-3-small",
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# blocks created for this agent
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memory_blocks=[{"label": "persona", "value": "I am a supervisor"}],
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# pre-existing shared block that is "attached" to this agent
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block_ids=[shared_block.id],
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)
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# create a worker agent
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worker_agent = client.agents.create(
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model="anthropic/claude-3-5-sonnet-20241022",
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embedding="openai/text-embedding-3-small",
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# blocks created for this agent
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memory_blocks=[{"label": "persona", "value": "I am a worker"}],
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# pre-existing shared block that is "attached" to this agent
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block_ids=[shared_block.id],
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)
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```
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```typescript title="node.js" maxLines=50
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// install letta-client with `npm install @letta-ai/letta-client`
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import { LettaClient } from '@letta-ai/letta-client'
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// create a client to connect to Letta
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const client = new LettaClient({
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token: "LETTA_API_KEY"
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});
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// create a shared memory block
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const sharedBlock = await client.blocks.create({
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label: "organization",
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description: "Shared information between all agents within the organization.",
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value: "Nothing here yet, we should update this over time."
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});
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// create a supervisor agent
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const supervisorAgent = await client.agents.create({
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model: "anthropic/claude-3-5-sonnet-20241022",
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embedding: "openai/text-embedding-3-small",
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// blocks created for this agent
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memoryBlocks: [{ label: "persona", value: "I am a supervisor" }],
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// pre-existing shared block that is "attached" to this agent
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blockIds: [sharedBlock.id]
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});
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// create a worker agent
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const workerAgent = await client.agents.create({
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model: "anthropic/claude-3-5-sonnet-20241022",
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embedding: "openai/text-embedding-3-small",
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// blocks created for this agent
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memoryBlocks: [{ label: "persona", value: "I am a worker" }],
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// pre-existing shared block that is "attached" to this agent
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blockIds: [sharedBlock.id]
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});
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```
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</CodeGroup>
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Memory blocks can also be accessed by other agents, even if not shared.
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For example, worker agents can write the output of their task to a memory block, which is then read by a supervisor agent.
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To access the memory blocks of other agents, you can simply use the SDK clients or API to access specific agent's memory blocks (using the [core memory routes](/api-reference/agents/core-memory)).
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