Multi-Agent Shared Memory

Agents can share state via shared memory blocks. This allows agents to have a “shared memory”. You can shared blocks between agents by attaching the same block ID to multiple agents.

In the example code below, we create a shared memory block and attach it to a supervisor agent and a worker agent. Because the memory block is shared, when one agent writes to it, the other agent can read the updates immediately.

1from letta_client import Letta
2
3client = Letta(base_url="http://127.0.0.1:8283")
4
5# create a shared memory block
6shared_block = client.blocks.create(label="organization", value="Organization: Letta")
7
8# create a supervisor agent
9supervisor_agent = client.agents.create(
10    model="anthropic/claude-3-5-sonnet-20241022",
11    embedding="openai/text-embedding-ada-002",
12    # blocks created for this agentj
13    memory_blocks=[{"persona": "I am a supervisor"}],
14    # pre-existing shared block that is "attached" to this agent
15    block_ids=[shared_block.id],
16)
17
18# create a worker agent
19worker_agent = client.agents.create(
20    model="anthropic/claude-3-5-sonnet-20241022",
21    embedding="openai/text-embedding-ada-002",
22    # blocks created for this agent
23    memory_blocks=[{"persona": "I am a worker"}],
24    # pre-existing shared block that is "attached" to this agent
25    block_ids=[shared_block.id],
26)

Memory blocks can also be accessed by other agents, even if not shared. For example, worker agents can write the output of their task to a memory block, which is then read by a supervisor agent. 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).

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