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a6bf85b01e
Co-authored-by: cpacker <packercharles@gmail.com> Co-authored-by: Sarah Wooders <sarahwooders@gmail.com> Co-authored-by: Shubham Naik <shubham.naik10@gmail.com> Co-authored-by: Matthew Zhou <mattzh1314@gmail.com> Co-authored-by: Shubham Naik <shub@memgpt.ai>
120 lines
3.3 KiB
Python
120 lines
3.3 KiB
Python
from letta_client import CreateBlock, Letta, MessageCreate
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"""
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Make sure you run the Letta server before running this example.
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See: https://docs.letta.com/quickstart
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If you're using Letta Cloud, replace 'baseURL' with 'token'
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See: https://docs.letta.com/api-reference/overview
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Execute this script using `poetry run python3 example.py`
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"""
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client = Letta(
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base_url="http://localhost:8283",
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)
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agent = client.agents.create(
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memory_blocks=[
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CreateBlock(
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value="Name: Caren",
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label="human",
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),
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],
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model="openai/gpt-4o-mini",
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embedding="openai/text-embedding-ada-002",
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)
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print(f"Created agent with name {agent.name}")
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message_text = "What's my name?"
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response = client.agents.messages.create(
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agent_id=agent.id,
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messages=[
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MessageCreate(
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role="user",
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content=message_text,
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),
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],
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)
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print(f"Sent message to agent {agent.name}: {message_text}")
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print(f"Agent thoughts: {response.messages[0].reasoning}")
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print(f"Agent response: {response.messages[1].content}")
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def secret_message():
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"""Return a secret message."""
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return "Hello world!"
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tool = client.tools.upsert_from_function(
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func=secret_message,
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)
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client.agents.tools.attach(agent_id=agent.id, tool_id=tool.id)
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print(f"Created tool {tool.name} and attached to agent {agent.name}")
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message_text = "Run secret message tool and tell me what it returns"
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response = client.agents.messages.create(
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agent_id=agent.id,
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messages=[
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MessageCreate(
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role="user",
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content=message_text,
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),
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],
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)
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print(f"Sent message to agent {agent.name}: {message_text}")
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print(f"Agent thoughts: {response.messages[0].reasoning}")
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print(f"Tool call information: {response.messages[1].tool_call}")
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print(f"Tool response information: {response.messages[2].status}")
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print(f"Agent thoughts: {response.messages[3].reasoning}")
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print(f"Agent response: {response.messages[4].content}")
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agent_copy = client.agents.create(
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model="openai/gpt-4o-mini",
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embedding="openai/text-embedding-ada-002",
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)
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block = client.agents.core_memory.retrieve_block(agent.id, "human")
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agent_copy = client.agents.core_memory.attach_block(agent_copy.id, block.id)
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print(f"Created agent copy with shared memory named {agent_copy.name}")
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message_text = "My name isn't Caren, it's Sarah. Please update your core memory with core_memory_replace"
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response = client.agents.messages.create(
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agent_id=agent_copy.id,
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messages=[
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MessageCreate(
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role="user",
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content=message_text,
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),
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],
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)
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print(f"Sent message to agent {agent_copy.name}: {message_text}")
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block = client.agents.core_memory.retrieve_block(agent_copy.id, "human")
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print(f"New core memory for agent {agent_copy.name}: {block.value}")
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message_text = "What's my name?"
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response = client.agents.messages.create(
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agent_id=agent_copy.id,
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messages=[
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MessageCreate(
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role="user",
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content=message_text,
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),
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],
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)
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print(f"Sent message to agent {agent_copy.name}: {message_text}")
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print(f"Agent thoughts: {response.messages[0].reasoning}")
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print(f"Agent response: {response.messages[1].content}")
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client.agents.delete(agent_id=agent.id)
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client.agents.delete(agent_id=agent_copy.id)
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print(f"Deleted agents {agent.name} and {agent_copy.name}")
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