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chore: officially migrate to submodule (#4502)

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* remove apps/core and apps/fern

* fix precommit

* add submodule updates in workflows

* submodule

* remove core tests

* update core revision

* Add submodules: true to all GitHub workflows

- Ensure all workflows can access git submodules
- Add submodules support to deployment, test, and CI workflows
- Fix YAML syntax issues in workflow files

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* remove core-lint

* upgrade core with latest main of oss

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Kian Jones
2025-09-09 12:45:53 -07:00
committed by GitHub
parent 48b5722095
commit 22f70ca07c
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166
examples/docs/example.py
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from letta_client import CreateBlock, Letta, MessageCreate
"""
Make sure you run the Letta server before running this example.
See: https://docs.letta.com/quickstart
If you're using Letta Cloud, replace 'baseURL' with 'token'
See: https://docs.letta.com/api-reference/overview
Execute this script using `uv run python3 example.py`
This will install `letta_client` and other dependencies.
"""
client = Letta(
base_url="http://localhost:8283",
)
agent = client.agents.create(
memory_blocks=[
CreateBlock(
value="Name: Caren",
label="human",
),
],
model="openai/gpt-4o-mini",
embedding="openai/text-embedding-3-small",
)
print(f"Created agent with name {agent.name}")
# Example without streaming
message_text = "What's my name?"
response = client.agents.messages.create(
agent_id=agent.id,
messages=[
MessageCreate(
role="user",
content=message_text,
),
],
)
print(f"Sent message to agent {agent.name}: {message_text}")
print(f"Agent thoughts: {response.messages[0].reasoning}")
print(f"Agent response: {response.messages[1].content}")
def secret_message():
"""Return a secret message."""
return "Hello world!"
tool = client.tools.upsert_from_function(
func=secret_message,
)
client.agents.tools.attach(agent_id=agent.id, tool_id=tool.id)
print(f"Created tool {tool.name} and attached to agent {agent.name}")
message_text = "Run secret message tool and tell me what it returns"
response = client.agents.messages.create(
agent_id=agent.id,
messages=[
MessageCreate(
role="user",
content=message_text,
),
],
)
for msg in response.messages:
if msg.message_type == "assistant_message":
print(msg.content)
elif msg.message_type == "reasoning_message":
print(msg.reasoning)
elif msg.message_type == "tool_call_message":
print(msg.tool_call.name)
print(msg.tool_call.arguments)
elif msg.message_type == "tool_return_message":
print(msg.tool_return)
print(f"Sent message to agent {agent.name}: {message_text}")
print(f"Agent thoughts: {response.messages[0].reasoning}")
print(f"Tool call information: {response.messages[1].tool_call}")
print(f"Tool response information: {response.messages[2].status}")
print(f"Agent thoughts: {response.messages[3].reasoning}")
print(f"Agent response: {response.messages[4].content}")
# send a message to the agent (streaming steps)
message_text = "Repeat my name."
stream = client.agents.messages.create_stream(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content=message_text,
),
],
# if stream_tokens is false, each "chunk" will have a full piece
# if stream_tokens is true, the chunks will be token-based (and may need to be accumulated client-side)
stream_tokens=True,
)
# print the chunks coming back
for chunk in stream:
if chunk.message_type == "assistant_message":
print(chunk.content)
elif chunk.message_type == "reasoning_message":
print(chunk.reasoning)
elif chunk.message_type == "tool_call_message":
if chunk.tool_call.name:
print(chunk.tool_call.name)
if chunk.tool_call.arguments:
print(chunk.tool_call.arguments)
elif chunk.message_type == "tool_return_message":
print(chunk.tool_return)
elif chunk.message_type == "usage_statistics":
print(chunk)
agent_copy = client.agents.create(
model="openai/gpt-4o-mini",
embedding="openai/text-embedding-3-small",
)
block = client.agents.blocks.retrieve(agent.id, block_label="human")
agent_copy = client.agents.blocks.attach(agent_copy.id, block.id)
print(f"Created agent copy with shared memory named {agent_copy.name}")
message_text = "My name isn't Caren, it's Sarah. Please update your core memory with core_memory_replace"
response = client.agents.messages.create(
agent_id=agent_copy.id,
messages=[
MessageCreate(
role="user",
content=message_text,
),
],
)
print(f"Sent message to agent {agent_copy.name}: {message_text}")
block = client.agents.blocks.retrieve(agent_copy.id, block_label="human")
print(f"New core memory for agent {agent_copy.name}: {block.value}")
message_text = "What's my name?"
response = client.agents.messages.create(
agent_id=agent_copy.id,
messages=[
MessageCreate(
role="user",
content=message_text,
),
],
)
print(f"Sent message to agent {agent_copy.name}: {message_text}")
print(f"Agent thoughts: {response.messages[0].reasoning}")
print(f"Agent response: {response.messages[1].content}")
client.agents.delete(agent_id=agent.id)
client.agents.delete(agent_id=agent_copy.id)
print(f"Deleted agents {agent.name} and {agent_copy.name}")