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chore: officially migrate to submodule (#4502)

Browse Source 
* 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
953 changed files with 0 additions and 181472 deletions
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70
examples/docs/agent_advanced.py
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@@ -1,70 +0,0 @@
from letta_client import CreateBlock, Letta, MessageCreate
from letta.prompts import gpt_system
"""
Make sure you run the Letta server before running this example.
```
letta server
```
"""
client = Letta(base_url="http://localhost:8283")
# create a new agent
agent_state = client.agents.create(
# agent's name (unique per-user, autogenerated if not provided)
name="agent_name",
# in-context memory representation with human/persona blocks
memory_blocks=[
CreateBlock(
label="human",
value="Name: Sarah",
),
CreateBlock(
label="persona",
value="You are a helpful assistant that loves emojis",
),
],
# LLM model & endpoint configuration
model="openai/gpt-4o-mini",
context_window_limit=8000,
# embedding model & endpoint configuration (cannot be changed)
embedding="openai/text-embedding-3-small",
# system instructions for the agent (defaults to `memgpt_chat`)
system=gpt_system.get_system_text("memgpt_chat"),
# whether to include base letta tools (default: True)
include_base_tools=True,
# list of additional tools (by name) to add to the agent
tool_ids=[],
)
print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
# message an agent as a user
response = client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content="hello",
)
],
)
print("Usage", response.usage)
print("Agent messages", response.messages)
# message a system message (non-user)
response = client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="system",
content="[system] user has logged in. send a friendly message.",
)
],
)
print("Usage", response.usage)
print("Agent messages", response.messages)
# delete the agent
client.agents.delete(agent_id=agent_state.id)

49
examples/docs/agent_basic.py
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@@ -1,49 +0,0 @@
from letta_client import CreateBlock, Letta, MessageCreate
"""
Make sure you run the Letta server before running this example.
```
letta server
```
"""
client = Letta(base_url="http://localhost:8283")
# create a new agent
agent_state = client.agents.create(
memory_blocks=[
CreateBlock(
label="human",
value="Name: Sarah",
),
],
# set automatic defaults for LLM/embedding config
model="openai/gpt-4o-mini",
embedding="openai/text-embedding-3-small",
)
print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
# Message an agent
response = client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content="hello",
)
],
)
print("Usage", response.usage)
print("Agent messages", response.messages)
# list all agents
agents = client.agents.list()
# get the agent by ID
agent_state = client.agents.retrieve(agent_id=agent_state.id)
# get the agent by name
agent_state = client.agents.list(name=agent_state.name)[0]
# delete an agent
client.agents.delete(agent_id=agent_state.id)

166
examples/docs/example.py
View File

@@ -1,166 +0,0 @@
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}")

0
examples/docs/memory.py
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148
examples/docs/node/example.ts
View File

@@ -1,148 +0,0 @@
import type {
AssistantMessage,
ReasoningMessage,
ToolCallMessage,
ToolReturnMessage,
} from '@letta-ai/letta-client/api/types';
/**
* 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 `npm run example`
*/
const client = new LettaClient({
baseUrl: 'http://localhost:8283',
});
const agent = await client.agents.create({
memoryBlocks: [
{
value: 'name: Caren',
label: 'human',
},
],
model: 'openai/gpt-4o-mini',
embedding: 'openai/text-embedding-3-small',
});
console.log('Created agent with name', agent.name);
let messageText = "What's my name?";
let response = await client.agents.messages.create(agent.id, {
messages: [
{
role: 'user',
content: messageText,
},
],
});
console.log(`Sent message to agent ${agent.name}: ${messageText}`);
console.log(
'Agent thoughts:',
(response.messages[0] as ReasoningMessage).reasoning,
);
console.log(
'Agent response:',
(response.messages[1] as AssistantMessage).content,
);
const CUSTOM_TOOL_SOURCE_CODE = `
def secret_message():
"""Return a secret message."""
return "Hello world!"
`.trim();
const tool = await client.tools.upsert({
sourceCode: CUSTOM_TOOL_SOURCE_CODE,
});
await client.agents.tools.attach(agent.id, tool.id);
console.log(`Created tool ${tool.name} and attached to agent ${agent.name}`);
messageText = 'Run secret message tool and tell me what it returns';
response = await client.agents.messages.create(agent.id, {
messages: [
{
role: 'user',
content: messageText,
},
],
});
console.log(`Sent message to agent ${agent.name}: ${messageText}`);
console.log(
'Agent thoughts:',
(response.messages[0] as ReasoningMessage).reasoning,
);
console.log(
'Tool call information:',
(response.messages[1] as ToolCallMessage).toolCall,
);
console.log(
'Tool response information:',
(response.messages[2] as ToolReturnMessage).status,
);
console.log(
'Agent thoughts:',
(response.messages[3] as ReasoningMessage).reasoning,
);
console.log(
'Agent response:',
(response.messages[4] as AssistantMessage).content,
);
let agentCopy = await client.agents.create({
model: 'openai/gpt-4o-mini',
embedding: 'openai/text-embedding-3-small',
});
let block = await client.agents.blocks.retrieve(agent.id, 'human');
agentCopy = await client.agents.blocks.attach(agentCopy.id, block.id);
console.log('Created agent copy with shared memory named', agentCopy.name);
messageText =
"My name isn't Caren, it's Sarah. Please update your core memory with core_memory_replace";
response = await client.agents.messages.create(agentCopy.id, {
messages: [
{
role: 'user',
content: messageText,
},
],
});
console.log(`Sent message to agent ${agentCopy.name}: ${messageText}`);
block = await client.agents.blocks.retrieve(agentCopy.id, 'human');
console.log(`New core memory for agent ${agentCopy.name}: ${block.value}`);
messageText = "What's my name?";
response = await client.agents.messages.create(agentCopy.id, {
messages: [
{
role: 'user',
content: messageText,
},
],
});
console.log(`Sent message to agent ${agentCopy.name}: ${messageText}`);
console.log(
'Agent thoughts:',
(response.messages[0] as ReasoningMessage).reasoning,
);
console.log(
'Agent response:',
(response.messages[1] as AssistantMessage).content,
);
await client.agents.delete(agent.id);
await client.agents.delete(agentCopy.id);
console.log(`Deleted agents ${agent.name} and ${agentCopy.name}`);

806
examples/docs/node/package-lock.json generated
View File

@@ -1,806 +0,0 @@
{
"name": "@letta-ai/core",
"version": "0.1.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "@letta-ai/core",
"version": "0.1.0",
"dependencies": {
"@letta-ai/letta-client": "^0.1.131"
},
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"@types/node": "^22.12.0",
"ts-node": "^10.9.2",
"typescript": "^5.7.3"
}
},
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"dev": true,
"license": "MIT",
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}
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18
examples/docs/node/package.json
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@@ -1,18 +0,0 @@
{
"name": "@letta-ai/core",
"version": "0.1.0",
"private": true,
"type": "module",
"scripts": {
"example": "node --no-warnings --import 'data:text/javascript,import { register } from \"node:module\"; import { pathToFileURL } from \"node:url\"; register(\"ts-node/esm\", pathToFileURL(\"./\"));' example.ts",
"build": "tsc"
},
"dependencies": {
"@letta-ai/letta-client": "^0.1.131"
},
"devDependencies": {
"@types/node": "^22.12.0",
"ts-node": "^10.9.2",
"typescript": "^5.7.3"
}
}

4
examples/docs/node/project.json
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@@ -1,4 +0,0 @@
{
"name": "node-example",
"$schema": "../../node_modules/nx/schemas/project-schema.json"
}

18
examples/docs/node/tsconfig.json
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@@ -1,18 +0,0 @@
{
"compilerOptions": {
"target": "es2017",
"module": "esnext",
"lib": ["es2017", "dom"],
"declaration": true,
"strict": true,
"moduleResolution": "node",
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"outDir": "./dist",
"rootDir": ".",
"resolveJsonModule": true
},
"include": ["*.ts"],
"exclude": ["node_modules", "dist"]
}

58
examples/docs/rest_client.py
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@@ -1,58 +0,0 @@
from letta_client import CreateBlock, Letta, MessageCreate
"""
Make sure you run the Letta server before running this example.
```
letta server
```
"""
def main():
# Connect to the server as a user
client = Letta(base_url="http://localhost:8283")
# list available configs on the server
llm_configs = client.models.list_llms()
print(f"Available LLM configs: {llm_configs}")
embedding_configs = client.models.list_embedding_models()
print(f"Available embedding configs: {embedding_configs}")
# Create an agent
agent_state = client.agents.create(
name="my_agent",
memory_blocks=[
CreateBlock(
label="human",
value="My name is Sarah",
),
CreateBlock(
label="persona",
value="I am a friendly AI",
),
],
model=llm_configs[0].handle,
embedding=embedding_configs[0].handle,
)
print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
# Send a message to the agent
print(f"Created agent: {agent_state.name} with ID {str(agent_state.id)}")
response = client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content="Whats my name?",
)
],
)
print(f"Received response:", response.messages)
# Delete agent
client.agents.delete(agent_id=agent_state.id)
print(f"Deleted agent: {agent_state.name} with ID {str(agent_state.id)}")
if __name__ == "__main__":
main()

110
examples/docs/tools.py
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@@ -1,110 +0,0 @@
from letta_client import CreateBlock, Letta, MessageCreate
from letta_client.types import TerminalToolRule
"""
Make sure you run the Letta server before running this example.
```
letta server
```
"""
client = Letta(base_url="http://localhost:8283")
# define a function with a docstring
def roll_d20() -> str:
"""
Simulate the roll of a 20-sided die (d20).
This function generates a random integer between 1 and 20, inclusive,
which represents the outcome of a single roll of a d20.
Returns:
int: A random integer between 1 and 20, representing the die roll.
Example:
>>> roll_d20()
15 # This is an example output and may vary each time the function is called.
"""
import random
dice_role_outcome = random.randint(1, 20)
output_string = f"You rolled a {dice_role_outcome}"
return output_string
# create a tool from the function
tool = client.tools.upsert_from_function(func=roll_d20)
print(f"Created tool with name {tool.name}")
# create a new agent
agent_state = client.agents.create(
memory_blocks=[
CreateBlock(
label="human",
value="Name: Sarah",
),
],
# set automatic defaults for LLM/embedding config
model="openai/gpt-4o-mini",
embedding="openai/text-embedding-3-small",
# create the agent with an additional tool
tool_ids=[tool.id],
tool_rules=[
# exit after roll_d20 is called
TerminalToolRule(tool_name=tool.name),
# exit after send_message is called (default behavior)
TerminalToolRule(tool_name="send_message"),
]
)
print(f"Created agent with name {agent_state.name} with tools {[t.name for t in agent_state.tools]}")
# Message an agent
response = client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content="roll a dice",
)
],
)
print("Usage", response.usage)
print("Agent messages", response.messages)
# remove a tool from the agent
client.agents.tools.detach(agent_id=agent_state.id, tool_id=tool.id)
# add a tool to the agent
client.agents.tools.attach(agent_id=agent_state.id, tool_id=tool.id)
client.agents.delete(agent_id=agent_state.id)
# create an agent with only a subset of default tools
send_message_tool = [t for t in client.tools.list() if t.name == "send_message"][0]
agent_state = client.agents.create(
memory_blocks=[
CreateBlock(
label="human",
value="username: sarah",
),
],
model="openai/gpt-4o-mini",
embedding="openai/text-embedding-3-small",
include_base_tools=False,
tool_ids=[tool.id, send_message_tool.id],
)
# message the agent to search archival memory (will be unable to do so)
client.agents.messages.create(
agent_id=agent_state.id,
messages=[
MessageCreate(
role="user",
content="search your archival memory",
)
],
)
print("Usage", response.usage)
print("Agent messages", response.messages)
client.agents.delete(agent_id=agent_state.id)