feat: add e2e example scripts for documentation (#1995)

examples/docs/agent_advanced.py

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+from letta import ChatMemory, EmbeddingConfig, LLMConfig, create_client
+from letta.prompts import gpt_system
+
+client = create_client()
+
+# create a new agent
+agent_state = client.create_agent(
+    name="agent_name",
+    memory=ChatMemory(human="Name: Sarah", persona="You are a helpful assistant that loves emojis"),
+    llm_config=LLMConfig(
+        model="gpt-4",
+        model_endpoint_type="openai",
+        model_endpoint="https://api.openai.com/v1",
+        context_window=8000,
+    ),
+    embedding_config=EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    ),
+    system=gpt_system.get_system_text("memgpt_chat"),
+    tools=[],
+    include_base_tools=True,
+)
+print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
+
+# message an agent as a user
+response = client.send_message(agent_id=agent_state.id, role="user", message="hello")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# message a system message (non-user)
+response = client.send_message(agent_id=agent_state.id, role="system", message="[system] user has logged in. send a friendly message.")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# delete the agent
+client.delete_agent(agent_id=agent_state.id)

examples/docs/agent_basic.py

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+from letta import EmbeddingConfig, LLMConfig, create_client
+
+client = create_client()
+
+# set automatic defaults for LLM/embedding config
+client.set_default_llm_config(
+    LLMConfig(model="gpt-4o-mini", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=128000)
+)
+client.set_default_embedding_config(
+    EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    )
+)
+
+
+# create a new agent
+agent_state = client.create_agent()
+print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
+
+# Message an agent
+response = client.send_message(agent_id=agent_state.id, role="user", message="hello")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# list all agents
+agents = client.list_agents()
+
+# get the agent by ID
+agent_state = client.get_agent(agent_id=agent_state.id)
+
+# get the agent by name
+agent_id = client.get_agent_id(agent_name=agent_state.name)
+agent_state = client.get_agent(agent_id=agent_id)
+
+# delete an agent
+client.delete_agent(agent_id=agent_state.id)

examples/docs/rest_client.py

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+from letta import create_client
+from letta.schemas.memory import ChatMemory
+
+"""
+Make sure you run the Letta server before running this example.
+```
+letta server
+```
+"""
+
+
+def main():
+    # Connect to the server as a user
+    client = create_client(base_url="http://localhost:8283")
+
+    # list available configs on the server
+    llm_configs = client.list_llm_configs()
+    print(f"Available LLM configs: {llm_configs}")
+    embedding_configs = client.list_embedding_configs()
+    print(f"Available embedding configs: {embedding_configs}")
+
+    # Create an agent
+    agent_state = client.create_agent(
+        name="my_agent",
+        memory=ChatMemory(human="My name is Sarah.", persona="I am a friendly AI."),
+        embedding_config=embedding_configs[0],
+        llm_config=llm_configs[0],
+    )
+    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.user_message(agent_id=agent_state.id, message="Whats my name?")
+    print(f"Recieved response:", response.messages)
+
+    # Delete agent
+    client.delete_agent(agent_id=agent_state.id)
+    print(f"Deleted agent: {agent_state.name} with ID {str(agent_state.id)}")
+
+
+if __name__ == "__main__":
+    main()

examples/docs/tools.py

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+from letta import EmbeddingConfig, LLMConfig, create_client
+
+client = create_client()
+# set automatic defaults for LLM/embedding config
+client.set_default_llm_config(
+    LLMConfig(model="gpt-4", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=8000)
+)
+client.set_default_embedding_config(
+    EmbeddingConfig(
+        embedding_endpoint_type="openai",
+        embedding_endpoint="https://api.openai.com/v1",
+        embedding_model="text-embedding-ada-002",
+        embedding_dim=1536,
+        embedding_chunk_size=300,
+    )
+)
+
+
+# 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
+
+
+tool = client.create_tool(roll_d20, name="roll_dice")
+
+# create a new agent
+agent_state = client.create_agent(tools=[tool.name])
+print(f"Created agent with name {agent_state.name} with tools {agent_state.tools}")
+
+# Message an agent
+response = client.send_message(agent_id=agent_state.id, role="user", message="roll a dice")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+# remove a tool from the agent
+client.remove_tool_from_agent(agent_id=agent_state.id, tool_id=tool.id)
+
+# add a tool to the agent
+client.add_tool_to_agent(agent_id=agent_state.id, tool_id=tool.id)
+
+client.delete_agent(agent_id=agent_state.id)
+
+# create an agent with only a subset of default tools
+agent_state = client.create_agent(include_base_tools=False, tools=[tool.name, "send_message"])
+
+# message the agent to search archival memory (will be unable to do so)
+response = client.send_message(agent_id=agent_state.id, role="user", message="search your archival memory")
+print("Usage", response.usage)
+print("Agent messages", response.messages)
+
+client.delete_agent(agent_id=agent_state.id)