Letta Integration

Deploy Letta memory-enabled agents with RunAgent

Prerequisites

Basic understanding of Letta
Completed Deploy Your First Agent tutorial
Python 3.8 or higher

Overview

Letta is a framework for building conversational AI agents with persistent memory and context awareness. RunAgent makes it easy to deploy Letta agents and access them from any programming language while maintaining conversation state.

Installation & Setup

1. Install Letta Server

pip install -U letta

2. Set Environment Variables

Letta requires API keys for LLM providers. Set them before starting the server:

export OPENAI_API_KEY=your_openai_api_key_here

3. Start Letta Server

The Letta server must be running before deploying RunAgent agents:

letta server

The server will start on http://localhost:8283. Keep this terminal window open. Note: If you need to use a different port, you can specify it with:

letta server --port 8284

4. Install Letta Client (Optional)

For Python SDK usage, install the client package:

pip install letta-client

Quick Start with RunAgent

1. Create a Letta Agent Project

runagent init my-letta-agent --framework letta
cd my-letta-agent

2. Install Dependencies

pip install -r requirements.txt

The generated requirements.txt will include:

letta-client
python-dotenv

3. Configure Environment

Create a .env file in your project directory:

OPENAI_API_KEY=your_openai_api_key_here
LETTA_SERVER_URL=http://localhost:8283

4. Review Configuration

The generated runagent.config.json will be pre-configured for Letta:

{
  "agent_name": "my-letta-agent",
  "description": "Letta conversational agent with memory",
  "framework": "letta",
  "version": "1.0.0",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "agent.py",
        "module": "letta_run",
        "tag": "chat"
      },
      {
        "file": "agent.py",
        "module": "letta_run_stream",
        "tag": "chat_stream"
      }
    ]
  }
}

Basic Letta Agent

Here’s a simple Letta agent demonstrating conversational capabilities:

# agent.py
import os
from dotenv import load_dotenv
from letta_client import CreateBlock, Letta

# Load environment variables
load_dotenv()


def _extract_message_from_input(*input_args, **input_kwargs) -> str:
    """Extract message text from various input formats"""
    # Try direct message parameter
    if input_kwargs.get("message"):
        return str(input_kwargs["message"])
    
    # Try messages list format
    if input_kwargs.get("messages"):
        messages = input_kwargs["messages"]
        if isinstance(messages, list) and messages:
            last_message = messages[-1]
            if isinstance(last_message, dict) and "content" in last_message:
                return last_message["content"]
    
    # Try first positional argument as string
    if input_args and isinstance(input_args[0], str):
        return input_args[0]
    
    # Try first positional argument as dict with messages
    if input_args and isinstance(input_args[0], dict):
        data = input_args[0]
        if "messages" in data:
            messages = data["messages"]
            if isinstance(messages, list) and messages:
                last_message = messages[-1]
                if isinstance(last_message, dict) and "content" in last_message:
                    return last_message["content"]
        if "message" in data:
            return str(data["message"])
    
    return "Hello"


def letta_run(*input_args, **input_kwargs):
    """Main entrypoint for the Letta agent"""
    try:
        # Initialize Letta client
        letta_url = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
        client = Letta(base_url=letta_url)
        
        # Create memory blocks
        memory_blocks = [
            CreateBlock(
                label="human",
                value="You are talking to a user through RunAgent framework",
            ),
            CreateBlock(
                label="persona",
                value="You are a helpful AI assistant. Be friendly and concise.",
            ),
        ]

        # Create agent
        agent = client.agents.create(
            name=f"runagent-letta-{os.getpid()}",
            memory_blocks=memory_blocks,
            system="You are a helpful AI assistant integrated with RunAgent. Respond naturally and helpfully.",
            model="openai/gpt-4o-mini",
            embedding="openai/text-embedding-ada-002",
            include_base_tools=True
        )

        print(f"✅ Letta agent created with ID: {agent.id}")
        
        # Extract message from input
        message = _extract_message_from_input(*input_args, **input_kwargs)
        
        # Send message to Letta agent
        response = client.agents.messages.create(
            agent_id=agent.id,
            messages=[{
                "role": "user",
                "content": message
            }]
        )
        
        # Clean up agent after use
        try:
            client.agents.delete(agent.id)
            print(f"🗑️ Cleaned up agent: {agent.id}")
        except:
            pass
        
        return response
        
    except Exception as e:
        return {"error": f"Letta execution error: {str(e)}"}


def letta_run_stream(*input_args, **input_kwargs):
    """Streaming entrypoint for the Letta agent"""
    try:
        # Initialize Letta client
        letta_url = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
        client = Letta(base_url=letta_url)
        
        # Create memory blocks
        memory_blocks = [
            CreateBlock(
                label="human",
                value="You are talking to a user through RunAgent framework",
            ),
            CreateBlock(
                label="persona",
                value="You are a helpful AI assistant. Be friendly and concise.",
            ),
        ]

        # Create agent
        agent = client.agents.create(
            name=f"runagent-letta-stream-{os.getpid()}",
            memory_blocks=memory_blocks,
            system="You are a helpful AI assistant integrated with RunAgent. Respond naturally and helpfully.",
            model="openai/gpt-4o-mini",
            embedding="openai/text-embedding-ada-002",
            include_base_tools=True
        )

        print(f"✅ Letta streaming agent created with ID: {agent.id}")
        
        # Extract message from input
        message = _extract_message_from_input(*input_args, **input_kwargs)
        
        # Create streaming response
        stream = client.agents.messages.create_stream(
            agent_id=agent.id,
            messages=[{
                "role": "user",
                "content": message
            }],
            stream_tokens=True,
        )
        
        # Yield chunks
        for chunk in stream:
            yield chunk
        
        # Clean up agent after streaming completes
        try:
            client.agents.delete(agent.id)
            print(f"🗑️ Cleaned up streaming agent: {agent.id}")
        except:
            pass
            
    except Exception as e:
        yield {"error": f"Letta streaming error: {str(e)}"}

Advanced: Letta Agent with Custom Tools

Here’s an example with custom tools (keyword extraction, RAG):

# agent.py (Advanced with Tools)
import os
from typing import Dict
from dotenv import load_dotenv
from letta_client import CreateBlock, Letta

# Load environment variables
load_dotenv()


def _register_tools(client: Letta) -> Dict[str, str]:
    """Register custom tools with Letta client"""
    from keyword_tool import extract_keywords
    from rag_tool import rag_tool
    
    tool_fns = [extract_keywords, rag_tool]
    tool_map: Dict[str, str] = {}
    
    for fn in tool_fns:
        try:
            print(f"  📝 Registering {fn.__name__}...")
            tool = client.tools.upsert_from_function(func=fn)
            tool_map[tool.name] = tool.id
            print(f"  ✅ Successfully registered: {tool.name}")
        except Exception as e:
            print(f"  ❌ Failed to register tool {fn.__name__}: {e}")
    
    return tool_map


def letta_run(message: str):
    """Advanced Letta agent with custom tools"""
    try:
        # Initialize Letta client
        letta_url = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
        client = Letta(base_url=letta_url)
        
        # Register tools
        print("🔧 Registering tools...")
        tool_map = _register_tools(client)
        
        # Create memory blocks
        memory_blocks = [
            CreateBlock(
                label="human",
                value="You are talking to a user through RunAgent framework",
            ),
            CreateBlock(
                label="persona",
                value="You are an expert assistant with access to research tools.",
            ),
        ]

        # Create agent with tools
        agent = client.agents.create(
            name=f"runagent-letta-tools-{os.getpid()}",
            memory_blocks=memory_blocks,
            system="You are an expert assistant with access to keyword extraction and RAG tools. When users ask for research or information, use the rag_tool. When they need keywords extracted, use extract_keywords.",
            model="openai/gpt-4o-mini",
            embedding="openai/text-embedding-ada-002",
            tool_ids=list(tool_map.values()),
            include_base_tools=True
        )

        print(f"✅ Letta agent created with {len(tool_map)} tools")
        
        # Send message to Letta agent
        response = client.agents.messages.create(
            agent_id=agent.id,
            messages=[{
                "role": "user",
                "content": message
            }]
        )
        
        # Clean up
        try:
            client.agents.delete(agent.id)
        except:
            pass
        
        return response
        
    except Exception as e:
        return {"error": f"Letta execution error: {str(e)}"}

Custom Tool Example: Keyword Extraction

# keyword_tool.py
def extract_keywords(text: str, num_keywords: int = 5) -> dict:
    """
    Extract keywords from input text.

    Args:
        text (str): The input text to extract keywords from
        num_keywords (int): Number of keywords to extract (default: 5)

    Returns:
        dict: Dictionary containing the extracted keywords
    """
    from langchain_openai import ChatOpenAI
    from langchain.prompts import PromptTemplate

    try:
        # Initialize the language model
        llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0.2)

        # Create prompt template
        prompt_template = """
        Extract exactly {num_keywords} relevant keywords from the following text. 
        Return them as a comma-separated list without numbering or bullet points.
        Only include the keywords, no additional information or explanation.

        Text: {input_text}

        Keywords:
        """

        prompt = PromptTemplate(
            input_variables=["input_text", "num_keywords"],
            template=prompt_template
        )
        
        # Create chain
        chain = prompt | llm

        # Run the chain
        result = chain.invoke({"input_text": text, "num_keywords": num_keywords})

        # Extract content
        content = result.content if hasattr(result, 'content') else str(result)
        keywords = [kw.strip() for kw in content.split(',')]

        return {
            "status": "success",
            "keywords": keywords,
            "message": f"Successfully extracted {len(keywords)} keywords."
        }

    except Exception as e:
        return {
            "status": "error",
            "keywords": [],
            "message": f"Error extracting keywords: {str(e)}"
        }

Testing Your Letta Agent

Python Client

# test_letta.py
from runagent import RunAgentClient

# Ensure Letta server is running on port 8283
client = RunAgentClient(
    agent_id="your_agent_id_here",
    entrypoint_tag="chat",
    local=True
)

# Test basic conversation
result = client.run(message="Hello, I'm interested in AI agents")
print(f"Response: {result}")

# Test with message dict format
result2 = client.run({
    "messages": [
        {"role": "user", "content": "What can you help me with?"}
    ]
})
print(f"Response 2: {result2}")

JavaScript Client

// test_letta.js
import { RunAgentClient } from 'runagent';

const client = new RunAgentClient({
    agentId: 'your_agent_id_here',
    entrypointTag: 'chat',
    local: true
});

await client.initialize();

// Test conversation
const result = await client.run({
    message: 'Tell me about Letta agents'
});

console.log('Response:', result);

Streaming Example

# test_letta_stream.py
from runagent import RunAgentClient

stream_client = RunAgentClient(
    agent_id="your_agent_id_here",
    entrypoint_tag="chat_stream",
    local=True
)

print("Streaming conversation:")
for chunk in stream_client.run(message="Explain the benefits of AI memory"):
    print(chunk, end="", flush=True)

Project Structure

my-letta-agent/
├── agent.py                 # Main agent code
├── keyword_tool.py          # Optional: Custom keyword tool
├── rag_tool.py             # Optional: Custom RAG tool
├── .env                    # Environment variables
├── requirements.txt        # Python dependencies
└── runagent.config.json    # RunAgent configuration

Configuration Options

Multiple Entrypoints

You can expose multiple entrypoints for different use cases:

{
  "agent_name": "advanced-letta-agent",
  "description": "Advanced Letta multi-entrypoint system",
  "framework": "letta",
  "agent_architecture": {
    "entrypoints": [
      {
        "file": "agent.py",
        "module": "letta_run",
        "tag": "chat"
      },
      {
        "file": "agent.py",
        "module": "letta_run_stream",
        "tag": "chat_stream"
      },
      {
        "file": "agent.py",
        "module": "letta_run_with_tools",
        "tag": "research"
      }
    ]
  }
}

Best Practices

1. Memory Management

Use appropriate memory blocks for context
Clean up agents after use to prevent memory leaks
Consider session-based memory for multi-user scenarios

2. Tool Design

Create focused, single-purpose tools
Provide clear docstrings for tool functions
Handle tool errors gracefully with try-catch blocks
Return structured dictionaries from tools

3. Error Handling

Always wrap Letta operations in try-catch blocks
Return meaningful error messages to users
Log errors for debugging purposes

4. Server Management

Ensure Letta server is running before deploying agents
Use environment variables for server URL configuration
Monitor server logs for debugging

5. Agent Cleanup

Delete temporary agents after use
Implement cleanup in both success and error paths
Use context managers for resource management

Common Patterns

Conversational Memory

Use Letta’s memory blocks to maintain context across interactions:

memory_blocks = [
    CreateBlock(
        label="human",
        value="User preferences: prefers technical explanations, interested in AI"
    ),
    CreateBlock(
        label="persona",
        value="You are a technical AI expert. Provide detailed, accurate information."
    ),
]

Tool Integration

def _register_tools(client: Letta) -> Dict[str, str]:
    tool_fns = [search_tool, calculate_tool, analyze_tool]
    tool_map = {}
    
    for fn in tool_fns:
        tool = client.tools.upsert_from_function(func=fn)
        tool_map[tool.name] = tool.id
    
    return tool_map

Session Management

For multi-user scenarios, create agents per session:

def create_session_agent(user_id: str, session_id: str):
    agent = client.agents.create(
        name=f"user-{user_id}-session-{session_id}",
        memory_blocks=load_user_memory(user_id),
        # ... other configuration
    )
    return agent

Troubleshooting

Common Issues

1. Connection Error: “Cannot connect to Letta server”

Solution: Ensure Letta server is running (letta server)
Check the server URL in your .env file
Verify the server is accessible at http://localhost:8283

2. API Key Error

Solution: Set OPENAI_API_KEY in your environment
Verify the key is valid and has sufficient credits
Check that the key is loaded in the Letta server environment

3. Tool Registration Fails

Solution: Ensure tool functions have proper docstrings
Check that tool function signatures are compatible with Letta
Verify all tool dependencies are installed

4. Memory Persistence Issues

Solution: Agents are ephemeral by default in this setup
Implement custom session storage if needed
Use Letta’s built-in persistence features for production

5. Streaming Not Working

Solution: Ensure you’re using create_stream method
Check that stream_tokens=True is set
Verify the entrypoint is correctly configured for streaming

Debug Tips

Enable verbose logging:

import logging
logging.basicConfig(level=logging.DEBUG)

def letta_run(message: str):
    print(f"Debug: Processing message: {message}")
    # ... rest of code

Test Letta server connection:

from letta_client import Letta

try:
    client = Letta(base_url="http://localhost:8283")
    print("✅ Successfully connected to Letta server")
except Exception as e:
    print(f"❌ Connection failed: {e}")

Performance Optimization

1. Connection Pooling

Reuse Letta client connections when possible:

# Initialize once
_letta_client = None

def get_letta_client():
    global _letta_client
    if _letta_client is None:
        _letta_client = Letta(base_url=os.getenv("LETTA_SERVER_URL"))
    return _letta_client

2. Tool Caching

_tool_registry = None

def get_tools(client: Letta):
    global _tool_registry
    if _tool_registry is None:
        _tool_registry = _register_tools(client)
    return _tool_registry

3. Memory Management

Clean up agents promptly:

finally:
    try:
        client.agents.delete(agent.id)
    except:
        pass

Next Steps

Advanced Patterns - Learn advanced Letta patterns
Production Deployment - Deploy to production
Multi-Language Access - Access from different languages
Performance Tuning - Optimize for production

Additional Resources

🎉 Great work! You’ve learned how to deploy Letta memory-enabled agents with RunAgent. Letta’s conversational memory capabilities combined with RunAgent’s multi-language access create powerful, context-aware AI systems!

​Letta Integration

​Prerequisites

​Overview

​Installation & Setup

​1. Install Letta Server

​2. Set Environment Variables

​3. Start Letta Server

​4. Install Letta Client (Optional)

​Quick Start with RunAgent

​1. Create a Letta Agent Project

​2. Install Dependencies

​3. Configure Environment

​4. Review Configuration

​Basic Letta Agent

​Advanced: Letta Agent with Custom Tools

​Custom Tool Example: Keyword Extraction

​Testing Your Letta Agent

​Python Client

​JavaScript Client

​Streaming Example

​Project Structure

​Configuration Options

​Multiple Entrypoints

​Best Practices

​1. Memory Management

​2. Tool Design

​3. Error Handling

​4. Server Management

​5. Agent Cleanup

​Common Patterns

​Conversational Memory

​Tool Integration

​Session Management

​Troubleshooting

​Common Issues

​Debug Tips

​Performance Optimization

​1. Connection Pooling

​2. Tool Caching

​3. Memory Management

​Next Steps

​Additional Resources