name	learning-sdk-integration
description	Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK

Learning SDK Integration

Overview

This skill provides universal patterns for adding persistent memory to LLM agents using the Learning SDK through a 3-line integration pattern that works with OpenAI, Anthropic, Gemini, and other LLM providers.

When to Use

Use this skill when:

Building LLM agents that need memory across sessions
Implementing conversation history persistence
Adding context-aware capabilities to existing agents
Creating multi-agent systems with shared memory
Working with any LLM provider (OpenAI, Anthropic, Gemini, etc.)

Core Integration Pattern

Basic 3-Line Integration

from agentic_learning import learning

# Wrap LLM SDK calls to enable memory
with learning(agent="my-agent"):
    response = openai.chat.completions.create(...)

Async Integration

from agentic_learning import learning_async

# For async LLM SDK usage
async with learning_async(agent="my-agent"):
    response = await claude.messages.create(...)

Provider-Specific Examples

OpenAI Integration

from openai import OpenAI
from agentic_learning import learning_async

class MemoryEnhancedOpenAIAgent:
    def __init__(self, api_key: str, agent_name: str):
        self.client = OpenAI(api_key=api_key)
        self.agent_name = agent_name
    
    async def chat(self, message: str, model: str = "gpt-4"):
        async with learning_async(agent=self.agent_name):
            response = await self.client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": message}]
            )
            return response.choices[0].message.content

Claude Integration

from anthropic import Anthropic
from agentic_learning import learning_async

class MemoryEnhancedClaudeAgent:
    def __init__(self, api_key: str, agent_name: str):
        self.client = Anthropic(api_key=api_key)
        self.agent_name = agent_name
    
    async def chat(self, message: str, model: str = "claude-3-5-sonnet-20241022"):
        async with learning_async(agent=self.agent_name):
            response = await self.client.messages.create(
                model=model,
                max_tokens=1000,
                messages=[{"role": "user", "content": message}]
            )
            return response.content[0].text

Gemini Integration

import google.generativeai as genai
from agentic_learning import learning_async

class MemoryEnhancedGeminiAgent:
    def __init__(self, api_key: str, agent_name: str):
        genai.configure(api_key=api_key)
        self.model = genai.GenerativeModel('gemini-pro')
        self.agent_name = agent_name
    
    async def chat(self, message: str):
        async with learning_async(agent=self.agent_name):
            response = await self.model.generate_content_async(message)
            return response.text

PydanticAI Integration

from pydantic_ai import Agent
from agentic_learning import learning

agent = Agent('anthropic:claude-sonnet-4-20250514')

with learning(agent="pydantic-demo"):
    result = agent.run_sync("Hello!")

For detailed patterns including structured output, tool usage, and async examples, see references/pydantic-ai.md.

Advanced Patterns

Memory-Only Mode (Capture Without Injection)

# Use capture_only=True to save conversations without memory injection
async with learning_async(agent="research-agent", capture_only=True):
    # Conversation will be saved but no memory will be retrieved/injected
    response = await llm_call(...)

Custom Memory Blocks

# Define custom memory blocks for specific context
custom_memory = [
    {"label": "project_context", "description": "Current project details"},
    {"label": "user_preferences", "description": "User's working preferences"}
]

async with learning_async(agent="my-agent", memory=custom_memory):
    response = await llm_call(...)

Multi-Agent Memory Sharing

# Multiple agents can share memory by using the same agent name
agent1 = MemoryEnhancedOpenAIAgent(api_key, "shared-agent")
agent2 = MemoryEnhancedClaudeAgent(api_key, "shared-agent")

# Both agents will access the same memory context
response1 = await agent1.chat("Research topic X")
response2 = await agent2.chat("Summarize our research")

Context-Aware Tool Selection

async def context_aware_tool_use():
    async with learning_async(agent="tool-selector"):
        # Memory will help agent choose appropriate tools
        memories = await get_memories("tool-selector")
        
        if "web_search_needed" in str(memories):
            return use_web_search()
        elif "data_analysis" in str(memories):
            return use_data_tools()
        else:
            return use_default_tools()

Best Practices

1. Agent Naming

Use descriptive agent names that reflect their purpose
For related functionality, use consistent naming patterns
Example: email-processor, research-assistant, code-reviewer

2. Memory Structure

# Good: Specific, purposeful memory blocks
memory_blocks = [
    {"label": "conversation_history", "description": "Recent conversation context"},
    {"label": "task_context", "description": "Current task and goals"},
    {"label": "user_preferences", "description": "User interaction preferences"}
]

3. Error Handling

async def robust_llm_call(message: str):
    try:
        async with learning_async(agent="my-agent"):
            return await llm_sdk_call(...)
    except Exception as e:
        # Fallback without memory if learning fails
        return await llm_sdk_call(...)

4. Provider Selection Patterns

def choose_provider(task_type: str, budget: str, latency_requirement: str):
    """Select LLM provider based on task requirements"""
    
    if task_type == "code_generation" and budget == "high":
        return "claude-3-5-sonnet"  # Best for code
    elif task_type == "general_chat" and budget == "low":
        return "gpt-3.5-turbo"  # Cost-effective
    elif latency_requirement == "ultra_low":
        return "gemini-1.5-flash"  # Fastest
    else:
        return "gpt-4"  # Good all-rounder

Memory Management

Retrieving Conversation History

from agentic_learning import AsyncAgenticLearning

async def get_conversation_context(agent_name: str):
    client = AsyncAgenticLearning()
    memories = await client.get_memories(agent_name)
    return memories

Clearing Memory

# When starting fresh contexts
client = AsyncAgenticLearning()
await client.clear_memory(agent_name)

Integration Examples

Universal Research Agent

class UniversalResearchAgent:
    def __init__(self, provider: str, api_key: str):
        self.provider = provider
        self.client = self._initialize_client(provider, api_key)
    
    def _initialize_client(self, provider: str, api_key: str):
        if provider == "openai":
            from openai import OpenAI
            return OpenAI(api_key=api_key)
        elif provider == "claude":
            from anthropic import Anthropic
            return Anthropic(api_key=api_key)
        elif provider == "gemini":
            import google.generativeai as genai
            genai.configure(api_key=api_key)
            return genai.GenerativeModel('gemini-pro')
    
    async def research(self, topic: str):
        async with learning_async(
            agent="universal-researcher",
            memory=[
                {"label": "research_history", "description": "Previous research topics"},
                {"label": "current_session", "description": "Current research session"}
            ]
        ):
            prompt = f"Research the topic: {topic}. Consider previous research context."
            response = await self._make_llm_call(prompt)
            return response

Multi-Provider Code Review Assistant

class CodeReviewAssistant:
    def __init__(self, providers: dict):
        self.providers = providers
        self.clients = {name: self._init_client(name, key) 
                       for name, key in providers.items()}
    
    async def review_with_multiple_perspectives(self, code: str):
        reviews = {}
        
        for provider_name, client in self.clients.items():
            async with learning_async(
                agent=f"code-reviewer-{provider_name}",
                memory=[
                    {"label": "review_history", "description": "Past code reviews"},
                    {"label": "coding_standards", "description": "Project standards"}
                ]
            ):
                prompt = f"Review this code from {provider_name} perspective: {code}"
                reviews[provider_name] = await self._make_llm_call(client, prompt)
        
        # Synthesize multiple perspectives
        return await self._synthesize_reviews(reviews)

Testing Integration

Unit Test Pattern

import pytest
from agentic_learning import learning_async

async def test_memory_integration():
    async with learning_async(agent="test-agent"):
        # Test that memory is working
        response = await llm_sdk_call("Remember this test")
        
        # Verify memory was captured
        client = AsyncAgenticLearning()
        memories = await client.get_memories("test-agent")
        assert len(memories) > 0

@pytest.mark.parametrize("provider", ["openai", "claude", "gemini"])
async def test_provider_memory_integration(provider):
    # Test memory works with each provider
    agent = create_agent(provider, api_key)
    response = await agent.chat("Test message")
    assert response is not None

Troubleshooting

Common Issues

Memory not appearing: Ensure agent name is consistent across calls
Performance issues: Use capture_only=True for logging-only scenarios
Context overflow: Regularly clear memory for long-running sessions
Async conflicts: Always use learning_async with async SDK calls
Provider compatibility: Check SDK version compatibility with Agentic Learning SDK

Debug Mode

# Enable debug logging to see memory operations
import logging
logging.basicConfig(level=logging.DEBUG)

async with learning_async(agent="debug-agent"):
    # Memory operations will be logged
    response = await llm_sdk_call(...)

Install Skill

SKILL.md