LangChain Integration

StateBase integrates seamlessly with LangChain to provide production-grade memory and state management for your LangChain agents.

Why StateBase + LangChain?

LangChain’s built-in memory (e.g., ConversationBufferMemory) is ephemeral and not production-ready:

Feature	LangChain Memory	StateBase
Persistence	In-memory only	PostgreSQL + Redis
Rollback	❌ Not supported	✅ Time-travel to any turn
Audit Trail	❌ No logging	✅ Complete trace history
Multi-Session	❌ Separate instances	✅ Unified memory across sessions
Semantic Search	❌ Basic retrieval	✅ Vector-based memory search

Installation

pip install langchain statebase

Basic Integration

Replace LangChain’s memory with StateBase:

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, AIMessage
from statebase import StateBase

# Initialize StateBase
sb = StateBase(api_key="your-key")

# Create session
session = sb.sessions.create(
    agent_id="langchain-agent",
    user_id="user_123"
)

# Initialize LangChain LLM
llm = ChatOpenAI(model="gpt-4")

def chat(user_message):
    # 1. Get conversation history from StateBase
    context = sb.sessions.get_context(
        session_id=session.id,
        turn_limit=10
    )
    
    # 2. Convert to LangChain message format
    messages = []
    for turn in context["recent_turns"]:
        messages.append(HumanMessage(content=turn["input"]["content"]))
        messages.append(AIMessage(content=turn["output"]["content"]))
    
    # 3. Add current message
    messages.append(HumanMessage(content=user_message))
    
    # 4. Generate response
    response = llm(messages)
    
    # 5. Log turn in StateBase
    sb.sessions.add_turn(
        session_id=session.id,
        input=user_message,
        output=response.content
    )
    
    return response.content

# Usage
print(chat("What's the capital of France?"))
# "The capital of France is Paris."

print(chat("What's the population?"))
# "Paris has a population of approximately 2.2 million people."
# ✅ StateBase remembers "it" refers to Paris

Custom Memory Class

Create a LangChain-compatible memory class:

from langchain.memory import BaseChatMemory
from langchain.schema import BaseMessage, HumanMessage, AIMessage
from statebase import StateBase

class StateBaseMemory(BaseChatMemory):
    """LangChain memory backed by StateBase"""
    
    session_id: str
    sb: StateBase
    turn_limit: int = 10
    
    def __init__(self, session_id: str, api_key: str, turn_limit: int = 10):
        super().__init__()
        self.session_id = session_id
        self.sb = StateBase(api_key=api_key)
        self.turn_limit = turn_limit
    
    @property
    def memory_variables(self):
        return ["history"]
    
    def load_memory_variables(self, inputs):
        """Load conversation history from StateBase"""
        context = self.sb.sessions.get_context(
            session_id=self.session_id,
            turn_limit=self.turn_limit
        )
        
        # Convert to LangChain messages
        messages = []
        for turn in context["recent_turns"]:
            messages.append(HumanMessage(content=turn["input"]["content"]))
            messages.append(AIMessage(content=turn["output"]["content"]))
        
        return {"history": messages}
    
    def save_context(self, inputs, outputs):
        """Save conversation turn to StateBase"""
        self.sb.sessions.add_turn(
            session_id=self.session_id,
            input=inputs.get("input", ""),
            output=outputs.get("output", "")
        )
    
    def clear(self):
        """Clear memory (not recommended with StateBase)"""
        # StateBase keeps history for audit - don't actually delete
        pass

# Usage with LangChain chains
from langchain.chains import ConversationChain

memory = StateBaseMemory(
    session_id=session.id,
    api_key="your-key"
)

chain = ConversationChain(
    llm=ChatOpenAI(),
    memory=memory
)

response = chain.run("What's the weather in SF?")

With LangChain Agents

Use StateBase with LangChain’s agent framework:

from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from statebase import StateBase

sb = StateBase(api_key="your-key")

# Define tools
tools = [
    Tool(
        name="Calculator",
        func=lambda x: eval(x),
        description="Useful for math calculations"
    ),
    Tool(
        name="Weather",
        func=lambda x: f"The weather in {x} is sunny",
        description="Get weather for a city"
    )
]

# Create session
session = sb.sessions.create(agent_id="langchain-tool-agent")

# Initialize agent
agent = initialize_agent(
    tools=tools,
    llm=ChatOpenAI(model="gpt-4"),
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    memory=StateBaseMemory(session_id=session.id, api_key="your-key"),
    verbose=True
)

# Run agent
response = agent.run("What's 25 * 4?")
print(response)

# Check StateBase for full trace
turns = sb.sessions.list_turns(session_id=session.id)
for turn in turns:
    print(f"Input: {turn.input}")
    print(f"Output: {turn.output}")
    print(f"Metadata: {turn.metadata}")

Checkpointing Tool Calls

Checkpoint before expensive tool calls:

from langchain.tools import BaseTool

class StateBaseCheckpointTool(BaseTool):
    """Tool wrapper that checkpoints before execution"""
    
    name: str
    description: str
    func: callable
    session_id: str
    sb: StateBase
    
    def _run(self, query: str) -> str:
        # Checkpoint before tool call
        self.sb.sessions.update_state(
            session_id=self.session_id,
            state={"pending_tool": self.name, "tool_input": query},
            reasoning=f"About to call {self.name}"
        )
        
        try:
            # Execute tool
            result = self.func(query)
            
            # Checkpoint after success
            self.sb.sessions.update_state(
                session_id=self.session_id,
                state={"last_tool_result": result},
                reasoning=f"Tool {self.name} succeeded"
            )
            
            return result
            
        except Exception as e:
            # Roll back on failure
            self.sb.sessions.rollback(session_id=self.session_id, version=-1)
            raise

# Usage
weather_tool = StateBaseCheckpointTool(
    name="Weather",
    description="Get weather for a city",
    func=get_weather_api,
    session_id=session.id,
    sb=sb
)

Semantic Memory with LangChain

Combine LangChain’s retrieval with StateBase’s memory:

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from statebase import StateBase

sb = StateBase(api_key="your-key")
session = sb.sessions.create(agent_id="rag-agent")

# LangChain vector store for documents
vectorstore = Chroma(
    embedding_function=OpenAIEmbeddings(),
    persist_directory="./chroma_db"
)

def rag_with_statebase(question):
    # 1. Get user's long-term memories from StateBase
    user_memories = sb.memory.search(
        query=question,
        session_id=session.id,
        limit=3
    )
    
    # 2. Get relevant documents from LangChain vectorstore
    docs = vectorstore.similarity_search(question, k=3)
    
    # 3. Combine both contexts
    context = {
        "user_memories": [m.content for m in user_memories],
        "documents": [doc.page_content for doc in docs]
    }
    
    # 4. Generate answer
    response = llm.generate(
        prompt=f"""
        User memories: {context['user_memories']}
        Documents: {context['documents']}
        Question: {question}
        
        Answer based on both user memories and documents.
        """
    )
    
    # 5. Log in StateBase
    sb.sessions.add_turn(
        session_id=session.id,
        input=question,
        output=response,
        metadata={
            "retrieved_docs": len(docs),
            "retrieved_memories": len(user_memories)
        }
    )
    
    return response

Migration from LangChain Memory

Migrate existing LangChain memory to StateBase:

from langchain.memory import ConversationBufferMemory

# Old LangChain memory
old_memory = ConversationBufferMemory()
old_memory.save_context(
    {"input": "Hi"},
    {"output": "Hello!"}
)

# Migrate to StateBase
sb = StateBase(api_key="your-key")
session = sb.sessions.create(agent_id="migrated-agent")

# Extract messages from LangChain memory
messages = old_memory.chat_memory.messages

# Import into StateBase
for i in range(0, len(messages), 2):
    if i + 1 < len(messages):
        sb.sessions.add_turn(
            session_id=session.id,
            input=messages[i].content,
            output=messages[i+1].content
        )

print(f"Migrated {len(messages)//2} turns to StateBase")

Best Practices

✅ Do This

Use StateBase for production (LangChain memory is for prototyping)
Checkpoint before tool calls (enables retry without re-execution)
Store user preferences in StateBase memory (cross-session persistence)
Log all agent actions (full audit trail)

❌ Avoid This

Don’t use ConversationBufferMemory in production (data loss risk)
Don’t mix memory backends (pick StateBase or LangChain, not both)
Don’t skip turn logging (you’ll need it for debugging)

Complete Example

from langchain.agents import initialize_agent, Tool, AgentType
from langchain.chat_models import ChatOpenAI
from statebase import StateBase

# Initialize StateBase
sb = StateBase(api_key="your-key")
session = sb.sessions.create(
    agent_id="production-langchain-agent",
    user_id="user_123"
)

# Define tools with checkpointing
def weather_with_checkpoint(city):
    sb.sessions.update_state(
        session_id=session.id,
        state={"pending_tool": "weather", "city": city},
        reasoning="Calling weather API"
    )
    
    try:
        result = get_weather(city)
        sb.sessions.update_state(
            session_id=session.id,
            state={"last_weather": result},
            reasoning="Weather API succeeded"
        )
        return result
    except Exception as e:
        sb.sessions.rollback(session_id=session.id, version=-1)
        return f"Weather unavailable: {e}"

tools = [
    Tool(
        name="Weather",
        func=weather_with_checkpoint,
        description="Get current weather for a city"
    )
]

# Create agent with StateBase memory
agent = initialize_agent(
    tools=tools,
    llm=ChatOpenAI(model="gpt-4"),
    agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
    memory=StateBaseMemory(session_id=session.id, api_key="your-key"),
    verbose=True
)

# Run agent
response = agent.run("What's the weather in Paris?")
print(response)

# Later: Analyze agent behavior
turns = sb.sessions.list_turns(session_id=session.id)
for turn in turns:
    print(f"Tool used: {turn.metadata.get('tool_name')}")
    print(f"Success: {turn.metadata.get('success')}")

Next Steps

LangGraph Integration: Use StateBase with LangGraph
Tool Calling Pattern: Advanced tool usage
Migration Guide: Detailed migration steps

Key Takeaway: LangChain is great for prototyping. StateBase makes it production-ready.

Getting Started

Core Concepts

Live Demos

API Reference

Agent Patterns

Integrations

SDKs

Production Playbook

Security & Compliance

Templates & Examples

LangChain Integration

LangChain Integration

Why StateBase + LangChain?

Installation

Basic Integration

Custom Memory Class

With LangChain Agents

Checkpointing Tool Calls

Semantic Memory with LangChain

Migration from LangChain Memory

Best Practices

✅ Do This

❌ Avoid This

Complete Example

Next Steps

​LangChain Integration

​Why StateBase + LangChain?

​Installation

​Basic Integration

​Custom Memory Class

​With LangChain Agents

​Checkpointing Tool Calls

​Semantic Memory with LangChain

​Migration from LangChain Memory

​Best Practices

​✅ Do This

​❌ Avoid This

​Complete Example

​Next Steps

LangChain Integration

Why StateBase + LangChain?

Installation

Basic Integration

Custom Memory Class

With LangChain Agents

Checkpointing Tool Calls

Semantic Memory with LangChain

Migration from LangChain Memory

Best Practices

✅ Do This

❌ Avoid This

Complete Example

Next Steps