Skip to main content
For the full source code and advanced implementation details, see the official LangChain Integration repository section..

​
Overview

Integrating MemMachine with LangChain implements the BaseMemory interface, allowing you to use MemMachine as a persistent memory backend. This enables:
  • Persistent Memory: Conversations and context persist across multiple sessions.
  • Semantic Search: Retrieve relevant memories based on semantic similarity.
  • Context Scoping: Automatic filtering by user_id, agent_id, and session_id.
  • Episodic & Semantic Memory: Access to both conversation history and extracted knowledge.

​
Configuration

The integration can be managed via environment variables or constructor parameters.
ParameterEnvironment VariableDefaultDescription
base_urlMEMORY_BACKEND_URLhttp://localhost:8080MemMachine server URL
org_idLANGCHAIN_ORG_IDlangchain_orgOrganization identifier
project_idLANGCHAIN_PROJECT_IDlangchain_projectProject identifier
user_idLANGCHAIN_USER_IDNoneScopes memory to a specific user
agent_idLANGCHAIN_AGENT_IDNoneScopes memory to a specific agent
session_idLANGCHAIN_SESSION_IDNoneScopes memory to a specific session
1

Install Dependencies

Install the core LangChain framework along with the MemMachine client:
pip install langchain memmachine-client
2

Initialize MemMachine Memory

Initialize the memory class with your project configuration.
from integrations.langchain.memory import MemMachineMemory

memory = MemMachineMemory(
    base_url="http://localhost:8080",
    org_id="my_org",
    project_id="my_project",
    user_id="user123",
    session_id="session456",
)
3

Integrate with a Chain

Pass the memory instance to your LangChain ConversationChain or LLMChain.
from langchain.llms import OpenAI
from langchain.chains import ConversationChain

llm = OpenAI(temperature=0)

# Create conversation chain with MemMachine memory
chain = ConversationChain(
    llm=llm,
    memory=memory,
    verbose=True,
)

# The chain will now remember context across runs
chain.run("Hello, my name is Alice")

​
Advanced Usage

​
Custom Prompt with Memory Context

You can use a PromptTemplate to explicitly include both conversation history and semantic facts (extracted context). 
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

prompt = PromptTemplate(
    input_variables=["history", "memmachine_context", "input"],
    template="""You are a helpful assistant with access to the user's memory.

Relevant context from memory:
{memmachine_context}

Conversation history:
{history}

User: {input}
Assistant:""",
)

chain = LLMChain(llm=llm, prompt=prompt, memory=memory)

​
Direct Memory Operations

For granular control, you can interface with the underlying memory storage directly. 
# Manually add a specific fact
memory._memory.add(
    content="I prefer working in the morning",
    role="user",
)

# Search memories manually
results = memory.load_memory_variables({"input": "What are my preferences?"})
print(results["memmachine_context"])
Pro Tip: Use the search_limit parameter (default: 10) in the constructor to control the number of memory fragments retrieved during each interaction.

​
Requirements

  • MemMachine Server: Must be reachable at the MEMORY_BACKEND_URL.
  • LLM API Key: An OPENAI_API_KEY (or equivalent) must be configured in your environment or within the MemMachine configuration.yml.
  • Python: 3.10 or higher.
  • Framework: langchain and memmachine.