Mem0 supports LangChain as a provider to access a wide range of LLM models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various LLM providers through a consistent interface.
For a complete list of available chat models supported by LangChain, refer to the LangChain Chat Models documentation.
Usage
import os
from mem0 import Memory
from langchain_openai import ChatOpenAI
# Set necessary environment variables for your chosen LangChain provider
os.environ["OPENAI_API_KEY"] = "your-api-key"
# Initialize a LangChain model directly
openai_model = ChatOpenAI(
model="gpt-4o",
temperature=0.2,
max_tokens=2000
)
# Pass the initialized model to the config
config = {
"llm": {
"provider": "langchain",
"config": {
"model": openai_model
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
Supported LangChain Providers
LangChain supports a wide range of LLM providers, including:
- OpenAI (
ChatOpenAI
)
- Anthropic (
ChatAnthropic
)
- Google (
ChatGoogleGenerativeAI
, ChatGooglePalm
)
- Mistral (
ChatMistralAI
)
- Ollama (
ChatOllama
)
- Azure OpenAI (
AzureChatOpenAI
)
- HuggingFace (
HuggingFaceChatEndpoint
)
- And many more
You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available providers, refer to the LangChain Chat Models documentation.
Provider-Specific Configuration
When using LangChain as a provider, you’ll need to:
- Set the appropriate environment variables for your chosen LLM provider
- Import and initialize the specific model class you want to use
- Pass the initialized model instance to the config
Make sure to install the necessary LangChain packages and any provider-specific dependencies.
Config
All available parameters for the langchain
config are present in Master List of All Params in Config.