LangChain

Mellea integrates with LangChain in two ways:

1. Tool bridging — wrap existing LangChain tools as MelleaTool objects and pass them to any MelleaSession call.
2. Message history — seed a Mellea ChatContext with conversation history from a LangChain session.

Using LangChain tools

Prerequisites: pip install langchain-core (or pip install langchain-community for community tools).

MelleaTool.from_langchain() wraps any LangChain BaseTool so it can be passed to instruct() or chat() via ModelOption.TOOLS:

# Requires: langchain-community
# Returns: ModelOutputThunk
from mellea import start_session
from mellea.backends import ModelOption
from mellea.backends.tools import MelleaTool

# Import any LangChain BaseTool subclass
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

# Wrap for use in Mellea
wiki = MelleaTool.from_langchain(WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper()))

m = start_session()
result = m.instruct(
    "What year was the Eiffel Tower completed? Use the Wikipedia tool.",
    model_options={ModelOption.TOOLS: [wiki]},
    tool_calls=True,
)

print(result)

# The model chose to call a tool — execute it
if result.tool_calls:
    tool_output = result.tool_calls[wiki.name].call_func()
    print(tool_output)

from_langchain() reads the tool's name and schema directly from the BaseTool instance, so any tool that follows the LangChain BaseTool interface works without further configuration.

Backend note: Tool calling requires a backend and model that support function calling (e.g., Ollama with granite4.1:3b, OpenAI with gpt-4o). The default Ollama setup supports this.

Seeding a session with LangChain message history

When migrating from LangChain or building a system that spans both libraries, you may want to start a Mellea session from an existing LangChain conversation. Mellea uses explicit ChatContext objects; the bridge is to convert LangChain messages to OpenAI format first, then build the context:

# Requires: langchain-core, mellea[ollama]
# Returns: str
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.messages import convert_to_openai_messages

from mellea import start_session
from mellea.stdlib.components import Message
from mellea.stdlib.context import ChatContext

# Existing LangChain conversation history
lc_messages = [
    SystemMessage(content="You are a helpful assistant"),
    HumanMessage(content="Hello!"),
    AIMessage(content="Hi there!"),
]

# 1. Convert to OpenAI format (a common interchange)
openai_messages = convert_to_openai_messages(messages=lc_messages)

# 2. Build a Mellea ChatContext from the converted messages
ctx = ChatContext()
for msg in openai_messages:
    # NOTE: if messages contain images or documents, extract those fields too
    ctx = ctx.add(Message(role=msg["role"], content=msg["content"]))

# 3. Continue the conversation in Mellea
m = start_session(ctx=ctx)
response = m.chat("What exact words did the AI assistant use in its most recent response?")
print(str(response))
# Output will vary — LLM responses depend on model and temperature.
# Expected: the model reports back "Hi there!" from the seeded context

convert_to_openai_messages normalises all LangChain message subtypes (system, human, AI, tool) into {"role": ..., "content": ...} dicts. Any library that exports to OpenAI chat format — LlamaIndex, Haystack, Semantic Kernel — works with the same pattern.

Full example: docs/examples/library_interop/langchain_messages.py

Which approach to use

Scenario	Use
Your tool exists as a LangChain BaseTool	MelleaTool.from_langchain(tool)
Your tool exists as a smolagents Tool	MelleaTool.from_smolagents(tool)
You have a plain Python function to expose	@tool decorator
You have LangChain message history to continue	convert_to_openai_messages → ChatContext
You want Mellea as an OpenAI endpoint for another framework	m serve

---

See also: Tools and Agents | Context and Sessions