Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.mellea.ai/llms.txt

Use this file to discover all available pages before exploring further.

m serve runs any Mellea program as an OpenAI-compatible chat endpoint. This lets any LLM client — LangChain, the OpenAI SDK, curl — call your Mellea program as if it were a model. Prerequisites: pip install mellea.

​
The serve() function

Your program must define a serve() function with this signature: 
from cli.serve.models import ChatMessage
from mellea.core import ModelOutputThunk, SamplingResult

def serve(
    input: list[ChatMessage],
    requirements: list[str] | None = None,
    model_options: dict | None = None,
) -> ModelOutputThunk | SamplingResult:
    """Your Mellea program logic here."""
    ...
m serve loads your file, finds serve(), and routes incoming requests to it. ChatMessage has role and content fields matching the OpenAI chat format.

​
Example serve program

import mellea
from cli.serve.models import ChatMessage
from mellea.core import ModelOutputThunk, Requirement, SamplingResult
from mellea.stdlib.context import ChatContext
from mellea.stdlib.requirements import simple_validate
from mellea.stdlib.sampling import RejectionSamplingStrategy

session = mellea.start_session(ctx=ChatContext())

def serve(
    input: list[ChatMessage],
    requirements: list[str] | None = None,
    model_options: dict | None = None,
) -> ModelOutputThunk | SamplingResult:
    """Takes a prompt as input and runs it through a Mellea program."""
    message = input[-1].content
    reqs = [
        Requirement(
            "Keep this under 50 words",
            validation_fn=simple_validate(lambda x: len(x.split()) < 50),
        ),
        *(requirements or []),
    ]
    return session.instruct(
        description=message,
        requirements=reqs,
        strategy=RejectionSamplingStrategy(loop_budget=3),
        model_options=model_options,
    )
The session is initialised at module level so it is reused across requests. This preserves the ChatContext conversation history across turns.

​
Starting m serve

m serve path/to/your_program.py
The server starts on port 8000 by default and exposes:
  • POST /v1/chat/completions — OpenAI-compatible chat completions endpoint
  • GET /health — health check
To see all options: 
m serve --help

​
Calling the served endpoint

Any OpenAI-compatible client works. Using curl: 
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Summarize this in one sentence."}]}'
Using the OpenAI Python SDK: 
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
response = client.chat.completions.create(
    model="mellea",
    messages=[{"role": "user", "content": "Summarize this in one sentence."}],
)
print(response.choices[0].message.content)
Full example: docs/examples/m_serve/m_serve_example_simple.py
See also: Context and Sessions | Backends and Configuration