Mellea — build predictable AI without guesswork
Inside every AI-powered pipeline, the unreliable part is the same: the LLM call itself.
Silent failures, untestable outputs, no guarantees.
Mellea is a Python library for writing generative programs — replacing brittle prompts and flaky agents
with structured, testable AI workflows built around type-annotated outputs, verifiable requirements, and automatic retries.
Install
uv pip install mellea
See installation docs for additional options, such as installing all extras via uv pip install 'mellea[all]'.
For source installation directly from this repo, see CONTRIBUTING.md.
Example
The @generative decorator turns a typed Python function into a structured LLM call.
Docstrings become prompts, type hints become schemas — no templates, no parsers:
from pydantic import BaseModel
from mellea import generative, start_session
class UserProfile(BaseModel):
name: str
age: int
@generative
def extract_user(text: str) -> UserProfile:
"""Extract the user's name and age from the text."""
m = start_session()
user = extract_user(m, text="User log 42: Alice is 31 years old.")
print(user.name) # Alice
print(user.age) # 31 — always an int, guaranteed by the schema
start_session() is the convenience entry point: it returns a MelleaSession
with sensible defaults you can override as needed.
What Mellea Does
- Structured output —
@generative turns typed functions into LLM calls; Pydantic schemas are enforced at generation time
- Requirements & repair — attach natural-language requirements to any call; Mellea validates and retries automatically
- Sampling strategies — run a generation multiple times and pick the best result; swap between rejection sampling, majority voting, and more with one parameter change
- Multiple backends — Ollama, OpenAI, HuggingFace, WatsonX, LiteLLM, Bedrock
- Legacy integration — easily drop Mellea into existing codebases with
mify
- MCP compatible — expose any generative program as an MCP tool
Learn More
| Resource | Description |
|---|
| docs.mellea.ai | Full docs — vision, tutorials, API reference, how-to guides |
| Colab notebooks | Interactive examples you can run immediately |
| Code examples | Runnable examples: RAG, agents, Instruct-Validate-Repair (IVR), MObjects, and more |
Contributing
We welcome contributions of all kinds — bug fixes, new backends, standard library components, examples, and docs.
Questions? See GitHub Discussions.
IBM ❤️ Open Source AI
Mellea was started by IBM Research in Cambridge, MA.
Licensed under the Apache-2.0 License. Copyright © 2026 Mellea.
