Get started
Install Mellea and run your first generative program in minutes.
Tutorial
Build a complete program with generation, validation, and repair.
Code examples
Runnable examples: RAG, agents, sampling, MObjects, and more.
API reference
Full public API — backends, session, components, requirements, sampling.
How Mellea works
Mellea’s design rests on three interlocking ideas.Python, not prose
@generative turns a typed function signature into an LLM-backed implementation.
Docstrings become prompts. Type hints become output schemas. No DSL required.Requirements driven
Declare what good output looks like with
req(). Mellea checks every response
before it leaves the session — using LLM verifiers, programmatic checks, or
domain-trained adapters.Instruct · Validate · Repair
When a requirement fails, Mellea feeds the failure back and tries again.
Rejection sampling, majority voting, and SOFAI are built in.
Key patterns
MObjects and mify
Add
@mify to any class to make it LLM-queryable and tool-accessible
without rewriting your data model.Context and sessions
Explicit context threading with push/pop state keeps multi-turn
workflows reproducible and debuggable.
Async and streaming
ainstruct(), aact(), and token-by-token streaming for production
throughput and responsive UIs.Safety checks
GuardianCheck detects harmful, off-topic, or hallucinated outputs
before they reach downstream code.Inference-time scaling
Best-of-n, SOFAI, majority voting — swap strategies in one line.
Tools and agents
@tool, MelleaTool, and the ReACT loop for goal-driven multi-step agents.Backends
Mellea is backend-agnostic. The same program runs on any inference engine.Ollama
Local inference, zero cloud costs.
OpenAI
GPT-4o, o3-mini, any OpenAI-compatible API.
AWS Bedrock
AWS Bedrock via Bedrock Mantle or LiteLLM.
IBM WatsonX
IBM WatsonX managed AI platform.
HuggingFace
Local inference with Transformers — aLoRA and constrained decoding.
vLLM
High-throughput batched local inference on Linux + CUDA.
LiteLLM / Vertex AI
Google Vertex AI, Anthropic, and 100+ providers via LiteLLM.
LangChain
Use LangChain tools in Mellea sessions or call Mellea from LangChain chains.
How-to guides
Enforce structured output
Pydantic models,
Literal types, and @generative for guaranteed schemas.Write custom verifiers
Python functions,
ValidationResult, and multi-field validation logic.Async and streaming
aact(), ainstruct(), and token-by-token streaming output.Use context and sessions
ChatContext, explicit context threading, and multi-session workflows.Configure model options
Temperature, seed, max tokens, system prompts — cross-backend with
ModelOption.Use images and vision
Pass images to
instruct() and chat() with any vision-capable backend.Build a RAG pipeline
Vector search, LLM relevance filtering, and grounded generation end-to-end.
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