generative-computing/mellea

Health v2 · maintenance only

Adoption not part of the score

1799 stars · 149 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Mellea is a Python library for writing generative programs, where LLM calls are first-class operations with type-annotated outputs, verifiable requirements, and automatic retry/repair loops. It supports multiple backends (OpenAI, Ollama, vLLM, HuggingFace, Watsonx, LiteLLM, Bedrock) and integrates with MCP for exposing tools.

Use cases

• extract structured data from text with guaranteed schemas
• validate LLM outputs against requirements with automatic retries
• build reliable AI pipelines without brittle prompt chains
• wrap Python functions as MCP tools for Claude Desktop or Cursor
• enforce grammar-constrained decoding for valid structured output
• build RAG and agent workflows with testable LLM calls

When to choose

• you need type-safe, schema-enforced LLM outputs in Python
• you want verifiable requirements and automatic repair instead of prompt guesswork
• you need token-level constrained decoding rather than retry-based validation
• you want to expose validated LLM functions as MCP tools

When to avoid

• you need a full agent framework with complex multi-agent orchestration
• you work outside Python
• you only need simple one-off prompt calls without validation

Facets

library · maturity active

llm-inference agent-framework rag mcp prompt-engineering sdk large-language-models artificial-intelligence developer-tools python cross-platform generative-programming structured-output constrained-decoding requirement-validation pydantic llm-workflows rejection-sampling ai-agents retrieval-augmented-generation

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