#3535th of 48 in Agent frameworks and SDKs
outlines
Python library that constrains LLM output to a type, schema or grammar
Documentation ↗
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- Stars
- 15.9k
- License
- Apache-2.0
- Last commit
- Aug 2026
- Last release
- Aug 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
Outlines enforces structured output during generation. You pass a Python type, such as Literal, int, a Pydantic model or a function signature, and the model returns output matching it. The same call works across transformers, llama.cpp, vLLM, Ollama, OpenAI and Gemini backends, and it includes Jinja-based prompt templates.
Who it is for: Python developers who need valid JSON or typed output from LLMs
Strengths
- Output type is a plain Python type: Literal, int, Pydantic model, union or function signature
- Same model(prompt, output_type) call across transformers, llama.cpp, vLLM, Ollama, OpenAI and Gemini
- Constrains generation itself, so no post-hoc JSON parsing or retry loop
- Built-in Jinja prompt templates, loadable from strings or files
Weaknesses
- Library only: no server, UI or deployment tooling of its own
- Dottxt API is early access and requires a request form
- README does not detail constraint limits for hosted API backends
- Hardware requirements for local models are not documented in the README
What it needs
- GPU optional
- Needs transformers, pydantic
- Models: transformers, llama.cpp, vLLM, Ollama, OpenAI
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