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Guidance

Constrained generation and Pythonic control flow for steering language models.

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About

Guidance is Microsoft's programming paradigm for controlling language models: instead of hoping the model emits valid JSON, you constrain generation itself — regexes, context-free grammars, and structured gen() calls guarantee output syntax while interleaving Python control flow (conditionals, loops, tool calls) with generation. It supports many backends (Transformers, llama.cpp, OpenAI-compatible APIs) and is designed to cut latency and cost versus repeated prompting or fine-tuning for structured tasks.

What it can do

  • **Constrained generation**: force outputs to match regexes, CFGs, JSON schemas, or token sets
  • **Pythonic control flow**: interleave `with system()/user()/assistant()` blocks, loops, and conditionals with generation
  • **Named captures**: `gen(name=...)` stores generations for reuse downstream
  • **Multi-backend**: Transformers, llama.cpp, OpenAI and compatible APIs
  • **Token healing & smart caching**: backend-aware optimizations that cut latency/cost
  • **Jupyter widget**: rich in-notebook rendering of generation traces

Setup

```bash

pip install guidance

```

```python

from guidance import system, user, assistant, gen

from guidance.models import Transformers

lm = Transformers("microsoft/Phi-4-mini-instruct")

with system():

lm += "You are a helpful assistant"

with user():

lm += "How old are you?"

with assistant():

lm += gen("age", regex=r"\d+", max_tokens=10)

print(lm["age"])

```

Backends: Transformers, LlamaCpp, OpenAI, and more — pick per model. Docs in the repo's docs/ folder.

Prerequisites

Python 3.10+. A backend: local model weights (Transformers/llama.cpp) or an OpenAI-compatible API key.

Use cases

structured outputsconstrained decodingJSON generationagents

Works with

Generic APIcustomClaude Code

Trust score — 8/10

/10 trustVerified

Scored 8/10 (verified tier, metrics-v1). Evidence: 21,796 GitHub stars (log-scaled vs catalog peers); blended with ~12,131 monthly package downloads; last push 144d ago; 2 releases/12mo; 351 open issues. Manual review: Microsoft-backed reference for constrained generation; 21.8k stars; strongest guarantees need local backends, not black-box APIs; repo activity slowed in 2026. License: MIT.

★ 21,796 GitHub stars

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
7.6/10
Maintenance · recency, releases, issue load
5.6/10
Openness · license + docs
10.0/10
Manual review · sanity read + red-flag check
9.0/10
  • · 21,796 GitHub stars (log-scaled vs catalog peers)
  • · blended with ~12,131 monthly package downloads
  • · last push 144d ago; 2 releases/12mo; 351 open issues
  • · manual review: Microsoft-backed reference for constrained generation; 21.8k stars; strongest guarantees need local backends, not black-box APIs; repo activity slowed in 2026

Sources

Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.