Our verdict
We have not reviewed Guidance yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About Guidance
Guidance is a framework for steering large language model outputs through a domain-specific language that constrains token generation. It lets you specify exact output formats, control branching logic, and enforce structured responses without post-processing.
Best for
Developers building production systems that need deterministic, schema-compliant LLM outputs
Use cases
- Enforce JSON or XML schema compliance in model outputs
- Build multi-turn workflows with conditional branching based on model responses
- Extract structured data from unstructured text with guaranteed format
Notes
21,486 stars on GitHub. Last updated 2026-05-21. Licensed MIT.
Pros
- Reduces hallucination and invalid outputs by constraining generation at token level
- Eliminates need for output parsing and validation in downstream code
- Works across multiple LLM providers and local models
Cons
- Adds latency due to constraint checking on every token
- Requires learning a new DSL syntax for non-trivial use cases
- Community-maintained with no commercial support guarantee
Key facts
What we have checked
- Latest version
- 0.3.2 (released 18 Mar 2026)
- GitHub stars
- 21,779
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How Guidance is growing
21,486 to 21,779
Alternatives
Alternatives to Guidance
| Record | Type | How we know | Checked |
|---|---|---|---|
| BAMLAlternative to this | Open source | Named in the listing | Not dated |
| DSPyAlternative to this | Open source | Named in the listing | Not dated |
| InstructorAlternative to this | Open source | Named in the listing | Not dated |
| LMQLAlternative to this | Open source | Named in the listing | Not dated |
| magenticAlternative to this | Open source | Named in the listing | Not dated |
| OutlinesAlternative to this | Open source | Named in the listing | Not dated |
| PromptifyAlternative to this | Open source | Named in the listing | Not dated |
Compare
Compare Guidance side by side
Sources
Where this comes from
- GitHubOfficial
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
Released 0.3.2
Released 0.3.1
Released 0.3.0
Released 0.2.5
Released 0.2.4
Released 0.2.3
Released 0.2.2
Released 0.2.0
Released 0.2.0rc1
Released 0.1.16
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
BAML
Boundary
A typed language for LLM functions. Define inputs, outputs, and prompts, get reliable structured output.
DSPy
Stanford NLP
Programming, not prompting. Declare what you want, compile prompts and weights against an objective.
Instructor
Jason Liu (community)
Structured output for LLMs via Pydantic. The cleanest answer to 'just give me a typed object back'.
LMQL
Community
Language Model Query Language
magentic
Community
Seamlessly integrate LLMs as Python functions
Outlines
Community
Structured Outputs
Promptify
Community
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.
Get the free Developer’s Field Guide
A 27-page field guide to the AI coding workflow with Claude. Claude Code, MCP servers, the prompt patterns that work, and what to delegate. Free.
Enter your work email. We send it straight over, plus a few short notes worth knowing. Unsubscribe any time.