Guidance AI

Constrains LLM generation at the token level using regex, JSON schemas, and context-free grammars, producing structured output faster and more reliably than prompt-only approaches.

Evaluated Mar 06, 2026 (0d ago) v0.1.x
Homepage ↗ Repo ↗ AI & Machine Learning llm structured-output constrained-generation python json-schema regex grammars microsoft
⚙ Agent Friendliness
61
/ 100
Can an agent use this?
🔒 Security
82
/ 100
Is it safe for agents?
⚡ Reliability
59
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
72
Error Messages
70
Auth Simplicity
100
Rate Limits
100

🔒 Security

TLS Enforcement
88
Auth Strength
85
Scope Granularity
70
Dep. Hygiene
78
Secret Handling
90

No network surface from the library itself. Use env vars or a secrets manager for any cloud LLM API keys; never hardcode.

⚡ Reliability

Uptime/SLA
55
Version Stability
58
Breaking Changes
55
Error Recovery
68
AF Security Reliability

Best When

You need guaranteed schema-valid structured output from a local or Transformers-backed LLM without retrying failed parses.

Avoid When

You are using cloud LLM APIs (OpenAI, Anthropic) and need low latency — constrained decoding acceleration only works with compatible local backends.

Use Cases

  • Guarantee valid JSON output from an LLM without post-processing or retry loops
  • Constrain agent tool-call arguments to an exact schema before passing them to downstream APIs
  • Generate structured data extraction outputs (named entities, classification labels) with zero parse failures
  • Build decision trees where each LLM branch is constrained to a finite set of options
  • Accelerate structured generation on llama.cpp or Transformers backends via token-level masking

Not For

  • General-purpose agent orchestration — Guidance is a generation control library, not a task or memory manager
  • Teams using cloud APIs like OpenAI where constrained decoding acceleration is unavailable
  • Workflows where free-form, unconstrained prose is the desired output

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: none
OAuth: No Scopes: No

Local library; API keys for cloud backends (OpenAI, Anthropic) passed via environment variables if using those backends.

Pricing

Model: open_source
Free tier: Yes
Requires CC: No

Microsoft-maintained, MIT licensed. Cloud LLM backend usage billed by provider.

Agent Metadata

Pagination
none
Idempotent
No
Retry Guidance
Not documented

Known Gotchas

  • Token-level acceleration only works with llama.cpp, Transformers, and a small set of compatible backends — OpenAI/Anthropic APIs get no speed benefit
  • Grammar constraints can cause the model to generate degenerate or repetitive outputs when the constraint space is too narrow
  • The API surface has changed significantly across minor versions — pin your version carefully
  • Async support is limited; long constrained generation blocks the event loop without explicit threading
  • Complex grammars (deeply nested JSON) can cause constraint compilation to be slow on first call

Alternatives

Full Evaluation Report

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Scores are editorial opinions as of 2026-03-06.

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