Master Outlines with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Define your first Outlines use case and success metric. Connect a foundation model and configure credentials. Attach retrieval/tools and set guardrails for execution. Run evaluation datasets to benchmark quality and latency. Deploy with monitoring, alerts, and iterative improvement loops.
💡 Quick Start: Follow these 1 steps in order to get up and running with Outlines quickly.
Explore the key features that make Outlines powerful for ai agent builders workflows.
Generate JSON guaranteed to conform to a Pydantic model or JSON Schema. The FSM ensures every generated token leads to valid JSON with correct types, required fields, and format constraints.
Extracting structured medical records from clinical notes using a local Llama model where guaranteed schema compliance is critical.
Constrain model output to match any regular expression pattern. Useful for formatted strings like phone numbers, dates, emails, or custom identifiers with guaranteed format compliance.
Generating synthetic test data (emails, phone numbers, dates) that always matches the required format without validation or retry.
Define output constraints using context-free grammars (EBNF notation), enabling structured generation for programming languages, mathematical expressions, or custom DSLs.
Generating syntactically valid SQL queries, Python code, or arithmetic expressions from a local model with guaranteed parser compatibility.
Unified API across Transformers (development), vLLM (production serving), llama.cpp/ExLlamaV2 (efficient local), and MLX (Apple Silicon). Same code works across all backends.
Developing on a laptop with Transformers, then deploying to production with vLLM for 10x throughput — same code, different backend.
Constrain generation to a predefined set of options. The model can only output one of the specified choices, enabling reliable classification without parsing.
Building a sentiment classifier that outputs exactly 'positive', 'negative', or 'neutral' — guaranteed with no parsing edge cases.
Decorator-based prompt templating using Jinja2 syntax with type-safe variable injection. Templates support conditionals, loops, and function calls.
Creating reusable prompt templates for different extraction tasks, with typed parameters and conditional prompt sections.
No. Outlines requires access to the model's logits to mask invalid tokens during generation. API providers don't expose logits for constrained decoding. For structured output from API models, use Instructor or the provider's native JSON mode. Outlines is specifically for local model inference.
First request has a cold-start for FSM construction (1-10 seconds depending on schema complexity), but the FSM is cached. Per-token overhead is roughly 5-15% slower. For complex schemas the overhead increases. vLLM's integration is optimized for production throughput.
It can slightly, by narrowing the model's probability distribution. Quality impact is minimal for well-structured schemas. Very restrictive constraints have more impact than flexible ones. The tradeoff — guaranteed validity vs. marginally reduced quality — is usually worth it.
Different tools for different architectures. Outlines uses constrained decoding with local models — output is mathematically guaranteed valid, zero retries. Instructor uses function calling with API models — validated post-hoc with retries. Use Outlines for local deployments; Instructor for API-based applications. They're complementary.
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Tutorial updated March 2026