{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"outlines","slug":"outlines","name":"Outlines","type":"framework","url":"https://github.com/outlines-dev/outlines","page_url":"https://unfragile.ai/outlines","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"outlines__cap_0","uri":"capability://text.generation.language.json.schema.constrained.generation","name":"json schema-constrained generation","description":"Enforces LLM outputs to conform to arbitrary JSON schemas by integrating with the model's token generation loop. Uses a finite state machine (FSM) built from the schema to mask invalid tokens at each generation step, ensuring 100% schema compliance without post-hoc parsing or validation. Works by computing allowed next tokens based on the current parse state of the JSON being generated.","intents":["I need to extract structured data from an LLM response without parsing failures or retry logic","I want to guarantee my LLM output fits a specific data contract for downstream processing","I need to generate valid JSON objects matching a Pydantic model or JSON Schema without manual validation"],"best_for":["Backend engineers building APIs that consume LLM outputs as structured data","Data pipeline builders extracting information into databases or data warehouses","Teams building LLM-powered agents that need deterministic output formats"],"limitations":["Schema complexity impacts generation speed — deeply nested schemas with many branches add token-masking overhead","Requires schema to be known at generation time; dynamic schema selection requires pre-computing FSMs for all variants","JSON schema constraints may force the model to generate semantically odd but syntactically valid outputs"],"requires":["Python 3.9+","A supported LLM backend (transformers, vLLM, llama.cpp, or OpenAI API)","JSON schema definition (Pydantic model, JSON Schema dict, or string)"],"input_types":["JSON Schema (dict or Pydantic model)","Prompt text (string)"],"output_types":["JSON string (guaranteed valid against schema)","Parsed Python dict or Pydantic model instance"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_1","uri":"capability://text.generation.language.regex.constrained.generation","name":"regex-constrained generation","description":"Constrains LLM token generation to match a regular expression pattern by building a DFA (deterministic finite automaton) from the regex and masking invalid tokens at each step. Enables generation of phone numbers, URLs, dates, or any text matching a specific pattern without post-generation validation or rejection sampling.","intents":["I need the LLM to generate phone numbers, email addresses, or dates in a specific format","I want to enforce that generated text matches a regex pattern without post-processing","I need to generate structured text like CSV rows or log entries with a fixed format"],"best_for":["Data extraction pipelines requiring formatted outputs (phone numbers, ZIP codes, dates)","Form-filling agents that need to generate valid field values","Text generation systems with strict formatting requirements (URLs, identifiers, codes)"],"limitations":["Complex regexes with many branches or backtracking can create large DFAs with performance overhead","Regex constraints may force semantically incorrect outputs (e.g., a valid but nonsensical phone number)","No support for lookahead/lookbehind assertions in regex patterns"],"requires":["Python 3.9+","A supported LLM backend","A valid regex pattern (Python re syntax)"],"input_types":["Regex pattern (string)","Prompt text (string)"],"output_types":["Text string (guaranteed to match regex)","Parsed values extracted from the matched text"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_10","uri":"capability://text.generation.language.guided.generation.with.custom.callbacks","name":"guided generation with custom callbacks","description":"Allows developers to hook into the generation loop with custom callbacks that can inspect or modify constraint state, token masks, or sampling behavior. 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Supports both synchronous and asynchronous callbacks.","intents":["I want to log which tokens were masked at each step for debugging","I need to dynamically relax constraints if generation gets stuck","I want to implement custom constraint logic beyond JSON, regex, and CFG"],"best_for":["Advanced users implementing custom constraint logic","Debugging and monitoring constrained generation","Research and experimentation with novel constraint strategies"],"limitations":["Callbacks add per-token overhead; complex callbacks can significantly impact generation speed","Callback API is not stable across Outlines versions; custom callbacks may break on upgrades","Debugging callback behavior requires understanding internal constraint state representation"],"requires":["Python 3.9+","A supported LLM backend","Understanding of Outlines' internal constraint state and masking API"],"input_types":["Callback function (sync or async)","Callback parameters (constraint state, logits, tokens, etc.)"],"output_types":["Modified constraint state or masks (optional)","Logging or monitoring data"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_11","uri":"capability://text.generation.language.constraint.composition.and.chaining","name":"constraint composition and chaining","description":"Enables combining multiple constraints (e.g., JSON schema AND regex pattern) by computing the intersection of their token masks at each generation step. Supports constraint chaining where the output of one constraint feeds into the next, enabling complex constraint hierarchies. Masks are combined using logical AND to ensure all constraints are satisfied simultaneously.","intents":["I want to generate JSON that also matches a specific regex pattern","I need to enforce both a schema constraint and a grammar constraint","I want to layer constraints for progressive refinement of outputs"],"best_for":["Complex constraint scenarios requiring multiple simultaneous constraints","Progressive constraint refinement workflows","Systems with layered validation requirements"],"limitations":["Composing constraints multiplies masking overhead — each constraint requires mask computation","Conflicting constraints can result in no valid tokens, causing generation to fail","Constraint composition order may affect performance; no automatic optimization"],"requires":["Python 3.9+","A supported LLM backend","Multiple constraint definitions (schema, regex, grammar)"],"input_types":["List of constraint definitions","Composition strategy (AND, OR, sequential)"],"output_types":["Generated text (string) satisfying all constraints","Constraint satisfaction metadata"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_12","uri":"capability://text.generation.language.quantized.model.support.with.llama.cpp.integration","name":"quantized model support with llama.cpp integration","description":"Integrates with llama.cpp to enable constrained generation on quantized models (GGUF format), allowing efficient inference on CPU or low-VRAM devices. 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Supports all constraint types (JSON, regex, CFG) on quantized models with minimal performance degradation.","intents":["I want to run constrained generation on a quantized model on my laptop","I need to deploy constrained generation on edge devices with limited VRAM","I want to use a 7B quantized model with schema constraints instead of a larger cloud model"],"best_for":["Edge deployment and on-device inference","Cost-sensitive applications avoiding cloud API costs","Privacy-critical systems requiring local model execution"],"limitations":["Quantized models may produce lower-quality outputs than full-precision models, especially with strict constraints","llama.cpp performance varies significantly based on CPU architecture and available VRAM","Some advanced features (batching, streaming) may have limited support on llama.cpp"],"requires":["Python 3.9+","llama-cpp-python 0.2.0+","GGUF-format quantized model weights","Sufficient CPU and RAM for the quantized model"],"input_types":["Path to GGUF model file","Constraint definition (schema, regex, grammar)","Prompt (string)"],"output_types":["Generated text (string)","Generation metadata (tokens, timing)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_13","uri":"capability://tool.use.integration.openai.and.anthropic.api.integration.with.function.calling","name":"openai and anthropic api integration with function calling","description":"Provides a unified interface for constrained generation via OpenAI and Anthropic APIs by translating Outlines constraints into native function-calling schemas. Handles schema conversion, API request formatting, and response parsing automatically. Supports both JSON mode (OpenAI) and tool_use (Anthropic) with transparent fallback and retry logic.","intents":["I want to use OpenAI's function calling with my Pydantic models","I need to generate structured outputs via Anthropic's tool_use without manual schema conversion","I want a unified API for constrained generation across OpenAI and Anthropic"],"best_for":["Teams using OpenAI or Anthropic APIs and needing structured outputs","Applications requiring cloud-based LLMs with constraint guarantees","Hybrid systems mixing local and cloud models"],"limitations":["API rate limits and costs apply; no local caching of model weights","Network latency adds 100-500ms per request compared to local inference","API schema support may lag behind Outlines' constraint capabilities","Function calling may not guarantee schema compliance on all model versions"],"requires":["Python 3.9+","OpenAI API key (for OpenAI models) or Anthropic API key (for Anthropic models)","Network connectivity to API endpoints"],"input_types":["Constraint definition (Pydantic model, JSON schema, or dict)","Prompt (string)","API credentials"],"output_types":["Generated text (string)","Parsed function call arguments (dict or Pydantic model)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_2","uri":"capability://text.generation.language.context.free.grammar.cfg.constrained.generation","name":"context-free grammar (cfg) constrained generation","description":"Enforces LLM outputs to conform to a context-free grammar by parsing the generated tokens against the grammar rules and masking tokens that would violate the grammar. Supports arbitrary CFGs (more expressive than regex) for generating code snippets, mathematical expressions, or domain-specific languages. Uses an Earley parser or similar to track valid next tokens based on the current parse state.","intents":["I need to generate valid code snippets or expressions in a specific language or DSL","I want to enforce that generated output follows a grammar (e.g., valid Python, SQL, or mathematical notation)","I need to generate structured text more complex than regex but simpler than full parsing"],"best_for":["Code generation systems that need syntactically valid output","DSL generators (SQL, GraphQL, configuration languages)","Mathematical expression generators requiring valid syntax"],"limitations":["Grammar complexity directly impacts generation speed — large grammars with many rules add significant overhead","Ambiguous grammars can cause parsing conflicts and unpredictable masking behavior","Requires grammar to be specified in EBNF or similar format; no automatic grammar inference"],"requires":["Python 3.9+","A supported LLM backend","A context-free grammar definition (EBNF string or grammar object)"],"input_types":["Context-free grammar (EBNF string or grammar definition)","Prompt text (string)"],"output_types":["Text string (guaranteed to parse against grammar)","Parse tree or AST representation"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_3","uri":"capability://tool.use.integration.multi.backend.model.abstraction","name":"multi-backend model abstraction","description":"Provides a unified Python API for constrained generation across heterogeneous LLM backends (transformers, vLLM, llama.cpp, OpenAI, Anthropic, etc.) by abstracting the token generation interface. Each backend implements a common interface for token sampling and masking, allowing the same constraint code to run on local models, quantized models, or cloud APIs without modification.","intents":["I want to switch between local and cloud LLM backends without rewriting my constraint code","I need to run constrained generation on a quantized model (llama.cpp) and compare results with OpenAI","I want to use vLLM for batched inference with schema constraints"],"best_for":["Teams evaluating multiple LLM backends and needing portable constraint code","Developers building hybrid systems (local + cloud models)","Production systems requiring fallback to alternative backends"],"limitations":["Backend-specific features (e.g., vLLM's guided generation) may not be fully exposed through the abstraction","Latency and throughput characteristics vary significantly across backends; abstraction hides these differences","Some backends (e.g., OpenAI API) have rate limits and cost implications not managed by the abstraction"],"requires":["Python 3.9+","Backend-specific dependencies (transformers, vLLM, llama-cpp-python, openai, anthropic, etc.)","Model weights or API credentials depending on backend"],"input_types":["Backend identifier (string: 'transformers', 'vllm', 'llama_cpp', 'openai', etc.)","Model name or path","Constraint definition (schema, regex, or grammar)"],"output_types":["Generated text (string)","Token logits or probabilities (backend-dependent)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_4","uri":"capability://automation.workflow.batched.constrained.generation.with.vllm.integration","name":"batched constrained generation with vllm integration","description":"Optimizes throughput for constrained generation by batching multiple requests and applying constraints at the batch level using vLLM's paged attention and continuous batching. Masks tokens for all sequences in a batch simultaneously, reducing per-request overhead and enabling higher throughput than sequential generation. Integrates with vLLM's scheduler to maintain constraint compliance across dynamic batches.","intents":["I need to generate structured outputs for 100+ requests with minimal latency overhead","I want to maximize GPU utilization while maintaining schema constraints","I need to serve constrained generation at scale with high throughput"],"best_for":["High-throughput inference servers processing many constrained generation requests","Batch processing pipelines (data extraction, content generation)","Production systems requiring efficient resource utilization"],"limitations":["Batch size and constraint complexity interact — large batches with complex schemas may exceed GPU memory","Constraint masking overhead scales with batch size; very large batches may see diminishing throughput gains","Requires vLLM; not available for other backends"],"requires":["Python 3.9+","vLLM 0.2.0+","CUDA-capable GPU with sufficient VRAM","Model weights compatible with vLLM"],"input_types":["List of prompts (strings)","Shared or per-request constraint definitions (schema, regex, grammar)","Batch size and sampling parameters"],"output_types":["List of generated texts (strings)","Per-request constraint compliance metadata"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_5","uri":"capability://text.generation.language.prompt.templating.with.constraint.integration","name":"prompt templating with constraint integration","description":"Provides a templating system for building prompts that automatically integrate with constraint definitions, allowing developers to define prompts and their expected output schemas in a single configuration. Supports Jinja2-style templating with variable substitution and constraint metadata, enabling reusable prompt-constraint pairs without manual synchronization.","intents":["I want to define a prompt and its expected JSON schema output in one place","I need to reuse the same prompt template with different constraints for A/B testing","I want to version control prompts and schemas together"],"best_for":["Teams managing large numbers of prompts and constraints","Prompt engineering workflows requiring version control and reproducibility","Systems with multiple prompt variants and corresponding output schemas"],"limitations":["Templating complexity is limited to Jinja2 syntax; no custom template engines","Constraint metadata must be manually synchronized with template variables","No built-in support for conditional constraints based on template variables"],"requires":["Python 3.9+","Jinja2 (typically included with Outlines)","Prompt and constraint definitions"],"input_types":["Jinja2 template string","Template variables (dict)","Constraint definition (schema, regex, or grammar)"],"output_types":["Rendered prompt (string)","Integrated constraint definition"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_6","uri":"capability://text.generation.language.token.masking.and.sampling.integration","name":"token masking and sampling integration","description":"Integrates constraint-based token masking with the model's sampling layer by intercepting logits before sampling and zeroing out invalid tokens. Supports multiple sampling strategies (greedy, temperature-based, top-k, top-p) while maintaining constraint compliance. Masks are computed efficiently using precomputed FSMs or parse states to avoid redundant computation.","intents":["I want to apply constraints while preserving the model's sampling behavior (temperature, top-k, etc.)","I need to generate diverse outputs that all satisfy a constraint","I want to use nucleus sampling with schema constraints"],"best_for":["Applications requiring both constraint compliance and output diversity","Systems using temperature-based sampling for creative outputs","Inference pipelines with custom sampling strategies"],"limitations":["Masking adds latency proportional to vocabulary size and constraint complexity","Some sampling strategies (e.g., very low temperature) may make masking ineffective if few valid tokens remain","Precomputed masks require memory proportional to vocabulary size × constraint states"],"requires":["Python 3.9+","A supported LLM backend with logits access","Constraint definition (schema, regex, or grammar)"],"input_types":["Logits tensor (shape: [batch_size, vocab_size])","Constraint state (FSM state or parse state)","Sampling parameters (temperature, top_k, top_p)"],"output_types":["Masked logits tensor","Sampled token indices"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_7","uri":"capability://text.generation.language.streaming.constrained.generation","name":"streaming constrained generation","description":"Enables token-by-token streaming of constrained outputs, yielding valid tokens as they are generated while maintaining constraint compliance. Maintains constraint state across streamed tokens and updates masks incrementally, allowing real-time output display without buffering the entire response. Supports streaming to HTTP clients, file handles, or custom callbacks.","intents":["I want to stream JSON generation to a client in real-time while guaranteeing schema compliance","I need to display generated text as it's produced while maintaining regex constraints","I want to implement a streaming API endpoint for constrained generation"],"best_for":["Web applications and APIs requiring real-time output streaming","Chat interfaces displaying LLM responses incrementally","Long-form generation tasks where latency to first token matters"],"limitations":["Streaming adds per-token overhead for constraint state updates and mask recomputation","Partial JSON or incomplete structures may be displayed before generation completes","Streaming state must be maintained across network boundaries (stateful servers required)"],"requires":["Python 3.9+","A supported LLM backend with streaming support","Constraint definition (schema, regex, or grammar)"],"input_types":["Prompt (string)","Constraint definition","Streaming callback or output handle"],"output_types":["Token stream (iterator of strings)","Streamed bytes to HTTP response or file"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_8","uri":"capability://text.generation.language.pydantic.model.integration.for.schema.generation","name":"pydantic model integration for schema generation","description":"Accepts Pydantic models as constraint definitions and automatically converts them to JSON schemas for constrained generation. Supports Pydantic v1 and v2 with field validation, nested models, and complex types. Enables type-safe constraint definitions where the schema is derived from Python type annotations.","intents":["I want to use my existing Pydantic models to constrain LLM outputs","I need to generate data that matches a Pydantic model without manual schema conversion","I want type safety for both my application code and LLM constraints"],"best_for":["Python applications already using Pydantic for data validation","Teams wanting to avoid manual JSON schema maintenance","Systems where the same model definition is used for both API validation and LLM constraints"],"limitations":["Pydantic model complexity directly impacts constraint overhead — deeply nested models with many validators add latency","Custom Pydantic validators are not enforced during generation; only schema structure is used","Pydantic v1 and v2 have different schema generation; both are supported but may produce different constraints"],"requires":["Python 3.9+","Pydantic 1.10+ or 2.0+","A supported LLM backend"],"input_types":["Pydantic model class","Prompt (string)"],"output_types":["Generated text (string)","Parsed Pydantic model instance"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__cap_9","uri":"capability://automation.workflow.efficient.fsm.caching.and.reuse","name":"efficient fsm caching and reuse","description":"Caches compiled finite state machines (FSMs) for regex and JSON schema constraints across multiple generation calls, avoiding redundant compilation overhead. Uses memoization keyed by constraint definition (schema, regex, or grammar) to reuse FSMs for identical constraints. Supports in-memory and persistent caching strategies.","intents":["I want to avoid recompiling the same regex constraint for every generation call","I need to cache FSMs for frequently-used schemas to reduce latency","I want to share precompiled constraints across multiple processes or servers"],"best_for":["High-throughput inference servers with repeated constraints","Applications generating many outputs with the same schema","Distributed systems where constraint compilation is a bottleneck"],"limitations":["In-memory caching uses heap memory proportional to the number of unique constraints","FSM size grows with constraint complexity; very large schemas may not be practical to cache","Cache invalidation requires manual intervention if constraints change"],"requires":["Python 3.9+","A supported LLM backend"],"input_types":["Constraint definition (schema, regex, or grammar)","Cache configuration (in-memory or persistent)"],"output_types":["Compiled FSM (cached or newly compiled)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"outlines__headline","uri":"capability://data.processing.analysis.structured.text.generation.framework","name":"structured text generation framework","description":"Outlines is a structured text generation framework that ensures LLM outputs adhere to a specified JSON schema, regex, or context-free grammar, facilitating reliable and guided generation across various backends.","intents":["best structured text generation framework","structured text generation for LLMs","top frameworks for guided text generation","how to ensure LLM output follows a schema","frameworks for reliable text generation"],"best_for":["developers needing structured outputs from LLMs"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","A supported LLM backend (transformers, vLLM, llama.cpp, or OpenAI API)","JSON schema definition (Pydantic model, JSON Schema dict, or string)","A supported LLM backend","A valid regex pattern (Python re syntax)","Understanding of Outlines' internal constraint state and masking API","Multiple constraint definitions (schema, regex, grammar)","llama-cpp-python 0.2.0+","GGUF-format quantized model weights","Sufficient CPU and RAM for the quantized model"],"failure_modes":["Schema complexity impacts generation speed — deeply nested schemas with many branches add token-masking overhead","Requires schema to be known at generation time; dynamic schema selection requires pre-computing FSMs for all variants","JSON schema constraints may force the model to generate semantically odd but syntactically valid outputs","Complex regexes with many branches or backtracking can create large DFAs with performance overhead","Regex constraints may force semantically incorrect outputs (e.g., a valid but nonsensical phone number)","No support for lookahead/lookbehind assertions in regex patterns","Callbacks add per-token overhead; complex callbacks can significantly impact generation speed","Callback API is not stable across Outlines versions; custom callbacks may break on upgrades","Debugging callback behavior requires understanding internal constraint state representation","Composing constraints multiplies masking overhead — each constraint requires mask computation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=outlines","compare_url":"https://unfragile.ai/compare?artifact=outlines"}},"signature":"q6e6/dJd9P3gmEw6WSMtFmnB2U94LsLdt04srmtlKLOlZIEQiBYJp5GhyMGlNX6JxO/lJ230KMS7v664mlxBCA==","signedAt":"2026-06-21T14:23:24.325Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/outlines","artifact":"https://unfragile.ai/outlines","verify":"https://unfragile.ai/api/v1/verify?slug=outlines","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}