{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-dottxt-ai--outlines","slug":"dottxt-ai--outlines","name":"outlines","type":"prompt","url":"https://dottxt-ai.github.io/outlines/","page_url":"https://unfragile.ai/dottxt-ai--outlines","categories":["prompt-engineering"],"tags":["cfg","generative-ai","json","llms","prompt-engineering","regex","structured-generation","symbolic-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-dottxt-ai--outlines__cap_0","uri":"capability://tool.use.integration.provider.agnostic.model.abstraction.with.unified.generation.interface","name":"provider-agnostic model abstraction with unified generation interface","description":"Outlines abstracts away provider differences through a layered Model Integration Layer that supports both steerable models (Transformers, LlamaCpp, MLXLM with direct logits access) and black box API models (OpenAI, Gemini, Anthropic, Mistral, Dottxt, vLLM, TGI, SGLang, Ollama). The framework uses factory functions (from_transformers(), from_openai(), etc.) that return Generator instances, enabling identical code to work across all providers while delegating constraint enforcement to provider-native capabilities or client-side logits masking.","intents":["Switch between local and cloud LLM providers without rewriting generation code","Build LLM applications that work with both proprietary APIs and open-source models","Avoid vendor lock-in by maintaining provider-agnostic inference logic"],"best_for":["Teams building multi-provider LLM applications","Developers migrating between OpenAI, Anthropic, and local inference stacks","Organizations requiring flexibility to swap inference backends"],"limitations":["API-based models enforce constraints server-side (OpenAI, Anthropic) while local models use client-side logits masking, creating different latency profiles","Not all providers support all constraint types equally — some APIs lack native regex or CFG support","Requires separate API keys or model downloads for each provider"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, Gemini, Mistral) OR local model files (for Transformers, LlamaCpp)","Appropriate tokenizer for each model backend"],"input_types":["text prompts","Jinja2 templates with variables"],"output_types":["text","structured JSON","regex-constrained strings","grammar-constrained text"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_1","uri":"capability://data.processing.analysis.json.schema.constrained.generation.with.automatic.schema.conversion","name":"json schema-constrained generation with automatic schema conversion","description":"Outlines converts Python type hints and JSON schemas into internal Term representations (JsonSchema objects) that guide token sampling during generation. The Type System Layer uses the ModelTypeAdapter pattern to handle input formatting and output type conversion, while the Constraint Enforcement Layer applies these schemas through pluggable backends that mask invalid tokens at each generation step, guaranteeing output conformance to the schema structure.","intents":["Generate structured JSON responses that always match a predefined schema without post-processing validation","Extract typed data from LLM outputs (e.g., user profiles with specific fields and types)","Build reliable data pipelines where LLM outputs feed directly into downstream systems without error handling"],"best_for":["Data extraction pipelines requiring guaranteed schema compliance","API builders returning structured responses to clients","Teams building LLM-powered ETL workflows"],"limitations":["Schema complexity impacts generation speed — deeply nested schemas with many constraints add latency per token","Large schemas may exceed context windows or tokenizer capacity","API-based models (OpenAI, Anthropic) have native schema support but may not support all JSON Schema features equally"],"requires":["Python 3.9+","JSON schema definition or Python type hints (Pydantic models, dataclasses, TypedDict)","Model with tokenizer access (for local models) or API support for structured outputs (for cloud models)"],"input_types":["JSON Schema objects","Python type hints (Pydantic BaseModel, dataclass, TypedDict)","Jinja2 prompts with schema context"],"output_types":["JSON strings conforming to schema","Python objects (deserialized from JSON)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_10","uri":"capability://tool.use.integration.vllm.server.integration.with.distributed.inference.support","name":"vllm server integration with distributed inference support","description":"Outlines integrates with vLLM servers (both local and remote) to enable distributed inference with structured generation support. The integration communicates with vLLM's OpenAI-compatible API, translating Outlines' constraint representations into vLLM's native guided generation format. This enables scaling inference across multiple GPUs or machines while maintaining constraint enforcement, providing a middle ground between local inference (single machine) and cloud APIs (vendor lock-in).","intents":["Scale local inference across multiple GPUs or machines using vLLM","Run structured generation on self-hosted infrastructure without cloud API costs","Build high-throughput LLM applications with constraint enforcement"],"best_for":["Teams with on-premise GPU infrastructure","High-throughput applications requiring distributed inference","Organizations avoiding cloud vendor lock-in while needing scale"],"limitations":["Requires vLLM server setup and maintenance — operational overhead vs. managed cloud APIs","Constraint enforcement in vLLM may have different performance characteristics than local Outlines backends","Network latency between client and vLLM server adds overhead vs. local inference","vLLM server must be running and accessible — adds deployment complexity"],"requires":["Python 3.9+","vLLM server running (local or remote)","Network connectivity to vLLM server","Model loaded in vLLM server"],"input_types":["text prompts","Jinja2 templates","constraint specifications (JSON schema, regex, CFG)"],"output_types":["text","structured JSON","constrained text"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_11","uri":"capability://automation.workflow.batch.generation.with.streaming.and.async.support","name":"batch generation with streaming and async support","description":"Outlines supports batch generation of multiple prompts with streaming token output and async/await patterns for non-blocking inference. The Generator interface provides methods for single-prompt generation, batch generation, and streaming generation, enabling developers to choose the appropriate pattern for their use case. Async support enables concurrent inference requests without blocking, improving throughput for I/O-bound applications.","intents":["Generate outputs for multiple prompts efficiently in a single batch","Stream tokens as they are generated for real-time user feedback","Build non-blocking LLM applications using async/await patterns"],"best_for":["Applications processing multiple prompts concurrently","Real-time LLM applications requiring token streaming","High-throughput inference services with async request handling"],"limitations":["Batch generation requires all prompts to fit in memory — very large batches may cause OOM errors","Streaming adds per-token overhead for I/O and serialization","Async support requires async-compatible model backends — not all backends support async","Batch size optimization is manual — no built-in auto-batching or dynamic batching"],"requires":["Python 3.9+","Model backend supporting batch generation (most backends support this)","Async runtime (asyncio) for async generation"],"input_types":["list of text prompts","list of constraint specifications"],"output_types":["list of generated texts","token streams","async iterators"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_12","uri":"capability://code.generation.editing.custom.type.and.schema.processing.with.extensible.type.system","name":"custom type and schema processing with extensible type system","description":"Outlines provides a pluggable type system that enables custom type definitions and schema processing beyond built-in types (JSON schema, regex, CFG). Developers can define custom types by implementing type adapters and constraint representations, enabling domain-specific structured generation. The Type System Layer automatically routes custom types to appropriate constraint backends, enabling seamless integration of custom constraints without modifying core framework code.","intents":["Define domain-specific output types (e.g., medical records, financial reports) with custom validation","Extend Outlines with custom constraint types beyond JSON, regex, and CFG","Build specialized LLM applications with custom structured generation logic"],"best_for":["Teams with domain-specific structured generation requirements","Developers building specialized LLM frameworks on top of Outlines","Applications requiring custom validation logic beyond standard constraints"],"limitations":["Custom type implementation requires understanding Outlines' type adapter and constraint backend interfaces","Custom types may not be compatible with all model backends — requires backend-specific implementation","Custom constraint enforcement adds latency — performance depends on implementation quality","Limited documentation and examples for custom type development"],"requires":["Python 3.9+","Understanding of Outlines' type adapter and constraint backend interfaces","Implementation of custom type adapter and constraint representation"],"input_types":["custom type definitions","custom constraint representations"],"output_types":["custom typed objects","constrained text matching custom types"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_13","uri":"capability://image.visual.vision.and.multimodal.model.support.with.image.input.handling","name":"vision and multimodal model support with image input handling","description":"Outlines provides integration with vision and multimodal models (e.g., GPT-4V, Gemini Vision, Claude 3 Vision) that accept image inputs alongside text prompts. The framework handles image encoding, tokenization, and constraint enforcement for multimodal outputs, enabling structured generation from image+text inputs. The Model Integration Layer automatically detects multimodal capabilities and routes requests appropriately.","intents":["Generate structured outputs from images and text (e.g., extract product info from product photos)","Build multimodal LLM applications with guaranteed output structure","Process images with structured extraction using vision models"],"best_for":["Applications combining image analysis with structured extraction","Teams building multimodal LLM pipelines","Developers requiring vision capabilities with guaranteed output format"],"limitations":["Multimodal models are slower and more expensive than text-only models","Image encoding and tokenization add latency before model inference","Not all vision models support all constraint types — some may lack native structured output support","Image quality and size impact generation quality and latency"],"requires":["Python 3.9+","Multimodal model with vision capabilities (GPT-4V, Gemini Vision, Claude 3 Vision, etc.)","Image files (PNG, JPEG, etc.) or image URLs","API key for vision model provider"],"input_types":["text prompts","image files or URLs","constraint specifications"],"output_types":["text","structured JSON","constrained text"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_2","uri":"capability://data.processing.analysis.regex.guided.token.generation.with.pattern.based.output.constraints","name":"regex-guided token generation with pattern-based output constraints","description":"Outlines converts regular expressions into constraint representations that guide the token sampling process, ensuring generated text matches the regex pattern at every step. The framework uses the Constraint Enforcement Layer to apply regex patterns through pluggable backends (outlines_core, xgrammar, llguidance) that mask logits for tokens violating the pattern, preventing invalid sequences from being sampled and guaranteeing regex conformance without post-processing.","intents":["Generate structured text matching specific patterns (phone numbers, dates, URLs, email addresses)","Enforce output format constraints (e.g., 'Answer: [yes/no]' or 'Price: $[0-9]+\\.[0-9]{2}')","Build format-aware LLM outputs for downstream parsing or API consumption"],"best_for":["Applications requiring strict output formatting (financial data, contact info, structured logs)","Developers building LLM-powered form filling or data entry systems","Teams needing deterministic output formats for downstream processing"],"limitations":["Complex regex patterns with many alternatives can create high logits masking overhead, slowing generation","Regex constraints may conflict with natural language generation, forcing awkward phrasing to match patterns","API-based models (OpenAI, Anthropic) have limited or no native regex support — requires client-side constraint enforcement"],"requires":["Python 3.9+","Valid regex pattern (Python re syntax)","Model with tokenizer access for local models; API models may fall back to post-generation validation"],"input_types":["regex pattern strings","Jinja2 prompts with embedded regex constraints"],"output_types":["text strings matching regex pattern"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_3","uri":"capability://code.generation.editing.context.free.grammar.cfg.guided.generation.with.symbolic.constraints","name":"context-free grammar (cfg) guided generation with symbolic constraints","description":"Outlines converts context-free grammars (in EBNF or similar formats) into constraint representations that enforce grammatical structure during token sampling. The Type System Layer converts grammars into Term representations, and the Constraint Enforcement Layer applies them through pluggable backends that track grammar state and mask tokens that would violate grammar rules, guaranteeing outputs conform to the specified grammar without post-processing.","intents":["Generate valid programming code or domain-specific languages (DSLs) that conform to syntax rules","Enforce structured natural language output (e.g., 'Sentence: [Noun] [Verb] [Object]')","Build LLM-powered code generators or configuration file creators with guaranteed syntactic validity"],"best_for":["Code generation and synthesis applications","DSL-based LLM applications (SQL, GraphQL, YAML generation)","Teams building LLM-powered configuration or template systems"],"limitations":["Grammar complexity directly impacts generation latency — complex grammars with many rules add significant per-token overhead","Large grammars may exceed memory constraints on resource-limited devices","API-based models (OpenAI, Anthropic) have no native CFG support — requires local model with logits access"],"requires":["Python 3.9+","Grammar definition in EBNF or similar format","Local model with tokenizer and logits access (Transformers, LlamaCpp, MLXLM, vLLM)","Pluggable backend (outlines_core, xgrammar, or llguidance) installed"],"input_types":["EBNF grammar strings","Grammar objects from xgrammar or llguidance","Jinja2 prompts with grammar context"],"output_types":["text conforming to grammar structure","code in specified language","DSL instances"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_4","uri":"capability://text.generation.language.jinja2.based.prompt.templating.with.variable.interpolation.and.control.flow","name":"jinja2-based prompt templating with variable interpolation and control flow","description":"Outlines provides a Template system built on Jinja2 that enables dynamic prompt construction with variable interpolation, conditional logic, and loops. Templates are rendered before being passed to the model, allowing developers to build parameterized prompts that adapt to input data, context, or runtime conditions. The Template class integrates with the Generator interface, enabling seamless prompt rendering and generation in a single call.","intents":["Build reusable prompt templates with variables that adapt to different inputs","Implement conditional prompts that change based on input data or context","Create few-shot examples dynamically based on input characteristics"],"best_for":["Teams managing multiple prompt variants across applications","Developers building prompt engineering workflows","Applications requiring dynamic few-shot example selection"],"limitations":["Jinja2 rendering adds latency before model inference — complex templates with loops or conditionals can slow down generation","Template syntax errors are caught at render time, not at definition time, requiring runtime debugging","Large templates with many variables may exceed context windows when rendered"],"requires":["Python 3.9+","Jinja2 library (included in Outlines dependencies)","Template string with valid Jinja2 syntax"],"input_types":["Jinja2 template strings","Dictionary of template variables","Python objects (accessible via dot notation in templates)"],"output_types":["rendered prompt strings","prompt + structured generation output"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_5","uri":"capability://tool.use.integration.pluggable.constraint.backend.selection.with.outlines.core.xgrammar.and.llguidance","name":"pluggable constraint backend selection with outlines_core, xgrammar, and llguidance","description":"Outlines abstracts constraint enforcement through a pluggable backend architecture that supports three implementations: outlines_core (Outlines' native Rust-based engine), xgrammar (NVIDIA's grammar-guided generation), and llguidance (Microsoft's guidance library integration). The Constraint Enforcement Layer automatically selects the appropriate backend based on model type and constraint complexity, or allows manual backend selection. Each backend implements the LogitsProcessor interface, masking invalid tokens during generation while maintaining provider independence.","intents":["Choose the optimal constraint enforcement backend for performance vs. feature coverage trade-offs","Switch between constraint backends without changing application code","Leverage specialized backends (xgrammar for complex grammars, llguidance for advanced patterns)"],"best_for":["Performance-critical applications requiring backend optimization","Teams using specialized constraint libraries (xgrammar, llguidance)","Developers building extensible LLM applications with pluggable constraint engines"],"limitations":["Each backend has different performance characteristics — xgrammar optimized for complex grammars, outlines_core for general use, llguidance for advanced patterns","Not all backends support all constraint types equally — some may lack regex or CFG support","Backend selection is manual or automatic based on heuristics; no built-in cost/performance optimizer"],"requires":["Python 3.9+","Local model with tokenizer and logits access","Optional: xgrammar or llguidance libraries installed for non-default backends"],"input_types":["constraint representations (JsonSchema, Regex, CFG)","backend selection parameter (string or enum)"],"output_types":["LogitsProcessor instances","constrained token sequences"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_6","uri":"capability://data.processing.analysis.type.adapter.pattern.for.input.formatting.and.output.deserialization","name":"type adapter pattern for input formatting and output deserialization","description":"Outlines uses the ModelTypeAdapter pattern to handle bidirectional type conversion: formatting inputs (e.g., converting Python types to prompt text) and deserializing outputs (e.g., parsing JSON strings back to Python objects). The Type System Layer applies adapters based on the output_type parameter, enabling seamless integration between Python type hints and LLM text generation. Adapters support Pydantic models, dataclasses, TypedDict, JSON schemas, and custom types through a pluggable interface.","intents":["Automatically convert Python objects to prompt context and LLM outputs back to typed Python objects","Integrate LLM generation with Python type systems (Pydantic, dataclasses) without manual serialization","Build end-to-end typed LLM pipelines from input to output"],"best_for":["Python developers building type-safe LLM applications","Teams using Pydantic or dataclasses for data validation","Applications requiring seamless Python ↔ LLM type conversion"],"limitations":["Custom type adapters require manual implementation for non-standard types","Deserialization assumes valid JSON output — invalid outputs cause parsing errors (mitigated by schema constraints)","Type adapter overhead adds latency for complex types with many fields"],"requires":["Python 3.9+","Type hint or schema definition (Pydantic BaseModel, dataclass, TypedDict, JSON schema)","JSON-serializable output from model"],"input_types":["Python type hints","Pydantic BaseModel instances","dataclass instances","TypedDict definitions"],"output_types":["Python objects matching input type","Pydantic model instances","dataclass instances"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_7","uri":"capability://tool.use.integration.tokenizer.protocol.abstraction.for.multi.model.compatibility","name":"tokenizer protocol abstraction for multi-model compatibility","description":"Outlines defines a Tokenizer Protocol that abstracts tokenizer implementations across different models and libraries (Transformers, LlamaCpp, MLXLM, etc.). The protocol enables constraint enforcement backends to work with any tokenizer implementation by providing standard encode/decode operations and token vocabulary access. This abstraction allows the same constraint logic to work across different model architectures and tokenizer libraries without reimplementation.","intents":["Use the same constraint enforcement code across models with different tokenizers","Integrate custom tokenizers without modifying constraint enforcement logic","Support new model backends by implementing the Tokenizer Protocol"],"best_for":["Teams supporting multiple model architectures and tokenizer libraries","Developers building custom tokenizers for specialized domains","Framework maintainers extending Outlines with new model backends"],"limitations":["Tokenizer differences (BPE vs. SentencePiece vs. WordPiece) can cause subtle constraint behavior differences","Custom tokenizers must implement the full Protocol interface, including vocabulary access and encoding/decoding","Tokenizer performance varies significantly — some tokenizers are slower than others, impacting constraint enforcement speed"],"requires":["Python 3.9+","Tokenizer implementation matching the Tokenizer Protocol","Model with compatible tokenizer"],"input_types":["text strings","token IDs"],"output_types":["token IDs","text strings","vocabulary information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_8","uri":"capability://tool.use.integration.local.model.inference.with.transformers.llamacpp.and.mlxlm.backends","name":"local model inference with transformers, llamacpp, and mlxlm backends","description":"Outlines provides steerable model integrations for local inference through Transformers (HuggingFace models), LlamaCpp (GGUF-format models), and MLXLM (Apple Silicon optimization). These backends provide direct logits access, enabling client-side constraint enforcement through logits masking. The framework handles model loading, tokenizer initialization, and generation loop management, exposing a unified Generator interface that works identically to API-based models.","intents":["Run LLM inference locally without cloud API dependencies or costs","Enforce structured generation constraints on local models through logits masking","Build privacy-preserving LLM applications that keep data on-device"],"best_for":["Teams with privacy requirements or offline deployment needs","Cost-sensitive applications running inference at scale","Developers building edge LLM applications on resource-constrained devices"],"limitations":["Local inference is slower than cloud APIs — typical latency 10-100x higher depending on hardware","Requires significant GPU/CPU resources — not suitable for serverless or resource-constrained environments","Model selection limited to open-source models (Llama, Mistral, etc.) — no access to proprietary models like GPT-4","Constraint enforcement adds per-token overhead on top of base inference latency"],"requires":["Python 3.9+","GPU with CUDA/ROCm support (recommended) or CPU (slow)","Model files (HuggingFace format for Transformers, GGUF for LlamaCpp, MLX for MLXLM)","Sufficient disk space for model weights (7B models ~15GB, 70B models ~150GB)"],"input_types":["text prompts","Jinja2 templates","constraint specifications (JSON schema, regex, CFG)"],"output_types":["text","structured JSON","constrained text"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-dottxt-ai--outlines__cap_9","uri":"capability://tool.use.integration.api.based.model.integration.with.native.constraint.support.openai.anthropic.gemini.mistral","name":"api-based model integration with native constraint support (openai, anthropic, gemini, mistral)","description":"Outlines integrates with cloud LLM APIs (OpenAI, Anthropic, Gemini, Mistral, Dottxt) by leveraging their native structured output features. For OpenAI, it uses JSON mode and function calling; for Anthropic, it uses tool_choice and structured outputs; for Gemini, it uses schema-based generation. The Model Integration Layer translates Outlines' constraint representations into provider-native formats, enabling server-side constraint enforcement without client-side logits masking, reducing latency and improving reliability.","intents":["Generate structured outputs using cloud LLM APIs with guaranteed schema compliance","Leverage provider-native structured generation features for optimal performance","Build cloud-based LLM applications without managing local inference infrastructure"],"best_for":["Teams using OpenAI, Anthropic, Gemini, or Mistral APIs","Applications requiring low-latency structured generation","Developers building cloud-native LLM applications"],"limitations":["API costs scale with token usage — structured generation may increase token consumption due to constraint overhead","Provider APIs have different feature coverage — not all providers support all constraint types equally","API rate limits and quota constraints may impact throughput for high-volume applications","Vendor lock-in — switching providers requires code changes to adapt to different API formats"],"requires":["Python 3.9+","API key for target provider (OpenAI, Anthropic, Gemini, Mistral, or Dottxt)","Network connectivity to provider API","Sufficient API quota and credits"],"input_types":["text prompts","Jinja2 templates","constraint specifications (JSON schema, regex, CFG)"],"output_types":["text","structured JSON","constrained text"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, Gemini, Mistral) OR local model files (for Transformers, LlamaCpp)","Appropriate tokenizer for each model backend","JSON schema definition or Python type hints (Pydantic models, dataclasses, TypedDict)","Model with tokenizer access (for local models) or API support for structured outputs (for cloud models)","vLLM server running (local or remote)","Network connectivity to vLLM server","Model loaded in vLLM server","Model backend supporting batch generation (most backends support this)","Async runtime (asyncio) for async generation"],"failure_modes":["API-based models enforce constraints server-side (OpenAI, Anthropic) while local models use client-side logits masking, creating different latency profiles","Not all providers support all constraint types equally — some APIs lack native regex or CFG support","Requires separate API keys or model downloads for each provider","Schema complexity impacts generation speed — deeply nested schemas with many constraints add latency per token","Large schemas may exceed context windows or tokenizer capacity","API-based models (OpenAI, Anthropic) have native schema support but may not support all JSON Schema features equally","Requires vLLM server setup and maintenance — operational overhead vs. managed cloud APIs","Constraint enforcement in vLLM may have different performance characteristics than local Outlines backends","Network latency between client and vLLM server adds overhead vs. local inference","vLLM server must be running and accessible — adds deployment complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3649305703019505,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-04-16T12:39:55Z"},"community":{"stars":13777,"forks":693,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dottxt-ai--outlines","compare_url":"https://unfragile.ai/compare?artifact=dottxt-ai--outlines"}},"signature":"ZZwnGoRJ1CvhbZuZImg3coEf3MxYNDzGHVIV3yxBSD+XPfauSnkGOVAfZUk0kRdsMrGIf6N76HP1XR7I9UBhCA==","signedAt":"2026-06-22T19:47:14.576Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dottxt-ai--outlines","artifact":"https://unfragile.ai/dottxt-ai--outlines","verify":"https://unfragile.ai/api/v1/verify?slug=dottxt-ai--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"}}