{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-guidance","slug":"pypi-guidance","name":"guidance","type":"framework","url":"https://github.com/guidance-ai/guidance","page_url":"https://unfragile.ai/pypi-guidance","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-guidance__cap_0","uri":"capability://text.generation.language.grammar.constrained.text.generation.with.token.aware.parsing","name":"grammar-constrained text generation with token-aware parsing","description":"Generates text from language models while enforcing constraints defined as an Abstract Syntax Tree (AST) of GrammarNode subclasses (LiteralNode, RegexNode, SelectNode, JsonNode). Uses TokenParser and ByteParser engines that work at the text level rather than token level, implementing token healing to correctly process text boundaries. The execution engine accumulates generated text into stateful lm objects that maintain both output and captured variables across generation steps.","intents":["I want to constrain LLM output to match a specific pattern or format without post-processing","I need to generate structured data (JSON, CSV, key-value pairs) directly from the model with guaranteed validity","I want to interleave conditional logic and loops with text generation in a single program","I need to capture specific parts of generated text into variables for downstream use"],"best_for":["developers building structured output pipelines (JSON extraction, form filling, code generation)","teams implementing guardrails for LLM outputs without external validation layers","researchers prototyping grammar-based control mechanisms for language models"],"limitations":["Token healing adds computational overhead compared to unconstrained generation; exact latency depends on constraint complexity","Grammar constraints must be defined upfront; dynamic constraint generation at runtime is not supported","ByteParser requires UTF-8 compatible tokenizers; some specialized tokenizers may not work correctly","Complex nested grammars can produce exponential parsing states, requiring careful grammar design"],"requires":["Python 3.8+","A supported language model backend (llama-cpp-python, transformers, OpenAI API, Anthropic API, or VertexAI)","llguidance library for tokenization integration"],"input_types":["Python function decorated with @guidance","Grammar node definitions (LiteralNode, RegexNode, SelectNode, JsonNode)","Prompt text with embedded generation directives"],"output_types":["Constrained text output matching grammar","Captured variables extracted from generation","Structured data (JSON, regex matches, selected options)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_1","uri":"capability://tool.use.integration.multi.backend.model.abstraction.with.unified.api","name":"multi-backend model abstraction with unified api","description":"Provides a unified interface for executing guidance programs across heterogeneous language model backends including local models (llama-cpp, Hugging Face Transformers) and remote APIs (OpenAI, Anthropic, Azure OpenAI, Google VertexAI). Each backend implements a common model interface that handles tokenization, state management, and generation, allowing the same guidance program to run on different models without code changes. The abstraction layer handles backend-specific details like API authentication, context window management, and token counting.","intents":["I want to write a guidance program once and run it on multiple model providers (local and cloud)","I need to switch between OpenAI and a local Llama model without rewriting my generation logic","I want to abstract away backend-specific API details (authentication, rate limiting, token counting) from my application code","I need to support both streaming and non-streaming backends with the same interface"],"best_for":["teams building multi-model applications that need provider flexibility","developers prototyping with cloud APIs but deploying with local models","organizations evaluating different model providers without rewriting application code"],"limitations":["Backend-specific features (e.g., vision capabilities, function calling schemas) may not be uniformly exposed across all backends","Performance characteristics vary significantly between backends; latency and throughput are not normalized","Some advanced features (e.g., token healing) may have different behavior across backends due to tokenizer differences","Remote API backends introduce network latency and rate limiting that local backends do not have"],"requires":["Python 3.8+","Backend-specific dependencies: llama-cpp-python for local Llama, transformers for Hugging Face, openai for OpenAI, anthropic for Anthropic, google-cloud-aiplatform for VertexAI","API keys for remote backends (OpenAI, Anthropic, VertexAI)"],"input_types":["Model identifier string (e.g., 'gpt-4', 'claude-3-opus', 'meta-llama/Llama-2-7b')","Backend configuration (API keys, model paths, context window size)","Guidance program (decorated Python function)"],"output_types":["Generated text with captured variables","Token usage statistics (if provided by backend)","Model state object for chaining multiple generations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_10","uri":"capability://automation.workflow.caching.and.stateless.execution.modes.for.performance.optimization","name":"caching and stateless execution modes for performance optimization","description":"Supports both stateful and stateless execution modes, with optional caching of generation results. Stateless mode allows guidance programs to be executed without maintaining state between calls, reducing memory overhead. Caching can be enabled to store results of expensive generations (e.g., long prompts with complex constraints) and reuse them for identical inputs. The caching layer integrates with the model backend to avoid redundant API calls or model inference.","intents":["I want to cache results of expensive generations to avoid redundant computation","I need to run guidance programs in stateless mode for scalability","I want to optimize performance by reusing cached results for identical inputs","I need to implement batch processing with caching for multiple similar prompts"],"best_for":["teams building production systems with high throughput requirements","developers optimizing performance for repeated or similar generations","applications with budget constraints on API calls (OpenAI, Anthropic)"],"limitations":["Caching is based on exact input matching; similar but not identical inputs are not cached","Cache invalidation is manual; no automatic cache expiration or versioning","Cached results are stored in memory by default; no built-in persistence to disk or database","Stateless mode prevents access to previous generation state; multi-step pipelines require explicit state passing"],"requires":["Python 3.8+","Guidance framework with a configured model backend","Optional: external cache store (Redis, memcached) for distributed caching"],"input_types":["Guidance program with cache=True or stateless=True parameter","Prompt and model configuration"],"output_types":["Generated text (from cache or fresh generation)","Cache hit/miss indicator","Captured variables"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_11","uri":"capability://planning.reasoning.programmatic.control.flow.with.python.integration","name":"programmatic control flow with python integration","description":"Allows guidance programs to interleave Python control flow (if/else, for loops, function calls) with constrained text generation using the @guidance decorator. The decorator transforms Python functions into guidance programs that can mix imperative logic with declarative grammar constraints. This enables complex workflows where generation decisions depend on previous outputs, external data, or application logic.","intents":["I want to implement conditional generation based on previous outputs or external data","I need to build complex workflows that mix Python logic with constrained generation","I want to call external functions or APIs within a guidance program","I need to implement loops that generate multiple constrained outputs based on application logic"],"best_for":["developers building complex agentic workflows with mixed logic and generation","teams implementing conditional generation pipelines","applications requiring tight integration between application code and language model generation"],"limitations":["Control flow is executed sequentially; no built-in parallelization of branches","Python function calls within guidance programs block generation; no async/await support","Debugging mixed Python/generation code can be complex; stack traces may be difficult to interpret","Performance depends on Python execution speed; complex logic can slow generation"],"requires":["Python 3.8+","Guidance framework with a configured model backend","Understanding of the @guidance decorator and guidance program syntax"],"input_types":["Python function decorated with @guidance","Embedded generation directives (gen(), select(), json())","External data or function calls"],"output_types":["Generated text with captured variables","Return value of the guidance function (any Python type)","lm state object for inspection"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_12","uri":"capability://text.generation.language.token.level.constraint.enforcement.with.llguidance.integration","name":"token-level constraint enforcement with llguidance integration","description":"Integrates with the llguidance library to enforce grammar constraints at the token level during model inference. The grammar AST is compiled into a state machine that tracks which tokens are valid at each generation step, preventing the model from generating invalid tokens. This is implemented through a custom sampling function that filters the model's token logits based on the current grammar state, ensuring only valid tokens are sampled.","intents":["I want to prevent the model from generating invalid tokens that violate grammar constraints","I need to enforce constraints at the token level for maximum reliability","I want to understand which tokens are valid at each generation step","I need to implement custom sampling logic that respects grammar constraints"],"best_for":["developers building systems where constraint violations are unacceptable","teams implementing safety-critical applications (medical, financial)","researchers studying token-level constraint enforcement"],"limitations":["Token-level filtering adds computational overhead; exact latency depends on grammar complexity","Some tokenizers may not be fully compatible with llguidance; custom tokenizer support requires integration work","Very large vocabularies (100k+ tokens) can slow token filtering","Constraint violations at the token level may cause the model to refuse generation if no valid tokens remain"],"requires":["Python 3.8+","llguidance library (C++ extension for performance)","Model with compatible tokenizer (most modern models)","Guidance framework with a configured model backend"],"input_types":["Grammar AST (compiled from grammar nodes)","Model tokenizer","Token logits from model"],"output_types":["Filtered token logits (only valid tokens have non-zero probability)","Valid token set at current generation step","Grammar state for next step"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_13","uri":"capability://text.generation.language.recursive.grammar.rules.and.reusable.constraint.patterns","name":"recursive grammar rules and reusable constraint patterns","description":"Supports RuleNode grammar constraints that define reusable patterns and recursive grammar rules. Rules can be defined once and referenced multiple times, reducing grammar duplication and improving maintainability. Recursive rules enable generation of nested structures (e.g., nested JSON, nested lists) without explicitly defining the nesting depth. Rules are compiled into the grammar AST and can be parameterized with arguments.","intents":["I want to define reusable grammar patterns that can be referenced multiple times","I need to generate nested structures (nested JSON, nested lists) with recursive rules","I want to parameterize grammar rules with arguments for flexibility","I need to reduce duplication in complex grammar definitions"],"best_for":["developers building complex grammar definitions with repeated patterns","teams generating nested or hierarchical data structures","applications with domain-specific grammar patterns"],"limitations":["Recursive rules can cause infinite grammar expansion if not carefully designed; base cases must be explicit","Parameterized rules add complexity to grammar definition; simple rules are easier to understand","Debugging recursive rules can be difficult; grammar visualization may not show recursion clearly","Very deep recursion (10+ levels) can produce large grammar states and slow generation"],"requires":["Python 3.8+","Guidance framework with a configured model backend","Understanding of recursive grammar patterns"],"input_types":["Rule definitions (RuleNode with name and pattern)","Rule references in grammar","Optional: rule parameters"],"output_types":["Generated text matching the recursive rule","Captured variables from rule matches","Grammar state after rule expansion"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_2","uri":"capability://planning.reasoning.stateful.execution.with.variable.capture.and.context.accumulation","name":"stateful execution with variable capture and context accumulation","description":"Maintains execution state through immutable lm objects that accumulate generated text, captured variables, and model state across multiple generation steps. Variables are captured using named capture groups in regex patterns or JSON schema fields, and can be referenced in subsequent generation steps. The stateful model object preserves the full generation history, enabling introspection, debugging, and chaining of multiple constrained generations in sequence.","intents":["I want to extract specific values from generated text and use them in subsequent prompts","I need to build multi-step generation pipelines where each step depends on the previous output","I want to debug generation by inspecting what was generated at each step and what variables were captured","I need to maintain conversation context across multiple turns of generation"],"best_for":["developers building multi-step reasoning chains or agentic workflows","teams implementing form-filling or data extraction pipelines with validation","researchers studying generation behavior and debugging constraint violations"],"limitations":["Immutable state objects create memory overhead for long generation sequences; no built-in garbage collection for old states","Variable capture is limited to regex groups and JSON schema fields; arbitrary Python object capture is not supported","State serialization is not built-in; persisting state across process boundaries requires custom serialization","No automatic state merging for parallel generation branches"],"requires":["Python 3.8+","Guidance framework with a configured model backend","Named capture groups in regex patterns or JSON schema definitions"],"input_types":["Guidance program with capture directives (regex named groups, JSON schema)","Previous lm state object (for chaining generations)"],"output_types":["Updated lm object with new text and captured variables","Dictionary of captured variables accessible via lm['variable_name']","Full generation history for debugging"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_3","uri":"capability://data.processing.analysis.json.schema.based.structured.output.generation","name":"json schema-based structured output generation","description":"Generates valid JSON output that conforms to a provided JSON schema by using JsonNode grammar constraints. The schema is converted into a grammar that enforces field types, required fields, nested objects, and arrays at generation time. The generated JSON is automatically parsed and made available as Python objects in the captured variables, eliminating the need for post-generation validation or repair.","intents":["I want to generate JSON that always matches my schema without post-processing or validation","I need to extract structured data from unstructured text and guarantee the output is valid JSON","I want to generate complex nested objects with arrays and optional fields in a single pass","I need to avoid hallucinated JSON fields that don't match my schema"],"best_for":["developers building data extraction pipelines (entity extraction, form filling, API response generation)","teams implementing structured output for downstream systems that require strict schema compliance","applications where JSON validation failures are unacceptable (financial data, medical records)"],"limitations":["Schema must be defined in JSON Schema format; other schema languages (Pydantic, Protocol Buffers) require conversion","Very large schemas (100+ fields) can produce complex grammars that slow generation; schema optimization is required","Numeric constraints (min/max values) are not enforced at generation time; only type constraints are applied","Recursive schema definitions may cause infinite grammar expansion; circular references must be flattened"],"requires":["Python 3.8+","JSON schema definition (as dict or JSON string)","Guidance framework with a configured model backend"],"input_types":["JSON schema (dict or JSON string)","Prompt text requesting JSON generation","Optional: seed data or examples"],"output_types":["Valid JSON string matching the schema","Parsed Python dict/list accessible via captured variables","Schema validation report (if debugging enabled)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_4","uri":"capability://text.generation.language.regex.based.pattern.matching.and.text.extraction","name":"regex-based pattern matching and text extraction","description":"Constrains generation to match regular expressions using RegexNode grammar nodes, enabling precise control over text format and structure. Named capture groups in regex patterns are automatically extracted into variables for downstream use. The regex constraints are compiled into the grammar AST and enforced at the token level during generation, preventing the model from generating text that violates the pattern.","intents":["I want to generate text that matches a specific format (phone numbers, dates, email addresses, code snippets)","I need to extract specific parts of generated text using regex groups","I want to enforce constraints like 'exactly 3 digits followed by a dash' without post-processing","I need to generate code or structured text that follows a specific syntax"],"best_for":["developers generating formatted data (dates, phone numbers, addresses, URLs)","teams building code generation tools with syntax constraints","applications extracting specific information from generated text"],"limitations":["Complex regex patterns can produce large grammar states, slowing generation; simple patterns are recommended","Regex lookahead/lookbehind assertions may not be fully supported; only basic regex features are guaranteed","Unicode character classes may have different behavior across tokenizers; ASCII patterns are most reliable","Very long regex patterns (100+ characters) can cause grammar compilation to be slow"],"requires":["Python 3.8+","Valid Python regex pattern (re module syntax)","Guidance framework with a configured model backend"],"input_types":["Regex pattern string (with optional named groups)","Prompt text requesting generation matching the pattern"],"output_types":["Generated text matching the regex pattern","Captured groups extracted into variables (if named groups are used)","Regex match object for further processing"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_5","uri":"capability://planning.reasoning.conditional.branching.and.selection.with.constrained.alternatives","name":"conditional branching and selection with constrained alternatives","description":"Implements SelectNode grammar constraints that force the model to choose from a predefined set of alternatives, with each alternative being a grammar subtree. The selection is enforced at generation time, preventing the model from generating text outside the allowed options. Supports both simple string selections and complex nested grammar selections, with captured variables from the selected branch available in the output state.","intents":["I want the model to choose from a specific set of options (yes/no, category A/B/C) without hallucinating other values","I need to implement conditional logic where different prompts are used based on model output","I want to generate multiple alternative outputs and select the best one based on criteria","I need to enforce categorical constraints (sentiment: positive/negative/neutral) in generated text"],"best_for":["developers building classification or categorization pipelines","teams implementing decision trees or branching workflows with LLMs","applications requiring strict categorical outputs (sentiment analysis, intent classification)"],"limitations":["Selection must be from a predefined list; dynamic option generation is not supported","Large numbers of alternatives (100+) can slow generation due to grammar complexity","Nested selections can produce exponential grammar states; deep nesting should be avoided","No built-in support for weighted selection; all alternatives are treated equally"],"requires":["Python 3.8+","List of alternative strings or grammar nodes","Guidance framework with a configured model backend"],"input_types":["List of alternative strings or grammar subtrees","Prompt text requesting selection from alternatives"],"output_types":["Selected alternative string","Captured variables from the selected branch","Index of selected alternative (if needed)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_6","uri":"capability://text.generation.language.chat.role.templating.with.multi.turn.conversation.support","name":"chat role templating with multi-turn conversation support","description":"Provides built-in support for multi-turn conversations with role-based message formatting (system, user, assistant). Chat templates are automatically applied based on the model's tokenizer, handling model-specific formatting requirements (e.g., ChatML, Llama2 chat format). The framework maintains conversation history as part of the lm state, enabling stateful multi-turn interactions where each turn can use grammar constraints.","intents":["I want to build multi-turn chatbots with constrained outputs at each turn","I need to format messages correctly for different models (OpenAI, Llama, Mistral) without manual template handling","I want to maintain conversation history and use it in subsequent generations","I need to implement system prompts that work across different model providers"],"best_for":["developers building chatbot applications with multiple turns","teams implementing conversational AI with structured output requirements","applications supporting multiple model providers with different chat formats"],"limitations":["Chat templates are model-specific; custom templates require manual definition","Conversation history is stored in memory; no built-in persistence to disk or database","Token counting for conversation history may be inaccurate for some models","Role-based access control or message filtering is not built-in"],"requires":["Python 3.8+","Model with chat template support (most modern models)","Guidance framework with a configured model backend"],"input_types":["Chat messages with roles (system, user, assistant)","Guidance program for generating assistant responses","Optional: custom chat template"],"output_types":["Formatted message string with role tags","Updated conversation history in lm state","Generated assistant response with constraints applied"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_7","uri":"capability://tool.use.integration.function.calling.and.tool.use.with.schema.based.dispatch","name":"function calling and tool use with schema-based dispatch","description":"Enables language models to call external functions or tools by generating function calls that conform to a schema-based registry. Functions are registered with their signatures, and the model generates structured function calls (typically as JSON) that are automatically parsed and dispatched to the registered functions. The framework handles schema validation, parameter extraction, and return value integration back into the generation context.","intents":["I want the model to call external APIs or functions and use the results in subsequent generation","I need to implement agent-like behavior where the model decides which tool to use and with what parameters","I want to constrain function calls to a predefined set of functions with validated parameters","I need to implement tool-use loops where the model iteratively calls tools and reasons about results"],"best_for":["developers building AI agents that interact with external systems","teams implementing tool-use workflows with validated function calls","applications requiring structured function invocation from language models"],"limitations":["Function schemas must be defined upfront; dynamic schema generation is not supported","Return values from functions must be serializable to text or JSON for re-injection into context","No built-in timeout or error handling for function execution; custom wrappers are required","Tool-use loops can consume significant context; no automatic context compression"],"requires":["Python 3.8+","Function definitions with type annotations or schema definitions","Guidance framework with a configured model backend","JSON schema definitions for function parameters"],"input_types":["Function registry (dict of function name -> function object)","Function schemas (JSON Schema format)","Prompt requesting tool use"],"output_types":["Parsed function call with parameters","Function return value (any serializable type)","Updated lm state with function result"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_8","uri":"capability://text.generation.language.repetition.and.iteration.patterns.with.grammar.based.loops","name":"repetition and iteration patterns with grammar-based loops","description":"Supports repetition patterns (one_or_more, zero_or_more, repeat) using RepeatNode grammar constraints that allow the model to generate multiple instances of a pattern. Useful for generating lists, arrays, or repeated structures where each repetition must conform to the same grammar. The framework handles variable capture across repetitions, accumulating results into lists that are accessible in the lm state.","intents":["I want to generate a list of items where each item matches a specific pattern","I need to generate multiple JSON objects in an array with guaranteed schema compliance","I want to implement loops in generation (e.g., 'generate 3 examples of X')","I need to extract multiple matches from generated text using the same regex pattern"],"best_for":["developers generating lists or arrays of structured data","teams building data generation pipelines with repeated patterns","applications requiring multiple instances of constrained output"],"limitations":["Repetition count must be bounded; infinite loops are not supported","Very large repetition counts (1000+) can consume significant memory and slow generation","Nested repetitions can produce exponential grammar complexity; deep nesting should be avoided","No built-in support for conditional repetition (e.g., 'repeat until condition')"],"requires":["Python 3.8+","Grammar pattern to repeat (string, regex, JSON schema, or selection)","Guidance framework with a configured model backend"],"input_types":["Grammar node to repeat","Repetition count or bounds (min, max)","Prompt requesting repeated generation"],"output_types":["List of generated items matching the pattern","Captured variables from each repetition (as list of dicts)","Full generation history for each repetition"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-guidance__cap_9","uri":"capability://automation.workflow.notebook.integration.with.interactive.visualization.and.debugging","name":"notebook integration with interactive visualization and debugging","description":"Provides Jupyter notebook widgets that visualize guidance program execution, showing the generated text, captured variables, and grammar constraints in real-time. The visualization includes token-by-token generation progress, constraint violations (if any), and interactive exploration of the grammar AST. Enables developers to debug guidance programs directly in notebooks without external tools.","intents":["I want to visualize how my guidance program is generating text step-by-step","I need to debug why a constraint is being violated or why a variable wasn't captured","I want to explore the grammar AST to understand how constraints are being applied","I need to inspect intermediate states during multi-step generation"],"best_for":["researchers prototyping and debugging guidance programs","developers learning how guidance constraints work","teams troubleshooting complex grammar definitions"],"limitations":["Visualization is limited to Jupyter notebooks; no CLI or web UI support","Real-time visualization adds overhead to generation; not suitable for production performance testing","Large grammar ASTs (100+ nodes) may be difficult to visualize in a single view","Interactive exploration requires manual navigation; no automated constraint analysis"],"requires":["Python 3.8+","Jupyter notebook environment","Guidance framework with a configured model backend","ipywidgets library for interactive widgets"],"input_types":["Guidance program (decorated Python function)","Model and prompt for generation"],"output_types":["Interactive Jupyter widget showing generation progress","Grammar AST visualization","Captured variables table","Constraint violation report (if applicable)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","A supported language model backend (llama-cpp-python, transformers, OpenAI API, Anthropic API, or VertexAI)","llguidance library for tokenization integration","Backend-specific dependencies: llama-cpp-python for local Llama, transformers for Hugging Face, openai for OpenAI, anthropic for Anthropic, google-cloud-aiplatform for VertexAI","API keys for remote backends (OpenAI, Anthropic, VertexAI)","Guidance framework with a configured model backend","Optional: external cache store (Redis, memcached) for distributed caching","Understanding of the @guidance decorator and guidance program syntax","llguidance library (C++ extension for performance)","Model with compatible tokenizer (most modern models)"],"failure_modes":["Token healing adds computational overhead compared to unconstrained generation; exact latency depends on constraint complexity","Grammar constraints must be defined upfront; dynamic constraint generation at runtime is not supported","ByteParser requires UTF-8 compatible tokenizers; some specialized tokenizers may not work correctly","Complex nested grammars can produce exponential parsing states, requiring careful grammar design","Backend-specific features (e.g., vision capabilities, function calling schemas) may not be uniformly exposed across all backends","Performance characteristics vary significantly between backends; latency and throughput are not normalized","Some advanced features (e.g., token healing) may have different behavior across backends due to tokenizer differences","Remote API backends introduce network latency and rate limiting that local backends do not have","Caching is based on exact input matching; similar but not identical inputs are not cached","Cache invalidation is manual; no automatic cache expiration or versioning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:12.848Z","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=pypi-guidance","compare_url":"https://unfragile.ai/compare?artifact=pypi-guidance"}},"signature":"365W0Yh4rqjwWhN3JepPMaveMZUIBT9dEVV5caYjTfyW+2Vqh8t5vcSgNu9qRBsEXPR3UhxCIHpvIktTsm2GBA==","signedAt":"2026-06-20T12:55:13.682Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-guidance","artifact":"https://unfragile.ai/pypi-guidance","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-guidance","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"}}