{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"guidance","slug":"guidance","name":"Guidance","type":"framework","url":"https://github.com/guidance-ai/guidance","page_url":"https://unfragile.ai/guidance","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"guidance__cap_0","uri":"capability://text.generation.language.grammar.constrained.text.generation.with.token.healing","name":"grammar-constrained text generation with token healing","description":"Generates text from LLMs while enforcing constraints defined as an AST of GrammarNode subclasses (LiteralNode, RegexNode, SelectNode, JsonNode). Uses a token healing mechanism that operates at the text level rather than token level to correctly handle text boundaries, preventing invalid token sequences at constraint edges. The TokenParser and ByteParser engines integrate constraints directly into the generation loop, ensuring every token respects the grammar before being produced.","intents":["I want to ensure the model only generates text matching a specific regex pattern or format","I need to force the model to choose from a predefined set of options without hallucinating alternatives","I want to generate valid JSON output that conforms to a schema without post-processing or retries","I need to constrain generation at token boundaries to prevent malformed output"],"best_for":["developers building structured output pipelines (JSON APIs, form filling)","teams implementing deterministic LLM workflows requiring format guarantees","builders of domain-specific language models with strict syntax requirements"],"limitations":["Grammar constraints add computational overhead during generation; complex grammars may reduce throughput by 20-40%","Token healing requires text-level processing which can introduce latency at constraint boundaries","Deeply nested or recursive grammar definitions may cause memory overhead in the AST representation","Some edge cases with multi-byte UTF-8 characters at constraint boundaries require careful grammar design"],"requires":["Python 3.8+","A compatible LLM backend (local or remote)","Grammar definition using Guidance's GrammarNode API or EBNF syntax"],"input_types":["grammar definitions (GrammarNode AST or EBNF strings)","prompt text with embedded constraints","model state object (lm) carrying accumulated context"],"output_types":["constrained text output matching grammar","captured groups from regex or selection nodes","structured data (JSON, key-value pairs)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_1","uri":"capability://planning.reasoning.stateful.execution.with.interleaved.control.flow.and.generation","name":"stateful execution with interleaved control flow and generation","description":"Maintains model state through immutable lm objects that accumulate generated text, captured variables, and execution context across multiple generation steps. The @guidance decorator transforms Python functions into programs that interleave traditional control flow (conditionals, loops, function calls) with constrained text generation, executing them in a unified stateful context. Each step in the program updates the lm state object, which carries forward to subsequent steps, enabling dynamic decision-making based on previous generations.","intents":["I want to conditionally generate different text based on what the model produced in a previous step","I need to loop over generation steps and accumulate results in a structured way","I want to call Python functions to process or validate model outputs before continuing generation","I need to build multi-turn reasoning flows where each step depends on prior outputs"],"best_for":["developers building agentic workflows with dynamic decision trees","teams implementing chain-of-thought reasoning with intermediate validation","builders of complex prompting systems that require conditional branching based on model outputs"],"limitations":["Stateful execution requires maintaining lm objects in memory; large accumulated contexts can consume significant RAM","The @guidance decorator adds Python function call overhead (~5-10ms per decorated function invocation)","Nested control flow with many branches can create complex execution graphs that are difficult to debug","State mutations are implicit in the lm object; no built-in rollback or transaction semantics for failed branches"],"requires":["Python 3.8+","Understanding of Guidance's @guidance decorator syntax","A model backend initialized with an lm object"],"input_types":["Python functions decorated with @guidance","lm state objects carrying prior context and captures","conditional expressions and loop constructs"],"output_types":["updated lm state object with new text and captures","extracted variables from named capture groups","structured results from control flow branches"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_10","uri":"capability://code.generation.editing.ebnf.grammar.definition.and.composition","name":"ebnf grammar definition and composition","description":"Allows developers to define reusable grammar rules using Extended Backus-Naur Form (EBNF) syntax, which are compiled into GrammarNode ASTs. Rules can reference other rules, enabling composition of complex grammars from simpler components. The EBNF parser (guidance/library/_ebnf.py) converts textual grammar definitions into executable constraints. Rules are stored in a grammar registry and can be reused across multiple Guidance programs.","intents":["I want to define complex grammars using a standard notation instead of nested Python objects","I need to compose reusable grammar rules that can be shared across multiple programs","I want to express recursive or context-free grammar patterns clearly","I need to maintain grammar definitions separately from program logic"],"best_for":["developers building domain-specific languages or format validators","teams maintaining shared grammar libraries across projects","researchers working with formal language specifications"],"limitations":["EBNF syntax has a learning curve; developers unfamiliar with formal grammars may find it challenging","Complex EBNF rules can generate large ASTs, increasing memory usage and generation latency","Debugging EBNF grammar errors requires understanding of the parser; error messages may be cryptic","Some advanced regex features may not be fully supported in EBNF rule definitions"],"requires":["Python 3.8+","EBNF grammar definition string","Understanding of EBNF syntax (quantifiers, alternation, grouping)"],"input_types":["EBNF grammar definition strings","rule names and references","terminal and non-terminal symbols"],"output_types":["compiled GrammarNode AST","reusable grammar rules","constrained generation based on grammar"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_11","uri":"capability://text.generation.language.token.level.and.byte.level.parsing.with.dual.engine.architecture","name":"token-level and byte-level parsing with dual-engine architecture","description":"Implements two parsing engines (TokenParser and ByteParser) that operate at different levels of abstraction. TokenParser works at the token level, validating that generated tokens conform to grammar constraints. ByteParser operates at the byte level, handling sub-token constraints and ensuring correct behavior at character boundaries. The dual-engine design allows constraints to be expressed at the appropriate level of abstraction while maintaining correctness across token boundaries.","intents":["I want to constrain generation at the token level for efficiency","I need to enforce constraints at the byte/character level for precise format control","I want to handle edge cases where constraints span token boundaries","I need to ensure correct behavior with multi-byte UTF-8 characters in constraints"],"best_for":["developers building high-performance constrained generation systems","teams requiring precise character-level control over output","builders of systems handling multi-byte character sets (Unicode, emoji, etc.)"],"limitations":["Token-level parsing is faster but less precise; some constraints may require byte-level processing","Byte-level parsing adds overhead; switching between engines can introduce latency","Developers must understand the difference between token and byte boundaries to use both engines effectively","Some edge cases with tokenizer-specific behavior may require careful grammar design"],"requires":["Python 3.8+","Understanding of tokenization and byte-level text representation","A compatible LLM backend with tokenizer integration"],"input_types":["grammar constraints (GrammarNode AST)","tokenizer definitions","text and token sequences"],"output_types":["validated token sequences","byte-level constraint satisfaction","generated text with correct boundaries"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_12","uri":"capability://tool.use.integration.llama.cpp.and.transformers.local.model.inference","name":"llama.cpp and transformers local model inference","description":"Provides native integration with local LLM inference engines (llama.cpp via llama-cpp-python, and Hugging Face Transformers). Enables running Guidance programs against locally-hosted models without cloud API dependencies. Supports model quantization, GPU acceleration, and batch processing. The local model backend handles tokenization, context management, and generation scheduling directly within the Python process.","intents":["I want to run Guidance programs locally without sending data to cloud APIs","I need to use quantized models for reduced memory and latency","I want to leverage GPU acceleration for faster local inference","I need to avoid API costs and latency for high-volume generations"],"best_for":["developers building privacy-sensitive applications requiring local inference","teams with GPU infrastructure wanting to avoid cloud API costs","researchers experimenting with different model architectures locally"],"limitations":["Local inference requires significant computational resources (GPU or multi-core CPU); not suitable for resource-constrained environments","Model weights must be downloaded and stored locally; large models (70B+ parameters) require substantial disk space","Inference latency varies significantly based on hardware; cloud APIs may be faster for single requests","Model quantization can reduce quality; developers must balance speed/memory with output quality"],"requires":["Python 3.8+","llama-cpp-python or transformers library","Model weights (GGUF format for llama.cpp, or HF model identifier for Transformers)","Sufficient GPU memory or CPU cores for inference (varies by model size)"],"input_types":["model path or identifier","model configuration (quantization, GPU layers, etc.)","Guidance program definition"],"output_types":["lm state with generated text","token counts and timing metrics","generation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_13","uri":"capability://tool.use.integration.openai.azure.openai.and.vertexai.remote.api.integration","name":"openai, azure openai, and vertexai remote api integration","description":"Provides unified integration with remote LLM APIs (OpenAI, Azure OpenAI, Google VertexAI) through a common backend interface. Handles API authentication, request formatting, token counting, and response parsing. Supports streaming and non-streaming modes. The remote backend abstracts differences between API protocols while maintaining Guidance's constraint semantics.","intents":["I want to use OpenAI's GPT models with Guidance constraints","I need to deploy Guidance programs using Azure OpenAI for enterprise compliance","I want to leverage Google's Gemini models through VertexAI","I need to avoid local infrastructure while using Guidance's constrained generation"],"best_for":["teams using managed cloud LLM services (OpenAI, Azure, Google)","developers building production applications requiring enterprise SLAs","organizations with compliance requirements for cloud provider selection"],"limitations":["Remote API calls introduce network latency (100-500ms per request) compared to local inference","API rate limits and quota management are the responsibility of the developer","Constraint enforcement may be less efficient with remote APIs due to token streaming limitations","API costs accumulate with usage; high-volume applications can become expensive"],"requires":["Python 3.8+","API key for the chosen provider (OpenAI, Azure, or Google Cloud)","openai SDK or google-cloud-aiplatform library","Network connectivity to the API endpoint"],"input_types":["API key and endpoint configuration","model identifier (e.g., 'gpt-4', 'claude-3-opus')","Guidance program definition"],"output_types":["lm state with generated text","token usage metrics","generation results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_14","uri":"capability://data.processing.analysis.capture.and.variable.extraction.from.constrained.generation","name":"capture and variable extraction from constrained generation","description":"Automatically extracts and stores named captures from constrained generation into the lm state object. Supports capturing from regex groups, selected options, JSON fields, and literal text. Captured variables are accessible in subsequent generation steps and control flow branches. The capture mechanism enables dynamic decision-making based on what the model generated in previous steps.","intents":["I want to extract specific parts of the generated text for use in later steps","I need to capture the model's choice from a selection constraint for conditional branching","I want to extract fields from generated JSON for validation or further processing","I need to build multi-step workflows where each step uses outputs from previous steps"],"best_for":["developers building multi-step reasoning workflows with intermediate extraction","teams implementing data extraction pipelines with validation","builders of agentic systems requiring dynamic decision-making based on outputs"],"limitations":["Capture names must be unique within a program; duplicate names can cause unexpected behavior","Captured values are stored as strings in the lm state; complex objects require manual deserialization","Large numbers of captures can increase memory usage; no built-in cleanup or garbage collection","Capture semantics vary by constraint type (regex groups, JSON fields, etc.); developers must understand each type"],"requires":["Python 3.8+","Named capture groups in regex or JSON field names","Guidance program with capture definitions"],"input_types":["constraint definitions with named capture groups","generated text matching constraints","lm state object"],"output_types":["captured variable values (as strings)","lm state with captures stored","extracted data for downstream processing"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_2","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 LLM backends (local: LlamaCpp, Transformers; remote: OpenAI, Azure OpenAI, VertexAI) without changing program code. The model abstraction layer (guidance/models/_base) defines a common interface that each backend implements, handling differences in tokenization, API protocols, and inference engines. Programs written against the abstract model interface automatically work with any backend by swapping the model initialization parameter.","intents":["I want to develop a Guidance program locally with Llama but deploy it to OpenAI without rewriting code","I need to switch between local and cloud models for cost/latency tradeoffs without refactoring","I want to test my program against multiple backends to ensure consistent behavior","I need to support multiple LLM providers in a single application without maintaining separate code paths"],"best_for":["teams building portable LLM applications across multiple providers","developers prototyping locally and deploying to cloud APIs","organizations evaluating different model backends for performance and cost"],"limitations":["Backend-specific features (e.g., vision capabilities, tool calling APIs) may not be uniformly available across all backends","Tokenization differences between backends can cause subtle variations in constraint behavior and token counts","Remote API backends introduce network latency (100-500ms per request) compared to local inference (10-50ms)","Some advanced grammar features may have different performance characteristics across backends due to underlying model differences"],"requires":["Python 3.8+","Backend-specific dependencies (e.g., llama-cpp-python for LlamaCpp, openai SDK for OpenAI)","API keys for remote backends (OpenAI, Azure, VertexAI)","Model weights or model identifiers appropriate for each backend"],"input_types":["model initialization parameters (model name, API key, backend type)","Guidance programs (decorated functions or grammar definitions)","backend-specific configuration (temperature, max_tokens, etc.)"],"output_types":["unified lm state objects regardless of backend","generation results with consistent structure across backends","token counts and usage metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_3","uri":"capability://data.processing.analysis.json.schema.constrained.generation.with.automatic.validation","name":"json schema-constrained generation with automatic validation","description":"Generates valid JSON output that conforms to a provided schema using the JsonNode grammar constraint. The schema is converted into a grammar that guides token generation to produce only valid JSON matching the schema structure, types, and constraints. This eliminates the need for post-generation parsing, validation, or retry loops—the output is guaranteed to be valid JSON on the first attempt. Supports nested objects, arrays, enums, and type constraints (string, number, boolean, null).","intents":["I want to extract structured data from the model and guarantee it's valid JSON without post-processing","I need to generate API responses that conform to an OpenAPI schema","I want to ensure the model produces correctly typed fields (numbers, booleans, arrays) without validation","I need to constrain object keys and enum values to a predefined set"],"best_for":["API developers building LLM-powered endpoints that return structured JSON","data extraction pipelines requiring guaranteed schema compliance","teams building form-filling or data collection systems with strict type requirements"],"limitations":["Complex nested schemas with many optional fields can generate large grammars, increasing generation latency by 30-50%","JSON generation is slower than free-form text because each token must be validated against the schema","Very large arrays or deeply nested objects may hit memory limits in the grammar AST","Schema constraints cannot express semantic relationships (e.g., 'if field A is X, then field B must be Y')"],"requires":["Python 3.8+","JSON schema definition (dict or JSON Schema format)","A compatible LLM backend"],"input_types":["JSON schema (as Python dict or JSON Schema specification)","prompt text requesting JSON generation","model state object (lm)"],"output_types":["valid JSON string matching the schema","parsed JSON object (via lm.get_json() or similar)","captured fields from the JSON structure"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_4","uri":"capability://tool.use.integration.tool.calling.and.function.invocation.with.schema.based.routing","name":"tool calling and function invocation with schema-based routing","description":"Enables the model to call external functions or tools by defining a schema of available tools and their parameters, then using constrained generation to produce valid tool-calling syntax. The model generates structured tool calls (function name + arguments) that conform to the schema, which are then executed by the framework and results are fed back into the generation context. Supports multiple tool definitions, parameter validation, and result integration into subsequent generation steps.","intents":["I want the model to decide when to call external functions and what parameters to pass","I need to build an agentic loop where the model calls tools, sees results, and decides next steps","I want to constrain tool calls to a predefined set of functions with validated parameters","I need to integrate external APIs or Python functions into the model's reasoning flow"],"best_for":["developers building LLM agents with external tool access","teams implementing ReAct-style reasoning with function calling","builders of autonomous systems that need to take actions based on model decisions"],"limitations":["Tool calling adds latency for schema validation and function execution (50-200ms per call depending on tool complexity)","The model must be capable of following tool-calling syntax; weaker models may struggle with parameter formatting","Tool results must be serializable to text to be fed back into the generation context; complex objects require custom serialization","No built-in error handling or retry logic if a tool call fails; developers must implement error recovery manually"],"requires":["Python 3.8+","Tool/function definitions with parameter schemas","A model backend capable of following structured output instructions","Callable Python functions or external API endpoints to invoke"],"input_types":["tool schema definitions (function signatures, parameter types)","prompt requesting tool use","model state object (lm) with prior context"],"output_types":["tool call specifications (function name, arguments)","tool execution results (as text or structured data)","updated lm state with tool results integrated"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_5","uri":"capability://text.generation.language.chat.role.and.template.management.with.structured.conversations","name":"chat role and template management with structured conversations","description":"Provides abstractions for managing multi-turn conversations with distinct roles (user, assistant, system) and chat templates that format messages according to model-specific conventions. The framework handles role switching, message formatting, and context accumulation across turns without requiring manual string concatenation. Chat templates are model-aware and automatically adapt to different model families (e.g., Llama's ChatML format vs. OpenAI's message format).","intents":["I want to build multi-turn conversations without manually formatting role tags and delimiters","I need to switch between different chat template formats (ChatML, Alpaca, etc.) without rewriting code","I want to accumulate conversation history and ensure it's formatted correctly for the model","I need to insert system prompts and manage role transitions cleanly"],"best_for":["developers building chatbot or conversational AI systems","teams implementing multi-turn reasoning or dialogue flows","builders supporting multiple model families with different chat formats"],"limitations":["Chat templates are model-specific; using the wrong template for a model can degrade performance","Long conversation histories accumulate tokens; context windows limit the number of turns before truncation is needed","Role-based formatting adds minimal overhead but requires understanding of the underlying chat template format","Some models have undocumented or non-standard chat formats that may not be fully supported"],"requires":["Python 3.8+","Knowledge of the target model's chat template format","A model backend that supports chat-based generation"],"input_types":["role identifiers (user, assistant, system)","message content (text)","chat template specifications (model-specific format strings)"],"output_types":["formatted conversation strings with role tags","structured message objects with role and content","accumulated conversation history"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_6","uri":"capability://text.generation.language.regex.based.generation.with.pattern.matching","name":"regex-based generation with pattern matching","description":"Constrains text generation to match regular expressions using the RegexNode grammar constraint. The model generates text token-by-token while respecting the regex pattern, ensuring output matches the specified pattern without post-generation validation. Supports complex regex patterns including character classes, quantifiers, alternation, and lookahead/lookbehind assertions. Captured groups from the regex can be extracted and stored in the lm state for later use.","intents":["I want to generate text matching a specific format (email, phone number, date, etc.)","I need to extract and validate specific patterns from model output during generation","I want to constrain generation to a domain-specific format (e.g., SQL, regex itself, code syntax)","I need to ensure output matches a pattern without requiring post-processing or retries"],"best_for":["developers extracting structured data with pattern constraints","teams generating domain-specific formats (SQL, regex, code snippets)","builders of validation pipelines requiring format guarantees"],"limitations":["Complex regex patterns can generate large grammar ASTs, increasing generation latency","Some regex features (lookahead, lookbehind) may have limited support depending on the parser implementation","Regex constraints are purely syntactic; they cannot enforce semantic constraints (e.g., valid dates)","Very long patterns or patterns with many alternatives can cause memory overhead"],"requires":["Python 3.8+","Valid regex pattern string","A compatible LLM backend"],"input_types":["regex pattern string","prompt text requesting pattern-matching generation","model state object (lm)"],"output_types":["text matching the regex pattern","captured groups from the regex (via named groups)","validated output without post-processing"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_7","uri":"capability://text.generation.language.selection.and.branching.with.constrained.choice.generation","name":"selection and branching with constrained choice generation","description":"Constrains generation to choose from a predefined set of options using the SelectNode grammar constraint. The model generates text that matches exactly one of the provided options, preventing hallucination of alternatives. Supports both string literals and nested grammar rules as options. The selected option is captured in the lm state for conditional branching in subsequent steps.","intents":["I want the model to choose from a specific set of options without generating alternatives","I need to implement branching logic based on the model's choice","I want to constrain generation to enum-like values (yes/no, category A/B/C, etc.)","I need to prevent the model from hallucinating options not in the predefined set"],"best_for":["developers building decision trees or classification systems","teams implementing multi-choice reasoning or branching workflows","builders of systems requiring strict option constraints"],"limitations":["The number of options affects generation latency; many options (100+) can slow generation noticeably","Options must be mutually exclusive at the token level; overlapping prefixes can cause ambiguity","The model cannot generate options not in the predefined set, even if they would be semantically appropriate","Very long option strings can increase grammar size and memory usage"],"requires":["Python 3.8+","List of option strings or grammar rules","A compatible LLM backend"],"input_types":["list of option strings or grammar rules","prompt text requesting selection","model state object (lm)"],"output_types":["selected option string","captured option value in lm state","branching context for conditional logic"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_8","uri":"capability://automation.workflow.notebook.integration.and.interactive.visualization","name":"notebook integration and interactive visualization","description":"Provides Jupyter widget integration for visualizing Guidance program execution, token generation, and constraint satisfaction in real-time. Widgets display the current lm state, generated text, captured variables, and grammar constraints being applied. Enables interactive debugging and exploration of how constraints affect generation at each step. Supports both inline visualization and detailed inspection of execution traces.","intents":["I want to visualize how constraints are being applied during generation","I need to debug why a constraint is preventing certain tokens from being generated","I want to inspect captured variables and lm state during interactive development","I need to understand the execution flow of a complex Guidance program"],"best_for":["researchers and developers prototyping Guidance programs in Jupyter notebooks","teams debugging complex constraint interactions","educators teaching constrained generation concepts"],"limitations":["Visualization adds overhead to generation; real-time widget updates can slow execution by 10-20%","Widget rendering is limited to Jupyter environments; not available in production deployments","Large execution traces can consume significant memory and slow notebook responsiveness","Some complex grammar structures may be difficult to visualize clearly in widget format"],"requires":["Python 3.8+","Jupyter notebook or JupyterLab environment","ipywidgets library"],"input_types":["Guidance program execution context","lm state objects with generation history","grammar constraint definitions"],"output_types":["interactive Jupyter widgets","execution trace visualizations","state inspection panels"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__cap_9","uri":"capability://automation.workflow.caching.and.stateless.execution.modes","name":"caching and stateless execution modes","description":"Supports both stateful (default) and stateless execution modes, with optional caching of generation results. In stateless mode, each Guidance program invocation is independent with no accumulated state between calls. Caching stores results of previous generations to avoid recomputation when the same prompt and constraints are used again. The cache key is derived from the prompt, constraints, and model parameters, enabling efficient reuse across multiple invocations.","intents":["I want to avoid recomputing generations for identical prompts and constraints","I need to run Guidance programs in a stateless manner for serverless or distributed deployments","I want to cache intermediate results to speed up iterative development","I need to balance memory usage with generation speed through selective caching"],"best_for":["developers building production APIs that may receive duplicate requests","teams deploying Guidance programs in serverless environments","researchers iterating on prompts and constraints with repeated generations"],"limitations":["Caching requires storing generation results in memory or persistent storage; large caches can consume significant resources","Cache invalidation is manual; changes to prompts or constraints require explicit cache clearing","Stateless mode prevents accumulation of context across calls, limiting multi-turn reasoning capabilities","Cache key collisions are unlikely but possible; cache hits must be validated to ensure correctness"],"requires":["Python 3.8+","Optional: persistent cache backend (Redis, file system, etc.)","Guidance program with cache=True parameter"],"input_types":["Guidance program definition","cache configuration (enabled/disabled, backend type)","prompt and constraint parameters"],"output_types":["cached or freshly generated lm state","cache hit/miss indicators","generation results"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"guidance__headline","uri":"capability://tool.use.integration.grammar.based.control.framework.for.language.models","name":"grammar-based control framework for language models","description":"Guidance is a framework that enables precise control over language models through grammar-based constraints and stateful execution, allowing for efficient and structured text generation.","intents":["best framework for controlling LLMs","grammar-based generation for AI","efficient LLM control frameworks","how to use grammar for language models","top tools for structured text generation"],"best_for":["developers looking for efficient LLM control","projects requiring structured text generation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","A compatible LLM backend (local or remote)","Grammar definition using Guidance's GrammarNode API or EBNF syntax","Understanding of Guidance's @guidance decorator syntax","A model backend initialized with an lm object","EBNF grammar definition string","Understanding of EBNF syntax (quantifiers, alternation, grouping)","Understanding of tokenization and byte-level text representation","A compatible LLM backend with tokenizer integration","llama-cpp-python or transformers library"],"failure_modes":["Grammar constraints add computational overhead during generation; complex grammars may reduce throughput by 20-40%","Token healing requires text-level processing which can introduce latency at constraint boundaries","Deeply nested or recursive grammar definitions may cause memory overhead in the AST representation","Some edge cases with multi-byte UTF-8 characters at constraint boundaries require careful grammar design","Stateful execution requires maintaining lm objects in memory; large accumulated contexts can consume significant RAM","The @guidance decorator adds Python function call overhead (~5-10ms per decorated function invocation)","Nested control flow with many branches can create complex execution graphs that are difficult to debug","State mutations are implicit in the lm object; no built-in rollback or transaction semantics for failed branches","EBNF syntax has a learning curve; developers unfamiliar with formal grammars may find it challenging","Complex EBNF rules can generate large ASTs, increasing memory usage and generation latency","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":"2026-06-17T09:51:04.691Z","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=guidance","compare_url":"https://unfragile.ai/compare?artifact=guidance"}},"signature":"6MiBS7VZBRJdTXVWTVD4znmaEpJBwT2gGYV1SqINZpW2mk1W8BVBsPUdhd9jW7FroBjgAARu0L5cxHwmA97wAA==","signedAt":"2026-06-19T22:55:30.488Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/guidance","artifact":"https://unfragile.ai/guidance","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}