{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"typechat","slug":"typechat","name":"TypeChat","type":"framework","url":"https://github.com/microsoft/TypeChat","page_url":"https://unfragile.ai/typechat","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"typechat__cap_0","uri":"capability://data.processing.analysis.schema.driven.llm.output.validation.with.automatic.repair","name":"schema-driven llm output validation with automatic repair","description":"TypeChat constructs a prompt that embeds TypeScript interface or Python dataclass definitions, sends it to an LLM, validates the response against the schema using type checkers, and automatically re-invokes the LLM with validation error details if the response fails to conform. This replaces manual prompt engineering with declarative type definitions that serve as the contract between natural language input and structured output.","intents":["I want to ensure LLM responses conform to my application's data structures without manual validation code","I need to automatically fix malformed LLM outputs by having the model correct itself based on type errors","I want to define valid response shapes once in my type system and reuse them across multiple LLM calls"],"best_for":["TypeScript/Node.js developers building type-safe LLM integrations","Python developers using dataclasses who want schema-driven LLM outputs","Teams migrating from prompt engineering to schema engineering patterns"],"limitations":["Repair loop adds latency — each validation failure triggers an additional LLM call, potentially 2-3x slower than unvalidated responses","Requires LLM to understand type definitions in natural language; less reliable with smaller models or non-English schemas","No built-in support for complex recursive types or circular references — flattening required","Repair attempts are bounded; after N failures, returns error rather than retrying indefinitely"],"requires":["TypeScript 4.7+ or Python 3.9+","API key for OpenAI, Anthropic, or compatible LLM provider","Type definitions (TypeScript interfaces or Python dataclasses) representing valid response shapes"],"input_types":["natural language text (user query or instruction)","TypeScript interface definitions or Python dataclass definitions"],"output_types":["JSON object conforming to schema","typed Python object or TypeScript object instance","validation error with repair suggestions if max retries exceeded"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_1","uri":"capability://data.processing.analysis.polyglot.type.to.prompt.translation.with.language.agnostic.schema.representation","name":"polyglot type-to-prompt translation with language-agnostic schema representation","description":"TypeChat translates TypeScript interfaces and Python dataclasses into a unified schema representation that is embedded into LLM prompts in a language-agnostic format. The translation pipeline converts native type syntax (TypeScript generics, Python type hints, union types, optional fields) into a normalized schema that the LLM can understand and use to generate conforming responses, enabling the same schema definition to work across multiple LLM providers.","intents":["I want to write types once in my language and have them automatically formatted for LLM consumption","I need to support both TypeScript and Python clients with the same schema definitions","I want to switch between different LLM providers without rewriting my schema definitions"],"best_for":["Polyglot teams using both TypeScript and Python","Developers building LLM integrations that need to support multiple model providers","Organizations standardizing on schema-driven LLM interactions across codebases"],"limitations":["Schema translation is one-way (type → prompt); no code generation from LLM-validated responses back to types","Complex TypeScript generics or advanced type features may not translate cleanly; requires flattening to basic types","Python dataclass translation requires explicit type hints; untyped or partially typed classes may produce incomplete schemas"],"requires":["TypeScript 4.7+ with type reflection capabilities or Python 3.9+ with dataclasses module","Type definitions must be serializable to JSON schema-like format"],"input_types":["TypeScript interface definitions","Python dataclass definitions"],"output_types":["Normalized schema representation (JSON-serializable format)","Formatted prompt text with embedded schema definitions"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_10","uri":"capability://safety.moderation.error.recovery.with.detailed.validation.feedback","name":"error recovery with detailed validation feedback","description":"When LLM responses fail validation, TypeChat generates detailed error messages explaining what went wrong (e.g., 'field \"price\" is missing', 'field \"quantity\" must be a number, got string'), formats these errors as natural language feedback, and includes them in the repair prompt to help the LLM understand and correct the mistake.","intents":["I want validation errors to be automatically converted to feedback that helps the LLM correct itself","I need detailed error messages that explain exactly what fields or types are wrong","I want to maximize repair success by giving the LLM clear guidance on what to fix"],"best_for":["Applications where automatic repair is preferred over user intervention","Scenarios where LLM errors are expected and detailed feedback improves correction rates","Teams that want to minimize manual error handling and user-facing error messages"],"limitations":["Error message generation adds latency; complex validation errors require time to format","LLM may not understand or act on detailed error feedback; repair success is not guaranteed","Error messages can be verbose and consume significant token budget in repair prompts","Some validation errors (e.g., semantic constraints) may not be expressible in natural language"],"requires":["Schema definition with clear field names and type information","Validation that produces detailed error messages (not just 'validation failed')"],"input_types":["validation error details (field name, expected type, actual value)","original LLM response"],"output_types":["natural language error feedback formatted for LLM consumption","repair prompt including error details"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_11","uri":"capability://planning.reasoning.multi.intent.schema.support.with.union.type.handling","name":"multi-intent schema support with union type handling","description":"TypeChat supports schemas with union types (e.g., 'response can be OrderConfirmation OR CancellationConfirmation OR ErrorResponse'), allowing a single LLM call to handle multiple possible intents. The library validates the response against all union members and identifies which intent the LLM chose, enabling flexible intent routing without separate LLM calls.","intents":["I want to handle multiple possible user intents in a single LLM call without separate prompts","I need to route responses to different handlers based on which union type was matched","I want to support open-ended user requests that could map to different intents"],"best_for":["Conversational AI systems handling diverse user intents","Applications with multiple response types that should be handled uniformly","Teams building flexible LLM interfaces that adapt to user input"],"limitations":["Union type validation is slower than single-type validation; must check response against all union members","LLM may struggle to choose between similar union members; ambiguous intents can cause validation failures","Error messages for union validation are more complex; harder for LLM to understand what went wrong","Repair logic must handle union type mismatches; may require re-prompting with clarification"],"requires":["Schema definition with union types (TypeScript | operator or Python Union)","Clear distinction between union members to avoid ambiguity"],"input_types":["user query or instruction","union type schema with multiple possible response types"],"output_types":["response object matching one of the union members","metadata indicating which union member was matched"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_12","uri":"capability://automation.workflow.context.window.management.with.schema.aware.token.budgeting","name":"context window management with schema-aware token budgeting","description":"TypeChat manages LLM context windows by accounting for schema size, user input, and repair attempts when constructing prompts. The library estimates token usage, warns if schema + prompt exceeds context limits, and can truncate or summarize context to fit within available tokens while preserving schema definitions.","intents":["I want to ensure my schema and prompt fit within the LLM's context window","I need to know how many tokens my schema is consuming so I can optimize prompts","I want to automatically handle context overflow by truncating or summarizing input"],"best_for":["Applications with large schemas or long user inputs that approach context limits","Teams using smaller context window models (e.g., GPT-3.5) where token budgeting is critical","Cost-conscious applications that want to minimize token usage"],"limitations":["Token estimation is approximate; actual token counts vary by tokenizer and model","Automatic context truncation may lose important information; requires manual tuning","Schema is always preserved in context; cannot be truncated without breaking validation","Context management adds complexity; requires careful tuning to avoid validation failures"],"requires":["Schema definition","Knowledge of LLM context window size","Optional: token counting library (e.g., js-tiktoken for OpenAI)"],"input_types":["schema definition","user input or prompt","LLM context window size"],"output_types":["token usage estimate","warning if context is exceeded","truncated or summarized prompt if needed"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_13","uri":"capability://text.generation.language.example.driven.schema.refinement.with.few.shot.learning","name":"example-driven schema refinement with few-shot learning","description":"TypeChat supports embedding examples (few-shot demonstrations) in prompts alongside schema definitions, showing the LLM concrete input-output pairs that illustrate how to map natural language to the schema. The library formats examples consistently with the schema and can use them to improve response quality without retraining the model.","intents":["I want to show the LLM examples of correct responses to improve output quality","I need to clarify ambiguous schema definitions with concrete demonstrations","I want to use few-shot learning to improve LLM performance without fine-tuning"],"best_for":["Applications with complex or ambiguous schemas that benefit from examples","Teams that want to improve LLM performance without fine-tuning or prompt engineering","Scenarios where a few good examples significantly improve response quality"],"limitations":["Examples consume significant tokens; each example adds 100-500 tokens to the prompt","Example quality directly impacts LLM performance; poor examples can degrade results","No automatic example selection; developers must manually choose representative examples","Examples may not generalize to out-of-distribution inputs; LLM may overfit to example patterns"],"requires":["Schema definition","Representative examples of input-output pairs","Manual curation of examples for quality"],"input_types":["schema definition","example input-output pairs","user query"],"output_types":["LLM response informed by examples","improved response quality compared to schema-only prompts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.request.response.interface","name":"multi-provider llm abstraction with unified request/response interface","description":"TypeChat provides a provider-agnostic abstraction layer that normalizes API calls to OpenAI, Anthropic, and other LLM providers through a unified interface. The library handles provider-specific request formatting, response parsing, and error handling, allowing developers to switch providers or use multiple providers in parallel without changing application code.","intents":["I want to switch between OpenAI and Anthropic without rewriting my LLM integration code","I need to abstract away provider-specific API differences in my application","I want to support multiple LLM providers and let users choose which one to use"],"best_for":["Developers building LLM applications that need provider flexibility","Teams evaluating multiple LLM providers and want to avoid lock-in","Applications requiring fallback to alternative providers if primary provider fails"],"limitations":["Abstraction layer adds ~50-100ms overhead per request due to normalization and response parsing","Provider-specific features (vision, function calling, streaming) may not be fully exposed through the abstraction","Requires separate API keys for each provider; no built-in key management or rotation","Response latency varies significantly between providers; abstraction does not normalize or optimize for this"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to provider endpoints","TypeScript 4.7+ or Python 3.9+"],"input_types":["schema definition","natural language prompt","provider configuration (API key, model name)"],"output_types":["LLM response (text or JSON)","provider-agnostic response object with metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_3","uri":"capability://safety.moderation.iterative.validation.and.repair.with.bounded.retry.logic","name":"iterative validation and repair with bounded retry logic","description":"TypeChat implements a validation loop that checks LLM responses against the schema using type validators (TypeScript's type system or Python's runtime type checking), and if validation fails, automatically re-invokes the LLM with detailed error messages explaining what went wrong. The retry logic is bounded by a configurable maximum attempt count to prevent infinite loops and excessive API costs.","intents":["I want validation failures to trigger automatic correction attempts rather than returning errors to the user","I need to set a maximum number of repair attempts to control API costs and latency","I want detailed error messages from validation failures to be fed back to the LLM to improve corrections"],"best_for":["Applications where response quality is critical and some latency is acceptable","Scenarios where LLM errors are expected and automatic correction is preferable to user intervention","Cost-conscious teams that want to limit retry attempts to prevent runaway API spending"],"limitations":["Each retry adds 1-3 seconds of latency; total time can be 5-10x longer than single LLM call for complex schemas","Repair success rate decreases with each retry; after 2-3 failures, additional retries rarely succeed","Complex validation errors may not be expressible in natural language; LLM may not understand what correction is needed","Bounded retries mean some valid requests will fail if they require more attempts than the limit allows"],"requires":["Schema definition with clear validation rules","LLM provider with reasonable latency (< 5 seconds per request)","Configurable retry limit (typically 2-5 attempts)"],"input_types":["LLM response (text or JSON)","validation error details","retry count and maximum attempts"],"output_types":["validated response conforming to schema","error with validation failure details if max retries exceeded"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_4","uri":"capability://code.generation.editing.typescript.type.reflection.and.schema.extraction","name":"typescript type reflection and schema extraction","description":"TypeChat uses TypeScript's type system and reflection capabilities to extract schema information from interface definitions at runtime or compile time. The library parses TypeScript interface syntax, resolves type references, handles union types and optional fields, and converts this information into a JSON-serializable schema representation that can be embedded in LLM prompts.","intents":["I want to automatically extract schema from my TypeScript interfaces without writing separate schema definitions","I need to handle complex TypeScript types (unions, optionals, generics) and convert them to LLM-friendly formats","I want my schema to stay in sync with my TypeScript types without manual updates"],"best_for":["TypeScript developers who want single-source-of-truth for both types and LLM schemas","Teams using strict TypeScript with full type coverage","Projects where type safety is critical and schema drift is unacceptable"],"limitations":["Requires TypeScript 4.7+ with type reflection; older versions have limited introspection capabilities","Complex generics and conditional types may not extract cleanly; flattening to basic types required","Type extraction happens at runtime or build time; no lazy evaluation of large type hierarchies","Circular type references require special handling; not all recursive types are supported"],"requires":["TypeScript 4.7+","Type definitions must be serializable to JSON schema format","Build step or runtime type reflection library (e.g., reflect-metadata)"],"input_types":["TypeScript interface definitions","type annotations and constraints"],"output_types":["JSON schema representation","schema metadata (field names, types, constraints)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_5","uri":"capability://data.processing.analysis.python.dataclass.to.schema.conversion.with.runtime.type.validation","name":"python dataclass-to-schema conversion with runtime type validation","description":"TypeChat provides a Python-specific implementation that converts dataclass definitions into schema representations using Python's type hints and runtime type checking. The library inspects dataclass fields, resolves type annotations, handles Optional and Union types, and validates LLM responses against the dataclass structure using Python's type system.","intents":["I want to use Python dataclasses as my schema definition and have them automatically validated","I need to convert LLM responses into typed Python objects that match my dataclass definitions","I want runtime type checking to ensure LLM responses conform to my dataclass structure"],"best_for":["Python developers using dataclasses for type safety","Data science and ML teams building LLM pipelines with structured outputs","Projects where Python is the primary language and TypeScript interop is not required"],"limitations":["Python's runtime type checking is less strict than TypeScript's compile-time checking; some type violations only caught at runtime","Dataclass validation requires explicit type hints; untyped or partially typed classes produce incomplete schemas","Complex type hierarchies with deep nesting may be slow to validate at runtime","No built-in support for custom validation logic; requires separate validator decorators or post-processing"],"requires":["Python 3.9+","dataclasses module (standard library)","Type hints on all dataclass fields","Optional: pydantic or other validation library for advanced type checking"],"input_types":["Python dataclass definitions","type annotations (int, str, List, Optional, Union, etc.)","LLM response (JSON or text)"],"output_types":["Python dataclass instance","validation error with type mismatch details"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_6","uri":"capability://text.generation.language.prompt.construction.with.embedded.schema.definitions","name":"prompt construction with embedded schema definitions","description":"TypeChat automatically constructs prompts that include the schema definition in a format the LLM can understand, along with the user's natural language request. The library formats the schema as part of the system prompt or user message, ensuring the LLM has clear guidance on the expected response structure without requiring manual prompt engineering.","intents":["I want the schema to be automatically included in the prompt without manual formatting","I need to customize how the schema is presented to the LLM (as JSON schema, TypeScript syntax, etc.)","I want to add additional context or instructions alongside the schema definition"],"best_for":["Developers who want to avoid manual prompt engineering and schema formatting","Teams building multiple LLM integrations and want consistent prompt structure","Applications where prompt quality directly impacts response quality"],"limitations":["Prompt construction adds schema to every request, increasing token usage and latency by 10-20%","LLM understanding of embedded schemas varies by model; smaller models may not parse complex schemas correctly","No built-in support for few-shot examples or in-context learning; requires manual prompt customization","Schema formatting is fixed; limited flexibility for domain-specific schema representations"],"requires":["Schema definition in TypeScript or Python","LLM provider that supports system prompts or long context windows"],"input_types":["schema definition","user query or instruction","optional: additional context or examples"],"output_types":["formatted prompt string ready for LLM API"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_7","uri":"capability://data.processing.analysis.json.response.parsing.with.type.aware.deserialization","name":"json response parsing with type-aware deserialization","description":"TypeChat parses LLM responses (which may be raw text, JSON, or mixed formats) and deserializes them into typed objects that conform to the schema. The parser handles JSON extraction from text responses, type coercion, and conversion of JSON objects into TypeScript objects or Python dataclass instances with proper type checking.","intents":["I want to automatically convert LLM JSON responses into typed objects without manual parsing","I need to extract JSON from LLM responses that include surrounding text or explanations","I want type-safe deserialization that validates each field against the schema"],"best_for":["Developers building LLM integrations that expect structured JSON responses","Applications where response parsing errors should trigger validation failures rather than crashes","Teams that want to avoid manual JSON parsing and type conversion code"],"limitations":["Parser assumes LLM response is valid or near-valid JSON; highly malformed responses may fail to parse","Type coercion can mask data quality issues; e.g., string '123' coerced to number 123 without warning","No built-in support for custom deserialization logic; requires post-processing for complex types","Parsing errors are caught and reported but not automatically repaired; requires validation loop to retry"],"requires":["LLM response in JSON format or extractable JSON within text","Schema definition matching expected response structure"],"input_types":["LLM response (text, JSON, or mixed)","schema definition for type validation"],"output_types":["typed object (TypeScript object or Python dataclass instance)","parsing error with details if JSON extraction or deserialization fails"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_8","uri":"capability://data.processing.analysis.zod.schema.integration.for.typescript.validation","name":"zod schema integration for typescript validation","description":"TypeChat integrates with Zod, a TypeScript-first schema validation library, allowing developers to define schemas using Zod's fluent API and leverage Zod's validation capabilities for LLM response checking. The integration enables more expressive validation rules (min/max lengths, regex patterns, custom validators) beyond basic type checking.","intents":["I want to use Zod schemas for more expressive validation than basic TypeScript types","I need to add validation constraints (min/max, regex, custom rules) to my LLM response schema","I want to leverage Zod's error reporting for detailed validation failure messages"],"best_for":["TypeScript developers already using Zod in their projects","Applications requiring complex validation rules beyond basic type checking","Teams that want detailed validation error messages for debugging and repair"],"limitations":["Zod integration is TypeScript-only; no equivalent for Python","Zod schemas may not translate cleanly to LLM-friendly formats; complex validators may be opaque to the model","Zod validation adds runtime overhead; complex schemas can add 50-100ms per validation","Custom Zod validators may not be expressible in natural language for LLM repair feedback"],"requires":["TypeScript 4.7+","Zod library (npm install zod)","Zod schema definitions matching expected response structure"],"input_types":["Zod schema definition","LLM response (JSON or text)"],"output_types":["validated object conforming to Zod schema","detailed validation error with field-level failure information"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__cap_9","uri":"capability://text.generation.language.streaming.response.handling.with.incremental.validation","name":"streaming response handling with incremental validation","description":"TypeChat supports streaming LLM responses where the model outputs tokens incrementally, allowing applications to process partial responses in real-time. The library buffers streamed tokens, attempts incremental validation as complete JSON objects arrive, and can trigger early termination or repair if validation fails mid-stream.","intents":["I want to display LLM responses to users as they arrive rather than waiting for the full response","I need to validate partial responses and fail fast if the LLM is generating invalid output","I want to reduce perceived latency by streaming responses while maintaining type safety"],"best_for":["User-facing applications where response latency is critical","Real-time chat or interactive applications requiring streaming responses","Applications with large response payloads where streaming reduces initial latency"],"limitations":["Streaming adds complexity to validation logic; partial JSON may be invalid until stream completes","Incremental validation can trigger false positives if partial JSON is incomplete but structurally invalid","Repair logic is more complex with streaming; cannot easily re-stream corrected responses","Not all LLM providers support streaming; fallback to buffered responses required for some providers"],"requires":["LLM provider with streaming support (OpenAI, Anthropic, etc.)","Schema definition for validation","Client-side streaming support (WebSocket, Server-Sent Events, etc.)"],"input_types":["streaming LLM response (token stream)","schema definition for incremental validation"],"output_types":["incremental response objects as complete JSON arrives","streaming error if validation fails mid-stream"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"typechat__headline","uri":"capability://tool.use.integration.type.safe.natural.language.interface.framework","name":"type-safe natural language interface framework","description":"TypeChat is a framework that allows developers to create natural language interfaces by using TypeScript types to validate and structure responses from large language models, replacing traditional prompt engineering with type engineering for better schema adherence.","intents":["best natural language interface framework","natural language processing for structured data","TypeScript types for LLM validation","schema engineering for AI responses","LLM integration with type safety"],"best_for":["developers building LLM applications"],"limitations":["requires knowledge of TypeScript or Python"],"requires":["TypeScript or Python environment"],"input_types":["natural language input"],"output_types":["structured, type-safe data"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["TypeScript 4.7+ or Python 3.9+","API key for OpenAI, Anthropic, or compatible LLM provider","Type definitions (TypeScript interfaces or Python dataclasses) representing valid response shapes","TypeScript 4.7+ with type reflection capabilities or Python 3.9+ with dataclasses module","Type definitions must be serializable to JSON schema-like format","Schema definition with clear field names and type information","Validation that produces detailed error messages (not just 'validation failed')","Schema definition with union types (TypeScript | operator or Python Union)","Clear distinction between union members to avoid ambiguity","Schema definition"],"failure_modes":["Repair loop adds latency — each validation failure triggers an additional LLM call, potentially 2-3x slower than unvalidated responses","Requires LLM to understand type definitions in natural language; less reliable with smaller models or non-English schemas","No built-in support for complex recursive types or circular references — flattening required","Repair attempts are bounded; after N failures, returns error rather than retrying indefinitely","Schema translation is one-way (type → prompt); no code generation from LLM-validated responses back to types","Complex TypeScript generics or advanced type features may not translate cleanly; requires flattening to basic types","Python dataclass translation requires explicit type hints; untyped or partially typed classes may produce incomplete schemas","Error message generation adds latency; complex validation errors require time to format","LLM may not understand or act on detailed error feedback; repair success is not guaranteed","Error messages can be verbose and consume significant token budget in repair prompts","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":"2026-06-17T09:51:05.297Z","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=typechat","compare_url":"https://unfragile.ai/compare?artifact=typechat"}},"signature":"joLCIh0v5fZbZDZlBw6YR2cNXX4jJPCbAcytIKkI2xFYsOZ0nkmqCMOOciqdvcImcOVC/7mV1Y+WSp+vYSExBQ==","signedAt":"2026-06-22T01:35:00.956Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/typechat","artifact":"https://unfragile.ai/typechat","verify":"https://unfragile.ai/api/v1/verify?slug=typechat","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"}}