{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-tensorzero","slug":"tensorzero","name":"TensorZero","type":"framework","url":"https://www.tensorzero.com/","page_url":"https://unfragile.ai/tensorzero","categories":["frameworks-sdks","deployment-infra"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-tensorzero__cap_0","uri":"capability://tool.use.integration.unified.llm.gateway.with.multi.provider.routing","name":"unified llm gateway with multi-provider routing","description":"Routes inference requests across multiple LLM providers (OpenAI, Anthropic, etc.) through a single abstraction layer, handling provider-specific API differences, authentication, and request/response normalization. Implements a provider registry pattern that abstracts away protocol differences and enables dynamic provider selection based on cost, latency, or capability constraints without application code changes.","intents":["I want to switch between OpenAI and Anthropic without rewriting my inference code","I need to route requests to the cheapest provider that meets my latency requirements","I want to fall back to a secondary provider if the primary one fails or is rate-limited","I need to normalize responses across providers with different output formats"],"best_for":["teams building multi-provider LLM applications to avoid vendor lock-in","cost-conscious builders who want to optimize provider selection dynamically","production systems requiring high availability with provider failover"],"limitations":["Provider-specific features (like vision capabilities or function calling schemas) may require conditional logic despite normalization","Latency overhead from abstraction layer adds ~10-50ms per request depending on provider","Not all providers support identical model families, requiring application-level capability detection"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to provider endpoints","Configuration file or environment variables for provider credentials"],"input_types":["text prompts","structured messages with role/content","system prompts","function/tool definitions"],"output_types":["text completions","structured JSON responses","streaming token sequences","tool/function calls"],"categories":["tool-use-integration","gateway-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_1","uri":"capability://automation.workflow.production.observability.with.structured.logging.and.metrics","name":"production observability with structured logging and metrics","description":"Captures detailed telemetry from every LLM inference including latency, token counts, costs, provider, model, and custom metadata through a structured logging pipeline. Integrates with observability backends (likely Datadog, New Relic, or similar) to enable real-time dashboards, alerting, and debugging of LLM application behavior in production without requiring manual instrumentation.","intents":["I need to track which provider/model combination is actually being used in production","I want to monitor token usage and costs per feature or user cohort","I need to debug why a specific inference failed or produced unexpected output","I want to set up alerts when latency exceeds thresholds or error rates spike"],"best_for":["production teams operating LLM applications at scale","cost-conscious organizations tracking LLM spend across teams","teams building observability-first LLM systems with compliance requirements"],"limitations":["Observability backend integration requires separate setup and configuration","High-volume inference workloads may incur significant storage costs for detailed telemetry","Custom metadata logging requires explicit instrumentation in application code","Latency impact of logging pipeline not quantified in public documentation"],"requires":["Observability backend (Datadog, New Relic, or similar) or local log aggregation","Network connectivity to observability endpoint","Configuration to specify which metrics to capture and export"],"input_types":["inference requests","inference responses","custom metadata objects","error/exception data"],"output_types":["structured logs","metrics (counters, histograms, gauges)","traces with spans","dashboards and alerts"],"categories":["automation-workflow","observability-monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_10","uri":"capability://memory.knowledge.request.response.caching.with.semantic.deduplication","name":"request/response caching with semantic deduplication","description":"Caches LLM responses based on exact request matching or semantic similarity, returning cached results for duplicate or similar requests without re-invoking the model. Implements cache invalidation strategies and provides cache hit/miss metrics to measure effectiveness and cost savings.","intents":["I want to avoid re-processing identical requests that I've seen before","I need to cache responses for similar but not identical queries","I want to measure how much cost I'm saving with caching","I need to invalidate cache entries when prompts or models change"],"best_for":["applications with repetitive user queries (FAQs, common tasks)","systems where semantic similarity matching is valuable","cost-sensitive applications where cache hit rates significantly reduce spend"],"limitations":["Exact matching caching is only effective if users ask identical questions","Semantic deduplication requires embeddings or similarity models, adding latency and cost","Cache invalidation is complex; stale cached responses may be returned after prompt changes","Cache storage requirements grow with dataset size and diversity"],"requires":["Cache storage backend (Redis, database, or in-memory)","Cache key generation logic (hashing for exact match, embeddings for semantic)","Cache invalidation strategy and TTL configuration","Similarity threshold for semantic deduplication"],"input_types":["inference requests","cache configuration (TTL, similarity threshold)","cache invalidation rules"],"output_types":["cached responses or cache misses","cache hit/miss metrics","cost savings reports","cache management logs"],"categories":["memory-knowledge","caching-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_11","uri":"capability://planning.reasoning.multi.step.reasoning.with.chain.of.thought.orchestration","name":"multi-step reasoning with chain-of-thought orchestration","description":"Orchestrates multi-step LLM reasoning workflows where outputs from one step feed into subsequent steps, with automatic prompt chaining, context passing, and error handling. Supports branching logic, conditional execution, and result aggregation across parallel branches, enabling complex reasoning tasks without manual orchestration code.","intents":["I want to break down a complex task into multiple LLM steps","I need to pass outputs from one LLM call as inputs to the next","I want to run multiple reasoning branches in parallel and combine results","I need to handle failures in one step and retry or fall back gracefully"],"best_for":["teams building LLM agents with multi-step reasoning","applications requiring complex task decomposition","systems where intermediate reasoning steps improve final output quality"],"limitations":["Multi-step workflows add latency (each step requires a separate API call)","Error in one step can cascade to subsequent steps; error handling is critical","Orchestration logic can become complex for deeply nested workflows","Cost increases linearly with number of steps"],"requires":["Workflow definition (step sequence, branching logic, context passing)","Error handling strategy for step failures","Mechanism to pass context between steps","Timeout and retry configuration"],"input_types":["workflow definitions (DAG or step sequence)","initial inputs and context","branching conditions","error handling rules"],"output_types":["final reasoning output","intermediate step results","execution trace with timing","error logs and retry attempts"],"categories":["planning-reasoning","workflow-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_12","uri":"capability://safety.moderation.guardrails.and.safety.filtering.with.custom.rules","name":"guardrails and safety filtering with custom rules","description":"Applies safety filters to both inputs and outputs using a combination of built-in rules (PII detection, toxicity filtering, jailbreak detection) and custom user-defined rules. Implements a rule engine that can block, redact, or flag content based on configurable criteria, with audit logging of all filtering decisions.","intents":["I want to prevent my LLM from processing personally identifiable information","I need to block toxic or harmful outputs before returning them to users","I want to detect and prevent prompt injection or jailbreak attempts","I need to audit what content was filtered and why"],"best_for":["applications handling sensitive data (healthcare, finance, PII)","public-facing LLM applications requiring content moderation","regulated industries with compliance requirements","teams building safety-critical LLM systems"],"limitations":["Built-in safety filters may have false positives/negatives; custom rules are needed for domain-specific safety","Safety filtering adds latency to inference pipeline","Redaction may degrade output quality or usability","Guardrails cannot guarantee safety; they reduce risk but don't eliminate it"],"requires":["Safety rule definitions (built-in or custom)","Mechanism to detect PII, toxicity, or other unsafe content","Action configuration (block, redact, flag, log)","Audit logging for compliance"],"input_types":["user inputs (prompts, messages)","LLM outputs","safety rule definitions","redaction patterns"],"output_types":["filtered/redacted content","safety violation flags","audit logs","filtering decision explanations"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_13","uri":"capability://planning.reasoning.provider.agnostic.model.selection.with.capability.matching","name":"provider-agnostic model selection with capability matching","description":"Automatically selects the best model for a given task based on required capabilities (vision, function calling, JSON mode, etc.) and constraints (cost, latency, quality). Maintains a capability matrix of all supported models and uses it to route requests to models that meet requirements without manual provider/model selection.","intents":["I want to use the cheapest model that supports vision for image analysis","I need to automatically select a model with function calling support","I want to route requests to models that support JSON output format","I need to find models that meet latency requirements without manual lookup"],"best_for":["teams using multiple models across providers","applications with heterogeneous capability requirements","cost-conscious teams optimizing model selection"],"limitations":["Capability matrix must be maintained and updated as new models are released","Capability matching is based on declared capabilities; actual performance may vary","Some capabilities (like quality) are subjective and hard to match automatically","Model selection is based on static capability data, not real-time performance"],"requires":["Capability matrix for all supported models","Mechanism to query capabilities by requirement","Fallback strategy if no model matches all requirements","Regular updates to capability matrix as models change"],"input_types":["capability requirements (vision, function calling, JSON mode, etc.)","cost/latency/quality constraints","capability matrix"],"output_types":["selected model and provider","capability matching explanation","alternative model suggestions","capability coverage reports"],"categories":["planning-reasoning","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_2","uri":"capability://planning.reasoning.experiment.driven.optimization.with.a.b.testing.framework","name":"experiment-driven optimization with a/b testing framework","description":"Provides built-in infrastructure for running controlled experiments on LLM applications by splitting traffic between variants (different prompts, models, providers, parameters) and measuring outcomes against defined metrics. Implements statistical significance testing and variant selection logic to automatically route traffic toward better-performing configurations without manual intervention.","intents":["I want to test if a new prompt template improves output quality without affecting all users","I need to compare the cost/quality tradeoff between GPT-4 and Claude for my use case","I want to gradually roll out a new model version and measure its impact on user satisfaction","I need to run multivariate tests on temperature, top-p, and system prompt simultaneously"],"best_for":["product teams iterating on LLM application quality through data-driven decisions","teams optimizing for cost/quality tradeoffs with real user feedback","researchers and ML engineers running systematic prompt engineering experiments"],"limitations":["Requires defining clear success metrics upfront; poorly chosen metrics can lead to misleading conclusions","Statistical significance testing requires minimum sample sizes that may take days/weeks to accumulate","Variant selection logic (bandit algorithms) may not be suitable for all use cases","Experiment results are specific to your user distribution and use case; external benchmarks may not transfer"],"requires":["Defined success metrics (e.g., user ratings, task completion, cost per inference)","Mechanism to collect outcome data (user feedback, automated evaluation, downstream metrics)","Sufficient traffic volume to reach statistical significance in reasonable timeframe"],"input_types":["variant configurations (prompts, models, parameters)","traffic split percentages","outcome metrics and measurement logic","statistical significance thresholds"],"output_types":["experiment results with confidence intervals","variant performance rankings","traffic allocation recommendations","statistical significance reports"],"categories":["planning-reasoning","experimentation-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_3","uri":"capability://data.processing.analysis.automated.evaluation.with.custom.metrics.and.benchmarks","name":"automated evaluation with custom metrics and benchmarks","description":"Evaluates LLM outputs against user-defined success criteria using a combination of automated metrics (BLEU, ROUGE, semantic similarity) and custom evaluation functions (LLM-as-judge, regex matching, structured validation). Runs evaluations on inference batches or in real-time to measure quality, cost, and latency tradeoffs across model/prompt variants.","intents":["I want to automatically score whether my LLM's output answers the user's question correctly","I need to measure semantic similarity between my output and a reference answer","I want to use another LLM to judge whether my output meets quality criteria","I need to validate that outputs conform to a required JSON schema or format"],"best_for":["teams building quality gates into their LLM pipelines","researchers benchmarking model/prompt combinations systematically","production systems that need automated quality monitoring without manual review"],"limitations":["Automated metrics (BLEU, ROUGE) correlate imperfectly with human judgment for many tasks","LLM-as-judge evaluations are expensive (require additional API calls) and may introduce bias from the judge model","Custom evaluation functions require domain expertise to define correctly","Evaluation results are task-specific and may not generalize to similar tasks"],"requires":["Reference outputs or ground truth data for comparison-based metrics","Custom evaluation function definitions (Python code or prompt templates)","Sufficient compute budget if using LLM-as-judge evaluations"],"input_types":["LLM outputs (text, JSON, structured data)","reference/ground truth outputs","evaluation metric configurations","custom evaluation function code"],"output_types":["numeric scores (0-1 range typically)","pass/fail verdicts","detailed evaluation reports","aggregated metrics across batches"],"categories":["data-processing-analysis","evaluation-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_4","uri":"capability://automation.workflow.prompt.versioning.and.management.with.rollback.capability","name":"prompt versioning and management with rollback capability","description":"Stores and versions prompts, system messages, and inference parameters as first-class artifacts with git-like history, enabling rollback to previous versions and comparison between variants. Integrates with the gateway so prompt changes can be deployed without application code changes, and tracks which prompt version was used for each inference in observability data.","intents":["I want to revert to a previous prompt version if a new one degrades quality","I need to compare the outputs of two prompt versions side-by-side","I want to deploy a new prompt to 10% of traffic first, then gradually increase","I need to track which prompt version was used for each inference in production"],"best_for":["teams iterating rapidly on prompts without code deployments","organizations with non-technical stakeholders who need to modify prompts","production systems requiring audit trails of prompt changes"],"limitations":["Prompt versioning doesn't capture dependencies on external data or context","Rollback is instantaneous but doesn't retroactively change outputs of past inferences","Version comparison tools may not highlight semantic differences in complex prompts","Requires discipline to document why prompt versions changed"],"requires":["Mechanism to store and retrieve prompt versions (likely database or version control)","Configuration to specify which prompt version to use for each inference","Integration with observability to tag inferences with prompt version"],"input_types":["prompt text","system messages","inference parameters (temperature, top_p, etc.)","metadata (author, description, tags)"],"output_types":["versioned prompt artifacts","version history with diffs","deployment configurations","audit logs"],"categories":["automation-workflow","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_5","uri":"capability://planning.reasoning.cost.optimization.with.provider.and.model.selection","name":"cost optimization with provider and model selection","description":"Analyzes inference costs across providers and models based on token counts and pricing, then automatically selects the cheapest option that meets latency and quality constraints. Uses historical cost and performance data to make routing decisions, and provides dashboards showing cost breakdown by provider, model, and feature.","intents":["I want to use cheaper models (like GPT-3.5) when quality requirements are low","I need to understand which features are driving my LLM costs","I want to automatically fall back to a cheaper provider if the primary one is too expensive","I need to set cost budgets per user or feature and enforce them"],"best_for":["cost-sensitive teams running high-volume LLM applications","organizations with heterogeneous quality requirements across features","teams needing to optimize LLM spend without sacrificing user experience"],"limitations":["Cost optimization may degrade quality if constraints are set too aggressively","Cheaper models may have higher latency or different output characteristics","Cost data requires accurate token counting which may differ between providers","Optimization decisions are based on historical data and may not adapt to sudden pricing changes"],"requires":["Pricing data for all supported providers and models","Token counting logic for each provider (may differ slightly)","Quality/latency constraints defined per inference or feature","Historical performance data to inform routing decisions"],"input_types":["inference requests with quality/latency constraints","provider pricing tables","historical cost and performance metrics","cost budget constraints"],"output_types":["cost-optimized provider/model selection","cost breakdown dashboards","budget utilization reports","cost savings estimates"],"categories":["planning-reasoning","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_6","uri":"capability://data.processing.analysis.structured.output.validation.with.schema.enforcement","name":"structured output validation with schema enforcement","description":"Validates LLM outputs against user-defined schemas (JSON Schema, Pydantic models, regex patterns) and automatically re-prompts or falls back if outputs don't conform. Integrates with providers that support constrained generation (like Anthropic's JSON mode) to enforce schemas at generation time, reducing invalid outputs and retry overhead.","intents":["I need to ensure my LLM always returns valid JSON matching my schema","I want to automatically retry with a corrected prompt if the output is malformed","I need to extract structured data (entities, relationships) from unstructured text","I want to use constrained generation to reduce invalid outputs without retries"],"best_for":["applications requiring structured outputs (APIs, databases, downstream processing)","teams building LLM-powered data extraction pipelines","systems where invalid outputs cause cascading failures"],"limitations":["Constrained generation support varies by provider; not all models support it","Schema enforcement may reduce output diversity or creativity","Retry logic adds latency and cost if outputs frequently fail validation","Complex schemas may be difficult for LLMs to follow consistently"],"requires":["Schema definition (JSON Schema, Pydantic model, or regex pattern)","Validation logic (likely JSON schema validator or custom code)","Retry strategy if initial output fails validation","Provider support for constrained generation (optional but recommended)"],"input_types":["LLM outputs (text, JSON)","schema definitions","validation rules","retry configurations"],"output_types":["validated structured data","pass/fail validation results","error messages with correction suggestions","retry logs"],"categories":["data-processing-analysis","validation-enforcement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_7","uri":"capability://memory.knowledge.context.management.and.memory.with.token.budgeting","name":"context management and memory with token budgeting","description":"Manages conversation history and context windows by automatically truncating, summarizing, or prioritizing messages to fit within model token limits. Implements strategies like sliding windows, importance-based pruning, and hierarchical summarization to preserve relevant context while staying within budget, and tracks token usage to prevent overages.","intents":["I want to maintain conversation history without exceeding the model's context window","I need to summarize old messages to make room for new ones","I want to prioritize recent or important messages when context is limited","I need to track token usage to avoid unexpected costs from long conversations"],"best_for":["conversational AI applications with long-running sessions","teams building chatbots or assistants with memory","cost-sensitive applications where context window size directly impacts cost"],"limitations":["Summarization may lose important details or context","Importance-based pruning requires heuristics that may not work for all domains","Token counting is approximate and may differ from actual provider counts","Aggressive context truncation can degrade conversation quality"],"requires":["Token counting logic for the target model","Context management strategy (sliding window, summarization, pruning)","Mechanism to store and retrieve conversation history","Token budget constraints per conversation"],"input_types":["conversation messages (user, assistant, system)","token budget constraints","context management strategy configuration","importance scoring rules"],"output_types":["truncated/summarized context","token usage reports","context management decisions (what was kept/removed)","conversation history with metadata"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_8","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.registry","name":"function calling with schema-based tool registry","description":"Provides a schema-based function registry that maps tool definitions to callable functions, handles provider-specific function calling APIs (OpenAI, Anthropic, etc.), and automatically executes selected tools with proper error handling and result formatting. Supports both synchronous and asynchronous tool execution, and integrates with the gateway to route tool calls transparently.","intents":["I want my LLM to call external APIs or functions to complete tasks","I need to support multiple providers' function calling APIs without rewriting code","I want to validate tool arguments against a schema before execution","I need to handle tool execution errors gracefully and retry or fall back"],"best_for":["teams building LLM agents with external tool access","applications requiring LLM-driven API orchestration","multi-provider systems where tool calling APIs differ significantly"],"limitations":["Function calling support varies by provider and model; not all models support it","Schema definitions must be precise; ambiguous schemas lead to incorrect tool calls","Tool execution errors may require custom error handling logic","Latency overhead from tool execution adds to total inference time"],"requires":["Tool definitions with JSON Schema descriptions","Callable functions or API endpoints for each tool","Provider support for function calling (OpenAI, Anthropic, etc.)","Error handling strategy for tool execution failures"],"input_types":["tool definitions (JSON Schema)","tool implementations (Python functions, API endpoints)","inference requests with tool context","tool execution results"],"output_types":["tool calls with arguments","tool execution results","error messages and retry logic","audit logs of tool usage"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tensorzero__cap_9","uri":"capability://automation.workflow.batch.processing.with.cost.and.latency.optimization","name":"batch processing with cost and latency optimization","description":"Processes large volumes of inferences in batches using provider-native batch APIs (where available) to reduce costs, or groups requests to maximize throughput and minimize latency. Handles batching logic transparently, tracks batch status, and provides progress monitoring and result aggregation.","intents":["I want to process 10,000 inferences at lower cost using batch APIs","I need to group requests to maximize throughput without overwhelming the provider","I want to monitor progress of long-running batch jobs","I need to aggregate results from batches and handle partial failures"],"best_for":["teams processing large datasets through LLMs (data labeling, classification, extraction)","cost-sensitive applications where batch processing discounts are significant","systems with flexible latency requirements (hours/days acceptable)"],"limitations":["Batch APIs have longer latency (hours to days) compared to real-time inference","Not all providers offer batch APIs; fallback to real-time inference is more expensive","Batch job failures may require manual intervention or retry logic","Batch processing is less suitable for interactive or real-time applications"],"requires":["Provider support for batch APIs (OpenAI Batch API, etc.)","Ability to wait for batch job completion (hours to days)","Mechanism to store and retrieve batch results","Error handling for partial batch failures"],"input_types":["large lists of inference requests","batch configuration (size, timeout, retry policy)","cost/latency tradeoff preferences"],"output_types":["batch job IDs and status","aggregated results from completed batches","cost savings estimates","progress monitoring data"],"categories":["automation-workflow","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Network connectivity to provider endpoints","Configuration file or environment variables for provider credentials","Observability backend (Datadog, New Relic, or similar) or local log aggregation","Network connectivity to observability endpoint","Configuration to specify which metrics to capture and export","Cache storage backend (Redis, database, or in-memory)","Cache key generation logic (hashing for exact match, embeddings for semantic)","Cache invalidation strategy and TTL configuration","Similarity threshold for semantic deduplication"],"failure_modes":["Provider-specific features (like vision capabilities or function calling schemas) may require conditional logic despite normalization","Latency overhead from abstraction layer adds ~10-50ms per request depending on provider","Not all providers support identical model families, requiring application-level capability detection","Observability backend integration requires separate setup and configuration","High-volume inference workloads may incur significant storage costs for detailed telemetry","Custom metadata logging requires explicit instrumentation in application code","Latency impact of logging pipeline not quantified in public documentation","Exact matching caching is only effective if users ask identical questions","Semantic deduplication requires embeddings or similarity models, adding latency and cost","Cache invalidation is complex; stale cached responses may be returned after prompt changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:04.050Z","last_scraped_at":"2026-05-03T14:00:23.056Z","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=tensorzero","compare_url":"https://unfragile.ai/compare?artifact=tensorzero"}},"signature":"4i/ZwL4oKL6ESDe1MRnaM+gjX+LcAF5SuhmetYATw+Fm1O+jj/3UOKI+3q/W1emjQZM5+2nlsc/Hq6pEvn5KCw==","signedAt":"2026-06-19T22:52:09.603Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tensorzero","artifact":"https://unfragile.ai/tensorzero","verify":"https://unfragile.ai/api/v1/verify?slug=tensorzero","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"}}