{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-inngest-ai","slug":"inngest-ai","name":"@inngest/ai","type":"repo","url":"https://github.com/inngest/inngest-js/tree/main/packages/ai#readme","page_url":"https://unfragile.ai/inngest-ai","categories":["automation"],"tags":["inngest","ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-inngest-ai__cap_0","uri":"capability://tool.use.integration.multi.provider.ai.model.abstraction.with.type.safe.interfaces","name":"multi-provider ai model abstraction with type-safe interfaces","description":"Provides a unified TypeScript interface layer that abstracts over heterogeneous AI provider APIs (OpenAI, Anthropic, Gemini, Grok, Azure OpenAI) with compile-time type safety. Uses provider-specific adapter classes that normalize request/response formats and handle provider-specific quirks, allowing developers to swap providers without changing application code. Each adapter implements a common interface contract that maps to Inngest's event-driven execution model.","intents":["Switch between AI providers without rewriting model invocation code","Ensure type safety when calling different LLM APIs with varying parameter schemas","Reduce boilerplate when integrating multiple AI providers in a single application","Maintain consistent error handling across provider-specific API differences"],"best_for":["Teams building multi-provider AI applications who want to avoid vendor lock-in","Developers integrating Inngest workflows with LLM calls who need type guarantees","Applications requiring provider fallback or A/B testing across different models"],"limitations":["Abstraction layer adds ~50-100ms overhead per provider call due to adapter marshaling","Not all provider-specific features are exposed through the unified interface (e.g., vision-specific parameters may require provider-specific code paths)","Requires explicit adapter registration per provider; no auto-discovery of available providers"],"requires":["TypeScript 4.7+","Node.js 16+","Valid API keys for at least one supported provider (OpenAI, Anthropic, Gemini, Grok, or Azure OpenAI)","@inngest/inngest package as peer dependency"],"input_types":["provider configuration objects","model identifiers (string)","message arrays with role/content structure","optional system prompts"],"output_types":["structured completion objects with role/content/finish_reason","token usage metadata","provider-specific response envelopes"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_1","uri":"capability://tool.use.integration.provider.specific.function.calling.with.schema.normalization","name":"provider-specific function calling with schema normalization","description":"Implements function calling (tool use) across providers with different schema formats by normalizing tool definitions into a canonical schema format, then translating to provider-specific representations (OpenAI's function_calling format, Anthropic's tool_use, etc.). Handles provider differences in how they declare parameters, return types, and tool selection logic. Automatically marshals function results back into the LLM context for multi-turn tool-use workflows.","intents":["Define tools once and use them across multiple AI providers without rewriting schemas","Implement multi-step tool-use workflows where the LLM iteratively calls functions and processes results","Ensure type-safe function invocation with compile-time validation of tool signatures","Handle provider-specific tool calling quirks (e.g., Anthropic's explicit tool_choice vs OpenAI's auto)"],"best_for":["Developers building agent-like workflows that need to call external functions/APIs through LLMs","Teams using multiple AI providers and wanting consistent tool-calling semantics","Applications requiring strict type safety between LLM-selected tools and actual function implementations"],"limitations":["Schema normalization requires explicit tool definition; no automatic inference from TypeScript function signatures","Parallel tool calling (multiple tools in one response) is provider-dependent and may require manual orchestration","Tool result context window management is the developer's responsibility; no automatic context pruning when results exceed token limits"],"requires":["TypeScript 4.7+","Tool definitions conforming to the @inngest/ai schema format","Provider API keys with function-calling support (OpenAI, Anthropic, Gemini)"],"input_types":["tool schema objects with name, description, parameters","function implementations (TypeScript functions or async callables)","LLM responses containing tool selections"],"output_types":["normalized tool call objects with tool_name and arguments","tool execution results formatted for LLM context injection","provider-specific tool_use response envelopes"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_10","uri":"capability://automation.workflow.batch.processing.of.llm.requests.with.cost.optimization","name":"batch processing of llm requests with cost optimization","description":"Provides batch processing capabilities for high-volume LLM requests, leveraging provider-native batch APIs (OpenAI Batch API, Anthropic Batch API) to reduce costs and latency. Automatically groups requests into batches, submits them to providers, and polls for results. Integrates with Inngest's event system to track batch status and emit events when batches complete. Supports cost optimization strategies like batching similar requests together and prioritizing cheaper models for batch processing.","intents":["Process large volumes of LLM requests at reduced cost using provider batch APIs","Implement asynchronous batch processing workflows without manual polling","Optimize costs by grouping requests and leveraging batch discounts","Track batch processing status and handle partial failures"],"best_for":["High-volume data processing pipelines (content generation, classification, summarization)","Cost-sensitive applications that can tolerate higher latency for lower costs","Batch processing jobs that don't require real-time responses"],"limitations":["Batch processing introduces significant latency (hours to days depending on provider queue)","Batch APIs have different feature support than real-time APIs (e.g., no streaming, limited tool calling)","Batch failures require manual retry logic; no automatic retry for failed batch items"],"requires":["TypeScript 4.7+","Provider API key with batch API support (OpenAI, Anthropic)","Batch requests must conform to provider-specific batch format"],"input_types":["arrays of LLM requests","optional batch configuration (size, priority)"],"output_types":["batch job identifiers","batch status (queued, processing, completed)","batch results (arrays of LLM responses)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_11","uri":"capability://memory.knowledge.request.response.caching.with.semantic.deduplication","name":"request/response caching with semantic deduplication","description":"Implements caching of LLM requests and responses with optional semantic deduplication (detecting similar prompts that would produce similar outputs). Uses configurable cache backends (in-memory, Redis, Inngest event store) and supports cache invalidation strategies. Automatically deduplicates requests based on exact match (fast) or semantic similarity (slower but catches paraphrased prompts). Integrates with Inngest's event system to track cache hits/misses and enable cost analysis.","intents":["Reduce costs by caching LLM responses and reusing them for similar requests","Improve latency by serving cached responses instead of making new API calls","Detect and deduplicate semantically similar requests to maximize cache hit rates","Analyze cache effectiveness to optimize caching strategies"],"best_for":["Applications with repetitive LLM requests (e.g., FAQ answering, classification)","Cost-sensitive applications where caching can provide significant savings","High-traffic applications where cache hits can reduce latency"],"limitations":["Semantic deduplication requires embedding models, adding latency (~100-200ms per request)","Cache invalidation is manual; no automatic invalidation when prompts or models change","Cached responses may become stale if the underlying model is updated"],"requires":["TypeScript 4.7+","Optional: embedding model for semantic deduplication (e.g., OpenAI embeddings)","Optional: Redis or other cache backend for distributed caching"],"input_types":["LLM requests (prompts, model, parameters)","optional cache configuration (TTL, backend, deduplication strategy)"],"output_types":["cached LLM response or new response if cache miss","cache hit/miss indicator","cache effectiveness metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_2","uri":"capability://data.processing.analysis.structured.output.extraction.with.provider.specific.formatting","name":"structured output extraction with provider-specific formatting","description":"Enables extraction of structured data (JSON, typed objects) from LLM responses by specifying output schemas and delegating to provider-specific structured output mechanisms (OpenAI's JSON mode, Anthropic's structured output, Gemini's schema constraints). Automatically validates responses against the declared schema and provides type-safe access to extracted fields. Handles provider differences in how they enforce schema compliance and error recovery when responses don't match the schema.","intents":["Extract structured data from unstructured text using LLMs with guaranteed schema compliance","Generate type-safe TypeScript objects from LLM responses without manual parsing","Reduce hallucination and format errors by leveraging provider-native structured output constraints","Validate LLM outputs against a schema before passing them to downstream workflow steps"],"best_for":["Data extraction pipelines that need reliable, schema-compliant LLM outputs","Workflows generating structured data (JSON, objects) from natural language inputs","Teams requiring type safety between LLM outputs and application code"],"limitations":["Structured output support varies by provider and model; not all models support schema enforcement","Schema complexity is limited by provider constraints (e.g., OpenAI JSON mode has nesting depth limits)","Validation failures require retry logic; no built-in fallback to unstructured parsing"],"requires":["TypeScript 4.7+","JSON Schema or TypeScript type definition for output structure","Provider API key for a model supporting structured outputs (GPT-4 Turbo+, Claude 3+, Gemini 1.5+)"],"input_types":["JSON Schema objects","TypeScript type definitions","prompt text with extraction instructions"],"output_types":["validated JSON objects","typed TypeScript objects matching the declared schema","validation error details if schema compliance fails"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_3","uri":"capability://automation.workflow.streaming.response.handling.with.inngest.event.integration","name":"streaming response handling with inngest event integration","description":"Provides streaming support for LLM responses (token-by-token output) with automatic integration into Inngest's event system. Streams are buffered and can be emitted as Inngest events, allowing downstream workflow steps to process partial results in real-time. Handles provider-specific streaming protocols (Server-Sent Events, WebSocket) and normalizes them into a common stream interface. Manages backpressure and ensures streamed data is durably logged in Inngest's event store.","intents":["Stream LLM responses to clients in real-time while maintaining Inngest workflow durability","Process partial LLM outputs incrementally without waiting for full completion","Emit streaming tokens as Inngest events for downstream workflow steps to consume","Build real-time AI features (e.g., streaming chat) on top of durable Inngest workflows"],"best_for":["Real-time AI applications (chat, code generation) built on Inngest workflows","Developers needing to process LLM outputs incrementally while maintaining event durability","Applications requiring both streaming UX and reliable event sourcing"],"limitations":["Streaming adds complexity to error handling and retry logic; partial results may need manual reconciliation","Backpressure handling requires careful tuning to avoid buffer overflow in high-throughput scenarios","Streamed events are logged to Inngest's event store, increasing storage overhead for high-volume streaming"],"requires":["TypeScript 4.7+","Provider API key supporting streaming (OpenAI, Anthropic, Gemini)","Inngest event sink configured to handle streaming event payloads"],"input_types":["streaming-enabled LLM requests","event emission configuration"],"output_types":["async iterables of token chunks","Inngest events containing streamed data","completion metadata (finish_reason, total tokens)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_4","uri":"capability://data.processing.analysis.token.usage.tracking.and.cost.estimation.across.providers","name":"token usage tracking and cost estimation across providers","description":"Automatically tracks token consumption (input/output tokens) for each LLM call and aggregates usage across providers with different pricing models. Provides cost estimation based on provider-specific pricing rates (updated periodically) and supports custom pricing configuration. Integrates with Inngest's event metadata to attach usage data to each workflow execution, enabling cost analysis and budgeting. Handles provider differences in how they report token counts (e.g., Claude's token counting API vs OpenAI's inline reporting).","intents":["Monitor token usage and costs across multiple AI providers in a single application","Estimate costs before executing expensive LLM calls and implement cost-based rate limiting","Analyze cost trends and optimize model selection based on usage patterns","Implement billing and chargeback logic for multi-tenant AI applications"],"best_for":["Cost-conscious teams building AI applications with budget constraints","Multi-tenant SaaS platforms needing per-customer cost tracking","Organizations requiring detailed cost analysis and optimization of AI spending"],"limitations":["Token counting is approximate for some providers; actual billing may differ slightly","Pricing rates are static and require manual updates when providers change pricing","No built-in cost enforcement; developers must implement their own rate limiting based on tracked costs"],"requires":["TypeScript 4.7+","Provider API keys (usage data is extracted from LLM responses, not a separate API call)"],"input_types":["LLM responses with token usage metadata","custom pricing configuration (optional)"],"output_types":["token usage objects (input_tokens, output_tokens)","cost estimates (USD or custom currency)","aggregated usage reports per provider/model"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_5","uri":"capability://automation.workflow.retry.and.error.handling.for.transient.provider.failures","name":"retry and error handling for transient provider failures","description":"Implements provider-aware retry logic that distinguishes between transient failures (rate limits, temporary outages) and permanent failures (invalid API key, model not found). Uses exponential backoff with jitter and provider-specific retry strategies (e.g., respecting Retry-After headers from OpenAI). Integrates with Inngest's built-in retry mechanism to ensure failed LLM calls are automatically retried as part of the workflow execution, with full durability guarantees. Provides configurable retry policies per provider or model.","intents":["Automatically retry failed LLM calls without manual intervention","Distinguish between transient and permanent failures to avoid wasting retries on unrecoverable errors","Respect provider rate limits and backoff requirements to avoid cascading failures","Implement custom retry policies for different models or providers"],"best_for":["Production AI applications requiring high reliability and fault tolerance","Teams using multiple providers and needing consistent retry semantics","Applications with variable load that may trigger provider rate limits"],"limitations":["Retry logic adds latency; exponential backoff can cause significant delays for flaky providers","Provider-specific retry headers (e.g., Retry-After) are only respected if the provider includes them in error responses","No built-in circuit breaker; developers must implement their own provider health checks"],"requires":["TypeScript 4.7+","Inngest workflow configured with retry policy (default: 3 retries with exponential backoff)"],"input_types":["LLM request configuration","optional custom retry policy"],"output_types":["successful LLM response after retries","detailed error information if all retries are exhausted"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_6","uri":"capability://memory.knowledge.context.window.management.and.token.limit.enforcement","name":"context window management and token limit enforcement","description":"Monitors token consumption relative to each model's context window limit and provides utilities to manage context (e.g., truncating message history, summarizing old messages). Automatically enforces token limits by rejecting requests that would exceed the model's maximum context size. Provides visibility into context utilization and warns when approaching limits. Supports dynamic context pruning strategies (e.g., remove oldest messages, summarize and replace) to keep conversations within bounds.","intents":["Prevent token limit errors by proactively managing context window usage","Implement long-running conversations that exceed a single model's context window","Optimize context usage to reduce costs while maintaining conversation quality","Debug context-related issues by tracking token consumption across conversation turns"],"best_for":["Multi-turn conversation applications (chatbots, assistants) with long conversation histories","Applications processing large documents that may exceed context limits","Cost-sensitive applications needing to optimize context usage"],"limitations":["Context pruning strategies are heuristic-based; removing or summarizing messages may lose important context","Token counting is approximate for some models; actual token usage may differ from estimates","No built-in strategy for choosing which messages to prune; developers must implement custom logic"],"requires":["TypeScript 4.7+","Model context window size (typically provided by @inngest/ai based on model identifier)"],"input_types":["message arrays with role/content structure","optional context pruning strategy configuration"],"output_types":["pruned message arrays within token limits","context utilization metrics (tokens used / context window)","warnings when approaching limits"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_7","uri":"capability://planning.reasoning.model.selection.and.fallback.with.capability.based.routing","name":"model selection and fallback with capability-based routing","description":"Provides a routing layer that selects the best model for a given task based on declared capabilities (e.g., vision support, function calling, structured output) and cost/latency constraints. Implements fallback chains where if the primary model fails or is unavailable, the request is automatically routed to a secondary model with similar capabilities. Supports cost-based model selection (e.g., prefer cheaper models for simple tasks) and latency-based selection (e.g., prefer faster models for real-time applications). Integrates with Inngest's event system to track model selection decisions.","intents":["Automatically select the most appropriate model for a task without hardcoding model names","Implement fallback strategies to handle model unavailability or rate limiting","Optimize costs by routing simple tasks to cheaper models and complex tasks to more capable models","Build resilient applications that can adapt to provider outages or model deprecations"],"best_for":["Multi-model applications that need intelligent model selection","Cost-conscious teams wanting to optimize model selection based on task complexity","Applications requiring high availability with automatic fallback to alternative models"],"limitations":["Capability-based routing requires explicit model capability declarations; no automatic capability detection","Fallback chains add latency if the primary model fails; no built-in circuit breaker to skip failed models","Cost-based selection requires accurate cost estimates; pricing changes may require manual updates"],"requires":["TypeScript 4.7+","Model capability declarations (provided by @inngest/ai or custom configuration)"],"input_types":["task description or capability requirements","optional cost/latency constraints","fallback model list"],"output_types":["selected model identifier","model selection rationale (for debugging)","fallback model if primary fails"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_8","uri":"capability://automation.workflow.prompt.versioning.and.a.b.testing.within.workflows","name":"prompt versioning and a/b testing within workflows","description":"Enables versioning of prompts as Inngest workflow artifacts, allowing different prompt versions to be tested against each other in A/B test configurations. Automatically routes requests to different prompt versions based on configurable split percentages or user cohorts. Tracks performance metrics (latency, cost, output quality) per prompt version and integrates with Inngest's event system to enable analysis of which prompts perform best. Supports rollback to previous prompt versions without code changes.","intents":["Test different prompts against each other to optimize output quality","Gradually roll out new prompts to a percentage of users before full deployment","Track performance metrics per prompt version to make data-driven optimization decisions","Rollback to previous prompts if a new version performs poorly"],"best_for":["Teams optimizing LLM output quality through prompt engineering","Applications requiring A/B testing of prompts at scale","Organizations wanting to track prompt performance over time"],"limitations":["A/B testing requires sufficient traffic to achieve statistical significance; low-traffic applications may not see meaningful results","Performance metrics are collected asynchronously; real-time performance feedback is not available","No built-in statistical significance testing; developers must implement their own analysis"],"requires":["TypeScript 4.7+","Inngest workflow configured to support prompt versioning"],"input_types":["prompt text or template","version identifier","optional A/B test configuration (split percentage, cohort rules)"],"output_types":["selected prompt version","performance metrics per version (latency, cost, quality scores)","A/B test results and statistical analysis"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-inngest-ai__cap_9","uri":"capability://safety.moderation.safety.and.content.filtering.with.provider.native.moderation","name":"safety and content filtering with provider-native moderation","description":"Integrates content moderation by leveraging provider-native safety features (OpenAI's moderation API, Anthropic's safety guidelines) and provides a unified interface for content filtering across providers. Automatically flags or blocks requests/responses that violate safety policies, with configurable severity levels and custom rules. Logs moderation decisions to Inngest's event store for auditing and compliance. Supports both input filtering (reject unsafe prompts) and output filtering (reject unsafe completions).","intents":["Prevent unsafe or policy-violating content from being sent to LLMs","Block unsafe LLM outputs before they reach users","Maintain audit trails of moderation decisions for compliance","Implement custom safety policies beyond provider defaults"],"best_for":["Applications handling sensitive content (healthcare, finance, legal)","Multi-tenant platforms needing per-tenant safety policies","Organizations with strict compliance requirements (HIPAA, SOC 2)"],"limitations":["Provider-native moderation is imperfect; false positives and false negatives are possible","Custom safety rules require manual implementation; no built-in ML-based content classification","Moderation adds latency (~100-200ms per request for external moderation APIs)"],"requires":["TypeScript 4.7+","Provider API key with moderation support (OpenAI, Anthropic)","Optional: custom safety policy configuration"],"input_types":["user prompts","LLM responses","optional custom safety rules"],"output_types":["moderation verdict (safe/unsafe)","violation categories and severity","moderation audit logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["TypeScript 4.7+","Node.js 16+","Valid API keys for at least one supported provider (OpenAI, Anthropic, Gemini, Grok, or Azure OpenAI)","@inngest/inngest package as peer dependency","Tool definitions conforming to the @inngest/ai schema format","Provider API keys with function-calling support (OpenAI, Anthropic, Gemini)","Provider API key with batch API support (OpenAI, Anthropic)","Batch requests must conform to provider-specific batch format","Optional: embedding model for semantic deduplication (e.g., OpenAI embeddings)","Optional: Redis or other cache backend for distributed caching"],"failure_modes":["Abstraction layer adds ~50-100ms overhead per provider call due to adapter marshaling","Not all provider-specific features are exposed through the unified interface (e.g., vision-specific parameters may require provider-specific code paths)","Requires explicit adapter registration per provider; no auto-discovery of available providers","Schema normalization requires explicit tool definition; no automatic inference from TypeScript function signatures","Parallel tool calling (multiple tools in one response) is provider-dependent and may require manual orchestration","Tool result context window management is the developer's responsibility; no automatic context pruning when results exceed token limits","Batch processing introduces significant latency (hours to days depending on provider queue)","Batch APIs have different feature support than real-time APIs (e.g., no streaming, limited tool calling)","Batch failures require manual retry logic; no automatic retry for failed batch items","Semantic deduplication requires embedding models, adding latency (~100-200ms per request)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4200891518932243,"quality":0.49,"ecosystem":0.46,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":794075,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=inngest-ai","compare_url":"https://unfragile.ai/compare?artifact=inngest-ai"}},"signature":"CET5x9LBRRJ3o3Axy2liDg3QPN1bi6N5JDlW9wLsZt0hFHO/23A4E3RPn0ihB3azm2q74F6WH6aiAXpnH8a3BA==","signedAt":"2026-06-19T19:11:10.477Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/inngest-ai","artifact":"https://unfragile.ai/inngest-ai","verify":"https://unfragile.ai/api/v1/verify?slug=inngest-ai","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"}}