{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-auto-engineerai-gateway","slug":"npm-auto-engineerai-gateway","name":"@auto-engineer/ai-gateway","type":"mcp","url":"https://www.npmjs.com/package/@auto-engineer/ai-gateway","page_url":"https://unfragile.ai/npm-auto-engineerai-gateway","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-auto-engineerai-gateway__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Abstracts API differences across multiple LLM providers (OpenAI, Anthropic, etc.) behind a single standardized interface, translating provider-specific request/response formats into a normalized schema. Implements adapter pattern with provider-specific client wrappers that handle authentication, rate limiting, and protocol differences, allowing developers to swap providers without changing application code.","intents":["I want to support multiple LLM providers without rewriting my application logic for each one","I need to switch between OpenAI and Anthropic APIs based on cost or availability without refactoring","I want to abstract away provider-specific quirks like different token counting or response formats"],"best_for":["teams building multi-provider AI applications","developers avoiding vendor lock-in with a single LLM provider","startups needing cost optimization by comparing provider pricing dynamically"],"limitations":["Abstraction layer adds latency overhead for request/response translation (~50-100ms per call)","Not all advanced provider-specific features (e.g., vision models, function calling variants) may be fully exposed through normalized interface","Requires maintaining adapter code as providers update their APIs"],"requires":["Node.js 16+","API keys for at least one supported provider (OpenAI, Anthropic, etc.)","TypeScript 4.5+ or JavaScript ES2020+"],"input_types":["text prompts","structured messages with role/content","system prompts","model configuration objects"],"output_types":["text completions","structured JSON responses","streaming token streams","usage metadata (tokens, cost)"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_1","uri":"capability://tool.use.integration.mcp.tool.schema.translation.and.function.calling","name":"mcp tool schema translation and function calling","description":"Translates MCP tool definitions (JSON schemas) into provider-native function calling formats (OpenAI function_calling, Anthropic tool_use, etc.), then routes tool execution results back through the LLM. Implements a schema normalization layer that maps between MCP's tool specification and each provider's function calling protocol, handling argument validation and result serialization.","intents":["I want to expose my MCP tools to any LLM provider without rewriting tool schemas","I need to handle tool execution and result feedback in a provider-agnostic way","I want to validate tool arguments before execution using the MCP schema"],"best_for":["developers building MCP-based agent systems with multiple LLM backends","teams integrating external tools (APIs, databases) with LLMs across providers","MCP server authors wanting to support multiple LLM providers"],"limitations":["Tool schema translation may lose provider-specific optimizations (e.g., OpenAI's parallel tool calling)","Requires tool implementations to be provider-agnostic; provider-specific tool features not supported","No built-in tool result caching — each execution re-runs the tool"],"requires":["MCP server with tool definitions","Provider API keys for function calling support (OpenAI, Anthropic, etc.)","Tool implementations compatible with MCP protocol"],"input_types":["MCP tool schemas (JSON Schema format)","tool invocation requests with arguments","tool execution results"],"output_types":["provider-native function calling payloads","tool execution results formatted for LLM consumption","structured tool call metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_10","uri":"capability://tool.use.integration.mcp.server.integration.and.tool.discovery","name":"mcp server integration and tool discovery","description":"Discovers and registers MCP servers and their tools, exposing them to LLM providers through the gateway. Implements MCP client protocol handling that connects to MCP servers, introspects available tools, and manages tool lifecycle (initialization, execution, cleanup), with automatic tool schema translation for function calling.","intents":["I want to expose my MCP server's tools to LLM providers without writing custom integrations","I need to discover available tools from MCP servers and make them available to LLMs","I want to handle tool execution and result routing through the gateway"],"best_for":["MCP server developers wanting multi-provider LLM support","teams building agent systems with MCP tools","developers integrating external tools (APIs, databases) with LLMs"],"limitations":["Requires MCP servers to be running and accessible (network/process overhead)","Tool discovery happens at startup; dynamic tool registration not supported","MCP protocol overhead adds latency to tool execution (~100-500ms per tool call)","No built-in tool result caching or memoization"],"requires":["MCP server(s) running and accessible","MCP client library","Tool implementations compatible with MCP protocol"],"input_types":["MCP server connection details","tool invocation requests"],"output_types":["discovered tools and schemas","tool execution results","tool metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_2","uri":"capability://tool.use.integration.streaming.response.aggregation.with.provider.normalization","name":"streaming response aggregation with provider normalization","description":"Handles streaming token responses from different providers (OpenAI streaming, Anthropic streaming, etc.) and normalizes them into a unified event stream. Implements a stream adapter that buffers partial tokens, detects stream completion, and emits normalized events (token, done, error) regardless of provider, enabling consistent streaming UX across backends.","intents":["I want to display streaming LLM responses in real-time without handling provider-specific stream formats","I need to aggregate streaming tokens and detect when the response is complete","I want to handle streaming errors gracefully across different providers"],"best_for":["web applications needing real-time LLM response display","chat interfaces supporting multiple LLM backends","developers building streaming-first AI applications"],"limitations":["Streaming latency varies by provider; no normalization of response time guarantees","Token buffering adds memory overhead for long responses (~1KB per 1000 tokens)","Provider-specific streaming metadata (e.g., stop reason, usage stats) may be lost in normalization"],"requires":["Provider with streaming API support (OpenAI, Anthropic, etc.)","Client capable of consuming event streams (Node.js, browser with fetch/EventSource)"],"input_types":["streaming responses from LLM providers","stream configuration (timeout, buffer size)"],"output_types":["normalized event stream (token, done, error events)","aggregated text response","completion metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_3","uri":"capability://automation.workflow.provider.configuration.and.credential.management","name":"provider configuration and credential management","description":"Centralizes API key management and provider configuration (model selection, temperature, max tokens, etc.) with support for environment variables, config files, and runtime overrides. Implements a configuration hierarchy where runtime settings override file-based config, which overrides environment variables, with validation of required credentials before API calls.","intents":["I want to manage API keys securely without hardcoding them in my application","I need to switch between different models or providers via configuration without code changes","I want to set default parameters (temperature, max_tokens) per provider and override them per request"],"best_for":["teams managing multiple environments (dev, staging, prod) with different provider configs","developers deploying to cloud platforms with environment variable injection","applications supporting user-configurable LLM backends"],"limitations":["No built-in secrets encryption — relies on environment variable security","Configuration validation happens at runtime, not at startup","No audit logging of credential access or configuration changes"],"requires":["Environment variables or config file with provider credentials","Support for .env files (if using dotenv integration)"],"input_types":["environment variables","JSON/YAML config files","runtime configuration objects"],"output_types":["validated provider configuration","credential objects for API calls"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_4","uri":"capability://automation.workflow.request.response.logging.and.observability.hooks","name":"request/response logging and observability hooks","description":"Provides hooks for logging and monitoring all LLM API calls, including request payloads, response metadata, latency, and token usage. Implements a middleware pattern where developers can attach custom logging handlers (e.g., to send metrics to Datadog, write to files, or track costs) without modifying core gateway code.","intents":["I want to track LLM API costs and token usage across providers","I need to debug what requests are being sent to LLM providers","I want to monitor latency and error rates for each provider"],"best_for":["teams tracking LLM costs and usage metrics","developers debugging multi-provider integrations","applications requiring audit trails of LLM interactions"],"limitations":["Logging hooks add latency to every request (~10-50ms depending on handler complexity)","No built-in cost calculation — requires manual mapping of model names to pricing","Sensitive data (prompts, responses) logged by default; requires custom filtering"],"requires":["Custom logging handler implementation or integration with logging library"],"input_types":["request objects (prompts, model, parameters)","response objects (completions, usage, metadata)"],"output_types":["structured logs (JSON, text)","metrics (latency, token count, cost)","error traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_5","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.provider.specific.fallbacks","name":"error handling and retry logic with provider-specific fallbacks","description":"Implements intelligent retry logic that handles provider-specific errors (rate limits, timeouts, API errors) with exponential backoff and optional fallback to alternative providers. Detects error types (transient vs permanent) and applies provider-specific retry strategies (e.g., longer backoff for Anthropic rate limits vs OpenAI).","intents":["I want automatic retries for transient errors without manual error handling","I need to fall back to a secondary provider if the primary one is rate-limited or down","I want to distinguish between retryable and permanent errors"],"best_for":["production applications requiring high availability across LLM providers","teams using multiple providers for redundancy","applications with variable load that may hit rate limits"],"limitations":["Retry logic adds latency for failed requests (exponential backoff can take 30+ seconds)","Provider fallback requires credentials for multiple providers","No built-in circuit breaker — will keep retrying even if provider is down long-term"],"requires":["Configuration for retry policy (max retries, backoff strategy)","Optional: credentials for fallback providers"],"input_types":["API requests","error responses from providers"],"output_types":["successful response (after retry or fallback)","error with retry metadata (attempts, final error)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_6","uri":"capability://tool.use.integration.model.capability.detection.and.feature.negotiation","name":"model capability detection and feature negotiation","description":"Automatically detects which features each provider/model supports (vision, function calling, streaming, etc.) and negotiates feature availability at runtime. Implements a capability registry that maps model names to supported features and prevents unsupported feature requests (e.g., vision on text-only models) before sending to the API.","intents":["I want to know which models support vision or function calling before making requests","I need to gracefully degrade features if a model doesn't support them","I want to validate feature requests before sending to the API to avoid wasted calls"],"best_for":["applications supporting multiple models with different capabilities","teams building feature-rich AI applications with fallback modes","developers avoiding API errors from unsupported features"],"limitations":["Capability registry requires manual updates as providers add new models/features","No real-time capability detection — relies on static configuration","Some capabilities (e.g., max context length) may vary by region or account tier"],"requires":["Capability registry (built-in or custom)","Model names/IDs for providers"],"input_types":["model identifiers","requested features (vision, function_calling, streaming, etc.)"],"output_types":["capability metadata (supported features, limits)","validation results (feature supported/not supported)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_7","uri":"capability://automation.workflow.request.batching.and.cost.optimization","name":"request batching and cost optimization","description":"Groups multiple LLM requests into batches for providers that support batch APIs (e.g., OpenAI Batch API), reducing per-request costs. Implements a batching queue that accumulates requests up to a size/time threshold, then submits them as a single batch job, with result deduplication and callback routing.","intents":["I want to reduce LLM API costs by batching non-urgent requests","I need to process many prompts efficiently without hitting rate limits","I want to trade latency for cost savings on non-real-time workloads"],"best_for":["applications with non-real-time LLM workloads (e.g., content generation, analysis)","teams with high-volume LLM usage looking to reduce costs","batch processing pipelines"],"limitations":["Batching adds latency (requests queued until batch threshold reached, then processing time)","Not all providers support batch APIs; fallback to individual requests required","Batch jobs may take hours to complete; not suitable for real-time applications","Requires persistent queue for reliability; no built-in persistence"],"requires":["Provider with batch API support (OpenAI Batch API, etc.)","Configuration for batch size/timeout thresholds"],"input_types":["individual LLM requests","batch configuration (size, timeout)"],"output_types":["batch job IDs","results routed to original requesters","cost savings metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_8","uri":"capability://data.processing.analysis.context.window.management.and.token.counting","name":"context window management and token counting","description":"Tracks token usage across requests and manages context windows to prevent exceeding model limits. Implements provider-specific token counters (using tokenizer libraries or provider APIs) and automatically truncates or summarizes context when approaching limits, with configurable truncation strategies (sliding window, summarization, etc.).","intents":["I want to know how many tokens my prompt will use before sending it","I need to fit long conversations into a model's context window without losing important context","I want to automatically truncate old messages when context is full"],"best_for":["chat applications with long conversation histories","developers building context-aware agents","teams managing token budgets"],"limitations":["Token counting varies by provider and model; estimates may be inaccurate (~5-10% error)","Automatic context truncation may lose important information","No built-in summarization — requires external summarization model","Context window limits vary by model and region; static configuration may be outdated"],"requires":["Tokenizer library or provider API for token counting","Model context window limits (built-in or configured)"],"input_types":["text prompts","conversation histories","token counting configuration"],"output_types":["token counts","truncated/summarized context","context window utilization metrics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-auto-engineerai-gateway__cap_9","uri":"capability://data.processing.analysis.provider.agnostic.response.parsing.and.structured.output","name":"provider-agnostic response parsing and structured output","description":"Parses LLM responses into structured formats (JSON, typed objects) with provider-agnostic handling of structured output modes (OpenAI JSON mode, Anthropic structured output, etc.). Implements schema validation and automatic fallback to regex/parsing if structured output fails, with error recovery for malformed responses.","intents":["I want to extract structured data from LLM responses without manual parsing","I need to validate LLM responses against a schema before using them","I want to handle malformed JSON responses gracefully"],"best_for":["applications extracting structured data from LLM responses","developers building data pipelines with LLM outputs","teams requiring validated, type-safe LLM responses"],"limitations":["Structured output modes not supported by all providers; fallback parsing may be less reliable","Schema validation adds latency (~50-100ms per response)","LLMs may still produce invalid JSON even with structured output mode enabled","No built-in retry for validation failures — requires manual handling"],"requires":["JSON schema or TypeScript type definition for response structure","Provider with structured output support (optional; fallback to parsing)"],"input_types":["LLM responses (text, JSON)","JSON schemas or type definitions"],"output_types":["parsed objects","validation results","type-safe responses"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","API keys for at least one supported provider (OpenAI, Anthropic, etc.)","TypeScript 4.5+ or JavaScript ES2020+","MCP server with tool definitions","Provider API keys for function calling support (OpenAI, Anthropic, etc.)","Tool implementations compatible with MCP protocol","MCP server(s) running and accessible","MCP client library","Provider with streaming API support (OpenAI, Anthropic, etc.)","Client capable of consuming event streams (Node.js, browser with fetch/EventSource)"],"failure_modes":["Abstraction layer adds latency overhead for request/response translation (~50-100ms per call)","Not all advanced provider-specific features (e.g., vision models, function calling variants) may be fully exposed through normalized interface","Requires maintaining adapter code as providers update their APIs","Tool schema translation may lose provider-specific optimizations (e.g., OpenAI's parallel tool calling)","Requires tool implementations to be provider-agnostic; provider-specific tool features not supported","No built-in tool result caching — each execution re-runs the tool","Requires MCP servers to be running and accessible (network/process overhead)","Tool discovery happens at startup; dynamic tool registration not supported","MCP protocol overhead adds latency to tool execution (~100-500ms per tool call)","No built-in tool result caching or memoization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"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":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-04-22T08:11:30.283Z","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=npm-auto-engineerai-gateway","compare_url":"https://unfragile.ai/compare?artifact=npm-auto-engineerai-gateway"}},"signature":"Y+HJWDaqA9UJy6TGqrswk3HTBhOds0R5VjAdfMnhPMt+SXh6cvd8Q+EmTK0QaHui2PHAtS9TUCmb7FuGUBBtBQ==","signedAt":"2026-06-20T03:04:42.001Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-auto-engineerai-gateway","artifact":"https://unfragile.ai/npm-auto-engineerai-gateway","verify":"https://unfragile.ai/api/v1/verify?slug=npm-auto-engineerai-gateway","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"}}