{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-multi-llm-ts","slug":"multi-llm-ts","name":"multi-llm-ts","type":"repo","url":"https://github.com/nbonamy/multi-llm-ts#readme","page_url":"https://unfragile.ai/multi-llm-ts","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-multi-llm-ts__cap_0","uri":"capability://tool.use.integration.unified.llm.provider.abstraction","name":"unified-llm-provider-abstraction","description":"Abstracts multiple LLM provider APIs (OpenAI, Anthropic, Google, Azure, Ollama, etc.) behind a single consistent TypeScript interface, normalizing request/response schemas and authentication mechanisms. Implements a provider-agnostic message format and parameter mapping layer that translates unified API calls into provider-specific protocol calls, eliminating the need to learn and maintain separate SDK integrations for each LLM service.","intents":["I want to switch between different LLM providers without rewriting my application code","I need to query multiple LLM providers in parallel and compare their responses","I want to abstract away provider-specific API differences in my agent or chatbot","I need a single SDK that works with both cloud providers and local models like Ollama"],"best_for":["TypeScript/Node.js developers building multi-provider LLM applications","teams evaluating multiple LLM providers and needing vendor lock-in prevention","LLM agent frameworks that need pluggable model backends","startups prototyping with different providers before committing to one"],"limitations":["Abstraction layer adds latency overhead (~50-100ms per request) due to schema translation","Not all provider-specific features are exposed — advanced parameters may be lost in normalization","Requires explicit configuration for each provider's authentication credentials","Rate limiting and quota management must be handled per-provider, not globally","Streaming response handling varies by provider and may not be fully normalized"],"requires":["Node.js 14+ or TypeScript 4.5+","API keys or credentials for at least one LLM provider (OpenAI, Anthropic, Google, Azure, etc.)","npm or yarn package manager"],"input_types":["text messages","message arrays with role/content structure","system prompts","structured parameters (temperature, max_tokens, top_p, etc.)"],"output_types":["text completions","structured message objects with role and content","token usage metadata","streaming token streams"],"categories":["tool-use-integration","llm-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_1","uri":"capability://tool.use.integration.provider.configuration.management","name":"provider-configuration-management","description":"Manages provider-specific configuration (API keys, endpoints, model names, authentication schemes) through a centralized configuration system that supports environment variables, constructor parameters, and provider-specific settings. Handles credential injection and validation at initialization time, allowing runtime provider switching without application restart.","intents":["I want to configure multiple LLM providers with different API keys and endpoints","I need to switch providers at runtime based on availability or cost","I want to use environment variables for sensitive credentials without hardcoding them","I need to support custom endpoints for self-hosted or on-premise LLM deployments"],"best_for":["developers managing multiple LLM provider accounts in production","teams with environment-specific configurations (dev/staging/prod)","applications requiring provider failover or dynamic provider selection","enterprises using self-hosted LLM deployments alongside cloud providers"],"limitations":["Configuration validation happens at initialization, not at request time","No built-in credential rotation or refresh token management","Environment variable naming conventions must be manually documented","Configuration changes require application restart unless explicitly handled in code"],"requires":["Node.js environment with access to process.env or constructor parameters","Valid API credentials for each configured provider"],"input_types":["environment variables","constructor configuration objects","provider-specific credential formats"],"output_types":["validated provider instances","configuration state"],"categories":["tool-use-integration","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_10","uri":"capability://automation.workflow.provider.health.monitoring.and.failover","name":"provider-health-monitoring-and-failover","description":"Monitors provider health and availability through periodic health checks, tracking response times and error rates to detect degraded service. Implements automatic failover to alternative providers when the primary provider becomes unavailable or degraded, with configurable failover strategies and health check intervals.","intents":["I want to automatically switch to a backup provider if the primary provider fails","I need to monitor provider health and availability in real-time","I want to detect and respond to provider degradation before it impacts users","I need to build highly available multi-provider LLM systems"],"best_for":["production applications requiring high availability across multiple LLM providers","mission-critical systems that cannot tolerate provider outages","applications with SLA requirements for LLM availability","teams building resilient multi-provider LLM infrastructure"],"limitations":["Health checks add latency and API quota consumption","Failover decisions are based on heuristics and may not reflect actual user experience","No built-in circuit breaker pattern for cascading failures","Health check intervals are fixed and not adaptive to provider conditions","Failover state is not persisted, making recovery from transient failures unpredictable"],"requires":["Multiple configured providers for failover","Background task execution capability (timers, scheduled jobs)"],"input_types":["health check configuration","failover strategy parameters"],"output_types":["health status metrics","failover events","provider availability reports"],"categories":["automation-workflow","health-monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_11","uri":"capability://memory.knowledge.response.caching.and.deduplication","name":"response-caching-and-deduplication","description":"Caches LLM responses based on request hash or semantic similarity, avoiding redundant API calls for identical or similar requests. Implements configurable cache backends (in-memory, Redis, etc.) and cache invalidation strategies, with support for semantic deduplication to avoid near-duplicate requests to different providers.","intents":["I want to cache LLM responses to avoid redundant API calls","I need to deduplicate similar requests across multiple providers","I want to reduce API costs by reusing cached responses","I need to implement response caching without external dependencies"],"best_for":["applications with repetitive LLM queries that benefit from caching","cost-sensitive applications that need to minimize API calls","systems with high request volume and potential for deduplication","applications that can tolerate slightly stale cached responses"],"limitations":["Cache invalidation is difficult for LLM responses with time-sensitive content","Semantic deduplication requires additional computation and may miss similar requests","Cache storage requirements grow with request volume and response size","No built-in cache warming or preloading strategies","Cache coherence across distributed systems is not handled"],"requires":["Cache backend (in-memory, Redis, or similar)","Cache key generation strategy"],"input_types":["LLM requests","cache configuration"],"output_types":["cached responses","cache hit/miss metrics","cache management operations"],"categories":["memory-knowledge","caching"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_12","uri":"capability://safety.moderation.request.logging.and.audit.trail","name":"request-logging-and-audit-trail","description":"Logs all LLM requests and responses with configurable detail levels, creating an audit trail for compliance, debugging, and analysis. Supports structured logging with metadata (provider, model, tokens, latency, etc.) and integrates with standard logging frameworks, enabling centralized log aggregation and analysis.","intents":["I want to log all LLM requests for debugging and troubleshooting","I need an audit trail of LLM usage for compliance and security","I want to analyze request patterns and performance metrics","I need to integrate LLM logging with my existing logging infrastructure"],"best_for":["production applications requiring audit trails for compliance","teams debugging multi-provider LLM issues","applications with security or privacy requirements","systems analyzing LLM usage patterns and performance"],"limitations":["Logging adds latency and storage overhead, especially for high-volume applications","Sensitive data in prompts and responses must be manually redacted","Log retention and storage is not managed by the library","Structured logging requires consistent schema across all log entries","No built-in log analysis or visualization tools"],"requires":["Logging framework (console, file, or external service)","Storage for log data"],"input_types":["LLM requests","LLM responses","logging configuration"],"output_types":["structured log entries","audit trail records"],"categories":["safety-moderation","logging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_2","uri":"capability://data.processing.analysis.message.format.normalization","name":"message-format-normalization","description":"Normalizes message formats across different LLM providers by translating between provider-specific message structures (OpenAI's role/content format, Anthropic's user/assistant format, etc.) into a unified internal representation. Handles role mapping, content type conversion, and message history formatting to ensure consistent behavior regardless of the underlying provider's API specification.","intents":["I want to use the same message format regardless of which LLM provider I'm querying","I need to convert message histories between different provider formats","I want to handle system prompts consistently across providers that treat them differently","I need to ensure message role semantics are preserved when switching providers"],"best_for":["developers building provider-agnostic chatbots or conversation systems","multi-provider agent frameworks that need consistent message handling","applications migrating between LLM providers and needing format compatibility","teams building abstractions over multiple LLM APIs"],"limitations":["Some provider-specific message features (e.g., vision content in OpenAI) may not normalize cleanly","Message history length limits vary by provider and are not automatically managed","Role semantics differ subtly between providers and normalization may lose nuance","Custom message metadata from specific providers may be stripped during normalization"],"requires":["Message objects conforming to the library's unified message schema","Understanding of role types (system, user, assistant) across providers"],"input_types":["message arrays with role and content","system prompts","provider-specific message formats"],"output_types":["normalized message arrays","provider-specific message formats for API calls"],"categories":["data-processing-analysis","message-normalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_3","uri":"capability://data.processing.analysis.parameter.mapping.and.translation","name":"parameter-mapping-and-translation","description":"Maps unified parameter names (temperature, max_tokens, top_p, etc.) to provider-specific parameter names and formats, handling differences in parameter ranges, defaults, and support across providers. Translates parameter values into provider-appropriate formats and validates that requested parameters are supported by the target provider before making API calls.","intents":["I want to use the same parameter names across different LLM providers","I need to know which parameters are supported by each provider before making a request","I want to automatically translate my unified parameters to provider-specific formats","I need to handle parameter range differences between providers (e.g., temperature 0-1 vs 0-2)"],"best_for":["developers building parameter-driven LLM applications that support multiple providers","frameworks that need to validate and translate parameters before API calls","applications with user-configurable LLM parameters that must work across providers","teams building LLM configuration UIs that need to show provider-specific constraints"],"limitations":["Parameter validation is static and based on provider documentation, not runtime API responses","Some advanced provider-specific parameters cannot be expressed in the unified schema","Parameter range normalization may lose precision (e.g., rounding temperature values)","New provider parameters require library updates to be supported"],"requires":["Unified parameter object with standard keys (temperature, max_tokens, top_p, etc.)","Knowledge of which parameters each provider supports"],"input_types":["unified parameter objects","provider identifiers"],"output_types":["provider-specific parameter objects","validation results","parameter support metadata"],"categories":["data-processing-analysis","parameter-translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_4","uri":"capability://tool.use.integration.streaming.response.handling","name":"streaming-response-handling","description":"Abstracts streaming response handling across providers with different streaming protocols (Server-Sent Events for OpenAI, event streams for Anthropic, etc.), providing a unified async iterator or callback interface for consuming streamed tokens. Handles stream parsing, error recovery, and token buffering transparently regardless of the underlying provider's streaming implementation.","intents":["I want to stream LLM responses in real-time without blocking on the full response","I need to handle streaming responses consistently across different LLM providers","I want to process tokens as they arrive without buffering the entire response","I need to handle streaming errors and connection issues gracefully"],"best_for":["developers building real-time chat interfaces or streaming applications","multi-provider applications that need consistent streaming behavior","applications with low-latency requirements that benefit from token-by-token processing","frameworks building streaming abstractions over multiple LLM providers"],"limitations":["Streaming protocol differences between providers may cause timing variations","Error handling in streams is provider-specific and may not be fully normalized","Stream interruption and reconnection logic is not built-in","Token buffering and batching strategies vary by provider and are not configurable","Some providers have lower streaming latency than others due to infrastructure differences"],"requires":["HTTP client with streaming support (Node.js fetch or similar)","Async/await or callback-based event handling in application code"],"input_types":["streaming request parameters","provider identifiers"],"output_types":["async iterators yielding tokens","callback-based token events","stream metadata (finish_reason, usage, etc.)"],"categories":["tool-use-integration","streaming-response"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_5","uri":"capability://safety.moderation.error.handling.and.retry.logic","name":"error-handling-and-retry-logic","description":"Implements provider-agnostic error handling that normalizes different error types and HTTP status codes across providers into a unified error schema, with built-in retry logic for transient failures (rate limits, timeouts, temporary service outages). Distinguishes between retryable errors (429, 503) and permanent failures (401, 404) to avoid wasting API quota on unrecoverable errors.","intents":["I want consistent error handling across different LLM providers","I need automatic retry logic for transient failures without manual implementation","I want to distinguish between rate limiting, authentication failures, and service errors","I need to handle provider-specific error messages and codes uniformly"],"best_for":["production applications requiring robust error handling across multiple providers","applications with high request volume that need intelligent retry strategies","teams building resilient multi-provider LLM systems","applications that need to gracefully degrade when providers are unavailable"],"limitations":["Retry logic uses exponential backoff with fixed parameters, not customizable per provider","Rate limit detection relies on HTTP status codes and headers, which vary by provider","Timeout handling is global and not provider-specific","No built-in circuit breaker pattern for cascading failures across providers","Retry budget and quota management must be handled at application level"],"requires":["HTTP client with timeout support","Understanding of retryable vs non-retryable error conditions"],"input_types":["API requests","HTTP responses with error status codes"],"output_types":["normalized error objects","retry decisions","error metadata (status code, message, retry-after, etc.)"],"categories":["safety-moderation","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_6","uri":"capability://data.processing.analysis.token.usage.tracking.and.reporting","name":"token-usage-tracking-and-reporting","description":"Tracks and aggregates token usage (input tokens, output tokens, total tokens) across multiple LLM providers with different token counting methodologies, providing unified usage metrics and cost estimation. Normalizes token counts even when providers report them differently or use different tokenization schemes, enabling cost tracking and quota management across heterogeneous provider environments.","intents":["I want to track token usage across multiple LLM providers for cost analysis","I need to estimate costs before making requests to different providers","I want to aggregate usage metrics across all my LLM provider accounts","I need to monitor token consumption and set usage quotas"],"best_for":["applications with multi-provider LLM usage that need cost tracking","teams managing LLM budgets across multiple providers","SaaS platforms offering LLM services to end users","applications with usage-based billing or quota enforcement"],"limitations":["Token counting varies by provider and tokenization scheme, making cross-provider comparison imprecise","Cost estimation requires maintaining current pricing data, which changes frequently","Some providers don't return token usage in responses, requiring client-side token counting","Token usage is reported after requests complete, not before, limiting pre-request quota checks","No built-in persistence for usage history — requires external storage"],"requires":["Token usage data from provider API responses","Optional: pricing data for cost estimation"],"input_types":["API responses with token usage metadata","model identifiers","pricing configuration"],"output_types":["token usage metrics (input, output, total)","cost estimates","aggregated usage reports"],"categories":["data-processing-analysis","usage-tracking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_7","uri":"capability://data.processing.analysis.model.capability.detection.and.validation","name":"model-capability-detection-and-validation","description":"Detects and validates model capabilities (vision support, function calling, streaming, etc.) at initialization or runtime, preventing requests that use unsupported features on a given model. Maintains a capability matrix for each supported model and provider, allowing applications to query which features are available before attempting to use them.","intents":["I want to know which models support vision, function calling, or other advanced features","I need to validate that a model supports the features I'm trying to use before making a request","I want to automatically select models that support specific capabilities","I need to handle graceful degradation when a model doesn't support a requested feature"],"best_for":["multi-model applications that need to select models based on capability requirements","applications using advanced features like vision or function calling across providers","frameworks that need to validate feature compatibility before API calls","teams building model selection logic based on capability requirements"],"limitations":["Capability matrix is static and based on documentation, not runtime API introspection","New model capabilities require library updates to be recognized","Some capabilities have nuanced support (e.g., partial vision support) that may not be captured","Capability detection is not real-time and may lag behind provider updates","No mechanism to detect deprecated or newly available capabilities at runtime"],"requires":["Model identifiers","Provider identifiers"],"input_types":["model names","provider identifiers","feature names"],"output_types":["capability metadata","boolean support indicators","capability matrices"],"categories":["data-processing-analysis","capability-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_8","uri":"capability://tool.use.integration.function.calling.and.tool.use.abstraction","name":"function-calling-and-tool-use-abstraction","description":"Abstracts function calling and tool use across providers with different function calling implementations (OpenAI's function calling, Anthropic's tool use, Google's function calling, etc.), providing a unified schema for defining tools and handling tool calls. Translates unified tool definitions into provider-specific formats and normalizes tool call responses into a consistent structure regardless of the underlying provider's implementation.","intents":["I want to define tools once and use them with multiple LLM providers","I need to handle function calling consistently across different providers","I want to abstract away provider-specific tool calling formats","I need to build agent systems that work with any LLM provider"],"best_for":["developers building multi-provider LLM agents with tool use","agent frameworks that need provider-agnostic function calling","applications using tools/functions across multiple LLM providers","teams building tool-using systems without provider lock-in"],"limitations":["Tool schema translation may lose provider-specific features or optimizations","Some providers have different tool calling semantics (parallel calls, sequential, etc.)","Tool response handling varies by provider and may not be fully normalized","Advanced tool features (like tool choice constraints) may not be uniformly supported","Tool definition validation is static and based on provider documentation"],"requires":["Tool definitions with name, description, and parameter schema","Provider support for function calling (not all providers support this)"],"input_types":["tool definitions with JSON schema","tool call responses from providers"],"output_types":["provider-specific tool definitions","normalized tool call objects","tool execution results"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-multi-llm-ts__cap_9","uri":"capability://automation.workflow.batch.request.processing.and.optimization","name":"batch-request-processing-and-optimization","description":"Optimizes batch requests across multiple LLM providers by grouping requests, managing rate limits, and parallelizing calls where possible. Implements intelligent batching strategies that respect provider-specific rate limits and quota constraints while maximizing throughput and minimizing latency for bulk operations.","intents":["I want to process multiple LLM requests efficiently without hitting rate limits","I need to parallelize requests across multiple providers for faster processing","I want to optimize batch operations to minimize total latency","I need to respect provider rate limits while maximizing throughput"],"best_for":["applications processing large batches of LLM requests","data processing pipelines that need efficient multi-provider LLM integration","teams building bulk processing systems with multiple LLM providers","applications with variable request volume that need intelligent rate limiting"],"limitations":["Batch optimization is heuristic-based and may not be optimal for all workloads","Rate limit information is not always available from providers, limiting optimization","Parallelization adds complexity and may increase memory usage for large batches","No built-in persistence for batch state, making recovery from failures difficult","Batch size and parallelism must be tuned per provider and workload"],"requires":["Multiple requests to process","Understanding of provider rate limits and quotas"],"input_types":["arrays of request objects","batch configuration parameters"],"output_types":["arrays of responses","batch processing metrics","error reports"],"categories":["automation-workflow","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or TypeScript 4.5+","API keys or credentials for at least one LLM provider (OpenAI, Anthropic, Google, Azure, etc.)","npm or yarn package manager","Node.js environment with access to process.env or constructor parameters","Valid API credentials for each configured provider","Multiple configured providers for failover","Background task execution capability (timers, scheduled jobs)","Cache backend (in-memory, Redis, or similar)","Cache key generation strategy","Logging framework (console, file, or external service)"],"failure_modes":["Abstraction layer adds latency overhead (~50-100ms per request) due to schema translation","Not all provider-specific features are exposed — advanced parameters may be lost in normalization","Requires explicit configuration for each provider's authentication credentials","Rate limiting and quota management must be handled per-provider, not globally","Streaming response handling varies by provider and may not be fully normalized","Configuration validation happens at initialization, not at request time","No built-in credential rotation or refresh token management","Environment variable naming conventions must be manually documented","Configuration changes require application restart unless explicitly handled in code","Health checks add latency and API quota consumption","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.13628593029586802,"quality":0.35,"ecosystem":0.39999999999999997,"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":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1153,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=multi-llm-ts","compare_url":"https://unfragile.ai/compare?artifact=multi-llm-ts"}},"signature":"jdffQWYNEcmQPWJmHdeEFoMdd1YMe+pu1Nk4hjFYJsZvtCKYPLWzRnAKNGd3LUvERN34qxJbX6pAqdAfAtvUCA==","signedAt":"2026-06-21T09:02:42.487Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/multi-llm-ts","artifact":"https://unfragile.ai/multi-llm-ts","verify":"https://unfragile.ai/api/v1/verify?slug=multi-llm-ts","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"}}