{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-litellm","slug":"pypi-litellm","name":"litellm","type":"framework","url":"https://pypi.org/project/litellm/","page_url":"https://unfragile.ai/pypi-litellm","categories":["llm-apis"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-litellm__cap_0","uri":"capability://tool.use.integration.unified.llm.api.abstraction.with.provider.detection","name":"unified-llm-api-abstraction-with-provider-detection","description":"Provides a single `completion()` function that automatically detects the LLM provider (OpenAI, Anthropic, Google Vertex, AWS Bedrock, Ollama, etc.) from model name patterns and routes requests to the correct provider SDK. Uses a provider detection registry that maps model identifiers to provider-specific API clients, normalizing request/response formats across 50+ providers into a unified interface. Internally handles provider-specific authentication, endpoint routing, and response parsing without requiring developers to write provider-specific code.","intents":["I want to switch between different LLM providers without rewriting my code","I need a single API that works with OpenAI, Anthropic, and Google models interchangeably","I want to abstract away provider-specific API differences in my application"],"best_for":["teams building multi-provider LLM applications","developers prototyping with multiple models to compare quality/cost","startups avoiding vendor lock-in with a single LLM provider"],"limitations":["Response normalization may lose provider-specific fields (e.g., OpenAI's `logprobs` not available from all providers)","Streaming behavior differs subtly across providers — buffering and chunk timing not perfectly uniform","Some advanced features (reasoning, extended thinking) only available on specific providers, requiring conditional logic"],"requires":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Environment variables or explicit credentials passed to litellm"],"input_types":["messages (list of dicts with role/content)","model name (string identifier)","optional parameters (temperature, max_tokens, etc.)"],"output_types":["completion response object with normalized fields (choices, usage, finish_reason)","streaming chunks (if stream=True)"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_1","uri":"capability://automation.workflow.intelligent.request.routing.with.load.balancing","name":"intelligent-request-routing-with-load-balancing","description":"The Router class implements weighted load balancing and failover logic across multiple model deployments (same model on different providers, or different models entirely). Routes requests based on configurable strategies: round-robin, least-busy, cost-optimized, or latency-based. Tracks per-deployment metrics (success rate, latency, cost) and automatically fails over to backup deployments if a primary provider returns errors or exceeds rate limits. Uses cooldown management to temporarily disable failing deployments and prevent cascading failures.","intents":["I want to distribute LLM requests across multiple providers to reduce latency and cost","I need automatic failover if my primary LLM provider goes down","I want to route requests to the cheapest available model that meets my quality threshold","I need to balance load across multiple OpenAI API keys or Azure deployments"],"best_for":["production LLM applications requiring high availability","cost-conscious teams wanting to optimize spend across providers","teams with multiple API keys/deployments seeking load distribution"],"limitations":["Routing decisions are stateless per-request — no session affinity or user-level routing","Cooldown timers are in-memory; restarting the application resets failure tracking","Cost-based routing requires accurate, up-to-date pricing data; stale pricing leads to suboptimal decisions","No built-in circuit breaker for cascading failures across all deployments"],"requires":["Python 3.8+","Multiple LLM provider credentials configured","Router configuration with deployment definitions (model, provider, weights)"],"input_types":["router config (list of deployments with model, provider, weight)","routing strategy (round-robin, least-busy, cost-optimized, latency-based)","completion request (messages, parameters)"],"output_types":["completion response from selected deployment","routing metadata (selected deployment, latency, cost)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_10","uri":"capability://automation.workflow.budget.and.spend.tracking.with.enforcement","name":"budget-and-spend-tracking-with-enforcement","description":"Tracks cumulative spend per user, team, and organization with configurable budget limits. Enforces hard limits (reject requests exceeding budget) or soft limits (warn but allow). Provides real-time spend dashboards and analytics. Integrates with cost calculation to track spend in real-time. Supports budget reset schedules (daily, monthly, etc.) and budget alerts via email or webhooks.","intents":["I want to enforce budget limits per user or team to control LLM costs","I need real-time visibility into LLM spending across my organization","I want to receive alerts when spending approaches budget limits","I need to reset budgets on a schedule (daily, monthly, etc.)"],"best_for":["SaaS platforms offering LLM features with per-customer budgets","enterprises with cost control requirements","teams wanting to prevent runaway LLM costs"],"limitations":["Budget enforcement is approximate; real-time cost calculation may lag, allowing overspend","Hard budget limits may reject legitimate requests if cost estimates are inaccurate","Budget reset schedules are UTC-based; timezone-aware resets require custom logic","No built-in budget carryover; unused budget is lost at reset time"],"requires":["Python 3.8+","Database for storing budget configurations and spend logs","Optional: email or webhook integration for alerts"],"input_types":["budget configuration (limit, reset schedule, enforcement mode)","completion request"],"output_types":["budget enforcement decision (allow/reject)","spend dashboard with real-time metrics","budget alerts (email, webhook)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_11","uri":"capability://automation.workflow.rate.limiting.and.throttling.with.token.bucket","name":"rate-limiting-and-throttling-with-token-bucket","description":"Implements rate limiting using a token bucket algorithm with configurable limits per user, team, or organization. Supports multiple rate limit dimensions (requests per minute, tokens per hour, etc.). Integrates with Redis for distributed rate limiting across multiple proxy instances. Returns rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset) for client-side backoff. Supports priority queuing for high-priority requests.","intents":["I want to rate limit LLM API calls per user or team","I need to prevent any single user from consuming all LLM quota","I want distributed rate limiting across multiple proxy instances","I need to prioritize certain requests over others"],"best_for":["SaaS platforms with multi-tenant LLM APIs","applications requiring fair resource allocation","teams with distributed proxy deployments"],"limitations":["Token bucket algorithm has inherent burst capacity; sustained high-rate requests may exceed limits","Redis dependency adds latency (~5-10ms) per rate limit check","Rate limit headers are advisory; clients may ignore them and continue sending requests","Priority queuing requires manual configuration; no automatic priority assignment"],"requires":["Python 3.8+","Redis instance for distributed rate limiting","Rate limit configuration (limits per user/team/org)"],"input_types":["rate limit configuration (requests per minute, tokens per hour, etc.)","completion request with user/team identifier"],"output_types":["rate limit decision (allow/reject)","rate limit headers (X-RateLimit-Remaining, X-RateLimit-Reset)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_12","uri":"capability://safety.moderation.guardrails.and.content.safety.with.custom.validators","name":"guardrails-and-content-safety-with-custom-validators","description":"Provides a guardrails system for validating and filtering LLM inputs and outputs. Supports pre-built guardrails (PII detection, toxicity filtering, jailbreak detection) and custom validators. Runs guardrails before sending requests to LLM (input validation) and after receiving responses (output validation). Integrates with external safety services (OpenAI Moderation API, etc.). Supports guardrail chaining and conditional logic.","intents":["I want to prevent users from sending harmful or PII-containing prompts to the LLM","I need to filter LLM responses for toxicity or harmful content before showing to users","I want to detect and block jailbreak attempts","I need custom validation logic for my application's safety requirements"],"best_for":["applications with strict safety requirements (healthcare, finance, etc.)","platforms serving diverse user bases with content moderation needs","teams implementing custom safety policies"],"limitations":["Pre-built guardrails have false positive/negative rates; no perfect detection","Custom validators add latency (~50-200ms per request depending on complexity)","Guardrail evasion is an arms race; determined users may bypass filters","External safety service integrations add dependencies and latency"],"requires":["Python 3.8+","Optional: external safety service API key (OpenAI Moderation, etc.)","Custom validator implementation for application-specific rules"],"input_types":["user prompt (for input validation)","LLM response (for output validation)","guardrail configuration (which guardrails to enable)"],"output_types":["validation decision (allow/reject/flag)","safety metadata (detected issues, confidence scores)"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_13","uri":"capability://automation.workflow.model.access.groups.and.wildcard.routing","name":"model-access-groups-and-wildcard-routing","description":"Allows organizing models into access groups with wildcard patterns (e.g., 'gpt-4*' matches all GPT-4 variants). Enables fine-grained access control where users/teams can only access specific model groups. Supports dynamic model discovery and routing based on access groups. Useful for enforcing organizational policies (e.g., 'only use approved models') and cost control (e.g., 'restrict expensive models to senior engineers').","intents":["I want to restrict which models different users or teams can access","I need to enforce organizational policies on model usage (e.g., only approved models)","I want to prevent cost overruns by restricting access to expensive models","I need dynamic model discovery based on user permissions"],"best_for":["enterprises with strict model governance policies","organizations with cost control requirements","teams managing access to multiple LLM providers"],"limitations":["Wildcard patterns are simple glob-style matching; complex access rules require custom logic","Access group enforcement is proxy-side; applications can bypass by calling providers directly","No built-in audit logging for access group violations","Dynamic model discovery requires periodic updates to access group definitions"],"requires":["Python 3.8+","Proxy server with access group configuration","User/team identifiers for access control"],"input_types":["access group configuration (model patterns, user/team assignments)","completion request with user/team identifier and model name"],"output_types":["access decision (allow/deny)","available models list (filtered by user permissions)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_14","uri":"capability://automation.workflow.admin.dashboard.and.management.ui","name":"admin-dashboard-and-management-ui","description":"Web-based dashboard for managing LiteLLM proxy server operations. Provides UI for API key management (create, rotate, revoke), team and user management, spend tracking and analytics, model access control, and system health monitoring. Supports role-based access to dashboard features (admin, team lead, user). Integrates with database for persistent configuration storage.","intents":["I want a UI to manage API keys without using CLI commands","I need to see real-time spend analytics and cost breakdowns","I want to manage teams and users with role-based access","I need to monitor proxy server health and performance"],"best_for":["non-technical users managing LiteLLM deployments","teams requiring centralized management UI","organizations with compliance/audit requirements"],"limitations":["Dashboard is web-based; requires browser access and network connectivity","UI complexity increases with number of users/teams; performance may degrade with large deployments","Role-based access control is coarse-grained; fine-grained permissions require custom logic","No built-in audit logging for dashboard actions"],"requires":["Python 3.8+","Proxy server deployed with database backend","Web browser for accessing dashboard"],"input_types":["dashboard configuration (UI settings, role definitions)","user actions (create key, manage team, view analytics)"],"output_types":["HTML dashboard UI","API responses for dashboard actions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_15","uri":"capability://data.processing.analysis.embedding.generation.and.vector.storage.integration","name":"embedding-generation-and-vector-storage-integration","description":"Provides a unified interface for generating embeddings across providers (OpenAI, Cohere, Hugging Face, etc.) with the same abstraction as completion API. Supports batch embedding generation for efficiency. Integrates with vector stores (Pinecone, Weaviate, Milvus, etc.) for storing and retrieving embeddings. Tracks embedding costs and usage. Supports semantic search and RAG workflows.","intents":["I want to generate embeddings from different providers with the same code","I need to store embeddings in a vector database for semantic search","I want to build RAG applications with embeddings and LLM completions","I need to track embedding costs alongside completion costs"],"best_for":["applications building semantic search or RAG systems","teams using multiple embedding providers","applications requiring cost tracking for embeddings"],"limitations":["Embedding quality varies significantly across providers; no automatic quality validation","Vector store integration requires manual configuration; no automatic schema management","Batch embedding generation may have different latency characteristics across providers","Embedding cost tracking assumes standard pricing; custom contracts require manual overrides"],"requires":["Python 3.8+","Embedding provider API key (OpenAI, Cohere, Hugging Face, etc.)","Optional: vector store account (Pinecone, Weaviate, Milvus, etc.)"],"input_types":["text to embed (string or list of strings)","embedding model name","optional batch configuration"],"output_types":["embedding vectors (list of floats)","embedding metadata (model, cost, tokens used)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_2","uri":"capability://tool.use.integration.streaming.response.handling.with.normalization","name":"streaming-response-handling-with-normalization","description":"Handles streaming responses from LLM providers by normalizing provider-specific streaming formats (Server-Sent Events, chunked HTTP, WebSocket) into a unified Python iterator. Buffers and parses streaming chunks, reconstructs partial tokens across chunk boundaries, and exposes a consistent `stream=True` parameter across all providers. Supports both sync and async streaming with proper resource cleanup and error handling mid-stream.","intents":["I want to stream LLM responses to users without waiting for the full completion","I need to handle streaming from different providers with the same code","I want to process tokens as they arrive for real-time applications"],"best_for":["real-time chat applications and conversational UIs","token-by-token processing pipelines","applications requiring low time-to-first-token latency"],"limitations":["Streaming chunks may arrive out-of-order or with variable timing depending on provider and network conditions","Token reconstruction across chunk boundaries adds ~5-10ms latency per chunk","Error handling mid-stream may leave partial tokens in the buffer; no automatic recovery","Async streaming requires event loop management; mixing sync/async can cause deadlocks"],"requires":["Python 3.8+","Provider API key with streaming support","Network connection with stable latency for streaming"],"input_types":["completion request with stream=True","optional async context for async_completion()"],"output_types":["iterator of streaming chunks (each chunk is a normalized response object)","async iterator for async streaming"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_3","uri":"capability://data.processing.analysis.cost.calculation.and.pricing.tracking","name":"cost-calculation-and-pricing-tracking","description":"Automatically calculates the cost of each LLM request based on provider pricing (per-token rates for input/output, or per-request flat fees). Maintains an internal pricing database with rates for 100+ models across providers, updated regularly. Tracks cumulative costs per request, per user, per team, and per organization. Exposes cost data in response metadata and integrates with spend tracking dashboards. Supports custom pricing overrides for enterprise contracts.","intents":["I want to know the cost of each LLM API call in real-time","I need to track total spend by user, team, or project","I want to enforce budget limits and alert when spending exceeds thresholds","I need to bill customers based on their LLM usage"],"best_for":["SaaS platforms offering LLM-powered features to customers","enterprises with chargeback models for LLM usage","cost-conscious teams optimizing spend across models"],"limitations":["Pricing data may lag behind provider updates; rates can change without notice","Cost calculation assumes standard pricing tiers; volume discounts or custom contracts require manual overrides","Streaming responses may have inaccurate token counts until the full response completes","No built-in currency conversion; all costs calculated in USD"],"requires":["Python 3.8+","Provider API key (cost calculation works with any provider)","Optional: custom pricing configuration for enterprise rates"],"input_types":["completion request (messages, model, parameters)","optional custom pricing overrides"],"output_types":["cost metadata in response (input_cost, output_cost, total_cost)","spend logs (per-user, per-team, per-organization aggregations)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_4","uri":"capability://memory.knowledge.caching.with.semantic.and.exact.match.strategies","name":"caching-with-semantic-and-exact-match-strategies","description":"Implements a multi-layer caching system with Redis backend supporting both exact-match caching (hash of messages → cached response) and semantic caching (embeddings-based similarity matching for semantically equivalent prompts). Caches completion responses with configurable TTL and supports cache invalidation by key, pattern, or age. Integrates with Redis for distributed caching across multiple application instances. Provides dynamic cache controls per-request (force refresh, skip cache, etc.).","intents":["I want to cache LLM responses to reduce API costs and latency for repeated queries","I need semantic caching so similar prompts return cached responses without re-querying the LLM","I want to share cached responses across multiple application instances","I need to invalidate specific cached responses when underlying data changes"],"best_for":["applications with repetitive user queries (FAQs, documentation search)","cost-sensitive applications where cache hit rates are high","multi-instance deployments requiring distributed cache"],"limitations":["Exact-match caching only works for identical prompts; minor wording changes miss the cache","Semantic caching requires embedding generation, adding ~50-200ms latency per cache lookup","Redis dependency adds operational complexity; no built-in fallback if Redis is unavailable","Cache invalidation is manual; no automatic invalidation when underlying data changes","Semantic similarity threshold is configurable but requires tuning; too loose matches reduce quality"],"requires":["Python 3.8+","Redis instance (for distributed caching; optional for in-memory caching)","Optional: embedding model for semantic caching"],"input_types":["completion request (messages, model, parameters)","cache configuration (ttl, strategy, similarity threshold)"],"output_types":["cached response (if cache hit) or fresh response (if cache miss)","cache metadata (hit/miss, latency saved)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_5","uri":"capability://tool.use.integration.tool.calling.and.function.integration.with.schema.validation","name":"tool-calling-and-function-integration-with-schema-validation","description":"Provides a unified interface for tool/function calling across providers with different function-calling APIs (OpenAI's function_calling, Anthropic's tool_use, Google's function_calling). Accepts a schema definition (JSON Schema or Pydantic models) and automatically converts it to the provider's native format. Validates LLM-generated function calls against the schema and provides structured output. Supports parallel tool calling, tool choice enforcement, and automatic retry if the LLM generates invalid function calls.","intents":["I want to call external functions/APIs from LLM responses in a type-safe way","I need function calling to work across different LLM providers with the same code","I want the LLM to generate structured function calls that I can validate and execute","I need to enforce that the LLM calls specific functions or chooses from a set"],"best_for":["agentic systems where LLMs call external tools/APIs","applications requiring structured output from LLMs","teams building multi-provider agents with consistent tool interfaces"],"limitations":["Schema conversion to provider formats may lose nuance; complex schemas may not translate perfectly","Some providers don't support all function-calling features (e.g., parallel tool calling not available on all models)","LLM-generated function calls may be invalid or hallucinated; validation catches errors but doesn't prevent them","Tool choice enforcement (e.g., 'must call this function') not supported by all providers","No built-in execution of called functions; developers must implement function dispatch logic"],"requires":["Python 3.8+","Provider API key with function-calling support","Function schema definition (JSON Schema or Pydantic model)"],"input_types":["completion request with tools parameter","tool schema (JSON Schema or Pydantic model)","optional tool_choice parameter (auto, required, specific tool)"],"output_types":["structured function call with name and arguments","validation errors if call doesn't match schema"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_6","uri":"capability://memory.knowledge.prompt.caching.with.provider.native.support","name":"prompt-caching-with-provider-native-support","description":"Leverages provider-native prompt caching features (OpenAI's prompt caching, Anthropic's prompt caching) to reduce costs and latency for requests with large, repeated context. Automatically identifies cacheable prompt segments (system prompts, long documents, conversation history) and marks them for caching. Tracks cache hit rates and cost savings. Falls back to non-cached requests for providers without caching support.","intents":["I want to cache large system prompts or documents to reduce API costs","I need faster responses when using the same context repeatedly","I want to leverage provider-native caching without manual optimization"],"best_for":["applications with large, repeated context (e.g., document Q&A, code analysis)","cost-sensitive applications where context reuse is common","teams using providers with native caching support (OpenAI, Anthropic)"],"limitations":["Not all providers support prompt caching; fallback to non-cached requests may be slower/more expensive","Cache invalidation is provider-managed; no control over cache lifetime","Caching overhead (cache write latency) may exceed savings for small, one-time contexts","Cache key generation is provider-specific; cache hits don't transfer between providers"],"requires":["Python 3.8+","Provider API key with prompt caching support (OpenAI, Anthropic)","Large, repeated context (system prompt, documents, etc.)"],"input_types":["completion request with large context","optional cache configuration (ttl, strategy)"],"output_types":["completion response with cache metadata (cache_creation_input_tokens, cache_read_input_tokens)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_7","uri":"capability://automation.workflow.observability.and.logging.with.callback.system","name":"observability-and-logging-with-callback-system","description":"Provides a callback system for logging and observability, allowing developers to hook into request/response lifecycle events (pre-request, post-response, error, etc.). Integrates with observability platforms (Langfuse, Arize, Datadog, etc.) via pre-built callbacks. Supports custom callbacks for application-specific logging. Logs include request details, response metadata, cost, latency, and errors. Supports message redaction for privacy (e.g., removing PII before logging).","intents":["I want to log all LLM API calls for debugging and auditing","I need to integrate LLM observability with my monitoring platform (Langfuse, Datadog, etc.)","I want to track latency, cost, and error rates across LLM calls","I need to redact sensitive information from logs for compliance"],"best_for":["production LLM applications requiring observability","teams using observability platforms (Langfuse, Arize, Datadog)","applications with compliance requirements (PII redaction, audit logs)"],"limitations":["Callback execution adds latency to each request (~5-50ms depending on callback complexity)","Custom callbacks must handle errors gracefully; callback failures don't block requests but may cause silent logging failures","Message redaction is pattern-based; complex PII patterns may not be caught","Observability platform integrations depend on external service availability; failures don't block LLM requests"],"requires":["Python 3.8+","Optional: observability platform account (Langfuse, Arize, Datadog, etc.)","Optional: custom callback implementation for application-specific logging"],"input_types":["completion request","callback configuration (which callbacks to enable)","optional redaction rules"],"output_types":["logs sent to observability platform or custom handler","structured log data (request, response, cost, latency, errors)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_8","uri":"capability://automation.workflow.fallback.and.retry.logic.with.exponential.backoff","name":"fallback-and-retry-logic-with-exponential-backoff","description":"Implements automatic retry logic with exponential backoff for transient failures (rate limits, timeouts, temporary outages). Supports fallback to alternative models or providers if the primary fails. Configurable retry policies (max retries, backoff strategy, retry-able error codes). Tracks retry metrics and integrates with cooldown management to avoid retrying failing deployments.","intents":["I want automatic retries for transient LLM API failures without manual error handling","I need to fall back to a cheaper or alternative model if my primary provider fails","I want to avoid overwhelming a failing provider with retry requests"],"best_for":["production applications requiring high reliability","applications with fallback models configured","teams wanting to reduce manual error handling"],"limitations":["Exponential backoff may cause unacceptable latency for time-sensitive requests","Retry logic can't distinguish between transient and permanent failures; some permanent errors may be retried","Fallback to alternative models may produce different quality responses; no automatic quality validation","Retry metrics are in-memory; restarting the application resets retry history"],"requires":["Python 3.8+","Provider API key","Optional: fallback model/provider configured"],"input_types":["completion request","retry configuration (max_retries, backoff_strategy, retry_codes)"],"output_types":["completion response (from primary or fallback provider)","retry metadata (retry count, final provider used)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-litellm__cap_9","uri":"capability://automation.workflow.litellm.proxy.server.with.multi.tenancy.and.auth","name":"litellm-proxy-server-with-multi-tenancy-and-auth","description":"A production-grade proxy server that sits between applications and LLM providers, providing centralized API key management, authentication, authorization, budget enforcement, rate limiting, and multi-tenancy. Exposes an OpenAI-compatible API endpoint that applications can call instead of directly calling providers. Manages API keys per user/team/organization with role-based access control. Enforces budget limits per user/team and tracks spend. Supports SCIM and SSO for enterprise deployments.","intents":["I want to centralize LLM API key management across my organization","I need to enforce budget limits and rate limits per user or team","I want to provide an OpenAI-compatible API endpoint to my internal teams","I need multi-tenancy with role-based access control for enterprise deployments"],"best_for":["enterprises deploying LLM applications across teams","SaaS platforms offering LLM features to customers","organizations requiring centralized API key management and compliance"],"limitations":["Proxy adds latency (~50-100ms) to each request due to request forwarding and auth checks","Proxy is a single point of failure; high-availability setup requires load balancing and database replication","Budget enforcement is approximate; real-time cost calculation may lag behind actual spend","SCIM/SSO integration requires external identity provider; setup complexity increases with enterprise requirements"],"requires":["Python 3.8+","Database (PostgreSQL, MySQL, SQLite) for storing keys, users, teams, spend logs","Optional: Redis for caching and rate limiting","Optional: identity provider (Okta, Azure AD, etc.) for SCIM/SSO"],"input_types":["proxy configuration (database, auth settings, budget limits)","API requests (OpenAI-compatible format with API key)"],"output_types":["OpenAI-compatible API responses","admin dashboard for key/team/user management","spend logs and analytics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API keys for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Environment variables or explicit credentials passed to litellm","Multiple LLM provider credentials configured","Router configuration with deployment definitions (model, provider, weights)","Database for storing budget configurations and spend logs","Optional: email or webhook integration for alerts","Redis instance for distributed rate limiting","Rate limit configuration (limits per user/team/org)","Optional: external safety service API key (OpenAI Moderation, etc.)"],"failure_modes":["Response normalization may lose provider-specific fields (e.g., OpenAI's `logprobs` not available from all providers)","Streaming behavior differs subtly across providers — buffering and chunk timing not perfectly uniform","Some advanced features (reasoning, extended thinking) only available on specific providers, requiring conditional logic","Routing decisions are stateless per-request — no session affinity or user-level routing","Cooldown timers are in-memory; restarting the application resets failure tracking","Cost-based routing requires accurate, up-to-date pricing data; stale pricing leads to suboptimal decisions","No built-in circuit breaker for cascading failures across all deployments","Budget enforcement is approximate; real-time cost calculation may lag, allowing overspend","Hard budget limits may reject legitimate requests if cost estimates are inaccurate","Budget reset schedules are UTC-based; timezone-aware resets require custom logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"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":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:12.848Z","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=pypi-litellm","compare_url":"https://unfragile.ai/compare?artifact=pypi-litellm"}},"signature":"G0YhBXuWpA1wJGNVrXmsVcxckhMToHRbVRf6oJjgEr5gbZNYVUWPT5Ht7EQ2atM8tsJ+gxrQYnvr6aEzaDQvCA==","signedAt":"2026-06-21T08:50:26.109Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-litellm","artifact":"https://unfragile.ai/pypi-litellm","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-litellm","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"}}