{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"litellm","slug":"litellm","name":"LiteLLM","type":"framework","url":"https://github.com/BerriAI/litellm","page_url":"https://unfragile.ai/litellm","categories":["llm-apis","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"litellm__cap_0","uri":"capability://tool.use.integration.unified.openai.compatible.completion.interface","name":"unified-openai-compatible-completion-interface","description":"Provides a single litellm.completion() API that normalizes requests across 100+ LLM providers (OpenAI, Anthropic, Google, Azure, Ollama, etc.) by translating OpenAI message format into provider-specific request schemas. Uses provider detection logic in get_llm_provider_logic.py to route requests and a parameter mapping system (get_supported_openai_params.py) to handle capability differences across providers, enabling write-once code that works with any LLM backend.","intents":["I want to write LLM code once and swap providers without rewriting","I need to call Claude, GPT-4, Gemini, and local models with identical code","I want to avoid vendor lock-in by using a standard interface"],"best_for":["developers building multi-provider LLM applications","teams evaluating different LLM providers without code refactoring","startups wanting provider flexibility as they scale"],"limitations":["Parameter normalization may lose provider-specific advanced features not in OpenAI spec","Response format translation adds ~50-100ms latency per request","Some providers have unique capabilities (e.g., vision, tool use) that require conditional code paths"],"requires":["Python 3.8+","API keys for target providers (OpenAI, Anthropic, Google, etc.)","litellm package installed via pip"],"input_types":["message list (role/content format)","model identifier string","optional parameters (temperature, max_tokens, etc.)"],"output_types":["completion response object","streaming token iterator","structured response with usage metadata"],"categories":["tool-use-integration","llm-abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_1","uri":"capability://automation.workflow.intelligent.provider.routing.with.load.balancing","name":"intelligent-provider-routing-with-load-balancing","description":"The Router class (litellm/router.py) distributes requests across multiple model deployments using configurable routing strategies (round-robin, least-busy, cost-optimized, latency-optimized) with real-time health tracking and automatic failover. Maintains per-deployment metrics (latency, error rates, availability) and selects the next deployment based on strategy weights, enabling cost optimization and high availability without manual intervention.","intents":["I want to distribute load across multiple API keys or regions to avoid rate limits","I need to automatically failover to a backup provider if the primary is down","I want to route requests to the cheapest provider that meets my latency SLA","I need to balance between cost and performance across multiple deployments"],"best_for":["production teams running multi-region or multi-provider LLM services","cost-conscious builders wanting to mix expensive and cheap models","high-traffic applications requiring load distribution"],"limitations":["Routing decisions are made per-request without global optimization across concurrent requests","Health tracking is in-memory; requires external persistence for multi-instance deployments","Cost-optimized routing requires accurate, up-to-date pricing data which may lag provider changes"],"requires":["Python 3.8+","Multiple model deployments configured (API keys, endpoints, model names)","Optional: Redis for distributed state in multi-instance setups"],"input_types":["router configuration (model list with weights/priorities)","completion request (messages, model, parameters)","routing strategy enum (round_robin, least_busy, cost_optimized, latency_optimized)"],"output_types":["completion response from selected deployment","routing metadata (selected model, latency, cost)","fallback response if primary deployment fails"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_10","uri":"capability://safety.moderation.model.access.groups.and.wildcard.routing","name":"model-access-groups-and-wildcard-routing","description":"Enables fine-grained model access control using model access groups (e.g., 'gpt-4-*' matches all GPT-4 variants) and wildcard patterns. Allows teams/users to be assigned to groups that grant access to specific model families without listing individual models. Supports dynamic model discovery where new models matching a wildcard pattern are automatically accessible.","intents":["I want to grant a team access to all GPT-4 variants without listing each one","I need to automatically grant access to new models as they're released","I want to restrict access to specific model families (e.g., only open-source models)"],"best_for":["organizations with many model variants and frequent model updates","multi-tenant platforms with tiered access levels"],"limitations":["Wildcard matching is done at request time; no pre-computation of matching models","Model discovery requires periodic updates to the model list"],"requires":["LiteLLM Proxy Server","Model access group configuration (in proxy config or database)"],"input_types":["model identifier (e.g., 'gpt-4-turbo')","user/team with assigned access groups"],"output_types":["allow/deny decision based on wildcard match","list of accessible models for a user/team"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_11","uri":"capability://automation.workflow.fallback.and.retry.logic.with.cooldown.management","name":"fallback-and-retry-logic-with-cooldown-management","description":"Implements automatic fallback to alternative providers/models if the primary fails, with exponential backoff retry logic and cooldown periods to prevent thrashing. Tracks failure patterns per deployment and temporarily deprioritizes failed providers. Supports custom fallback chains (e.g., GPT-4 → Claude → Gemini) defined in router configuration.","intents":["I want requests to automatically retry if the LLM API is temporarily down","I need to fallback to a cheaper model if the primary is rate-limited","I want to prevent cascading failures by temporarily disabling failed providers","I need to define custom fallback chains for different use cases"],"best_for":["production applications requiring high availability","cost-sensitive applications that can trade quality for availability","multi-provider setups with heterogeneous reliability"],"limitations":["Fallback may result in different response quality if using cheaper/weaker models","Cooldown periods are fixed; no adaptive cooldown based on failure severity","Retry logic doesn't distinguish between retryable (rate limit) and non-retryable (auth) errors"],"requires":["Multiple model deployments configured in router","Fallback chain defined in router configuration"],"input_types":["completion request","fallback chain (list of models in priority order)","retry configuration (max_retries, backoff_factor)"],"output_types":["completion response from primary or fallback provider","metadata indicating which provider was used and if fallback occurred"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_12","uri":"capability://automation.workflow.litellm.proxy.server.as.centralized.api.gateway","name":"litellm-proxy-server-as-centralized-api-gateway","description":"Provides a standalone HTTP server (litellm/proxy/proxy_server.py) that acts as a centralized gateway for all LLM requests, implementing authentication, rate limiting, cost tracking, and observability. Exposes OpenAI-compatible REST API endpoints (/v1/chat/completions, /v1/embeddings, etc.) and management endpoints for key/team/user management. Supports deployment as Docker container or standalone Python service.","intents":["I want to centralize LLM API access across my organization","I need a single gateway to enforce policies (rate limits, budgets, access control)","I want to migrate from direct provider APIs to a managed gateway","I need to expose LLM APIs to external customers with billing"],"best_for":["organizations with multiple teams using LLMs","SaaS platforms offering LLM APIs to customers","enterprises requiring centralized governance"],"limitations":["Adds network latency (~10-50ms) compared to direct SDK calls","Requires operational overhead (deployment, monitoring, scaling)","Single point of failure if not deployed with high availability"],"requires":["Python 3.8+","PostgreSQL or SQLite database","Optional: Redis for caching and rate limiting","Docker for containerized deployment"],"input_types":["HTTP requests in OpenAI API format","Authorization header with API key"],"output_types":["HTTP responses in OpenAI API format","Management API responses (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_13","uri":"capability://automation.workflow.admin.dashboard.for.key.team.and.spend.management","name":"admin-dashboard-for-key-team-and-spend-management","description":"Provides a web-based dashboard (litellm/proxy/admin_ui/) for managing API keys, teams, users, and viewing spend analytics. Enables non-technical users to create/rotate keys, set rate limits, view cost breakdowns by model/team/user, and monitor API health. Supports role-based access (admin, team lead, viewer) with granular permissions.","intents":["I want to manage API keys without using the CLI or API","I need to see spend breakdowns by team and model","I want to grant team leads access to manage their own keys and budgets","I need to monitor API health and error rates"],"best_for":["non-technical users managing LLM access","organizations with many teams needing self-service key management","finance teams tracking LLM costs"],"limitations":["Dashboard is read-only for some operations (e.g., can't modify router config)","Real-time updates require polling or WebSocket support (not all features have real-time updates)","Limited to Proxy Server; not available in SDK-only mode"],"requires":["LiteLLM Proxy Server running","Web browser with JavaScript support"],"input_types":["user interactions (clicks, form submissions)","API calls to management endpoints"],"output_types":["HTML/CSS/JavaScript UI","API responses (JSON)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_14","uri":"capability://data.processing.analysis.model.pricing.and.context.window.database","name":"model-pricing-and-context-window-database","description":"Maintains a comprehensive database of model pricing and context windows (model_prices_and_context_window.json) covering 100+ models across all major providers. Automatically updates pricing for new models and provider price changes. Enables cost calculation, context window validation, and model selection based on budget/capability constraints.","intents":["I want to know the cost of calling a specific model","I need to validate that my prompt fits within a model's context window","I want to find the cheapest model that meets my requirements","I need to track pricing changes across providers"],"best_for":["cost-conscious applications selecting models dynamically","applications with variable context sizes needing validation","teams tracking LLM costs across many models"],"limitations":["Pricing data is static; real-time pricing changes require manual updates","Context window sizes are approximate; actual limits may vary by provider","New models may not be in the database immediately after release"],"requires":["model_prices_and_context_window.json file (included in litellm package)"],"input_types":["model identifier (e.g., 'gpt-4-turbo')"],"output_types":["pricing (input_cost_per_token, output_cost_per_token)","context window (max_tokens)","model metadata (provider, release date, capabilities)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_15","uri":"capability://tool.use.integration.pass.through.endpoints.for.provider.specific.features","name":"pass-through-endpoints-for-provider-specific-features","description":"Provides pass-through endpoints that forward requests directly to provider APIs without modification, enabling access to provider-specific features not yet supported by LiteLLM's unified interface. Useful for new provider features, experimental APIs, or edge cases. Maintains authentication and applies Proxy policies (rate limiting, cost tracking) even for pass-through requests.","intents":["I want to use a new provider feature that LiteLLM doesn't support yet","I need to call a provider's experimental API endpoint","I want to use provider-specific parameters without modification"],"best_for":["early adopters of new provider features","applications with provider-specific requirements","teams needing flexibility beyond LiteLLM's unified interface"],"limitations":["Pass-through requests bypass LiteLLM's normalization; responses may not be in OpenAI format","No automatic fallback or retry logic for pass-through requests","Cost tracking may be inaccurate for non-standard request formats"],"requires":["LiteLLM Proxy Server","Knowledge of provider's API format"],"input_types":["HTTP request in provider's native format"],"output_types":["HTTP response in provider's native format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_16","uri":"capability://tool.use.integration.mcp.server.gateway.for.tool.integration","name":"mcp-server-gateway-for-tool-integration","description":"Integrates with Model Context Protocol (MCP) servers to expose tools/resources as LLM-callable functions. Acts as a gateway between LLMs and MCP servers, translating tool definitions and handling tool invocations. Enables LLMs to access external tools (web search, code execution, database queries) via a standardized protocol.","intents":["I want to give LLMs access to external tools via MCP","I need to integrate web search, code execution, or database access into LLM workflows","I want to use a standardized protocol for tool integration"],"best_for":["applications building LLM agents with external tool access","teams using MCP-compatible tools (Claude Desktop, etc.)","complex workflows requiring multiple tool integrations"],"limitations":["MCP is a relatively new protocol; tool ecosystem is still growing","Tool invocation adds latency; no built-in caching of tool results","Error handling for tool failures requires custom logic"],"requires":["MCP server running and accessible","Tool definitions in MCP format"],"input_types":["completion request with MCP tools enabled"],"output_types":["completion response with tool_calls for MCP tools","tool results from MCP server"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_17","uri":"capability://tool.use.integration.mcp.server.gateway.and.agent.protocol.support","name":"mcp-server-gateway-and-agent-protocol-support","description":"Implements MCP (Model Context Protocol) server gateway that enables LLMs to interact with external tools and services via standardized protocol. Supports MCP clients connecting to LiteLLM proxy, which routes tool calls to registered MCP servers. Implements A2A (Agent-to-Agent) protocol for agent-to-agent communication. Provides tool registry and automatic tool discovery from MCP servers. Integrates with function calling to enable seamless tool use across providers.","intents":["Enable LLMs to call external tools and services via MCP protocol","Implement agent-to-agent communication using A2A protocol","Discover and register tools from MCP servers automatically","Support complex agentic workflows with multiple tool interactions"],"best_for":["Teams building complex agentic systems with multiple tools","Applications requiring standardized tool integration via MCP","Enterprises implementing agent-to-agent communication"],"limitations":["MCP server integration requires external MCP servers to be running","Tool discovery and registration adds startup latency","A2A protocol is experimental and may change","Complex tool chains may require careful error handling and retry logic"],"requires":["MCP servers implementing tools to be exposed","MCP client library for connecting to servers"],"input_types":["tool calls from LLM (function name, arguments)"],"output_types":["tool execution results (returned to LLM)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_2","uri":"capability://data.processing.analysis.multi.provider.spend.tracking.and.cost.calculation","name":"multi-provider-spend-tracking-and-cost-calculation","description":"Automatically calculates per-request costs using provider-specific pricing models stored in model_prices_and_context_window.json and litellm/llms/openai/cost_calculation.py. Tracks cumulative spend per user, team, organization, and tag via the Proxy's database layer (db_spend_update_writer.py) with Redis buffering for high-throughput scenarios. Supports budget enforcement at multiple levels (user, team, organization) with configurable alerts and hard limits.","intents":["I need to track how much each team/user is spending on LLM API calls","I want to enforce budget limits to prevent runaway costs","I need to allocate LLM costs back to internal teams or customers","I want to identify which models or features are most expensive"],"best_for":["SaaS platforms offering LLM features to multiple customers","enterprises managing LLM costs across teams","startups needing cost visibility before scaling"],"limitations":["Cost calculation relies on static pricing data; real-time pricing changes require manual updates","Spend tracking has eventual consistency due to Redis buffering; real-time accuracy is ~1-5 seconds behind","Context window pricing is approximate; actual token counts may vary by provider's tokenizer"],"requires":["LiteLLM Proxy Server running (not available in SDK-only mode)","PostgreSQL or SQLite database for spend logs","Optional: Redis for high-throughput buffering (db_transaction_queue/redis_update_buffer.py)","API keys with spend tracking enabled"],"input_types":["completion request with user/team/tag identifiers","model name (to look up pricing)","prompt and completion token counts"],"output_types":["cost in USD (float)","spend logs (database records with timestamp, user, model, cost)","budget alerts (if threshold exceeded)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_3","uri":"capability://memory.knowledge.request.response.caching.with.semantic.matching","name":"request-response-caching-with-semantic-matching","description":"Caches LLM responses using both exact-match (hash of messages + parameters) and semantic-match strategies via Redis integration (litellm/proxy/cache.py). Exact-match caching returns identical responses for identical requests; semantic caching uses embeddings to find similar past requests and return cached responses for semantically equivalent queries, reducing API calls and latency. Supports dynamic cache controls (TTL, cache-key customization) per request.","intents":["I want to avoid re-calling the LLM for identical user queries","I need to cache responses for similar questions without exact duplicates","I want to reduce latency for common queries by serving from cache","I need to control cache behavior per-request (e.g., bypass cache for this call)"],"best_for":["chatbot applications with repeated user queries","customer support systems with FAQ-like patterns","applications with high query volume and acceptable staleness"],"limitations":["Semantic caching requires embedding model (adds ~100-200ms per cache miss)","Cache hit rate depends on query similarity; exact-match only works for identical inputs","Cached responses may become stale; no automatic invalidation based on external data changes","Redis required for distributed caching; in-memory cache only works in single-process mode"],"requires":["Redis instance for cache storage","Optional: embedding model for semantic caching (e.g., OpenAI embeddings, local model)","LiteLLM Proxy Server (caching not available in SDK-only mode)"],"input_types":["completion request (messages, model, parameters)","cache control headers (cache=true/false, ttl=seconds, cache_key=custom_string)"],"output_types":["cached completion response (if hit)","fresh completion response (if miss)","cache metadata (hit/miss, age, similarity score for semantic matches)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_4","uri":"capability://automation.workflow.rate.limiting.and.throttling.with.multi.level.enforcement","name":"rate-limiting-and-throttling-with-multi-level-enforcement","description":"Enforces rate limits at multiple levels (per-user, per-team, per-organization, per-model) using token bucket algorithms stored in Redis or in-memory. Tracks request counts and token usage (input + output tokens) against configurable limits, returning 429 errors when limits are exceeded. Supports both hard limits (reject requests) and soft limits (log warnings) with customizable reset windows and burst allowances.","intents":["I want to prevent users from overwhelming the LLM API with too many requests","I need to enforce fair-share quotas across teams","I want to allow burst traffic but enforce average rate limits","I need to rate-limit by token consumption, not just request count"],"best_for":["multi-tenant SaaS platforms with shared LLM resources","API services with tiered pricing based on usage","applications protecting against abuse or runaway costs"],"limitations":["In-memory rate limiting doesn't work across multiple server instances; requires Redis for distributed enforcement","Token-based rate limiting requires accurate token counts; may be off by 1-2% due to tokenizer differences","Rate limit resets are time-window based; no support for sliding windows or adaptive limits"],"requires":["LiteLLM Proxy Server","Redis for distributed rate limiting (or in-memory for single-instance)","Rate limit configuration per user/team/organization"],"input_types":["user/team/organization identifier","request (to count tokens)","rate limit policy (requests_per_minute, tokens_per_day, etc.)"],"output_types":["allow/reject decision","remaining quota (requests/tokens)","reset time (when quota resets)","429 error response if limit exceeded"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_5","uri":"capability://text.generation.language.streaming.response.handling.with.provider.normalization","name":"streaming-response-handling-with-provider-normalization","description":"Normalizes streaming responses from 100+ providers into a unified Server-Sent Events (SSE) format compatible with OpenAI's streaming API. Handles provider-specific streaming formats (Anthropic's event stream, Google's chunked responses, Azure's streaming) and converts them to OpenAI's delta-based format. Supports both SDK streaming (Python generators) and Proxy streaming (HTTP SSE), with automatic error handling and graceful fallback to non-streaming if provider fails.","intents":["I want to stream LLM responses to users in real-time regardless of provider","I need to handle streaming errors without losing partial responses","I want to use the same streaming code for OpenAI, Claude, and Gemini","I need to stream responses over HTTP to web clients"],"best_for":["chatbot UIs requiring real-time token streaming","applications with long-running LLM tasks needing progress feedback","multi-provider applications needing consistent streaming behavior"],"limitations":["Streaming normalization adds ~10-20ms latency per chunk due to format conversion","Some providers (e.g., Ollama) have inconsistent streaming behavior; may require provider-specific workarounds","Error recovery during streaming is limited; partial responses may be lost if connection drops mid-stream"],"requires":["Provider supports streaming (most do, but some legacy APIs don't)","For HTTP streaming: LiteLLM Proxy Server","For SDK streaming: Python 3.8+ with async support"],"input_types":["completion request with stream=true","model identifier (to determine provider format)"],"output_types":["Python generator yielding delta objects (SDK mode)","HTTP SSE stream with delta events (Proxy mode)","each delta contains: role, content, finish_reason"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_6","uri":"capability://tool.use.integration.tool.calling.and.function.integration.with.schema.mapping","name":"tool-calling-and-function-integration-with-schema-mapping","description":"Normalizes function/tool calling across providers by translating OpenAI's function_call format to provider-specific schemas (Anthropic's tool_use, Google's function_calling, Ollama's tools). Accepts OpenAI-style tool definitions (name, description, parameters as JSON schema) and maps them to each provider's expected format, enabling write-once tool-calling code. Handles tool response routing and automatic re-invocation for multi-turn tool use.","intents":["I want to define tools once and use them with any LLM provider","I need to call external functions/APIs from LLM responses","I want to implement agent loops that call tools and feed results back to the LLM","I need to handle multi-turn tool use where the LLM calls multiple tools"],"best_for":["developers building LLM agents with tool use","applications integrating LLMs with external APIs/databases","multi-provider agent systems"],"limitations":["Tool schema translation may lose provider-specific features (e.g., Anthropic's input_schema vs OpenAI's parameters)","Tool response handling requires manual routing; no automatic function execution","Some providers have limits on tool count or schema complexity"],"requires":["Provider supports tool/function calling (OpenAI, Anthropic, Google, Ollama, etc.)","Tool definitions in OpenAI format (name, description, parameters JSON schema)"],"input_types":["completion request with tools array","each tool: {name, description, parameters (JSON schema)}","tool_choice parameter (auto, required, specific tool name)"],"output_types":["completion response with tool_calls array","each tool_call: {id, function: {name, arguments (JSON string)}}","finish_reason='tool_calls' when LLM wants to call a tool"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_7","uri":"capability://safety.moderation.multi.tenant.api.key.and.access.control.management","name":"multi-tenant-api-key-and-access-control-management","description":"Manages API keys, user permissions, and team hierarchies via the Proxy's database layer (schema.prisma) with role-based access control (RBAC). Supports key rotation, per-key rate limits, model access restrictions (which models a key can call), and audit logging. Integrates with SCIM and SSO for enterprise identity management, enabling centralized user/team provisioning.","intents":["I want to issue API keys to customers with different permission levels","I need to restrict which models each key can access","I want to rotate keys without downtime","I need to audit who called which models and when","I want to integrate with our SSO/SCIM provider for user management"],"best_for":["SaaS platforms offering LLM APIs to customers","enterprises managing LLM access across teams","organizations with compliance/audit requirements"],"limitations":["Key rotation requires API clients to update their keys; no automatic client-side rotation","SCIM/SSO integration requires manual configuration per identity provider","Permission checks add ~5-10ms latency per request"],"requires":["LiteLLM Proxy Server","PostgreSQL or SQLite database","Optional: SCIM/SSO provider (Okta, Azure AD, etc.)"],"input_types":["API key (in Authorization header)","user/team identifiers","permission scope (model access, rate limits, budget)"],"output_types":["API key object (key, created_at, last_used, permissions)","user/team object (id, name, role, permissions)","audit log entry (timestamp, user, action, resource)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_8","uri":"capability://data.processing.analysis.observability.and.logging.with.custom.callbacks","name":"observability-and-logging-with-custom-callbacks","description":"Provides a callback system (litellm/integrations/custom_logger.py) that hooks into every LLM request/response for logging, monitoring, and analytics. Supports custom callbacks (user-defined functions) and pre-built integrations (Langfuse, Datadog, New Relic, Weights & Biases). Logs request metadata (model, tokens, latency, cost), responses, and errors with optional message redaction for privacy. Integrates with observability platforms for distributed tracing and analytics.","intents":["I want to log all LLM API calls for debugging and auditing","I need to monitor LLM latency and error rates in production","I want to track which models are being used and their costs","I need to integrate LLM observability with my existing monitoring stack","I want to redact sensitive data from logs for privacy compliance"],"best_for":["production LLM applications requiring observability","teams using Langfuse, Datadog, or similar monitoring platforms","applications with privacy/compliance requirements"],"limitations":["Callback execution adds ~5-20ms latency per request depending on callback complexity","Custom callbacks are synchronous; async callbacks require manual implementation","Message redaction is pattern-based; may miss sensitive data or over-redact"],"requires":["Optional: Langfuse account for pre-built integration","Optional: Datadog, New Relic, or other monitoring platform API key","Custom callbacks require Python function definition"],"input_types":["completion request (messages, model, parameters)","completion response (choices, usage, latency)"],"output_types":["log entry (JSON or structured format)","observability platform event (Langfuse trace, Datadog metric, etc.)","audit log (database record)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litellm__cap_9","uri":"capability://memory.knowledge.prompt.caching.with.provider.native.support","name":"prompt-caching-with-provider-native-support","description":"Leverages provider-native prompt caching (OpenAI's prompt_cache_control, Anthropic's cache_control) to reduce costs and latency for repeated context. Automatically detects provider support and applies caching headers to system prompts or long context blocks. 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