{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-berriai--litellm","slug":"berriai--litellm","name":"litellm","type":"mcp","url":"https://docs.litellm.ai/docs/","page_url":"https://unfragile.ai/berriai--litellm","categories":["mcp-servers"],"tags":["ai-gateway","anthropic","azure-openai","bedrock","gateway","langchain","litellm","llm","llm-gateway","llmops","mcp-gateway","openai","openai-proxy","vertex-ai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-berriai--litellm__cap_0","uri":"capability://tool.use.integration.unified.llm.api.abstraction.with.provider.detection","name":"unified-llm-api-abstraction-with-provider-detection","description":"Abstracts 100+ LLM provider APIs (OpenAI, Anthropic, Azure, Bedrock, VertexAI, Cohere, HuggingFace, VLLM, NVIDIA NIM, Ollama) behind a single OpenAI-compatible interface. Uses provider detection logic that maps model names to their native providers and automatically translates request/response formats, handling provider-specific parameter mappings, authentication schemes, and response structures without requiring developers to write provider-specific code.","intents":["Switch between LLM providers without rewriting application code","Support multiple providers simultaneously for fallback and load-balancing scenarios","Avoid vendor lock-in by maintaining a consistent API surface across providers","Automatically detect which provider a model belongs to based on model name patterns"],"best_for":["Teams building multi-provider LLM applications","Developers wanting to avoid vendor lock-in","LLMOps engineers managing heterogeneous model deployments"],"limitations":["Provider-specific features (e.g., Claude's extended thinking, GPT-4's vision) require explicit parameter handling","Response format normalization adds ~50-100ms latency per request due to translation overhead","Some advanced provider features may not be fully exposed through the abstraction layer"],"requires":["Python 3.8+","Valid API keys for target providers (OpenAI, Anthropic, Azure, AWS, Google Cloud, etc.)","Network access to provider endpoints"],"input_types":["text prompts","structured message arrays with role/content","system prompts","tool/function definitions"],"output_types":["text completions","streaming token chunks","structured JSON responses","tool call objects"],"categories":["tool-use-integration","llm-gateway"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_1","uri":"capability://automation.workflow.intelligent.request.routing.with.load.balancing","name":"intelligent-request-routing-with-load-balancing","description":"Routes requests across multiple LLM deployments using configurable strategies (round-robin, least-busy, cost-optimized, latency-based) with real-time health checks and fallback chains. The Router class maintains deployment metadata (model, provider, cost, latency), tracks request distribution, and automatically retries failed requests on alternate deployments while respecting cooldown periods to avoid cascading failures.","intents":["Distribute load across multiple model instances to reduce latency and improve availability","Automatically failover to backup models when primary provider is down","Optimize costs by routing requests to cheaper models when quality thresholds are met","Balance between latency, cost, and model capability based on request characteristics"],"best_for":["Production systems requiring high availability and fault tolerance","Cost-conscious teams managing multiple model deployments","Teams needing dynamic routing based on real-time performance metrics"],"limitations":["Routing decisions are made per-request without global optimization across concurrent requests","Cooldown management adds complexity when managing many deployments (>20 models)","Cost-based routing requires accurate, up-to-date pricing data for all models","No built-in request queuing or backpressure handling for traffic spikes"],"requires":["Python 3.8+","Multiple LLM deployments configured with model names and provider credentials","Optional: Redis for distributed state tracking across multiple proxy instances"],"input_types":["completion requests with model specifications","routing configuration with deployment metadata","optional: custom routing weights and priorities"],"output_types":["completion responses from selected deployment","routing metadata (selected model, latency, cost)","fallback responses if all deployments fail"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_10","uri":"capability://safety.moderation.model.access.groups.and.wildcard.pattern.matching","name":"model-access-groups-and-wildcard-pattern-matching","description":"Manages model access control through model access groups that use wildcard patterns (e.g., 'gpt-4*', 'claude-*-v1') to grant users/teams access to sets of models. Evaluates patterns at request time to determine if a user can access a requested model, supporting hierarchical access (e.g., admin can access all models, team members can access team-specific models).","intents":["Grant users access to specific model families without listing each model individually","Implement hierarchical access control (admin > team lead > team member)","Dynamically add new models to existing access groups via wildcard patterns","Prevent unauthorized access to expensive or restricted models"],"best_for":["Multi-tenant platforms with complex access control requirements","Teams managing many models and wanting to avoid per-model configuration","Organizations with role-based access patterns"],"limitations":["Wildcard patterns can be ambiguous (e.g., 'gpt-*' matches both gpt-3.5 and gpt-4); requires careful naming conventions","Pattern matching at request time adds ~5-10ms latency; caching helps but requires invalidation on group changes","No support for negative patterns (e.g., 'gpt-4 except gpt-4-turbo'); requires explicit allow lists","Debugging access denials can be complex with many overlapping patterns"],"requires":["Python 3.8+","Database for storing model access groups and patterns","Model naming convention that supports wildcard matching"],"input_types":["model name in completion request","user/team identifier","model access group definitions with wildcard patterns"],"output_types":["access allowed/denied decision","matching model access group"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_11","uri":"capability://automation.workflow.rate.limiting.and.throttling.with.distributed.state","name":"rate-limiting-and-throttling-with-distributed-state","description":"Enforces rate limits per API key, user, or team using token bucket or sliding window algorithms. Tracks rate limit state in Redis for distributed enforcement across multiple proxy instances, supporting different limit strategies (requests per minute, tokens per hour, cost per day). Returns HTTP 429 with retry-after headers when limits are exceeded, and integrates with cooldown management to prevent cascading failures.","intents":["Prevent API abuse by limiting requests per user or team","Enforce token-based rate limits to control LLM usage","Implement cost-based rate limits to prevent budget overruns","Provide fair resource allocation across multiple users/teams"],"best_for":["Multi-tenant SaaS platforms needing per-user rate limits","Teams wanting to prevent accidental cost overruns","Public APIs exposed to external users"],"limitations":["Distributed rate limiting requires Redis; single-instance deployments can't enforce limits across instances","Token bucket algorithm has edge cases with burst traffic; sliding window is more accurate but slower","Rate limit state in Redis can become bottleneck at very high request rates (>10k req/s)","No built-in support for adaptive rate limiting based on provider load"],"requires":["Python 3.8+","Redis for distributed rate limit state","Rate limit configuration per API key or user"],"input_types":["API key or user identifier","request metadata (tokens, cost)","rate limit configuration"],"output_types":["rate limit decision (allowed/denied)","remaining quota","retry-after header if denied"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_12","uri":"capability://automation.workflow.health.checks.and.model.monitoring.with.provider.fallback","name":"health-checks-and-model-monitoring-with-provider-fallback","description":"Continuously monitors provider health by sending periodic test requests to each configured model, tracking response times and error rates. Marks providers as unhealthy when error rates exceed thresholds, automatically removing them from routing until they recover. Integrates with cooldown management to prevent repeated requests to failing providers, and exposes health status via /health endpoints for load balancer integration.","intents":["Detect provider outages and automatically failover to healthy providers","Monitor model performance and latency trends","Prevent cascading failures by avoiding repeated requests to failing providers","Expose health status for load balancer integration"],"best_for":["Production systems requiring high availability","Multi-provider deployments where provider failures are common","Teams using external load balancers (Kubernetes, AWS ALB)"],"limitations":["Health checks consume API quota and incur costs; must be tuned to balance detection speed vs. cost","Health check frequency is fixed; no adaptive health checking based on provider stability","Cooldown management can cause cascading failures if all providers fail simultaneously","No built-in alerting; requires integration with monitoring systems"],"requires":["Python 3.8+","Configured models for health checking","Optional: External monitoring system for alerting"],"input_types":["model configuration","health check frequency and thresholds"],"output_types":["health status (healthy/unhealthy)","error rates and latency metrics","/health endpoint response"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_13","uri":"capability://tool.use.integration.assistants.api.compatibility.and.openai.feature.parity","name":"assistants-api-compatibility-and-openai-feature-parity","description":"Provides OpenAI Assistants API compatibility by translating Assistants API requests to underlying LLM completion calls, managing conversation state, file uploads, and tool execution. Supports OpenAI-specific features (code interpreter, retrieval) through abstraction layers that map to provider-agnostic implementations, enabling applications built for OpenAI Assistants to work with alternative providers.","intents":["Use OpenAI Assistants API with alternative providers (Anthropic, Azure, etc.)","Migrate from OpenAI Assistants to multi-provider setup without code changes","Leverage Assistants API features (file handling, tool execution) across providers","Maintain conversation state and thread management across providers"],"best_for":["Teams built on OpenAI Assistants wanting to reduce vendor lock-in","Applications needing Assistants API features with cost optimization","Teams migrating from OpenAI to multi-provider setup"],"limitations":["Some Assistants API features (code interpreter, retrieval) require custom implementation for non-OpenAI providers","File handling and storage must be implemented separately; no built-in file storage","Thread management and conversation state require database; adds operational complexity","Feature parity is not guaranteed; some OpenAI-specific features may not be available"],"requires":["Python 3.8+","Database for storing threads and conversation state","File storage (S3, local filesystem) for file uploads","Provider support for tool calling and streaming"],"input_types":["Assistants API requests (create thread, add message, run)","file uploads","tool definitions"],"output_types":["Assistants API responses (thread, message, run)","streaming responses","tool call results"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_14","uri":"capability://planning.reasoning.reasoning.and.extended.thinking.support","name":"reasoning-and-extended-thinking-support","description":"Supports provider-specific reasoning features (OpenAI o1 reasoning, Claude extended thinking) by translating reasoning parameters to provider-native formats and handling extended thinking responses. Manages longer processing times and higher costs associated with reasoning models, and provides access to reasoning traces for debugging and analysis.","intents":["Use reasoning models (o1, extended thinking) for complex problem-solving","Access reasoning traces for debugging and understanding model decisions","Manage costs and latency of reasoning models transparently","Switch between reasoning and non-reasoning models based on task complexity"],"best_for":["Applications requiring complex reasoning (math, logic, code generation)","Teams wanting to understand model reasoning for debugging","Cost-conscious teams using reasoning models selectively"],"limitations":["Reasoning models are significantly more expensive (10-100x) than standard models","Processing times are longer (30s-5min); not suitable for real-time applications","Reasoning traces are provider-specific; no standardized format across providers","Not all providers support reasoning; feature availability is limited"],"requires":["Python 3.8+","Access to reasoning models (OpenAI o1, Claude extended thinking, etc.)","Higher API quotas and budgets due to increased costs"],"input_types":["completion requests with reasoning parameters","complex prompts requiring reasoning"],"output_types":["reasoning traces (if available)","final response","usage statistics including reasoning tokens"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_15","uri":"capability://tool.use.integration.mcp.server.gateway.for.tool.standardization","name":"mcp-server-gateway-for-tool-standardization","description":"Acts as an MCP (Model Context Protocol) server gateway, translating MCP tool definitions to LLM-compatible function schemas and vice versa. Enables LLMs to call MCP-compatible tools through a standardized interface, supporting tool discovery, execution, and result handling. Integrates with MCP servers for external tool access (file systems, databases, APIs).","intents":["Enable LLMs to call MCP-compatible tools without custom integration","Standardize tool definitions across different LLM providers","Discover and expose available tools from MCP servers","Execute tools and return results to LLMs in agentic loops"],"best_for":["Teams using MCP-compatible tools and wanting LLM integration","Agentic applications requiring standardized tool access","Organizations standardizing on MCP for tool definitions"],"limitations":["MCP server availability is required; no fallback if MCP server is down","Tool execution is synchronous; no support for parallel tool calls","MCP protocol overhead adds latency (~50-100ms per tool call)","Limited MCP server ecosystem; not all tools have MCP implementations"],"requires":["Python 3.8+","MCP-compatible servers for tools","MCP client library for communication"],"input_types":["completion requests with tool use","MCP tool definitions"],"output_types":["tool calls parsed from LLM response","tool execution results from MCP servers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_2","uri":"capability://data.processing.analysis.real.time.cost.tracking.and.calculation","name":"real-time-cost-tracking-and-calculation","description":"Calculates per-request costs by parsing model pricing from a centralized registry, tracking input/output token counts, and aggregating costs across users, teams, and deployments. Integrates with the proxy database to store spend logs with timestamps, model names, and token counts, enabling cost analytics, budget enforcement, and FinOps reporting via FOCUS cost export format.","intents":["Track actual LLM costs per API call for chargeback and billing","Monitor spend trends across teams and users to identify cost anomalies","Enforce budget limits and alert when spending exceeds thresholds","Export cost data in FinOps-standard formats for financial reconciliation"],"best_for":["Multi-tenant SaaS platforms charging users for LLM usage","Enterprise teams needing cost accountability across departments","FinOps teams reconciling cloud spend with actual usage"],"limitations":["Pricing data must be manually updated when providers change rates; no automatic price feed integration","Token counting accuracy depends on provider's tokenizer; estimates may differ from actual billed tokens","Cost calculation happens post-request, so budget enforcement is reactive rather than predictive","FOCUS export requires specific database schema; custom deployments may need schema migration"],"requires":["Python 3.8+","Database (PostgreSQL, MySQL, or SQLite) for storing spend logs","Pricing configuration file or API with model costs (input/output per 1K tokens)","Optional: Redis for real-time spend aggregation across distributed proxy instances"],"input_types":["completion requests with token counts","model names and provider information","user/team identifiers for cost attribution"],"output_types":["per-request cost calculations","aggregated spend reports by user/team/model","FOCUS-formatted cost export files","budget alert notifications"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_3","uri":"capability://safety.moderation.multi.tenant.authentication.and.authorization","name":"multi-tenant-authentication-and-authorization","description":"Manages API keys, user identities, and team memberships through a database-backed authentication system with role-based access control (RBAC). Supports multiple authentication methods (API keys, OAuth via SCIM/SSO), enforces per-key rate limits and budget caps, and tracks which users/teams can access which models via model access groups and wildcard patterns.","intents":["Issue and revoke API keys for external users or internal teams","Enforce per-user or per-team rate limits and spending budgets","Control which models each user/team can access based on roles or groups","Integrate with enterprise SSO/SCIM for automated user provisioning"],"best_for":["Multi-tenant SaaS platforms exposing LLM APIs to customers","Enterprise teams managing internal LLM access across departments","Organizations with complex access control requirements (model-level, budget-level)"],"limitations":["SCIM/SSO integration requires specific identity provider support; custom providers need custom implementation","Rate limiting is enforced per-key but lacks distributed rate limiting across multiple proxy instances without Redis","Model access groups use wildcard patterns which can be complex to manage at scale (>100 models)","No built-in audit logging for access control decisions; requires custom callbacks for compliance"],"requires":["Python 3.8+","Database (PostgreSQL, MySQL) for storing keys, users, teams, and access control rules","Optional: SCIM-compatible identity provider (Okta, Azure AD, etc.) for SSO","Optional: Redis for distributed rate limit tracking across proxy instances"],"input_types":["API key in request headers","user/team identifiers","model names for access control checks"],"output_types":["authentication success/failure","authorization decision (allowed/denied model access)","rate limit status and remaining quota"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_4","uri":"capability://text.generation.language.streaming.response.handling.with.event.normalization","name":"streaming-response-handling-with-event-normalization","description":"Handles streaming responses from diverse providers (OpenAI, Anthropic, Azure, etc.) by normalizing their different streaming formats (Server-Sent Events, JSON Lines, custom formats) into a unified stream of choice objects. Implements buffering, error handling, and graceful degradation when streaming fails, allowing clients to consume a consistent stream interface regardless of underlying provider.","intents":["Stream LLM responses in real-time to reduce perceived latency","Handle provider-specific streaming formats transparently","Implement streaming fallback when a provider's streaming fails","Aggregate streaming responses across multiple providers for ensemble responses"],"best_for":["Real-time chat applications requiring low-latency responses","Streaming-first applications where buffering entire responses is unacceptable","Teams using multiple providers and needing consistent streaming behavior"],"limitations":["Streaming adds complexity to error handling; partial responses may be sent before errors occur","Provider-specific streaming features (e.g., token usage in final chunk) may not be available until stream ends","Streaming over HTTP/1.1 connections can be slower than HTTP/2; requires client support for proper streaming","No built-in support for streaming from multiple providers simultaneously (e.g., ensemble voting on tokens)"],"requires":["Python 3.8+","HTTP client supporting streaming (aiohttp, httpx, requests with iter_lines)","Provider APIs supporting streaming (most modern LLM APIs do)"],"input_types":["completion requests with stream=true flag","provider-specific streaming parameters"],"output_types":["Server-Sent Events stream","normalized choice objects with delta content","final message with usage statistics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_5","uri":"capability://memory.knowledge.prompt.caching.with.semantic.deduplication","name":"prompt-caching-with-semantic-deduplication","description":"Caches LLM responses using both exact-match caching (identical prompts) and semantic caching (similar prompts via embeddings). Stores cached responses in Redis with configurable TTL, supports cache invalidation strategies, and integrates with provider-native prompt caching (e.g., Claude's prompt caching) to reduce costs and latency for repeated or similar queries.","intents":["Reduce costs by caching responses to repeated prompts","Improve latency for similar queries by returning cached responses","Leverage provider-native prompt caching features to reduce input token costs","Implement semantic caching to handle prompt variations that should return similar results"],"best_for":["Applications with repetitive queries (e.g., FAQ bots, documentation search)","Cost-sensitive deployments where prompt caching ROI is high","Teams using providers with native prompt caching (Claude, GPT-4 Turbo)"],"limitations":["Semantic caching requires embedding model; adds ~100-200ms latency for embedding computation","Cache invalidation is manual or TTL-based; no automatic invalidation when source data changes","Redis dependency adds operational complexity; single Redis instance becomes bottleneck at scale","Semantic similarity threshold tuning is application-specific and requires experimentation"],"requires":["Python 3.8+","Redis instance for cache storage","Optional: Embedding model (local or API-based) for semantic caching","Optional: Provider support for native prompt caching (Claude, GPT-4 Turbo)"],"input_types":["completion requests with cache_control parameters","optional: semantic cache configuration with similarity threshold"],"output_types":["cached response if hit","fresh response with cache metadata if miss","cache statistics (hit rate, savings)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_6","uri":"capability://safety.moderation.guardrails.and.content.safety.enforcement","name":"guardrails-and-content-safety-enforcement","description":"Enforces content safety policies by running requests and responses through configurable guardrails before reaching LLMs or returning to clients. Supports built-in guardrails (PII detection, prompt injection detection, toxicity filtering) and custom guardrails via a plugin architecture. Integrates with third-party safety services (e.g., Presidio for PII, custom ML models) and can block, redact, or flag requests based on policy violations.","intents":["Prevent prompt injection attacks by detecting malicious prompt patterns","Redact personally identifiable information (PII) before sending to LLMs","Filter toxic or harmful content in requests and responses","Implement custom safety policies specific to your application domain"],"best_for":["Regulated industries (healthcare, finance) requiring PII protection","Public-facing LLM applications vulnerable to prompt injection","Teams with custom safety requirements beyond standard guardrails"],"limitations":["Guardrail execution adds latency (~50-200ms per request depending on guardrail complexity)","PII detection has false positive/negative rates; requires tuning for specific use cases","Prompt injection detection is heuristic-based and can be evaded by sophisticated attacks","Custom guardrails require Python development; no low-code guardrail builder"],"requires":["Python 3.8+","Optional: Presidio library for PII detection","Optional: Third-party safety APIs (e.g., Perspective API for toxicity)","Custom guardrail code for domain-specific policies"],"input_types":["user prompts","system prompts","LLM responses"],"output_types":["pass/fail decision","redacted content","safety violation details","optional: modified request/response"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_7","uri":"capability://automation.workflow.observability.and.logging.with.callback.system","name":"observability-and-logging-with-callback-system","description":"Provides comprehensive observability through a callback system that hooks into request/response lifecycle events (pre-request, post-request, on-error). Logs all LLM interactions to configurable backends (Langfuse, Datadog, custom webhooks) with full context (model, tokens, cost, latency, user). Supports message redaction for privacy, custom logging logic via callback plugins, and integration with APM tools for distributed tracing.","intents":["Log all LLM API calls for debugging and auditing","Track performance metrics (latency, token usage) across models and providers","Integrate with observability platforms (Langfuse, Datadog) for centralized monitoring","Redact sensitive information from logs for compliance (PII, API keys)"],"best_for":["Production systems requiring full audit trails","Teams using observability platforms (Langfuse, Datadog, New Relic)","Compliance-heavy industries needing detailed logging"],"limitations":["Callback execution adds ~10-50ms latency per request; async callbacks help but don't eliminate overhead","Message redaction requires regex patterns or custom logic; no automatic PII detection in logs","Langfuse integration requires external service; self-hosted logging requires custom callback implementation","High-volume logging can create I/O bottlenecks; requires async logging or batching for scale"],"requires":["Python 3.8+","Optional: Langfuse account for centralized logging","Optional: Datadog, New Relic, or other APM tool for metrics","Custom callback implementations for non-standard logging backends"],"input_types":["completion requests","LLM responses","error information"],"output_types":["structured logs with full context","metrics (latency, tokens, cost)","traces in observability platform"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_8","uri":"capability://tool.use.integration.tool.calling.and.function.integration.with.schema.validation","name":"tool-calling-and-function-integration-with-schema-validation","description":"Enables function calling by accepting tool/function definitions as JSON schemas, translating them to provider-specific formats (OpenAI function_calling, Anthropic tools, etc.), and parsing tool calls from responses. Validates tool schemas, handles tool execution orchestration, and supports automatic retry loops where the LLM can call tools and receive results until a final response is generated.","intents":["Enable LLMs to call external functions or APIs as part of reasoning","Automatically translate function definitions to provider-specific formats","Parse tool calls from responses and execute them","Implement agentic loops where LLMs iteratively call tools and refine responses"],"best_for":["Agentic applications where LLMs need to call external APIs or functions","Teams using multiple providers and needing consistent tool-calling interface","Applications requiring structured function calling with schema validation"],"limitations":["Tool execution is synchronous; no built-in support for parallel tool calls","Schema validation is JSON Schema only; no support for other schema formats","Tool call parsing is provider-specific; some providers may not support all schema features","No built-in tool execution sandbox; developers must implement security controls"],"requires":["Python 3.8+","Tool definitions in JSON Schema format","Provider support for function calling (OpenAI, Anthropic, Cohere, etc.)"],"input_types":["completion requests with tools parameter","tool definitions as JSON schemas","tool execution results for agentic loops"],"output_types":["tool calls parsed from LLM response","final text response after tool execution","tool execution results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-berriai--litellm__cap_9","uri":"capability://automation.workflow.ai.gateway.proxy.server.with.pass.through.endpoints","name":"ai-gateway-proxy-server-with-pass-through-endpoints","description":"Deploys as a standalone HTTP proxy server that intercepts LLM API requests, applies routing, authentication, cost tracking, and guardrails before forwarding to providers. Implements OpenAI-compatible endpoints (/v1/chat/completions, /v1/embeddings, /v1/models) plus pass-through endpoints for provider-specific features. Supports Docker deployment, horizontal scaling with Redis state sharing, and management APIs for key/team/user administration.","intents":["Deploy a central LLM gateway for multi-tenant access control","Intercept and modify requests/responses for cost tracking and safety","Provide OpenAI-compatible API surface while routing to any provider","Scale horizontally across multiple proxy instances with shared state"],"best_for":["SaaS platforms exposing LLM APIs to customers","Enterprise teams centralizing LLM access control","Teams needing request/response interception for compliance or cost control"],"limitations":["Proxy adds network latency (~10-50ms per request) compared to direct provider calls","Horizontal scaling requires Redis for state sharing; single-instance deployments don't scale","Management APIs require separate authentication; no built-in API key rotation","Pass-through endpoints expose provider-specific features; breaks OpenAI compatibility guarantee"],"requires":["Python 3.8+","Docker for containerized deployment (optional but recommended)","PostgreSQL or MySQL for storing keys, users, teams, spend logs","Redis for distributed state (optional but required for horizontal scaling)","Provider API keys for all supported providers"],"input_types":["HTTP requests in OpenAI format","provider-specific requests via pass-through endpoints"],"output_types":["HTTP responses in OpenAI format","provider-specific responses via pass-through endpoints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Valid API keys for target providers (OpenAI, Anthropic, Azure, AWS, Google Cloud, etc.)","Network access to provider endpoints","Multiple LLM deployments configured with model names and provider credentials","Optional: Redis for distributed state tracking across multiple proxy instances","Database for storing model access groups and patterns","Model naming convention that supports wildcard matching","Redis for distributed rate limit state","Rate limit configuration per API key or user","Configured models for health checking"],"failure_modes":["Provider-specific features (e.g., Claude's extended thinking, GPT-4's vision) require explicit parameter handling","Response format normalization adds ~50-100ms latency per request due to translation overhead","Some advanced provider features may not be fully exposed through the abstraction layer","Routing decisions are made per-request without global optimization across concurrent requests","Cooldown management adds complexity when managing many deployments (>20 models)","Cost-based routing requires accurate, up-to-date pricing data for all models","No built-in request queuing or backpressure handling for traffic spikes","Wildcard patterns can be ambiguous (e.g., 'gpt-*' matches both gpt-3.5 and gpt-4); requires careful naming conventions","Pattern matching at request time adds ~5-10ms latency; caching helps but requires invalidation on group changes","No support for negative patterns (e.g., 'gpt-4 except gpt-4-turbo'); requires explicit allow lists","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8346341363932976,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:24.501Z","last_commit":"2026-05-03T12:13:43Z"},"community":{"stars":45540,"forks":7738,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=berriai--litellm","compare_url":"https://unfragile.ai/compare?artifact=berriai--litellm"}},"signature":"ehhJqWA7nZTPFmkfdma7bBfBtGI0+FAWv7Me6IVcqlUgX6iHXpWCqUuLk1dIaRSMfVwJYPwvqwaVHhWapL0GCQ==","signedAt":"2026-06-21T20:07:02.809Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/berriai--litellm","artifact":"https://unfragile.ai/berriai--litellm","verify":"https://unfragile.ai/api/v1/verify?slug=berriai--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"}}