LiteLLM: Unified API for Multiple AI Models

The Problem LiteLLM Solves

Every AI model provider has a different API format. Anthropic uses a messages API with its own authentication and parameter structure. OpenAI uses a completions API with function calling in a specific format. Google uses a generativeai API with yet another structure. When you want to use models from multiple providers in the same application, you need separate client libraries, separate authentication handling, separate request formatting, and separate response parsing for each one.

This integration burden grows with every provider you add. Two providers means two sets of integration code. Four providers means four. Each provider updates their API independently, which means maintaining compatibility across all of them as they evolve. For teams building multi-model agent systems that need to route between providers dynamically, this maintenance cost becomes a real drag on development velocity.

LiteLLM solves this by providing one function call that works with any supported model. You call litellm.completion() with a model identifier and a messages array in the OpenAI format, and LiteLLM handles the translation to the target provider. Switching from Claude to GPT to Gemini requires changing a single string parameter, not rewriting integration code.

How LiteLLM Works

At its core, LiteLLM is a translation layer. It accepts requests in the OpenAI completion format (the most widely adopted standard) and translates them into the native format of whatever provider you specify. The response comes back in a standardized format regardless of which provider processed the request.

The translation handles parameter mapping automatically. Temperature, max tokens, top-p, stop sequences, and other generation parameters are mapped from the OpenAI format to the equivalent parameters for each provider. Where providers support features that others do not, LiteLLM documents the differences clearly so you know what to expect.

Authentication is handled through environment variables or explicit configuration. You set your API keys for each provider you want to use, and LiteLLM picks up the right credentials based on which model you request. This means your application code never needs to manage multiple authentication flows.

The library also normalizes error handling across providers. Different providers return errors in different formats, but LiteLLM catches provider-specific exceptions and raises standardized exceptions that your application can handle uniformly. This is particularly valuable for fallback logic where you need to catch failures from one provider and retry with another.

Supported Providers and Models

LiteLLM supports all major commercial providers: Anthropic (Claude family), OpenAI (GPT family), Google (Gemini family), Cohere, Mistral AI, and many more. It also supports self-hosted models through Ollama, vLLM, and other local inference servers that expose OpenAI-compatible endpoints.

For Anthropic models, LiteLLM translates the OpenAI format into the Anthropic messages API, handling the differences in how system prompts, tool definitions, and multi-turn conversations are structured. You specify models like "claude-sonnet-4-20250514" or "claude-opus-4-20250514" and LiteLLM routes them correctly.

For Google models, LiteLLM handles the translation to the Gemini API format, including support for the extended context windows and multimodal inputs that Gemini offers. Local models through Ollama are accessed by prefixing the model name with "ollama/" and LiteLLM routes the request to your local Ollama server.

The full list of supported providers exceeds 100 and continues to grow as new providers emerge and existing providers release new models. The open-source community actively maintains provider integrations, which means new models are typically supported within days of their release.

Routing and Load Balancing

LiteLLM includes a built-in router that distributes requests across multiple model deployments. You define a list of model deployments with their provider, model name, and API credentials, and the router distributes incoming requests across them based on configurable strategies.

The simplest routing strategy is round-robin, which distributes requests evenly across all available deployments. This works well for load balancing across multiple API keys for the same provider, or across different providers for the same capability tier.

Cost-based routing sends each request to the cheapest available model that meets the specified requirements. You can set minimum capability thresholds and the router selects the cheapest model that clears the bar. This is the foundation of the cost optimization strategy discussed throughout this guide.

Latency-based routing tracks response times across deployments and sends requests to the fastest available option. For agent workflows where total completion time matters, this routing strategy minimizes end-to-end latency automatically.

Fallback Chains

One of the most valuable features for production agent systems is automatic fallback handling. You define an ordered list of models, and if the primary model fails (rate limit, server error, timeout), LiteLLM automatically retries with the next model in the chain. The application code sees a single request that either succeeds or fails after exhausting all fallback options.

Fallback chains are essential for production reliability. Cloud AI providers experience occasional outages, rate limits, and degraded performance. Without fallbacks, a single provider outage can take your entire agent system offline. With LiteLLM fallbacks, the system automatically shifts traffic to alternative providers during disruptions.

The fallback configuration supports different models at each level. A typical chain might start with Claude Sonnet for primary processing, fall back to GPT for the same task if Claude is unavailable, and fall back to Gemini as a last resort. Each fallback maintains the same request format because LiteLLM handles the translation for each provider.

Cost Tracking and Budgets

LiteLLM tracks token usage and estimated costs for every request automatically. Each response includes metadata showing input tokens, output tokens, and the estimated cost based on current provider pricing. This data feeds directly into monitoring and alerting systems without requiring custom instrumentation.

Budget controls allow you to set spending limits per user, per project, or per time period. When a budget threshold is reached, LiteLLM can block further requests, downgrade to cheaper models, or send alerts. This prevents unexpected cost spikes from runaway agent loops or unexpected traffic increases.

The cost tracking data also enables analysis of which models and which task types account for the most spending. This visibility is the starting point for cost optimization, because you cannot optimize what you cannot measure. Teams typically discover that a small number of task types account for a large portion of total spending, and targeting those tasks for routing optimization yields the highest returns.

Integration with Agent Frameworks

LiteLLM integrates with every major agent framework through its OpenAI-compatible interface. LangChain, CrewAI, AutoGen, and other frameworks that support the OpenAI API format can use LiteLLM as a drop-in replacement by pointing the base URL to a LiteLLM proxy server or by using LiteLLM as the completion backend directly.

The LiteLLM proxy server mode deserves special attention. Running LiteLLM as a proxy creates a central gateway that all model requests flow through, regardless of which application or framework generated the request. This centralizes routing logic, fallback handling, cost tracking, and authentication in one place rather than duplicating it across every application.

For teams running multiple agent systems or multiple applications that use AI models, the proxy pattern is particularly powerful. All traffic goes through one gateway, which means one place to configure routing rules, one place to monitor costs, and one place to manage API keys. Adding a new model or changing routing logic is a proxy configuration change, not an application code change.