> ## Documentation Index > Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt > Use this file to discover all available pages before exploring further. # LiteLLM Integration > Use Helicone AI Gateway with LiteLLM to get top tier observability for your LLM requests. export const strings = { additionalHeadersForSessions: "Helicone provides additional headers to help you manage and analyze your sessions.", azureOpenAIDocs: `To learn more about the differences between OpenAI and AzureOpenAI, review the documentation here.`, chainOfThoughtPromptingCookbookDescription: "Craft effective prompts, ideal for complex responses requiring multi-step problem solving.", chatbotCookbookDescription: "This step-by-step guide covers function calling, response formatting and monitoring with Helicone.", createHeliconeManualLogger: "Create a new HeliconeManualLogger instance", configureWebSocketConnection: "Configure WebSocket connection", environmentTrackingCookbookDescription: "Effortlessly track and manage your environments with Helicone across different deployment contexts.", exportBaseUrl: tool => `Export your ${tool} base URL`, getStartedWithPackage: "To get started, install the @helicone/helpers package", generateKey: "Create an account and generate an API key", generateKeyInstructions: `Log into Helicone or create an account. Once you have an account, you can generate an API key here.`, generateSessionId: "Generate the unique session ID that will be used to track the session.", gettingUserRequestsCookbookDescription: "Retrieve user-specific requests to monitor, debug, and track costs for individual users.", githubActionsCookbookDescription: "Automate the monitoring and caching of your LLM calls in your CI pipelines for better deployment processes.", groupingCallsWithSessions: "Grouping Calls with Helicone Sessions", handleWebSocketEvents: "Handle WebSocket events", heliconeLoggerAPIReference: `To learn more about the HeliconeManualLogger API, see the API Reference here.`, howToIntegrate: "How to Integrate", howToPromptThinkingModelsCookbookDescription: "Best practices to to effectively prompt thinking models like Deepseek and OpenAI o1-o3 for optimal results.", howToUseSessions: "To group related API calls and analyze them collectively, you can use Helicone's session tracking features. This is useful for grouping all interactions within a single conversation or user session.", includeHeadersInRequests: "Include headers in your requests", includeSessionHeaders: "Include the session headers when you make API requests. This way, the session information is attached to each request, allowing Helicone to group and analyze them together.", installRequiredDependencies: "Install required dependencies", installSDK: tool => `Install ${tool}`, logYourRequest: "Log your request", modelRegistryDescription: "You can find all 100+ supported models at helicone.ai/models.", modifyBasePath: "Modify the base URL path", optional: "Optional", relatedGuides: "Related Guides", replayLlmSessionsCookbookDescription: "Learn how to replay and modify LLM sessions using Helicone to optimize your AI agents and improve their performance.", sessionManagement: "Session Management", setApiKey: "Set up your Helicone API key in your .env file", setUpToolBaseUrl: tool => `Set up your ${tool} base URL`, setUpToolApiKey: tool => `Set up your ${tool} API key as an environment variable`, startUsing: tool => `Start using ${tool} with Helicone`, useTheSDK: tool => `Use the ${tool} SDK`, verifyInHelicone: "Verify your requests in Helicone", verifyInHeliconeDesciption: tool => `With the above setup, any calls to ${tool} will automatically be logged and monitored by Helicone. Review them in your Helicone dashboard.`, viewRequestsInDashboard: "View requests in the Helicone dashboard", viewRequestsInDashboardDescription: product => `All your ${product} requests are now visible in your Helicone dashboard.`, whyUseSessions: "By including the session headers in each request, you have more granular control over session tracking. This approach is especially useful if you want to handle sessions dynamically or manage multiple sessions concurrently." }; ## Introduction [LiteLLM](https://www.litellm.ai/) is an self-hosted interface for calling LLM APIs. ## Integration Steps
```env theme={null} HELICONE_API_KEY=sk-helicone-... ```

{strings.installRequiredDependencies}

```bash theme={null} pip install litellm python-dotenv ``` Add the `helicone/` prefix to any model name to logg requests for Helicone: ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() # Route through Helicone by adding "helicone/" prefix response = completion( model="helicone/gpt-4o", messages=[{"role": "user", "content": "What is the capital of France?"}], api_key=os.getenv("HELICONE_API_KEY") ) print(response.choices[0].message.content) ```
While you're here, why not give us a star on GitHub? It helps us a lot! ## Complete Working Examples ### Basic Completion ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() # Simple completion response = completion( model="helicone/gpt-4o-mini", messages=[{"role": "user", "content": "Tell me a fun fact about space"}], api_key=os.getenv("HELICONE_API_KEY") ) print(response.choices[0].message.content) ``` ### Streaming Responses ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() # Streaming example response = completion( model="helicone/claude-4.5-sonnet", messages=[{"role": "user", "content": "Write a short story about a robot learning to paint"}], stream=True, api_key=os.getenv("HELICONE_API_KEY") ) print("🤖 Assistant (streaming):") for chunk in response: if hasattr(chunk.choices[0].delta, 'content') and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") ``` ### Custom Properties and Session Tracking Add metadata to track and filter your requests: ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() response = completion( model="helicone/gpt-4o-mini", messages=[{"role": "user", "content": "What's the weather like?"}], api_key=os.getenv("HELICONE_API_KEY"), metadata={ "Helicone-Session-Id": "session-abc-123", "Helicone-Session-Name": "Weather Assistant", "Helicone-User-Id": "user-789", "Helicone-Property-Environment": "production", "Helicone-Property-App-Version": "2.1.0", "Helicone-Property-Feature": "weather-query" } ) print(response.choices[0].message.content) ``` ## Provider Selection and Fallback Helicone's AI Gateway supports automatic failover between providers: ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() # Automatic routing (cheapest provider) response = completion( model="helicone/gpt-4o", messages=[{"role": "user", "content": "Hello!"}], api_key=os.getenv("HELICONE_API_KEY") ) # Manual provider selection response = completion( model="helicone/claude-4.5-sonnet/anthropic", messages=[{"role": "user", "content": "Hello!"}], api_key=os.getenv("HELICONE_API_KEY") ) # Multiple provider fallback chain # Try OpenAI first, then Anthropic if it fails response = completion( model="helicone/gpt-4o/openai,claude-4.5-sonnet/anthropic", messages=[{"role": "user", "content": "Hello!"}], api_key=os.getenv("HELICONE_API_KEY") ) ``` ## Advanced Features ### Caching Enable caching to reduce costs and latency for repeated requests: ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() # Enable caching for this request response = completion( model="helicone/gpt-4o", messages=[{"role": "user", "content": "What is 2+2?"}], api_key=os.getenv("HELICONE_API_KEY"), metadata={ "Helicone-Cache-Enabled": "true" } ) print(response.choices[0].message.content) # Subsequent identical requests will be served from cache response2 = completion( model="helicone/gpt-4o", messages=[{"role": "user", "content": "What is 2+2?"}], api_key=os.getenv("HELICONE_API_KEY"), metadata={ "Helicone-Cache-Enabled": "true" } ) print(response2.choices[0].message.content) ``` ### Rate Limiting Apply rate limiting policies to control request rates: ```python theme={null} import os from litellm import completion from dotenv import load_dotenv load_dotenv() response = completion( model="helicone/gpt-4o", messages=[{"role": "user", "content": "Hello"}], api_key=os.getenv("HELICONE_API_KEY"), metadata={ "Helicone-Rate-Limit-Policy": "basic-100" } ) print(response.choices[0].message.content) ``` ## Related Documentation Learn about Helicone's AI Gateway features and capabilities Configure intelligent routing and automatic failover Browse all available models and providers Add metadata to track and filter your requests Track multi-turn conversations and user sessions Configure rate limits for your applications Reduce costs and latency with intelligent caching Official LiteLLM documentation