# LiteLLM LLM analytics installation - Docs 1. 1 ## LiteLLM Requirements Required > **Note:** LiteLLM can be used as a Python SDK or as a proxy server. PostHog observability requires LiteLLM version 1.77.3 or higher. 2. 2 ## Install LiteLLM Required Choose your installation method based on how you want to use LiteLLM: PostHog AI ### SDK ```bash pip install litellm ``` ### Proxy ```bash # Install via pip pip install 'litellm[proxy]' # Or run via Docker docker run --rm -p 4000:4000 ghcr.io/berriai/litellm:latest ``` 3. 3 ## Configure PostHog observability Required Configure PostHog by setting your project token and host as well as adding `posthog` to your LiteLLM callback handlers. You can find your project token in [your project settings](https://app.posthog.com/settings/project). PostHog AI ### SDK ```python import os import litellm # Set environment variables os.environ["POSTHOG_API_KEY"] = "" os.environ["POSTHOG_API_URL"] = "https://us.i.posthog.com" # Optional, defaults to https://app.posthog.com # Enable PostHog callbacks litellm.success_callback = ["posthog"] litellm.failure_callback = ["posthog"] # Optional: also log failures ``` ### Proxy ```yaml # config.yaml model_list: - model_name: gpt-5-mini litellm_params: model: gpt-5-mini litellm_settings: success_callback: ["posthog"] failure_callback: ["posthog"] # Optional: also log failures environment_variables: POSTHOG_API_KEY: "" POSTHOG_API_URL: "https://us.i.posthog.com" # Optional ``` 4. 4 ## Call LLMs through LiteLLM Required Now, when you use LiteLLM to call various LLM providers, PostHog automatically captures an `$ai_generation` event. PostHog AI ### SDK ```python response = litellm.completion( model="gpt-5-mini", messages=[ {"role": "user", "content": "Tell me a fun fact about hedgehogs"} ], metadata={ "user_id": "user_123", # Maps to PostHog distinct_id "company": "company_id_in_your_db" # Custom property } ) print(response.choices[0].message.content) ``` ### Proxy ```bash # Start the proxy (if not already running) litellm --config config.yaml # Make a request to the proxy curl -X POST http://localhost:4000/chat/completions -H "Content-Type: application/json" -d '{ "model": "gpt-5-mini", "messages": [ {"role": "user", "content": "Tell me a fun fact about hedgehogs"} ], "metadata": { "user_id": "user_123", "company": "company_id_in_your_db" # Custom property } }' ``` > **Notes:** > > - This works with streaming responses by setting `stream=True`. > - To disable logging for specific requests, add `{"no-log": true}` to metadata. > - If you want to capture LLM events anonymously, **don't** pass a `user_id` in metadata. > > See our docs on [anonymous vs identified events](/docs/data/anonymous-vs-identified-events.md) to learn more. You can expect captured `$ai_generation` events to have the following properties: | Property | Description | | --- | --- | | $ai_model | The specific model, like gpt-5-mini or claude-4-sonnet | | $ai_latency | The latency of the LLM call in seconds | | $ai_time_to_first_token | Time to first token in seconds (streaming only) | | $ai_tools | Tools and functions available to the LLM | | $ai_input | List of messages sent to the LLM | | $ai_input_tokens | The number of tokens in the input (often found in response.usage) | | $ai_output_choices | List of response choices from the LLM | | $ai_output_tokens | The number of tokens in the output (often found in response.usage) | | $ai_total_cost_usd | The total cost in USD (input + output) | | [[...]](/docs/llm-analytics/generations.md#event-properties) | See [full list](/docs/llm-analytics/generations.md#event-properties) of properties | 5. 5 ## Capture embeddings Optional PostHog can also capture embedding generations as `$ai_embedding` events through LiteLLM: PostHog AI ### SDK ```python response = litellm.embedding( input="The quick brown fox", model="text-embedding-3-small", metadata={ "user_id": "user_123", # Maps to PostHog distinct_id "company": "company_id_in_your_db" # Custom property } ) ``` ### Proxy ```bash # Make an embeddings request to the proxy curl -X POST http://localhost:4000/embeddings -H "Content-Type: application/json" -d '{ "input": "The quick brown fox", "model": "text-embedding-3-small", "metadata": { "user_id": "user_123", "company": "company_id_in_your_db" # Custom property } }' ``` 6. ## Verify traces and generations Recommended *Confirm LLM events are being sent to PostHog* Let's make sure LLM events are being captured and sent to PostHog. Under **LLM analytics**, you should see rows of data appear in the **Traces** and **Generations** tabs. ![LLM generations in PostHog](https://res.cloudinary.com/dmukukwp6/image/upload/SCR_20250807_syne_ecd0801880.png)![LLM generations in PostHog](https://res.cloudinary.com/dmukukwp6/image/upload/SCR_20250807_syjm_5baab36590.png) [Check for LLM events in PostHog](https://app.posthog.com/llm-analytics/generations) 7. 6 ## Next steps Recommended Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform. | Resource | Description | | --- | --- | | [Basics](/docs/llm-analytics/basics.md) | Learn the basics of how LLM calls become events in PostHog. | | [Generations](/docs/llm-analytics/generations.md) | Read about the $ai_generation event and its properties. | | [Traces](/docs/llm-analytics/traces.md) | Explore the trace hierarchy and how to use it to debug LLM calls. | | [Spans](/docs/llm-analytics/spans.md) | Review spans and their role in representing individual operations. | | [Anaylze LLM performance](/docs/llm-analytics/dashboard.md) | Learn how to create dashboards to analyze LLM performance. | ### Community questions Ask a question ### Was this page useful? 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