--- title: "LangSmith" url: "/docs/observability/langsmith_integration" canonical_url: "https://docs.litellm.ai/docs/observability/langsmith_integration" type: "docs" last_updated: "2026-10-03" summary: "An all-in-one developer platform for every step of the application lifecycle" related: - "/docs/observability/langfuse_integration" - "/docs/observability/langtrace_integration" --- # LangSmith > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt An all-in-one developer platform for every step of the application lifecycle https://smith.langchain.com/ :::info We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or join our [discord](https://discord.gg/wuPM9dRgDw) ::: ## Pre-Requisites ```shell uv add litellm ``` ## Quick Start Use just 2 lines of code, to instantly log your responses **across all providers** with Langsmith **SDK** ```python litellm.callbacks = ["langsmith"] ``` ```python import litellm import os os.environ["LANGSMITH_API_KEY"] = "" os.environ["LANGSMITH_PROJECT"] = "" # defaults to litellm-completion os.environ["LANGSMITH_DEFAULT_RUN_NAME"] = "" # defaults to LLMRun # LLM API Keys os.environ['OPENAI_API_KEY']="" # set langsmith as a callback, litellm will send the data to langsmith litellm.callbacks = ["langsmith"] # openai call response = litellm.completion( model="gpt-5.6-luna", messages=[ {"role": "user", "content": "Hi 👋 - i'm openai"} ] ) ``` **LiteLLM Proxy** 1. Setup config.yaml ```yaml model_list: - model_name: gpt-5.6-luna litellm_params: model: openai/gpt-5.6-luna api_key: os.environ/OPENAI_API_KEY litellm_settings: callbacks: ["langsmith"] ``` 2. Start LiteLLM Proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! ```bash curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-eWkpOhYaHiuIZV-29JDeTQ' \ -d '{ "model": "gpt-5.6-luna", "messages": [ { "role": "user", "content": "Hey, how are you?" } ], "max_completion_tokens": 250 }' ``` ## Advanced ### Local Testing - Control Batch Size Set the size of the batch that Langsmith will process at a time, default is 512. Set `langsmith_batch_size=1` when testing locally, to see logs land quickly. **SDK** ```python import litellm import os os.environ["LANGSMITH_API_KEY"] = "" # LLM API Keys os.environ['OPENAI_API_KEY']="" # set langsmith as a callback, litellm will send the data to langsmith litellm.callbacks = ["langsmith"] litellm.langsmith_batch_size = 1 # 👈 KEY CHANGE response = litellm.completion( model="gpt-5.6-luna", messages=[ {"role": "user", "content": "Hi 👋 - i'm openai"} ] ) print(response) ``` **LiteLLM Proxy** 1. Setup config.yaml ```yaml model_list: - model_name: gpt-5.6-luna litellm_params: model: openai/gpt-5.6-luna api_key: os.environ/OPENAI_API_KEY litellm_settings: langsmith_batch_size: 1 callbacks: ["langsmith"] ``` 2. Start LiteLLM Proxy ```bash litellm --config /path/to/config.yaml ``` 3. Test it! ```bash curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-eWkpOhYaHiuIZV-29JDeTQ' \ -d '{ "model": "gpt-5.6-luna", "messages": [ { "role": "user", "content": "Hey, how are you?" } ], "max_completion_tokens": 250 }' ``` ### Set Langsmith fields ```python import litellm import os os.environ["LANGSMITH_API_KEY"] = "" # LLM API Keys os.environ['OPENAI_API_KEY']="" # set langsmith as a callback, litellm will send the data to langsmith litellm.success_callback = ["langsmith"] response = litellm.completion( model="gpt-5.6-luna", messages=[ {"role": "user", "content": "Hi 👋 - i'm openai"} ], metadata={ "run_name": "litellmRUN", # langsmith run name "project_name": "litellm-completion", # langsmith project name "run_id": "497f6eca-6276-4993-bfeb-53cbbbba6f08", # langsmith run id "parent_run_id": "f8faf8c1-9778-49a4-9004-628cdb0047e5", # langsmith run parent run id "trace_id": "df570c03-5a03-4cea-8df0-c162d05127ac", # langsmith run trace id (root runs override this, see note below) "session_id": "1ffd059c-17ea-40a8-8aef-70fd0307db82", # langsmith run session id (must be an existing tracer session) "tags": ["model1", "prod-2"], # langsmith run tags "metadata": { # langsmith run metadata "key1": "value1" }, "dotted_order": "20240429T004912090000Z497f6eca-6276-4993-bfeb-53cbbbba6f08" } ) print(response) ``` A run that posts as a root, meaning it carries neither `parent_run_id` nor `dotted_order`, gets its `trace_id` set to the run id so the batch passes LangSmith's dotted_order validation. Supply `parent_run_id` or `dotted_order` to keep your own `trace_id`. `session_id` must reference a tracer session that already exists in LangSmith and is ignored when it equals `trace_id`, because that shape is produced by the proxy's request-header fan-out rather than a deliberate session ### Make LiteLLM Proxy use Custom `LANGSMITH_BASE_URL` If you're using a custom LangSmith instance, you can set the `LANGSMITH_BASE_URL` environment variable to point to your instance. For example, you can make LiteLLM Proxy log to a local LangSmith instance with this config: ```yaml litellm_settings: success_callback: ["langsmith"] environment_variables: LANGSMITH_BASE_URL: "http://localhost:1984" LANGSMITH_PROJECT: "litellm-proxy" ``` ## Support & Talk to Founders - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai ## Related pages - [Langfuse](https://docs.litellm.ai/docs/observability/langfuse_integration.md) - [Langtrace](https://docs.litellm.ai/docs/observability/langtrace_integration.md)