LangSmith Client SDK

This package contains the Python client for interacting with the LangSmith platform.

To install:

pip install -U langsmith
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=ls_...

Then trace:

import openai
from langsmith.wrappers import wrap_openai
from langsmith import traceable

# Auto-trace LLM calls in-context
client = wrap_openai(openai.Client())


@traceable  # Auto-trace this function
def pipeline(user_input: str):
    result = client.chat.completions.create(messages=[{"role": "user", "content": user_input}], model="gpt-5.4")
    return result.choices[0].message.content


pipeline("Hello, world!")

Every LLM call inside pipeline is nested under a single trace in the LangSmith UI.

LangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM application.

Cookbook: For tutorials on how to get more value out of LangSmith, check out the Langsmith Cookbook repo.

A typical workflow looks like:

1. Set up an account with LangSmith.
2. Log traces while debugging and prototyping.
3. Run benchmark evaluations and continuously improve with the collected data.

We'll walk through these steps in more detail below.

Sandbox AWS Auth Proxy

When sandbox code needs to call AWS services, use the sandbox AWS auth proxy. The proxy keeps the real AWS credentials outside the sandbox and signs supported AWS HTTPS requests with SigV4, so code in the sandbox can use AWS SDKs normally without storing long-lived AWS keys in files, environment variables, shell history, or logs.

Store AWS credentials as LangSmith workspace secrets using names that make sense for your workspace. Then create the sandbox with an AWS auth proxy config:

from langsmith.sandbox import (
    SandboxClient,
    aws_auth,
    proxy_config,
    workspace_secret,
)

client = SandboxClient()
auth_config = proxy_config(
    rules=[
        aws_auth(
            access_key_id=workspace_secret("SANDBOX_AWS_ACCESS_KEY_ID"),
            secret_access_key=workspace_secret("SANDBOX_AWS_SECRET_ACCESS_KEY"),
        )
    ],
)

with client.sandbox(
    name="aws-sandbox",
    proxy_config=auth_config,
) as sandbox:
    result = sandbox.run("python your_aws_script.py")
    print(result.stdout)

Use opaque_secret("...") instead of workspace_secret(...) when your application needs to pass short-lived write-only AWS credentials at sandbox creation time. Plaintext AWS credential values are not accepted directly; wrap them as opaque_secret(...) values.

Sandbox GCP Auth Proxy

When sandbox code needs to call Google APIs, use the sandbox GCP auth proxy. The proxy keeps the service account JSON outside the sandbox and injects OAuth bearer tokens for Google API hosts matched automatically by the sandbox proxy.

Store the service account JSON as a LangSmith workspace secret. Then create the sandbox with a GCP auth proxy config:

from langsmith.sandbox import (
    SandboxClient,
    gcp_auth,
    proxy_config,
    workspace_secret,
)

client = SandboxClient()
auth_config = proxy_config(
    rules=[
        gcp_auth(
            service_account_json=workspace_secret("SANDBOX_GCP_SERVICE_ACCOUNT_JSON"),
            scopes=["https://www.googleapis.com/auth/devstorage.read_write"],
        )
    ],
)

with client.sandbox(
    name="gcp-sandbox",
    proxy_config=auth_config,
) as sandbox:
    result = sandbox.run("python your_gcp_script.py")
    print(result.stdout)

Use opaque_secret("...") for short-lived write-only service account JSON. Plaintext service account JSON is not accepted directly.

Sandbox Mounts

When you create a LangSmith sandbox that needs filesystem access to external data such as object storage buckets or public Git repositories, pass a mount_config on sandbox creation. Mount specs contain only the mount target. Provider credentials stay in mount_config.auth; the backend expands them into runtime proxy auth rules. You can also pass proxy_config for non-mount proxy behavior such as custom headers, callbacks, access control, and generic egress rules. Explicit AWS/GCP proxy auth rules conflict with mount_config auth for the same provider.

S3 mounts require AWS auth:

from langsmith.sandbox import (
    aws_auth,
    mount_config,
    s3_mount,
    workspace_secret,
)

mount_cfg = mount_config(
    auth=[
        aws_auth(
            access_key_id=workspace_secret("SANDBOX_AWS_ACCESS_KEY_ID"),
            secret_access_key=workspace_secret("SANDBOX_AWS_SECRET_ACCESS_KEY"),
        )
    ],
    mounts=[
        s3_mount(
            id="customer_data",
            mount_path="/mnt/mounts/customer-data",
            bucket="example-bucket",
            prefix="datasets/customer-data",
            region="us-east-1",
            endpoint_url="https://s3.amazonaws.com",
            path_style=False,
            read_only=False,
        )
    ],
)

with client.sandbox(
    name="s3-mount-sandbox",
    mount_config=mount_cfg,
) as sandbox:
    result = sandbox.run("ls /mnt/mounts/customer-data")
    print(result.stdout)

GCS mounts require GCP auth:

from langsmith.sandbox import (
    gcp_auth,
    gcs_mount,
    mount_config,
    workspace_secret,
)

mount_cfg = mount_config(
    auth=[gcp_auth(service_account_json=workspace_secret("SANDBOX_GCP_SERVICE_ACCOUNT_JSON"))],
    mounts=[
        gcs_mount(
            id="customer_data",
            mount_path="/mnt/mounts/customer-data",
            bucket="example-bucket",
            prefix="datasets/customer-data",
        )
    ],
)

with client.sandbox(
    name="gcs-mount-sandbox",
    mount_config=mount_cfg,
) as sandbox:
    result = sandbox.run("ls /mnt/mounts/customer-data")
    print(result.stdout)

Public Git mounts do not require AWS or GCP auth:

from langsmith.sandbox import git_mount, mount_config

mount_cfg = mount_config(
    mounts=[
        git_mount(
            id="repo",
            mount_path="/mnt/repo",
            remote_url="https://github.com/langchain-ai/langsmith-python.git",
            ref={"type": "branch", "name": "main"},
            refresh_interval_seconds=60,
        )
    ],
)

with client.sandbox(
    name="git-mount-sandbox",
    mount_config=mount_cfg,
) as sandbox:
    result = sandbox.run("ls /mnt/repo")
    print(result.stdout)

Private Git repositories can use low-level proxy_config rules when the remote requires proxy-managed auth. There is not yet a high-level private Git auth helper.

1. Connect to LangSmith

Sign up for LangSmith using your GitHub, Discord accounts, or an email address and password. If you sign up with an email, make sure to verify your email address before logging in.

Then, create a unique API key on the Settings Page, which is found in the menu at the top right corner of the page.

2. Log Traces

You can log traces natively using the LangSmith SDK or within your LangChain application.

Logging Traces with LangChain

LangSmith seamlessly integrates with the Python LangChain library to record traces from your LLM applications.

1. Copy the environment variables from the Settings Page and add them to your application.

Tracing can be activated by setting the following environment variables or by manually specifying the LangChainTracer.

import os

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com"
# os.environ["LANGSMITH_ENDPOINT"] = "https://eu.api.smith.langchain.com" # If signed up in the EU region
os.environ["LANGSMITH_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>"
# os.environ["LANGSMITH_PROJECT"] = "My Project Name" # Optional: "default" is used if not set
# os.environ["LANGSMITH_WORKSPACE_ID"] = "<YOUR-WORKSPACE-ID>" # Required for org-scoped API keys

Tip: Projects are groups of traces. All runs are logged to a project. If not specified, the project is set to default.

2. Run an Agent, Chain, or Language Model in LangChain

If the environment variables are correctly set, your application will automatically connect to the LangSmith platform.

from langchain_core.runnables import chain


@chain
def add_val(x: dict) -> dict:
    return {"val": x["val"] + 1}


add_val({"val": 1})

Logging Traces Outside LangChain

You can still use the LangSmith development platform without depending on any LangChain code.

1. Copy the environment variables from the Settings Page and add them to your application.

import os

os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGSMITH_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>"
# os.environ["LANGSMITH_PROJECT"] = "My Project Name" # Optional: "default" is used if not set

2. Log traces

The easiest way to log traces using the SDK is via the @traceable decorator. Below is an example.

from datetime import datetime
from typing import List, Optional, Tuple

import openai
from langsmith import traceable
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())


@traceable
def argument_generator(query: str, additional_description: str = "") -> str:
    return (
        client.chat.completions.create(
            model="gpt-5.4",
            messages=[
                {
                    "role": "system",
                    "content": "You are a debater making an argument on a topic."
                    f"{additional_description}"
                    f" The current time is {datetime.now()}",
                },
                {"role": "user", "content": f"The discussion topic is {query}"},
            ],
        )
        .choices[0]
        .message.content
    )


@traceable
def argument_chain(query: str, additional_description: str = "") -> str:
    argument = argument_generator(query, additional_description)
    # ... Do other processing or call other functions...
    return argument


argument_chain("Why is blue better than orange?")

Alternatively, you can manually log events using the Client directly or using a RunTree, which is what the traceable decorator is meant to manage for you!

A RunTree tracks your application. Each RunTree object is required to have a name and run_type. These and other important attributes are as follows:

• name: str - used to identify the component's purpose
• run_type: str - Currently one of "llm", "chain" or "tool"; more options will be added in the future
• inputs: dict - the inputs to the component
• outputs: Optional[dict] - the (optional) returned values from the component
• error: Optional[str] - Any error messages that may have arisen during the call

from langsmith.run_trees import RunTree

parent_run = RunTree(
    name="My Chat Bot",
    run_type="chain",
    inputs={"text": "Summarize this morning's meetings."},
    # project_name= "Defaults to the LANGSMITH_PROJECT env var"
)
parent_run.post()
# .. My Chat Bot calls an LLM
child_llm_run = parent_run.create_child(
    name="My Proprietary LLM",
    run_type="llm",
    inputs={"prompts": ["You are an AI Assistant. The time is XYZ. Summarize this morning's meetings."]},
)
child_llm_run.post()
child_llm_run.end(
    outputs={"generations": ["I should use the transcript_loader tool to fetch meeting_transcripts from XYZ"]}
)
child_llm_run.patch()
# ..  My Chat Bot takes the LLM output and calls
# a tool / function for fetching transcripts ..
child_tool_run = parent_run.create_child(
    name="transcript_loader",
    run_type="tool",
    inputs={"date": "XYZ", "content_type": "meeting_transcripts"},
)
child_tool_run.post()
# The tool returns meeting notes to the chat bot
child_tool_run.end(outputs={"meetings": ["Meeting1 notes.."]})
child_tool_run.patch()

child_chain_run = parent_run.create_child(
    name="Unreliable Component",
    run_type="tool",
    inputs={"input": "Summarize these notes..."},
)
child_chain_run.post()

try:
    # .... the component does work
    raise ValueError("Something went wrong")
    child_chain_run.end(outputs={"output": "foo"})
    child_chain_run.patch()
except Exception as e:
    child_chain_run.end(error=f"I errored again {e}")
    child_chain_run.patch()
    pass
# .. The chat agent recovers

parent_run.end(outputs={"output": ["The meeting notes are as follows:..."]})
res = parent_run.patch()
res.result()

Create a Dataset from Existing Runs

Once your runs are stored in LangSmith, you can convert them into a dataset. For this example, we will do so using the Client, but you can also do this using the web interface, as explained in the LangSmith docs.

from langsmith import Client

client = Client()
dataset_name = "Example Dataset"
# We will only use examples from the top level AgentExecutor run here,
# and exclude runs that errored.
runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)

dataset = client.create_dataset(dataset_name, description="An example dataset")
for run in runs:
    client.create_example(
        inputs=run.inputs,
        outputs=run.outputs,
        dataset_id=dataset.id,
    )

Evaluating Runs

Check out the LangSmith Testing & Evaluation dos for up-to-date workflows.

For generating automated feedback on individual runs, you can run evaluations directly using the LangSmith client.

from typing import Optional
from langsmith.evaluation import StringEvaluator


def jaccard_chars(output: str, answer: str) -> float:
    """Naive Jaccard similarity between two strings."""
    prediction_chars = set(output.strip().lower())
    answer_chars = set(answer.strip().lower())
    intersection = prediction_chars.intersection(answer_chars)
    union = prediction_chars.union(answer_chars)
    return len(intersection) / len(union)


def grader(run_input: str, run_output: str, answer: Optional[str]) -> dict:
    """Compute the score and/or label for this run."""
    if answer is None:
        value = "AMBIGUOUS"
        score = 0.5
    else:
        score = jaccard_chars(run_output, answer)
        value = "CORRECT" if score > 0.9 else "INCORRECT"
    return dict(score=score, value=value)


evaluator = StringEvaluator(evaluation_name="Jaccard", grading_function=grader)

runs = client.list_runs(
    project_name="my_project",
    execution_order=1,
    error=False,
)
for run in runs:
    client.evaluate_run(run, evaluator)

Integrations

LangSmith easily integrates with your favorite LLM framework.

OpenAI SDK

We provide a convenient wrapper for the OpenAI SDK.

In order to use, you first need to set your LangSmith API key.

export LANGSMITH_API_KEY=<your-api-key>

Next, you will need to install the LangSmith SDK:

pip install -U langsmith

After that, you can wrap the OpenAI client:

from openai import OpenAI
from langsmith import wrappers

client = wrappers.wrap_openai(OpenAI())

Now, you can use the OpenAI client as you normally would, but now everything is logged to LangSmith!

client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Say this is a test"}],
)

Oftentimes, you use the OpenAI client inside of other functions. You can get nested traces by using this wrapped client and decorating those functions with @traceable. See this documentation for more documentation how to use this decorator

from langsmith import traceable


@traceable(name="Call OpenAI")
def my_function(text: str):
    return client.chat.completions.create(
        model="gpt-5.4",
        messages=[{"role": "user", "content": f"Say {text}"}],
    )


my_function("hello world")

Instructor

We provide a convenient integration with Instructor, largely by virtue of it essentially just using the OpenAI SDK.

In order to use, you first need to set your LangSmith API key.

export LANGSMITH_API_KEY=<your-api-key>

Next, you will need to install the LangSmith SDK:

pip install -U langsmith

After that, you can wrap the OpenAI client:

from openai import OpenAI
from langsmith import wrappers

client = wrappers.wrap_openai(OpenAI())

After this, you can patch the OpenAI client using instructor:

import instructor

client = instructor.patch(OpenAI())

Now, you can use instructor as you normally would, but now everything is logged to LangSmith!

from pydantic import BaseModel


class UserDetail(BaseModel):
    name: str
    age: int


user = client.chat.completions.create(
    model="gpt-5.4",
    response_model=UserDetail,
    messages=[
        {"role": "user", "content": "Extract Jason is 25 years old"},
    ],
)

Oftentimes, you use instructor inside of other functions. You can get nested traces by using this wrapped client and decorating those functions with @traceable. See this documentation for more documentation how to use this decorator

@traceable()
def my_function(text: str) -> UserDetail:
    return client.chat.completions.create(
        model="gpt-5.4",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": f"Extract {text}"},
        ],
    )


my_function("Jason is 25 years old")

Pytest Plugin

The LangSmith pytest plugin lets Python developers define their datasets and evaluations as pytest test cases. See online docs for more information.

This plugin is installed as part of the LangSmith SDK, and is enabled by default. See also official pytest docs: How to install and use plugins

Additional Documentation

To learn more about the LangSmith platform, check out the docs.

License

The LangSmith SDK is licensed under the MIT License.

The copyright information for certain dependencies' are reproduced in their corresponding COPYRIGHT.txt files in this repo, including the following:

• uuid-utils
• zstandard

Generated REST client

The low-level, generated client (Langsmith / AsyncLangsmith) ships in the same package. Everything below is maintained by Stainless.

Documentation

The full API of this library can be found in api.md.

Installation

# install from the production repo
pip install git+ssh://git@github.com/langchain-ai/langsmith-python.git

Usage

The full API of this library can be found in api.md.

import os
from langsmith import Langsmith

client = Langsmith(
    api_key=os.environ.get("LANGSMITH_API_KEY"),  # This is the default and can be omitted
    tenant_id=os.environ.get("LANGSMITH_TENANT_ID"),  # This is the default and can be omitted
)

page = client.runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)
print(page.items)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add LANGSMITH_API_KEY="My API Key" to your .env file so that your API Key is not stored in source control.

Async usage

Simply import AsyncLangsmith instead of Langsmith and use await with each API call:

import os
import asyncio
from langsmith import AsyncLangsmith

client = AsyncLangsmith(
    api_key=os.environ.get("LANGSMITH_API_KEY"),  # This is the default and can be omitted
    tenant_id=os.environ.get("LANGSMITH_TENANT_ID"),  # This is the default and can be omitted
)


async def main() -> None:
    page = await client.runs.query_v2(
        project_ids=["00000000-0000-0000-0000-000000000000"],
    )
    print(page.items)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses httpx for HTTP requests. However, for improved concurrency performance you may also use aiohttp as the HTTP backend.

You can enable this by installing aiohttp:

# install from the production repo
pip install 'langsmith[aiohttp] @ git+ssh://git@github.com/langchain-ai/langsmith-python.git'

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import os
import asyncio
from langsmith import DefaultAioHttpClient
from langsmith import AsyncLangsmith


async def main() -> None:
    async with AsyncLangsmith(
        api_key=os.environ.get("LANGSMITH_API_KEY"),  # This is the default and can be omitted
        tenant_id=os.environ.get("LANGSMITH_TENANT_ID"),  # This is the default and can be omitted
        http_client=DefaultAioHttpClient(),
    ) as client:
        page = await client.runs.query_v2(
            project_ids=["00000000-0000-0000-0000-000000000000"],
        )
        print(page.items)


asyncio.run(main())

Using types

Nested request parameters are TypedDicts. Responses are Pydantic models which also provide helper methods for things like:

• Serializing back into JSON, model.to_json()
• Converting to a dictionary, model.to_dict()

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.

Pagination

List methods in the LangChain API are paginated.

This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:

from langsmith import Langsmith

client = Langsmith()

all_runs = []
# Automatically fetches more pages as needed.
for run in client.runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
):
    # Do something with run here
    all_runs.append(run)
print(all_runs)

Or, asynchronously:

import asyncio
from langsmith import AsyncLangsmith

client = AsyncLangsmith()


async def main() -> None:
    all_runs = []
    # Iterate through items across all pages, issuing requests as needed.
    async for run in client.runs.query_v2(
        project_ids=["00000000-0000-0000-0000-000000000000"],
    ):
        all_runs.append(run)
    print(all_runs)


asyncio.run(main())

Alternatively, you can use the .has_next_page(), .next_page_info(), or .get_next_page() methods for more granular control working with pages:

first_page = await client.runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)
if first_page.has_next_page():
    print(f"will fetch next page using these details: {first_page.next_page_info()}")
    next_page = await first_page.get_next_page()
    print(f"number of items we just fetched: {len(next_page.items)}")

# Remove `await` for non-async usage.

Or just work directly with the returned data:

first_page = await client.runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)

print(f"next page cursor: {first_page.next_cursor}")  # => "next page cursor: ..."
for run in first_page.items:
    print(run.id)

# Remove `await` for non-async usage.

Nested params

Nested parameters are dictionaries, typed using TypedDict, for example:

from langsmith import Langsmith

client = Langsmith()

product_feedback = client.product_feedback.create(
    category="BUG",
    message="x",
    source="LANGSMITH_CLI",
    client={},
)
print(product_feedback.client)

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of langsmith.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of langsmith.APIStatusError is raised, containing status_code and response properties.

All errors inherit from langsmith.APIError.

import langsmith
from langsmith import Langsmith

client = Langsmith()

try:
    client.runs.query_v2(
        project_ids=["00000000-0000-0000-0000-000000000000"],
    )
except langsmith.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except langsmith.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except langsmith.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Error codes are as follows:

Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the max_retries option to configure or disable retry settings:

from langsmith import Langsmith

# Configure the default for all requests:
client = Langsmith(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries=5).runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)

Timeouts

By default requests time out after 1.5 minutes. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from langsmith import Langsmith

# Configure the default for all requests:
client = Langsmith(
    # 20 seconds (default is 1.5 minutes)
    timeout=20.0,
)

# More granular control:
client = Langsmith(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request:
client.with_options(timeout=5.0).runs.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

Logging

We use the standard library logging module.

You can enable logging by setting the environment variable LANGCHAIN_LOG to info.

$ export LANGCHAIN_LOG=info

Or to debug for more verbose logging.

How to tell whether None means null or missing

In an API response, a field may be explicitly null, or missing entirely; in either case, its value is None in this library. You can differentiate the two cases with .model_fields_set:

if response.my_field is None:
    if "my_field" not in response.model_fields_set:
        print('Got json like {}, without a "my_field" key present at all.')
    else:
        print('Got json like {"my_field": null}.')

Accessing raw response data (e.g. headers)

The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,

from langsmith import Langsmith

client = Langsmith()
response = client.runs.with_raw_response.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
)
print(response.headers.get("X-My-Header"))

run = response.parse()  # get the object that `runs.query_v2()` would have returned
print(run.id)

These methods return an APIResponse object.

The async client returns an AsyncAPIResponse with the same structure, the only difference being awaitable methods for reading the response content.

.with_streaming_response

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use .with_streaming_response instead, which requires a context manager and only reads the response body once you call .read(), .text(), .json(), .iter_bytes(), .iter_text(), .iter_lines() or .parse(). In the async client, these are async methods.

with client.runs.with_streaming_response.query_v2(
    project_ids=["00000000-0000-0000-0000-000000000000"],
) as response:
    print(response.headers.get("X-My-Header"))

    for line in response.iter_lines():
        print(line)

The context manager is required so that the response will reliably be closed.

Making custom/undocumented requests

This library is typed for convenient access to the documented API.

If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can make requests using client.get, client.post, and other http verbs. Options on the client will be respected (such as retries) when making this request.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.headers.get("x-foo"))

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

To access undocumented response properties, you can access the extra fields like response.unknown_prop. You can also get all the extra fields on the Pydantic model as a dict with response.model_extra.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

• Support for proxies
• Custom transports
• Additional advanced functionality

import httpx
from langsmith import Langsmith, DefaultHttpxClient

client = Langsmith(
    # Or use the `LANGCHAIN_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083",
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

You can also customize the client on a per-request basis by using with_options():

client.with_options(http_client=DefaultHttpxClient(...))

Managing HTTP resources

By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.

from langsmith import Langsmith

with Langsmith() as client:
    # make requests here
    ...

# HTTP client is now closed

Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

1. Changes that only affect static types, without breaking runtime behavior.
2. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
3. Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Determining the installed version

If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.

You can determine the version that is being used at runtime with:

import langsmith

print(langsmith.__version__)

Requirements

Python 3.10 or higher.

Contributing

See the contributing documentation.