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How to query traces and runs

LangSmith makes it easy to query for traces and runs. In addition to the filtering experience presented in the UI, you can also use the SDK or API to query for traces and runs.

Using the list_runs method in the SDK or /runs/query endpoint in the API, you can filter runs to analyze and export. Most simple requests can be satisfied using simple top level arguments:

  • project_id / project_name: The project(s) to fetch runs from - can be a single project or a list of projects.
  • trace_id: Fetch runs that are part of a specific trace.
  • run_type: The type of run to get, such as llm, chain, tool, retriever, etc.
  • dataset_name / dataset_id: Fetch runs that are associated with an example row in the specified dataset. This is useful for comparing prompts or models over a given dataset.
  • reference_example_id: Fetch runs that are associated with a specific example row. This is useful for comparing prompts or models on a given input.
  • parent_run_id - Fetch runs that are children of a given run. This is useful for fetching runs grouped together using the context manager or for fetching an agent trajectory.
  • error: Fetch runs that errored or did not error.
  • run_ids: Fetch runs with a given list of run ids. Note: This will ignore all other filtering arguments.
  • filter: Fetch runs that match a given structured filter statement. See the run filtering guide below for more information.
  • query (experimental): Query the experimental natural language API, which translates your query into a filter statement.

Using keyword arguments​

For simple queries, such as filtering by project, run time, name, or run ID's, you can directly use keyword arguments in the list_runs method. These correspond directly to query params in the REST API. All the examples below assume you have created a LangSmith client and configured it with your API key to connect to the LangSmith server.

from langsmith import Client

client = Client()

Below are some examples of ways to list runs using keyword arguments:

List all runs in a project​

project_runs = client.list_runs(project_name="<your_project>")

List LLM and Chat runs in the last 24 hours​

todays_llm_runs = client.list_runs(
    project_name="<your_project>",
    start_time=datetime.now() - timedelta(days=1),
    run_type="llm",
)

List traces in a project​

Root runs (or run traces), are runs that have no parents. These are assigned an 'execution_order' of 1. You can use this to filter for root runs.

root_runs = client.list_runs(
    project_name="<your_project>",
    execution_order=1
)

List runs without errors​

correct_runs = client.list_runs(project_name="<your_project>", error=False)

List runs by run ID​

If you have a list of run IDs, you can list them directly:

run_ids = ['a36092d2-4ad5-4fb4-9c0d-0dba9a2ed836','9398e6be-964f-4aa4-8ae9-ad78cd4b7074']
selected_runs = client.list_runs(id=run_ids)
Ignores Other Arguments

If you provide a list of run IDs in the way described above, it will ignore all other filtering arguments like project_name, run_type, etc. and directly return the runs matching the given IDs.

Run Filtering​

Listing runs with query params is useful for simple queries, but doesn't support many common needs, such as filtering by metadata, tags, or other fields.

LangSmith supports a filter query language to permit more complex filtering operations when fetching runs. This guide will provide a high level overview of the grammar as well as a few examples of when it can be useful.

If you'd prefer a more visual guide, you can get a taste of the language by viewing the table of runs on any of your projects' pages. We provide some recommended filters to get you started that you can copy and use the SDK.

Grammar​

The filtering grammar is based on common comparators on fields in the run object. Supported comparators include:

  • gte (greater than or equal to)
  • gt (greater than)
  • lte (less than or equal to)
  • lt (less than)
  • eq (equal to)
  • neq (not equal to)
  • has (check if run contains a tag or metadata json blob)
  • search (search for a substring in a string field)

Additionally, you can combine multiple comparisons through and and or operators.

These can be applied on fields of the run object, such as its id, name, run_type, start_time / end_time, latency, total_tokens, error, execution_order, tags, and any associated feedback through feedback_key and feedback_score.

Examples​

The following examples assume you have configured your environment appropriately and have runs stored in LangSmith.

List runs with "star_rating" key whose score is greater than 4​

client.list_runs(project_name="<your_project>", filter='and(eq(feedback_key, "star_rating"), gt(feedback_score, 4))')

List runs that took longer than 5 seconds to complete​

client.list_runs(project_name="<your_project>", filter='gt(latency, "5s")')

List all runs where total_tokens is greater than 5000​

client.list_runs(project_name="<your_project>", filter='gt(total_tokens, 5000)')

List all runs that have "error" not equal to null​

client.list_runs(project_name="<your_project>", filter='neq(error, null)')

List all runs where start_time is greater than a specific timestamp​

client.list_runs(project_name="<your_project>", filter='gt(start_time, "2023-07-15T12:34:56Z")')

List all runs that contain the string "substring"​

client.list_runs(project_name="<your_project>", filter='search("substring")')

List all runs that are tagged with the git hash "2aa1cf4"​

client.list_runs(project_name="<your_project>", filter='has(tags, "2aa1cf4")')

Complex query: List all "chain" type runs that took more than 10 seconds and had total_tokens greater than 5000​

client.list_runs(
  project_name="<your_project>",
  filter='and(eq(run_type, "chain"), gt(latency, 10), gt(total_tokens, 5000))'
)

Complex query: List all runs that started after a specific timestamp and either have "error" not equal to null or a "Correctness" feedback score equal to 0​

client.list_runs(
  project_name="<your_project>",
  filter='and(gt(start_time, "2023-07-15T12:34:56Z"), or(neq(error, null), and(eq(feedback_key, "Correctness"), eq(feedback_score, 0.0))))'
)

Complex query: List all runs where tags include "experimental" or "beta" and latency is greater than 2 seconds​

client.list_runs(
  project_name="<your_project>",
  filter='and(or(has(tags, "experimental"), has(tags, "beta")), gt(latency, 2))'
)

Check for presence of metadata​

If you want to check for the presence of metadata, you can use the eq operator, optionally with an and statement to match by value. This is useful if you want to log more structured information about your runs.


to_search = {
    "user_id": ""
}

# Check for any run with the "user_id" metadata key
client.list_runs(
  project_name="default",
  filter="eq(metadata_key, 'user_id')"
)
# Check for runs with user_id=4070f233-f61e-44eb-bff1-da3c163895a3
client.list_runs(
  project_name="default",
  filter="and(eq(metadata_key, 'user_id'), eq(metadata_value, '4070f233-f61e-44eb-bff1-da3c163895a3'))"
)

Check for environment details in metadata​

A common pattern is to add environment information to your traces via metadata. If you want to filter for runs containing environment metadata, you can use the same pattern as above:

client.list_runs(
  project_name="default",
  filter="and(eq(metadata_key, 'environment'), eq(metadata_value, 'production'))"
)

Check for conversation ID in metadata​

Another common way to associate traces in the same conversation is by using a shared conversation ID. If you want to filter runs based on a conversation ID in this way, you can search for that ID in the metadata.

client.list_runs(
  project_name="default",
  filter="and(eq(metadata_key, 'conversation_id'), eq(metadata_value, 'a1b2c3d4-e5f6-7890'))"
)

If you want to combine multiple conditions to refine your search, you can use the and operator along with other filtering functions. Here's how you can search for runs named "ChatOpenAI" that also have a specific conversation_id in their metadata:

client.list_runs(
  project_name="default",
  filter="and(eq(name, 'ChatOpenAI'), eq(metadata_key, 'conversation_id'), eq(metadata_value, '69b12c91-b1e2-46ce-91de-794c077e8151'))"
)

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