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Bulk export handles the full pipeline: discover conversations in your LangSmith project, export the complete run trees (including child LLM and tool runs), parse into Trajectories, and upload to the Trajectory platform.

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Prerequisites

Before running a bulk export, you need three pieces of information from LangSmith:
  1. API key — your LangSmith API key (starts with lsv2_sk_... or lsv2_pt_...)
  2. Workspace ID — the tenant/workspace UUID
  3. Destination ID — a pre-configured bulk export destination UUID

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Finding your Workspace ID

  1. Go to LangSmith and open Settings (gear icon)
  2. Under Workspaces, select your workspace
  3. The workspace ID is in the URL: https://smith.langchain.com/o/.../workspaces/{workspace_id}
Alternatively, you can find it via the API: 
from langsmith import Client

client = Client(api_key="lsv2_sk_...")
import requests
resp = requests.get(
    "https://api.smith.langchain.com/workspaces",
    headers={"X-API-Key": "lsv2_sk_..."},
)
for ws in resp.json():
    print(f"{ws['display_name']}: {ws['id']}")

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Finding your Destination ID

A bulk export destination tells LangSmith where to write the exported data (e.g. a GCS bucket). Destinations are configured in the LangSmith UI:
  1. Go to Settings → Bulk Exports in LangSmith
  2. Create or select an export destination (e.g. a GCS bucket)
  3. Copy the destination ID
You can also list existing destinations via the API: 
import requests

resp = requests.get(
    "https://api.smith.langchain.com/api/v1/bulk-export-destinations",
    headers={
        "X-API-Key": "lsv2_sk_...",
        "X-Tenant-Id": "your-workspace-id",
    },
)
for dest in resp.json():
    print(f"{dest['display_name']}: {dest['id']}")

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Finding your Project ID

  1. Go to your project in LangSmith
  2. The project ID is in the URL: https://smith.langchain.com/o/.../projects/p/{project_id}

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E2E Bulk Export

With all three IDs, the full pipeline is three lines: 
import trajectory_sdk as tj

tj.init(
    provider="langsmith",
    api_key="lsv2_sk_...",                  # or set LANGSMITH_API_KEY env var
    project_id="your-project-id",
    workspace_id="your-workspace-id",
    destination_id="your-destination-id",
    trajectory_api_key="your-trajectory-api-key",  # or set TRAJECTORY_API_KEY env var
)

# Export everything, parse, and return Trajectories
trajectories = tj.import_conversations(bulk=True)

# Upload to the Trajectory platform
tj.upload(trajectories, dataset="my_dataset")

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What happens under the hood

  1. Discover trace IDs — lists all root runs in the project and collects their trace_id values. This is necessary because child runs (LLM calls, tool calls) don’t carry thread_id metadata, so filtering by thread ID alone would miss them.
  2. Trigger bulk export — sends a POST to the LangSmith bulk exports API with an in(trace_id, [...]) filter, which captures the full run tree for each conversation (parents and all children).
  3. Poll for completion — the export job runs asynchronously. The SDK polls every 5 seconds until it completes (typically 1-2 minutes).
  4. Download parquet — fetches the exported parquet file from the configured GCS destination bucket.
  5. Parse into Trajectories — groups runs by conversation_id (from metadata) or trace_id, builds run trees, extracts messages, and constructs Trajectory objects using multiprocessing for speed.

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Time-Scoped Export

Export only recent conversations by passing the since parameter: 
from datetime import timedelta

# Export conversations from the last hour
trajectories = tj.import_conversations(bulk=True, since=timedelta(hours=1))
You can also pass an absolute datetime: 
from datetime import datetime

trajectories = tj.import_conversations(
    bulk=True,
    since=datetime(2025, 3, 1),
)
When since is omitted, the SDK exports all conversations in the project.

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From a Local Parquet File

If you already have a parquet file (e.g. from a previous export or manual download), you can skip the export steps: 
import trajectory_sdk as tj

tj.init(
    provider="langsmith",
    project_id="your-project-id",
)

trajectories = tj.import_conversations(
    bulk=True,
    source="./langsmith_export.parquet",
)

tj.save(trajectories, "./exports")
No workspace_id or destination_id is needed when providing a local file.

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Upload

After importing, upload trajectories to the Trajectory platform: 
tj.upload(trajectories, dataset="my_dataset")
trajectory_api_key must be set in tj.init() (or via the TRAJECTORY_API_KEY env var) for upload to work.

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Limits and Caveats

  • The LangSmith bulk export API has a maximum of 100 runs per page when listing root runs. The SDK auto-paginates through all pages.
  • Export jobs are asynchronous and typically take 1-2 minutes to complete.
  • The destination_id must point to a GCS bucket that your service account can read from.
  • Empty conversations (root runs with no child LLM/tool runs) produce Trajectories with 0 steps. The upload step automatically skips these.