Trajectory SDK
Generated Python client for the Trajectory API, with high-level workflows for uploading trajectories, telemetry, and runtime-backed benchmarks.
Install
pip install trajectory-sdk
Quick start
Set TRAJECTORY_API_KEY, then create a client:
from trajectory import Client
client = Client()
benchmarks = client.benchmarks.list(limit=10)
Managed benchmark runtimes inject TRAJECTORY_TOKEN, TRAJECTORY_BASE_URL,
MODEL_ENDPOINT_ID, and TRAJECTORY_TID. The same client uses them for inference, rewards, and
completion:
from trajectory import Client
client = Client()
response = client.chat.completions.create(model="policy", messages=messages)
client.trajectories.log_reward(reward_id="primary", name="correct", value=reward)
client.trajectories.complete(termination_reason="ENV_DONE")
Explicit authentication, URLs, model endpoint headers, and trajectory IDs continue to override the runtime environment.
SDK-owned HTTP clients use a 600-second read/write/pool timeout and a 5-second connection timeout.
Supplying http_client= inherits that client's timeouts, including a bare HTTPX client's 5-second
default. An explicit Client(timeout=...) overrides the supplied client; a resource method's
timeout= overrides that request. timeout=None disables timeouts. Retry counts are unchanged.
The generated resource methods map directly to the public HTTP API. Higher-level operations that
coordinate multiple API calls are available from trajectory.lib:
from trajectory import Client
from trajectory.lib import ingest_events
client = Client()
result = ingest_events(
client,
[
{
"event_type": "agent.completed",
"session_id": "session-123",
"timestamp": "2026-08-28T00:00:00+00:00",
"properties": {"model": "example-model"},
}
],
)
print(result.ingested, result.skipped)
LiteLLM harnesses
Starting with SDK 0.6.6, an existing LiteLLM harness can use this import for policy calls:
from trajectory.lib.litellm import completion
response = completion(model=model, messages=messages, temperature=1.0)
This always uses the injected Trajectory endpoint and credentials. Messages and tools are unchanged.
Keep user/judge calls on native LiteLLM and retain the original loop and grader, then report their
result with the existing
client.trajectories.log_reward(...) and client.trajectories.complete(...) methods.
Development
uv run pytest
uv run ruff check .
uv run ruff format --check .
The generated client lives in src/trajectory/. Handwritten workflows live in
src/trajectory/lib/, and tests live in tests/.
License
1 maintainer
Release files for trajectory-sdk 0.6.19
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| trajectory_sdk-0.6.19.tar.gz | 90.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| trajectory_sdk-0.6.19-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 251.7 kB
Release files / trajectory_sdk-0.6.19.tar.gz
| Download URL | trajectory_sdk-0.6.19.tar.gz |
|---|---|
| Size | 90.4 kB |
| Tags | Source |
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