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)
For task execution, follow the native benchmark harness and registration and image builds examples below.
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. Benchmark submission
and file-upload workflows are available from trajectory.lib. Detailed authored guides are
not currently available without documentation access; the core workflow is included here.
Native benchmark harness
A task's run_command starts your program inside its runtime. Supply task input through
TaskSpec.env_vars, such as QUESTION and EXPECTED_ANSWER below. input_messages is stored
task metadata; the native runtime does not send it to the model or to your program's stdin.
Your harness reads the input, calls the model, grades its answer, records the reward, and
completes the trajectory before exiting. The SDK has no trajectory CLI executable.
Managed runs supply TRAJECTORY_BASE_URL, TRAJECTORY_TOKEN, and model-endpoint credentials.
Use Client() without embedding API keys in the image. MODEL_ENDPOINT_ACCESS_TOKEN makes
trajectories.create() resolve the precreated trajectory and authenticates model requests.
The SDK does not read TRAJECTORY_TID; pass the returned ID explicitly to related calls.
Outside a managed run, configure TRAJECTORY_API_KEY and a model endpoint for model access.
import os
from trajectory import Client
client = Client()
tid = client.trajectories.create().tid
try:
response = client.chat.completions.create(
model="policy",
messages=[{"role": "user", "content": os.environ["QUESTION"]}],
extra_headers={"X-Trajectory-Id": tid},
)
answer = response.choices[0].message.content or ""
reward = float(answer.strip() == os.environ["EXPECTED_ANSWER"])
client.trajectories.log_reward(tid, reward_id="answer", name="accuracy", value=reward)
except Exception as error:
try:
client.trajectories.complete(tid, termination_reason="ERROR")
except Exception as completion_error:
error.add_note(f"Error completion failed: {type(completion_error).__name__}")
raise
else:
client.trajectories.complete(tid, termination_reason="ENV_DONE")
Package this file and its dependencies in your Dockerfile. For example, copying it to
/app/harness.py makes python /app/harness.py the task's run_command. A wrong answer gets
reward zero and ENV_DONE; a model or grader error remains an error, even if reporting its
completion also fails. Process exit alone does not complete a trajectory.
Registration and image builds
Define a BenchmarkSpec named benchmark with your tasks, distinct train/test splits,
and a runtime such as DockerfileBuild("Dockerfile"). With the Dockerfile and harness under
the local benchmark/ directory, register it and build its runtime:
from pathlib import Path
from trajectory import Client
from trajectory.lib import push, wait_for_benchmark_images
client = Client()
result = push(client, benchmark, root=Path("benchmark"))
images = wait_for_benchmark_images(client, result.bench_id)
push() uploads files and waits for task registration; it does not start image builds.
wait_for_benchmark_images() first calls client.benchmarks.images.build(bench_id) and then
polls for completion. client.benchmarks.images.list(bench_id) only reads status. Once the
runtime is ready, use the benchmark ID to start evaluation or training; uploading does not
execute the tasks. Run this registration code with your organization's API key.
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 0.6.24
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-0.6.24.tar.gz | 88.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| trajectory_sdk-0.6.24-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 253.9 kB
Release files / trajectory_sdk-0.6.24.tar.gz
| Download URL | trajectory_sdk-0.6.24.tar.gz |
|---|---|
| Size | 88.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Download URL | trajectory_sdk-0.6.24-py3-none-any.whl |
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| Size | 165.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
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