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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

Install the optional agent integration used by the full CLI with:

pip install "trajectory-sdk[cli]"

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.

Command line

The package installs a handwritten trajectory command that delegates API operations to the same generated client and high-level workflows:

export TRAJECTORY_API_KEY="tj_key_..."

trajectory trajectories upload trajectories.json --dataset my-dataset
trajectory telemetry ingest events.json
trajectory traces upload trajectories.json --events events.json --dataset my-dataset
trajectory secrets set OPENAI_API_KEY
trajectory secrets list

API-backed commands use a 30-minute timeout by default. Override it with --timeout SECONDS. The existing repository and benchmark tools remain available from the new CLI package:

trajectory bench validate ./benchmark
trajectory bench push ./benchmark --build-images
trajectory extract harness

Benchmark pushes use your organization's only Agent, or create Default Agent if none exists. With multiple Agents, add --agent-id agt_example to select the owner.

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/, the handwritten CLI lives in cmd/trajectory/cli/trajectory_cli/, and tests live in tests/.

License

Apache 2.0

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