openreward 0.1.77

Python SDK for the OpenReward platform.

OpenReward Python SDK

The official Python SDK for OpenReward — a platform for building, hosting, and training on RL environments for language models.

The SDK has two complementary roles:

• Build environments — define evaluation tasks, expose tools, and serve them via a standards-compliant API that can be deployed on the OpenReward platform.
• Train agents — connect to any environment (local or hosted), run agent loops, and log rollouts with rewards back to OpenReward.

Installation

pip install openreward

For environments that process documents (PDF, DOCX, Excel, PowerPoint):

pip install "openreward[tools]"

Requires Python 3.11+.

Core concepts

Environment

An Environment subclass defines a benchmark or task distribution. Implement three required methods:

Method	Purpose
list_splits()	Return split names, e.g. ["train", "test"]
list_tasks(split)	Return a deterministically ordered list of task dicts
get_prompt()	Return the task instructions as a list of TextBlock / ImageBlock

Actions are defined as async methods decorated with @tool. Each tool receives a Pydantic model as input and returns a ToolOutput.

ToolOutput

Every tool returns a ToolOutput containing:

• blocks — a list of TextBlock or ImageBlock results
• reward — optional float reward signal
• finished — whether the episode is complete
• metadata — optional arbitrary metadata

Server

Server wraps one or more Environment classes in a FastAPI app and exposes the Open Reward Standard API over HTTP with SSE streaming.

Key endpoints:

Endpoint	Description
POST /create	Spawn a new environment session
POST /{env}/call	Execute a tool (streamed via SSE)
GET /{env}/prompt	Get the current task prompt
GET /{env}/tools	List available tools
POST /{env}/tasks	List all tasks for a split

Sandboxes

Environments that need isolated compute (e.g. code execution) can spin up Docker containers via the sandbox API using SandboxSettings. Containers are managed automatically — started in setup() and torn down in teardown().

Toolsets

Group reusable tools into Toolset classes and compose them across environments via the toolsets class attribute.

Rollout logging

Log agent trajectories with reward signals back to OpenReward for analysis and training. The client's rollout API supports normalized message types as well as raw outputs from Anthropic, OpenAI, and Google GenAI SDKs.

Scaffolding a new environment

The orwd CLI generates a project skeleton:

# Minimal environment
orwd init my-env

# Environment with a Docker sandbox for code execution
orwd init my-env --template sandbox

Deploying to OpenReward

1. Push your environment to a GitHub repository.
2. Connect the repository in the OpenReward dashboard.
3. Configure compute resources (CPU, memory, scaling).
4. Every push to the connected branch triggers an automatic build and deployment.

Your environment is then accessible to any agent via the OpenReward API using the username/environment-name namespace.

Environment variables

Variable	Description
OPENREWARD_API_KEY	API key for authentication
OPENREWARD_URL	Override base URL (default: https://openreward.ai)
OPENREWARD_USE_STRUCTURED_LOGS	Set to 1 for JSON logging (recommended in production)
OPENREWARD_ROLLOUT_LOGGING_FORMAT	pretty or structured for rollout log output

Documentation

Full documentation, guides, and examples are at docs.openreward.ai.

License

Apache 2.0