strands-env 0.1.0

RL environment abstraction for Strands agents — step, observe, reward.

strands-env

RL environments for Strands agents — step, observe, reward.

strands-agents is designed for serving, not training. strands-env integrates strands-sglang to bridge this gap.

Define an environment

Subclass Environment and customize your tools:

from strands_tools import calculator
from strands_env.core.environment import Environment

class MathEnv(Environment):
    def get_tools(self):
        return [calculator]

Run it

env = MathEnv(model_factory=factory, reward_fn=reward_fn)
result = await env.step(Action(message="What is 2^10?", task_context=TaskContext(ground_truth="1024")))

result.observation.final_response   # "1024"
result.observation.tokens           # TokenObservation (SGLang only, for on-policy RL training)
result.reward.reward                # 1.0
result.termination_reason           # task_complete

Each step() runs a full agent loop (reasoning + tool calls), not a single model call. Strands' hook-based design makes it easy to customize what happens within each step.

Install

pip install strands-env

For development:

git clone <repo-url> && cd strands-env
pip install -e ".[dev]"

See examples/math_env.py for a complete runnable example:

python examples/math_env.py --backend sglang --sglang-base-url http://localhost:30000
python examples/math_env.py --backend bedrock --model-id us.anthropic.claude-sonnet-4-20250514

Development

# Lint
ruff check src/ && ruff format --check src/

# Unit tests
pytest tests/unit/ -v

# Integration tests (requires running SGLang server)
pytest tests/integration/ -v --sglang-base-url=http://localhost:30000

License

Apache License 2.0 - see LICENSE.