rleaas 1.0.1

Release SDK — RL Environments as a Service by Centific

rleaas — Release SDK

Python SDK for the Release (RLEaaS) platform by Centific — RL Environments as a Service.

Installation

pip install rleaas

API Key

Setup	API key needed?	How to get it
Local dev server (http://localhost:8000)	No	Run the AgentWork-Simulator locally — no auth required
Hosted / production	Yes	Log in to the Release dashboard → Settings → API Keys → create a key

Set the key as an environment variable so you never hardcode it:

export RLEAAS_API_KEY="rleaas_sk_your_key_here"

The SDK reads it automatically:

import rleaas

client = rleaas.Client()                          # reads RLEAAS_API_KEY from env
# or
client = rleaas.Client(api_key="rleaas_sk_...")   # pass explicitly

Local development (no key required)

import rleaas

client = rleaas.Client(base_url="http://localhost:8000")
print(client.ping())
# {'message': 'RL Environment & Agent API', 'version': '1.0.0', ...}

Sub-clients

Attribute	Purpose
client.Environment	Create and manage simulation environments
client.Tools	Register and configure agent tools
client.Agent	Register and export trained agents
client.Verifier	Define scoring verifiers (rule-based, LLM judge, composite)
client.Scenario	Create and browse training scenarios
client.ScenarioSuite	Organize scenarios into training/evaluation suites
client.TrainingJob	Launch and monitor GRPO/PPO/DQN/A2C training runs
client.Evaluation	Run evaluations and retrieve rollouts
client.Metrics	Query KPIs and training metrics
client.AuditLog	Access audit logs and governance configuration

Example

import rleaas

client = rleaas.Client()   # reads RLEAAS_API_KEY from environment

# Create environment
env = client.Environment.create(name="FinSim-Prod-v1", vertical="FinSim")
env.wait_until_ready()

# Create verifier
rule_v = client.Verifier.create(
    name="AML Compliance Check",
    verifier_type="rule_based",
    environment="FinSim-Prod-v1",
    config={
        "conditions": ["'run_aml_check' in trajectory.tool_calls"],
        "condition_logic": "AND",
        "reward_on_pass": 1.0,
        "reward_on_fail": 0.0,
    },
)

# Train
job = client.TrainingJob.run(
    environment_name="FinSim-Prod-v1",
    algorithm="GRPO",
    config={"episodes": 10000, "max_steps_per_episode": 20},
    verifier_ids=[rule_v.id],
)
job.wait_until_complete()
best = job.get_best_checkpoint()

# Evaluate
eval_job = client.Evaluation.run(
    agent_checkpoint_id=best["id"],
    scenario_suite_id="suite_eval_01",
    verifier_ids=[rule_v.id],
)
report = eval_job.wait_until_complete()
print(report["overall_score"])

Async support

async with rleaas.AsyncClient() as client:
    status = await client.ping()

License

MIT