epsilab 0.15.1

Python SDK and CLI for the Epsilab RL Environment Hub and Marketplace.

Epsilab Python SDK

Python SDK and CLI for the Epsilab RL Environment Hub.

What is Epsilab?

Epsilab is an open hub for RL environments — like HuggingFace, but for RL. Search, run, and export training data from hosted environments, or publish your own with a single command.

• Researchers and teams training models — search and run environments, export GRPO/DPO/SFT/KTO training data, batch evaluation
• Environment builders — publish with epsilab deploy, immutable content-addressed releases, usage analytics
• Open by default — public, shared, unlisted, or private visibility; auto-qualified releases

Installation

pip install epsilab

Quick Start

Deploy an environment

epsilab login
epsilab env init my-environment
cd my-environment
# implement your logic in server.py, add tasks to tasks.json
epsilab deploy

One command builds, uploads, and registers. No registry credentials needed.

Run an environment

from epsilab import Epsilab

client = Epsilab(api_key="sk-...")

# Find environments
envs = client.search_environments(domain="coding", min_quality_score=0.8)

# Create a session and interact
session = client.create_environment_session("deployment-id", task_id="task-001", seed=42)
result = client.environment_step(
    session.session_id,
    "def fibonacci(n): ...",
    session_token=session.session_token,
)
print(f"Reward: {result.reward}, Done: {result.done}")

# Export training data
export = client.create_environment_export(deployment_id="deployment-id", format="grpo")

TRL GRPO integration

def reward_fn(completions, task_ids, **kwargs):
    rewards = []
    for completion, task_id in zip(completions, task_ids):
        session = client.create_environment_session("deployment-id", task_id=task_id)
        result = client.environment_step(
            session.session_id,
            completion,
            session_token=session.session_token,
        )
        rewards.append(result.reward or 0.0)
    return rewards

Creating an Environment

An RL environment is a containerized task server. At minimum it needs:

File	Purpose
Dockerfile	Builds the runtime container image
server.py	HTTP server implementing the environment protocol
tasks.json	JSON array defining the task set

Environment protocol

Your server must expose two HTTP endpoints:

POST /reset   -> {"observation": str}
POST /step    -> {"observation": str, "reward": float, "terminated": bool, "truncated": bool}

/reset receives {"task_id": "...", "seed": 42} and returns the initial observation. /step receives {"action": "..."} and returns the step result.

tasks.json format

[
  {
    "task_id": "find-the-bug-001",
    "prompt": "Find and fix the bug in the following code...",
    "difficulty": "easy",
    "split": "train",
    "max_steps": 10
  }
]

Creating an Application Tool

Application tools are reusable plugins (e.g. GitHub, Slack, Calendar) that environments can compose together. A tool needs:

File	Purpose
plugin.py	AppPlugin subclass defining the tool's identity and lifecycle
api.py	API route handlers (FastAPI-style)
state.py	Deterministic state model for the tool

cd my-tool/
epsilab deploy    # auto-detects tool structure

CLI Commands

Command	Description
epsilab login	Authenticate with your API key
epsilab logout	Remove stored credentials
epsilab whoami	Show current auth and profile status
epsilab deploy	Build, upload, and register an environment or tool
epsilab env init [slug]	Scaffold a new environment project
epsilab env list	List your environment listings
epsilab env search [query]	Search the hub
epsilab env verify	Run local preflight checks
epsilab rl sessions	List your RL sessions
epsilab rl trajectory <id>	View step-by-step trajectory
epsilab namespace create <slug>	Create a namespace

All commands support --json for machine-readable output and -v for verbose logging.

Configuration

Environment Variable	Constructor Param	Description
EPSILAB_API_KEY	api_key	Your API key
EPSILAB_API_BASE	api_base	API base URL (default: production)
EPSILAB_HTTP_TIMEOUT	timeout_seconds	Request timeout in seconds (default: 120)
	max_retries	Auto-retry count for 429/5xx (default: 3)
	load_dotenv	Also read a local .env file (default: false)

Error Handling

from epsilab import Epsilab, AuthError, InsufficientCreditsError, RateLimitError, ApiError

client = Epsilab(api_key="sk-...")

try:
    session = client.create_environment_session("dep-id", task_id="task-001")
except AuthError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except ApiError as e:
    print(f"API error: {e.status_code}")

The SDK retries automatically on rate limits (429) and transient server errors (500, 502, 503, 504) with exponential backoff and jitter.

Examples

Script	Description
examples/run_environment.py	Browse, run sessions, inspect trajectories, export data
examples/grpo_training.py	Use environments as live reward functions for TRL GRPO
examples/batch_evaluation.py	Batch evaluation across tasks with server-side parallelism
examples/marketplace_example.py	Creator and buyer marketplace workflows

Documentation

Document	Description
API Reference	Full method reference for all SDK features
Evaluations & More	Model evaluations, voice, routing, capability matrix

License

Apache 2.0 — see LICENSE.