### Install Example Trainer

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/mcp_tool_calling/GRPO_README.md

Install the example_trainer package from the repository root. Use the '[all]' extra for all optional dependencies.

```bash
pip install -e ".[example_trainer]"
```

```bash
pip install -e ".[all]"
```

--------------------------------

### Install Example Trainer Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/example_trainer/README.md

Install the example trainer and its dependencies using pip. You can install just the example trainer or all available extras.

```bash
pip install -e ".[example_trainer]"
# or everything: pip install -e ".[all]"
```

--------------------------------

### Install Atropos for Running Examples

Source: https://github.com/nousresearch/atropos/blob/main/README.md

Install Atropos in editable mode with example dependencies. Use this if you want to run the provided examples.

```bash
pip install -e .[examples]
```

--------------------------------

### Start API and Environment Servers

Source: https://github.com/nousresearch/atropos/blob/main/llms.txt

Use these commands to start the central API server and an example environment server. Ensure the model name is adjusted for your setup and set '--slurm' appropriately. CLI arguments can override configuration files.

```bash
run-api &

python environments/gsm8k_server.py serve --openai.model_name="Qwen/Qwen2.5-1.5B-Instruct" --slurm False
# Alternatively, using a config file:
# python environments/gsm8k_server.py serve --config environments/configs/example.yaml
# CLI arguments can override config settings:
# python environments/gsm8k_server.py serve --config environments/configs/example.yaml --env.group_size 8
```

--------------------------------

### Environment Setup with Example Configuration

Source: https://github.com/nousresearch/atropos/blob/main/environments/pydantic_schema_following_environment/README.md

Initialize the PydanticSchemaFollowingEnv using a pre-defined example configuration function. This simplifies setup when using standard dataset structures.

```python
from atropos.environments.pydantic_schema_following_environment.example_config import create_config_with_dataset

# Create configuration for your dataset
env_config, server_configs = create_config_with_dataset("your_username/your_pydantic_dataset")

# Initialize environment
env = PydanticSchemaFollowingEnv(env_config, server_configs)
```

--------------------------------

### Set up Pokemon Showdown Battle Simulator

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/pokemon-showdown/README.md

Clone the Pokemon Showdown repository, install dependencies, and start the simulator. Ensure the simulator is running before launching the RL trainer.

```bash
git clone https://github.com/smogon/pokemon-showdown.git
cd pokemon-showdown
npm install
node pokemon-showdown start --no-security
```

--------------------------------

### Launch Example GRPO Trainer

Source: https://github.com/nousresearch/atropos/blob/main/environments/dataset_environment/LOCAL_TESTING.md

Starts the example GRPO trainer in a separate terminal. Configure model, training steps, and batch size.

```bash
python -m example_trainer.grpo.train \
  --model_name Qwen/Qwen2.5-1.5B-Instruct \
  --training_steps 20 \
  --batch_size 2 \
  --gradient_accumulation_steps 2 \
  --seq_len 512
```

--------------------------------

### Run Renderer Example

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Execute the renderer example script.

```bash
python test_renderer_example.py
```

--------------------------------

### Setup Development Environment

Source: https://github.com/nousresearch/atropos/blob/main/llms.txt

Set up a Python virtual environment and install the core Atropos package along with development dependencies. Ensure you are using Python 3.10+.

```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip install -e ".[dev]"
```

--------------------------------

### Start Atropos Environment Microservice (GSM8K Example)

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/FAQ.md

Launch a specific environment microservice, such as the GSM8K server. This command can be configured via command-line arguments or a YAML file.

```bash
python environments/gsm8k_server.py serve --openai.model_name YourModelName --slurm false
```

```bash
# python environments/gsm8k_server.py serve --config environments/configs/example.yaml
```

--------------------------------

### Initialize StarMapCompressionEnv

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Demonstrates the basic setup for the StarMapCompressionEnv, which is the starting point for compression tasks.

```python
from environments.community.starmap_compression.starmap_compression import StarMapCompressionEnv

```

--------------------------------

### Install All Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/conversational_style_dpo/requirements.txt

Install all available dependencies for the project, including optional extras, by using the editable install option with the 'all' extra.

```bash
pip install -e .[all]

```

--------------------------------

### Base Environment Setup

Source: https://github.com/nousresearch/atropos/blob/main/llms.txt

Implement the `setup` method for initial environment configuration, such as loading data or models.

```python
async def setup(self):
    # Called once at the start. Use for initial setup (loading data, models, etc.).
    pass
```

--------------------------------

### Install Prime CLI

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

Install the Prime CLI tool using `uv`.

```bash
uv tool install prime
```

--------------------------------

### Setup and Run LiveCodeBench with Modal Sandbox

Source: https://github.com/nousresearch/atropos/blob/main/environments/eval_environments/README.md

Instructions for setting up and running the LiveCodeBench evaluation using the Modal sandbox for secure code execution. This includes installing Modal, obtaining a token, deploying the sandbox, and running the evaluation script.

```bash
# First, deploy Modal sandbox (one-time setup)
pip install modal
modal token new
modal deploy modal_sandbox.py

# Run with Modal sandbox (secure)
python lcb_eval.py evaluate \
    --openai.base_url https://api.openai.com/v1 \
    --openai.api_key $OPENAI_API_KEY \
    --openai.model_name gpt-4o \
    --env.use_modal True \
    --env.thinking_mode True \
    --env.data_dir_to_save_evals ../evals/lcb/gpt-4o
```

--------------------------------

### Install Foundry/Anvil

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/ethereum_virtual_machine/README.md

Installs Foundry, which includes Anvil (EVM node) and Cast (CLI tool). Recommended for quick setup.

```bash
curl -L https://foundry.paradigm.xyz | bash
foundryup
```

--------------------------------

### Connect to Atropos Trainer

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Command to start the Atropos trainer service. Ensure PyPantograph is installed for full Lean integration.

```bash
python environments/community/lean_proof_env/lean_env.py serve
```

--------------------------------

### Install Requirements and Run GoofyMath

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/goofy_math/README.md

Installs necessary packages and runs the GoofyMath environment in process mode to generate examples. Ensure you set your OpenAI API key before running.

```bash
pip install -r requirements.txt

export OPENAI_API_KEY=your_key_here
cd atropos
python environments/hack0/goofy_math_server.py process \
  --env.data_path_to_save_groups goofy_math_demo.jsonl \
  --env.total_steps 3
```

--------------------------------

### Environment Setup for OpenVLA Robotics

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

This snippet outlines the basic setup for the OpenVLA robotics environment. It is typically used as a starting point for configuring and running simulations.

```python
import gymnasium as gym

env = gym.make("openvla_robotics/NutAssemblySquare-v0", render_mode="rgb_array")
observation, info = env.reset()

for _ in range(1000):
    action = env.action_space.sample()  # Replace with your action
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()
env.close()

```

--------------------------------

### Serve Text Reversal Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

Starts the text reversal environment for basic training with default settings. This includes a 16k train context, 32k eval context, and a sampled eval set of 100 examples.

```bash
python text_reversal_environment.py serve
```

--------------------------------

### Install KernelBench from Source

Source: https://github.com/nousresearch/atropos/blob/main/environments/kernelbench_env/README.md

Installs KernelBench using pip from a Git repository and sets up the project locally.

```bash
pip install git@github.com:ScalingIntelligence/KernelBench.git
cd KernelBench
pip install -r requirements.txt
pip install -e .
cd -
```

--------------------------------

### Start Visualization Server

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/padres_spatial/README.md

Navigates to the visualization directory and starts an HTTP server to view the environment. Access it via http://localhost:8080.

```bash
cd visualization
python3 -m http.server 8080
```

--------------------------------

### Install Atropos for Local Development

Source: https://github.com/nousresearch/atropos/blob/main/llms.txt

Install the Atropos library from a local clone for development purposes. This includes installing it in editable mode.

```bash
git clone https://github.com/NousResearch/atropos.git
cd atropos
pip install -e .
```

--------------------------------

### Install InternBootcamp Library

Source: https://github.com/nousresearch/atropos/blob/main/environments/intern_bootcamp/README.md

Navigate to the environment directory and install the InternBootcamp library, which is included as a submodule. This is a prerequisite for running the training environment.

```bash
cd environments/intern_bootcamp
cd internbootcamp_lib && uv pip install -e .
```

--------------------------------

### Install Atropos Library

Source: https://github.com/nousresearch/atropos/blob/main/README.md

Install the core Atropos library using pip. This is the basic installation for using Atropos.

```bash
pip install atroposlib
```

--------------------------------

### Install Prime Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

Install a specific environment, such as Wordle or GSM8K, using the Prime CLI.

```bash
prime env install will/wordle
```

```bash
prime env install primeintellect/gsm8k
```

--------------------------------

### Troubleshooting: Start Atropos API Server

Source: https://github.com/nousresearch/atropos/blob/main/example_trainer/README.md

Command to start the Atropos API server. Ensure this is running before attempting to connect to it.

```bash
# Start the API server first
run-api --port 8002
```

--------------------------------

### Install Dependencies and Run Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/metric_card_generator/README.md

Installs necessary dependencies and runs the metric card generator to produce rollouts. The generated HTML visualization can then be opened in a browser.

```bash
pip install -r requirements.txt

python metric_card_generator.py process --env.data_path_to_save_groups artifacts/metric_rollouts.jsonl

# Open artifacts/metric_rollouts.html in a browser
```

--------------------------------

### SWE-RL Example Edit

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

An example demonstrating the SEARCH/REPLACE edit format for the SWE-RL environment.

```python
Here is an example:
```python
```

--------------------------------

### Run GRPO Example Trainer

Source: https://github.com/nousresearch/atropos/blob/main/llms.txt

Execute the GRPO example trainer script from the project root. Ensure Atropos API and an environment are running.

```bash
python example_trainer/grpo.py
```

--------------------------------

### Start Environment

Source: https://github.com/nousresearch/atropos/blob/main/README.md

Run this command in a separate terminal to start an environment. Replace 'gsm8k_server.py serve' with your chosen environment.

```bash
python gsm8k_server.py serve --slurm False
```

--------------------------------

### Start Atropos API Server

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/FAQ.md

Run the Atropos API server to enable environment interactions. This is a prerequisite for starting environment microservices.

```bash
run-api
```

--------------------------------

### Set Up Environment Variables

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/padres_spatial/README.md

Copies the example environment file and instructs to add the OpenAI API key.

```bash
cp .env.example .env
# Add your OpenAI API key to .env
```

--------------------------------

### Set Up Development Environment

Source: https://github.com/nousresearch/atropos/blob/main/CONTRIBUTING.md

Create and activate a Python virtual environment, then install project dependencies including development packages.

```bash
# Ensure you have Python 3.10+ installed
# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

# Install dependencies, including development dependencies
pip install -e ".[dev]"
```

--------------------------------

### Startup Order for Components

Source: https://github.com/nousresearch/atropos/blob/main/example_trainer/README.md

Provides the recommended startup sequence for all components: API server, vLLM server, environment, and trainer. Includes health check for vLLM.

```bash
# Follow this startup order
# 1. Start API first
run-api --port 8002

# 2. Wait 5s, then start vLLM
# Check health: curl http://localhost:9001/health
python -m example_trainer.vllm_api_server --model ... --enable-lora --enforce-eager

# 3. Wait for vLLM health endpoint to return 200
while ! curl -s http://localhost:9001/health > /dev/null; do sleep 1; done

# 4. Start environment (use --openai.server_type vllm for logprobs)
python environments/gsm8k_server.py serve \
    --env.group_size 4 \
    --env.batch_size 16 \
    --env.total_steps 200 \
    --env.steps_per_eval 50 \
    --env.max_num_workers_per_node 8 \
    --env.rollout_server_url "http://localhost:8002" \
    --env.use_wandb true \
    --env.wandb_name "gsm8k-train-env" \
    --openai.base_url "http://localhost:9001/v1" \
    --openai.model_name "NousResearch/Hermes-3-Llama-3.1-8B" \
    --openai.server_type vllm

# 5. Start trainer (will register with API and begin training)
python -m example_trainer.grpo --weight-bridge-mode lora_only ...
```

--------------------------------

### setup

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/envs/README.md

This method is called once at the beginning of the environment's lifecycle. Use it for any initial setup required for your specific environment, such as loading datasets, initializing models, or connecting to external resources.

```APIDOC
## async def setup(self)

### Description
Initializes the environment. This method is called once at the beginning of the environment's lifecycle.

### Method
Asynchronous Function Call

### Parameters
None

### Response
None
```

--------------------------------

### Basic Configuration Setup

Source: https://github.com/nousresearch/atropos/blob/main/environments/answer_format_environment/README.md

Imports necessary classes for setting up the Answer Format Environment configuration.

```python
from atropos.environments.answer_format_environment import AnswerFormatEnv, AnswerFormatEnvConfig
```

--------------------------------

### Environment Setup

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Provides the initial command to set up the environment's configuration file by copying a template. This is a prerequisite for configuring API keys and other settings.

```bash
# Environment setup
cp .env.template .env
```

--------------------------------

### Initializing and Running ReasoningGymEnv

Source: https://github.com/nousresearch/atropos/blob/main/environments/reasoning_gym_environment/README.md

Initialize the Reasoning Gym environment with configuration and server settings, then set it up for use. Assumes 'await' is available in the execution context.

```python
from atropos.environments.reasoning_gym_environment import ReasoningGymEnv

# Initialize environment
env_config, server_configs = ReasoningGymEnv.config_init()
env = ReasoningGymEnv(env_config, server_configs)

# Setup and run
await env.setup()
# Training loop handled by atropos framework
```

--------------------------------

### Python Flask App Example

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

A basic Flask application setup in Python. Ensure proper indentation when performing search and replace operations.

```python
import math
from flask import Flask

```

--------------------------------

### Training Loop Example

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

A basic training loop structure for an environment. It shows how to get training items, collect trajectories, and includes a placeholder for policy updates.

```python
for episode in range(num_episodes):
    # Get next training item (resets environment)
    item = await env.get_next_item()

    # Collect robot trajectory
    scored_data, backlog = await env.collect_trajectories(item)

    # Process rewards and update policy
    # (Policy update logic would be implemented here)
```

--------------------------------

### Start Lean Environment in Serve Mode

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/lean_proof_env/README.md

Run the environment as a server for the Atropos trainer. Ensure your LLM API server is running and accessible.

```bash
python environments/lean_env.py serve
```

--------------------------------

### Environment Setup for FOB and NePS

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/pytorch_optimizer_coding/FOB/examples/neps/README.md

These commands set up a Conda environment, activate it, and install necessary dependencies for running FOB with NePS, including downgrading some packages as required.

```bash
conda create -n fob-neps python=3.10 -y
conda activate fob-neps
pip install -r requirements.txt
pip install -r examples/neps/requirements.txt # this will downgrade some packages
pip install -e .
```

--------------------------------

### Serve Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/README.md

Starts the instruction following algorithm environment in serve mode.

```bash
python instruction_following_algorithm_environment.py serve
```

--------------------------------

### Environment Setup: Set Rendering Backend

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Configures the OpenGL rendering backend. Choose 'egl' for GPU acceleration or 'osmesa' for CPU-only environments.

```bash
export PYOPENGL_PLATFORM=egl    # For GPU acceleration
export PYOPENGL_PLATFORM=osmesa # For CPU-only environments
```

--------------------------------

### Get Environment Status Response

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/api/README.md

Example JSON response from the `/status-env` endpoint, providing the current server step, queue size, and the environment's calculated sampling weight relative to others.

```json
{"current_step": <step>, "queue_size": <size>, "env_weight": <calculated_weight_float>}
```

--------------------------------

### Start Environment Server

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/mcp_tool_calling/GRPO_README.md

Launches the environment server, configuring it to connect to the Atropos API and use vLLM for OpenAI compatibility.

```bash
python environments/gsm8k_server.py serve \
  --env.rollout_server_url "http://localhost:8002" \
  --openai.server_type vllm \
  --openai.base_url "http://localhost:9001/v1" \
  --openai.api_key "dummy"
```

--------------------------------

### Install Core Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Installs the necessary Python packages for the environment. Ensure you have pip installed.

```bash
pip install modal verdict torch lightning
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/router_env/engine/tools/mcp/spotify/README.md

Installs project dependencies and builds the project. Ensure Node.js v16+ is installed.

```bash
git clone https://github.com/marcelmarais/spotify-mcp-server.git
cd spotify-mcp-server
npm install
npm run build
```

--------------------------------

### Serve Atropos Environment for Trainer

Source: https://github.com/nousresearch/atropos/blob/main/environments/smolagents_integration/README.md

Start the Atropos environment in serve mode, making it available for a trainer to connect.

```bash
python -m environments.smolagents_integration.smolagents_env serve \
  --env.rollout_server_url "http://localhost:8000" \
  --env.use_chat_completion true \
  --env.max_concurrent_processes 5 \
  --env.group_size 8 \
  --openai.model_name "your-model-name" \
  --openai.base_url "http://localhost:8000/v1"
```

--------------------------------

### Set Up API Keys

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Exports required API keys as environment variables. Replace placeholders with your actual keys.

```bash
export GROK_API_KEY="your_grok_api_key"
export MODAL_TOKEN="your_modal_token"
```

--------------------------------

### Install Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/accessibility_env/README.md

Install the necessary Python packages for the accessibility environment. Ensure Atropos core is also installed.

```bash
pip install -r requirements.txt
pip install lxml
```

```bash
# From the Atropos root:
pip install -e .[dev]
```

--------------------------------

### Run Custom Configuration Training

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/quantum_hybrid/README.md

Starts training with custom configuration parameters for qubits, layers, steps, and quantum weight.

```bash
python atropos.py process \
  --env.n_qubits 16 \
  --env.n_layers 5 \
  --env.total_steps 100 \
  --env.quantum_weight 0.5
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/physical_space_stl/README.md

Installs essential Python libraries for 3D rendering, machine learning, and data handling. Ensure you have Python and pip installed.

```shell
pip install pyrender trimesh pyglet matplotlib torch transformers pydantic vllm numpy requests tenacity wandb
```

--------------------------------

### Install Core Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Installs the essential Python packages required for the compression workflow.

```bash
pip install numpy scipy scikit-learn openai python-dotenv matplotlib pillow
```

--------------------------------

### Install Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/meteorology_forecast/README.md

Installs the necessary Python dependencies for the project. Ensure you have Python 3.10+.

```bash
pip install -r requirements.txt  # or manually install atroposlib, wandb, httpx, etc.
```

--------------------------------

### Initialize DynastAI Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Demonstrates how to create an instance of the DynastAI environment with custom configuration. Ensure the FastAPI backend is running and provide your OpenRouter API key.

```python
from environments.community.dynastai.src.dynastai_env import DynastAIEnv, DynastAIEnvConfig

# Create environment
config = DynastAIEnvConfig(
    api_host="localhost",
    api_port=9001,
    openrouter_api_key="your_key",
    web_ui=True,
    web_port=3000
)
env = DynastAIEnv(config)

```

--------------------------------

### Install Dependencies with Bun

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/deepsacrifice_chess/README.md

Installs all project dependencies using the Bun package manager.

```sh
bun install
```

--------------------------------

### Start Trainer (Shared vLLM Mode)

Source: https://github.com/nousresearch/atropos/blob/main/example_trainer/README.md

Initiates the trainer process in shared vLLM mode. Specify the model name, weight bridge mode, vLLM port, vLLM configuration path, and Atropos URL.

```bash
python -m example_trainer.grpo \
    --model-name NousResearch/Hermes-3-Llama-3.1-8B \
    --weight-bridge-mode shared_vllm \
    --vllm-port 9001 \
    --vllm-config-path /tmp/grpo_training/vllm_bridge_config.json \
    --atropos-url "http://localhost:8002" \
    --warmup-steps 20 \
    --clip-eps 0.2
```

--------------------------------

### Install SmolaGents and Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/smolagents_integration/README.md

Install the SmolaGents library and necessary dependencies for web search tools.

```bash
pip install smolagents
pip install datasets pandas huggingface_hub
pip install tavily-python  # Required for web search tools (needed by most GAIA tasks)
```

--------------------------------

### Install Dependencies and Run Environment

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/selcube/README.md

Installs project dependencies and then runs the Rubik's Cube environment for processing. Configure API keys, model names, and environment parameters as needed.

```bash
pip install -r requirements.txt

cd atropos/environments/hack0

(OPENAI_API_KEY="OPENAI_KEY" \
      python rubiks_cube_environment.py process \
      --slurm false \
      --openai.model_name gpt-4.1-nano \
      --env.tokenizer_name "NousResearch/DeepHermes-3-Llama-3-3B-Preview" \
      --env.use_wandb true \
      --env.group_size 4 \
      --env.max_steps 15 \
      --env.scramble_moves 5 \
      --env.data_path_to_save_groups "rubiks_process_results.jsonl" \
      --env.wandb_name "rubiks_cube_hackathon" \
      --env.debug_mode true \
      --env.use_curriculum true \
      --env.generate_visualizations true \
      --env.visualizations_dir "./rubiks_visualizations" \
      --env.provide_solving_strategies true)
```

--------------------------------

### Install Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/game_environments/diplomacy_environment/README.md

Installs required packages and the AI_Diplomacy library. Ensure you are in the correct project directory.

```bash
pip install -r requirements.txt
cd AI_Diplomacy
pip install -e .
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/ethereum_virtual_machine/README.md

Installs necessary Python libraries for interacting with OpenAI and configuration files.

```bash
pip install openai pydantic PyYAML
```

--------------------------------

### Verify Foundry Installation

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/ethereum_virtual_machine/README.md

Checks if Anvil, Cast, and Forge are installed correctly by displaying their versions.

```bash
anvil --version
cast --version
forge --version
```

--------------------------------

### Start Cat Behavior Environment Server

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/cat_behavior_env/README.md

Run the main server file to launch the cat behavior communication environment. Ensure you are in the correct project directory.

```bash
python environments/community/cat_behavior_env/cat_server.py
```

--------------------------------

### Test DynastAI Installation

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/dynastai/README.md

Run various test scripts to verify the DynastAI installation and functionality.

```bash
python verify_install.py
```

```bash
python run_dynastai.py
```

```bash
python test_card_generation.py
```

```bash
python test_dynastai_env.py
```

--------------------------------

### Dataset Setup Command

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/pytorch_optimizer_coding/FOB/README.md

Command to set up datasets based on the experiment configuration file.

```bash
python -m pytorch_fob.dataset_setup experiment.yaml
```

--------------------------------

### Initialize and Evaluate Optimizer

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Demonstrates how to initialize the OptimizerBenchmarkEnvironment and evaluate a generated optimizer code string. The environment returns a reward based on the optimizer's performance.

```python
from environments.community.pytorch_optimizer_coding.optimizer_benchmark_environmenr import OptimizerBenchmarkEnvironment

# Initialize environment
env = OptimizerBenchmarkEnvironment(config=config)

# Generate optimizer code (from agent)
optimizer_code = """
class NovelOptimizer(torch.optim.Optimizer):
    def __init__(self, params, lr=1e-3, momentum=0.9):
        defaults = dict(lr=lr, momentum=momentum)
        super().__init__(params, defaults)

    def step(self, closure=None):
        # Novel optimization logic here
        pass
"""

# Evaluate optimizer
reward = env.evaluate(optimizer_code)
print(f"Total reward: {reward}")
```

--------------------------------

### Install Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/examcraft/README.md

Installs all necessary Python packages for the ExamCraft environment. Ensure you have a requirements.txt file.

```bash
pip install -r requirements.txt
```

--------------------------------

### GET /status

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/api/README.md

Get the current training step count and the number of trajectories currently in the queue.

```APIDOC
## GET /status

### Description
Get the current training step (based on batches served) and queue size.

### Method
GET

### Endpoint
/status

### Response
#### Success Response (200)
- **current_step** (integer) - The current training step count.
- **queue_size** (integer) - The number of trajectories currently in the queue.
```

--------------------------------

### Bootcamp Registry Interaction

Source: https://github.com/nousresearch/atropos/blob/main/environments/intern_bootcamp/README.md

Demonstrates how to create a bootcamp instance and retrieve information about available bootcamps using the registry. Ensure the necessary imports are present.

```python
from environments.intern_bootcamp.bootcamp_registry import (
    create_bootcamp,
    get_available_bootcamps,
    bootcamp_registry
)

# Create a bootcamp instance
bootcamp = create_bootcamp("Game24bootcamp", num_numbers=4, range_max=50)

# Get information about a bootcamp
info = bootcamp_registry.get_bootcamp_info("Game24bootcamp")
print(info["parameters"])
```

--------------------------------

### Explore Available Tasks (Python)

Source: https://github.com/nousresearch/atropos/blob/main/environments/intern_bootcamp/README.md

Programmatically list the first 20 available bootcamp tasks using the registry. This is useful for exploration and understanding the variety of tasks supported.

```python
from environments.intern_bootcamp.bootcamp_registry import get_available_bootcamps

tasks = get_available_bootcamps()
for task in tasks[:20]:  # Show first 20
    print(task)
```

--------------------------------

### Install Pre-commit Hooks

Source: https://github.com/nousresearch/atropos/blob/main/CONTRIBUTING.md

Install pre-commit hooks to automatically run code quality checks on commit.

```bash
pre-commit install
```

--------------------------------

### Using DummyManagedServer with OpenAI Endpoints

Source: https://github.com/nousresearch/atropos/blob/main/atroposlib/envs/server_handling/MANAGED_SERVER.md

Example demonstrating how to opt-in to DummyManagedServer for OpenAI endpoints and access its interface, noting that token and logprob data are placeholders.

```python
import os

# Opt-in to dummy managed server for OpenAI
os.environ["ATROPOS_ALLOW_DUMMY_MANAGED_SERVER"] = "1"

# Now this works with OpenAI endpoints
async with self.server.managed_server() as managed:
    response = await managed.chat_completion(messages=messages, n=4)
    state = managed.get_state()
    nodes = state["nodes"]

    # nodes contain placeholder token data - DO NOT use for training
    for node in nodes:
        print(node.full_text)  # Real completion text
        print(node.tokens[:5])     # placeholder values
        print(node.logprobs[:5])   # placeholder values

    # Strict mode: get_logprobs is not available on DummyManagedServer
    # and will raise NotImplementedError.
```

--------------------------------

### Install Optional Visualization Dependencies

Source: https://github.com/nousresearch/atropos/blob/main/environments/community/README.md

Installs additional Python packages for enhanced 3D plotting and animation generation.

```bash
pip install matplotlib pillow  # For 3D plotting and animation generation
```