Environments, Benchmarks & Data Generation

Environments, Benchmarks & Data Generation

Hermes Agent includes a full environment framework that connects its tool-calling capabilities to the Atropos RL training framework. This enables three workflows:

1. RL Training — Train language models on multi-turn agentic tasks with GRPO
2. Benchmarks — Evaluate models on standardised agentic benchmarks
3. Data Generation — Generate SFT training data from agent rollouts

All three share the same core: an environment class that defines tasks, runs an agent loop, and scores the output.

:::info Repo environments vs RL training tools The Python environment framework documented here lives under the repo’s environments/ directory and is the implementation-level API for Hermes/Atropos integration. This is separate from the user-facing rl_* tools, which operate as an orchestration surface for remote RL training workflows. :::

:::tip Quick Links

• Want to run benchmarks? Jump to Available Benchmarks
• Want to train with RL? See RL Training Tools for the agent-driven interface, or Running Environments for manual execution
• Want to create a new environment? See Creating Environments :::

Architecture

The environment system is built on a three-layer inheritance chain:

classDiagram
    class BaseEnv {
      Server management
      Worker scheduling
      Wandb logging
      CLI: serve / process / evaluate
    }

    class HermesAgentBaseEnv {
      Terminal backend configuration
      Tool resolution
      Agent loop engine
      ToolContext access
    }

    class TerminalTestEnv {
      Stack testing
    }

    class HermesSweEnv {
      SWE training
    }

    class TerminalBench2EvalEnv {
      Benchmark evaluation
    }

    class TBLiteEvalEnv {
      Fast benchmark
    }

    class YCBenchEvalEnv {
      Long-horizon benchmark
    }

    BaseEnv <|-- HermesAgentBaseEnv
    HermesAgentBaseEnv <|-- TerminalTestEnv
    HermesAgentBaseEnv <|-- HermesSweEnv
    HermesAgentBaseEnv <|-- TerminalBench2EvalEnv
    TerminalBench2EvalEnv <|-- TBLiteEvalEnv
    TerminalBench2EvalEnv <|-- YCBenchEvalEnv

BaseEnv (Atropos)

The foundation from atroposlib. Provides:

• Server management — connects to OpenAI-compatible APIs (VLLM, SGLang, OpenRouter)
• Worker scheduling — parallel rollout coordination
• Wandb integration — metrics logging and rollout visualisation
• CLI interface — three subcommands: serve, process, evaluate
• Eval logging — evaluate_log() saves results to JSON + JSONL

HermesAgentBaseEnv

The hermes-agent layer (environments/hermes_base_env.py). Adds:

• Terminal backend configuration — sets TERMINAL_ENV for sandboxed execution (local, Docker, Modal, Daytona, SSH, Singularity)
• Tool resolution — _resolve_tools_for_group() calls hermes-agent’s get_tool_definitions() to get the right tool schemas based on enabled/disabled toolsets
• Agent loop integration — collect_trajectory() runs HermesAgentLoop and scores the result
• Two-phase operation — Phase 1 (OpenAI server) for eval/SFT, Phase 2 (VLLM ManagedServer) for full RL with logprobs
• Async safety patches — monkey-patches Modal backend to work inside Atropos’s event loop

Concrete Environments

Your environment inherits from HermesAgentBaseEnv and implements five methods:

Method	Purpose
setup()	Load dataset, initialise state
get_next_item()	Return the next item for rollout
format_prompt(item)	Convert an item into the user message
compute_reward(item, result, ctx)	Score the rollout (0.0–1.0)
evaluate()	Periodic evaluation logic

Core Components

Agent Loop

HermesAgentLoop (environments/agent_loop.py) is the reusable multi-turn agent engine. It runs the same tool-calling pattern as hermes-agent’s main loop:

1. Send messages + tool schemas to the API via server.chat_completion()
2. If the response contains tool_calls, dispatch each via handle_function_call()
3. Append tool results to the conversation, go back to step 1
4. If no tool_calls, the agent is done

Tool calls execute in a thread pool (ThreadPoolExecutor(128)) so that async backends (Modal, Docker) don’t deadlock inside Atropos’s event loop.

Returns an AgentResult:

@dataclass
class AgentResult:
    messages: List[Dict[str, Any]]       # Full conversation history
    turns_used: int                       # Number of LLM calls made
    finished_naturally: bool              # True if model stopped on its own
    reasoning_per_turn: List[Optional[str]]  # Extracted reasoning content
    tool_errors: List[ToolError]          # Errors encountered during tool dispatch
    managed_state: Optional[Dict]         # VLLM ManagedServer state (Phase 2)

Tool Context

ToolContext (environments/tool_context.py) gives reward functions direct access to the same sandbox the model used during its rollout. The task_id scoping means all state (files, processes, browser tabs) is preserved.

async def compute_reward(self, item, result, ctx: ToolContext):
    # Run tests in the model's terminal sandbox
    test = ctx.terminal("pytest -v")
    if test["exit_code"] == 0:
        return 1.0

    # Check if a file was created
    content = ctx.read_file("/workspace/solution.py")
    if content.get("content"):
        return 0.5

    # Download files for local verification
    ctx.download_file("/remote/output.bin", "/local/output.bin")
    return 0.0

Available methods:

Category	Methods
Terminal	terminal(command, timeout)
Files	read_file(path), write_file(path, content), search(query, path)
Transfers	upload_file(), upload_dir(), download_file(), download_dir()
Web	web_search(query), web_extract(urls)
Browser	browser_navigate(url), browser_snapshot()
Generic	call_tool(name, args) — escape hatch for any hermes-agent tool
Cleanup	cleanup() — release all resources

Tool Call Parsers

For Phase 2 (VLLM ManagedServer), the server returns raw text without structured tool calls. Client-side parsers in environments/tool_call_parsers/ extract tool_calls from raw output:

from environments.tool_call_parsers import get_parser

parser = get_parser("hermes")  # or "mistral", "llama3_json", "qwen", "deepseek_v3", etc.
content, tool_calls = parser.parse(raw_model_output)

Available parsers: hermes, mistral, llama3_json, qwen, qwen3_coder, deepseek_v3, deepseek_v3_1, kimi_k2, longcat, glm45, glm47.

In Phase 1 (OpenAI server type), parsers are not needed — the server handles tool call parsing natively.

Available Benchmarks

TerminalBench2

89 challenging terminal tasks with per-task Docker sandbox environments.

What it tests	Single-task coding/sysadmin ability
Scoring	Binary pass/fail (test suite verification)
Sandbox	Modal cloud sandboxes (per-task Docker images)
Tools	terminal + file
Tasks	89 tasks across multiple categories
Cost	~$50–200 for full eval (parallel execution)
Time	~2–4 hours

python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \
    --config environments/benchmarks/terminalbench_2/default.yaml

# Run specific tasks
python environments/benchmarks/terminalbench_2/terminalbench2_env.py evaluate \
    --config environments/benchmarks/terminalbench_2/default.yaml \
    --env.task_filter fix-git,git-multibranch

Dataset: NousResearch/terminal-bench-2 on HuggingFace.

TBLite (OpenThoughts Terminal Bench Lite)

100 difficulty-calibrated tasks — a faster proxy for TerminalBench2.

What it tests	Same as TB2 (coding/sysadmin), calibrated difficulty tiers
Scoring	Binary pass/fail
Sandbox	Modal cloud sandboxes
Tools	terminal + file
Tasks	100 tasks: Easy (40), Medium (26), Hard (26), Extreme (8)
Correlation	r=0.911 with full TB2
Speed	2.6–8× faster than TB2

python environments/benchmarks/tblite/tblite_env.py evaluate \
    --config environments/benchmarks/tblite/default.yaml

TBLite is a thin subclass of TerminalBench2 — only the dataset and timeouts differ. Created by the OpenThoughts Agent team (Snorkel AI + Bespoke Labs). Dataset: NousResearch/openthoughts-tblite.

YC-Bench

Long-horizon strategic benchmark — the agent plays CEO of an AI startup.

What it tests	Multi-turn strategic coherence over hundreds of turns
Scoring	Composite: 0.5 × survival + 0.5 × normalised_funds
Sandbox	Local terminal (no Modal needed)
Tools	terminal only
Runs	9 default (3 presets × 3 seeds), sequential
Cost	~$50–200 for full eval
Time	~3–6 hours

# Install yc-bench (optional dependency)
pip install "hermes-agent[yc-bench]"

# Run evaluation
bash environments/benchmarks/yc_bench/run_eval.sh

# Or directly
python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \
    --config environments/benchmarks/yc_bench/default.yaml

# Quick single-preset test
python environments/benchmarks/yc_bench/yc_bench_env.py evaluate \
    --config environments/benchmarks/yc_bench/default.yaml \
    --env.presets '["fast_test"]' --env.seeds '[1]'

YC-Bench uses collinear-ai/yc-bench — a deterministic simulation with 4 skill domains (research, inference, data_environment, training), prestige system, employee management, and financial pressure. Unlike TB2’s per-task binary scoring, YC-Bench measures whether an agent can maintain coherent strategy over hundreds of compounding decisions.

Training Environments

TerminalTestEnv

A minimal self-contained environment with inline tasks (no external dataset). Used for validating the full stack end-to-end. Each task asks the model to create a file at a known path; the verifier checks the content.

# Process mode (saves rollouts to JSONL, no training server needed)
python environments/terminal_test_env/terminal_test_env.py process \
    --env.data_path_to_save_groups terminal_test_output.jsonl

# Serve mode (connects to Atropos API for RL training)
python environments/terminal_test_env/terminal_test_env.py serve

HermesSweEnv

SWE-bench style training environment. The model gets a coding task, uses terminal + file + web tools to solve it, and the reward function runs tests in the same Modal sandbox.

python environments/hermes_swe_env/hermes_swe_env.py serve \
    --openai.model_name YourModel \
    --env.dataset_name bigcode/humanevalpack \
    --env.terminal_backend modal

Running Environments

Every environment is a standalone Python script with three CLI subcommands:

evaluate — Run a benchmark

For eval-only environments (benchmarks). Runs all items, computes metrics, logs to wandb.

python environments/benchmarks/tblite/tblite_env.py evaluate \
    --config environments/benchmarks/tblite/default.yaml \
    --openai.model_name anthropic/claude-sonnet-4.6

No training server or run-api needed. The environment handles everything.

process — Generate SFT data

Runs rollouts and saves scored trajectories to JSONL. Useful for generating training data without a full RL loop.

python environments/terminal_test_env/terminal_test_env.py process \
    --env.data_path_to_save_groups output.jsonl \
    --openai.model_name anthropic/claude-sonnet-4.6

Output format: each line is a scored trajectory with the full conversation history, reward, and metadata.

serve — Connect to Atropos for RL training

Connects the environment to a running Atropos API server (run-api). Used during live RL training.

# Terminal 1: Start the Atropos API
run-api

# Terminal 2: Start the environment
python environments/hermes_swe_env/hermes_swe_env.py serve \
    --openai.model_name YourModel

The environment receives items from Atropos, runs agent rollouts, computes rewards, and sends scored trajectories back for training.

Two-Phase Operation

Phase 1: OpenAI Server (Eval / SFT)

Uses server.chat_completion() with tools= parameter. The server (VLLM, SGLang, OpenRouter, OpenAI) handles tool call parsing natively. Returns ChatCompletion objects with structured tool_calls.

• Use for: evaluation, SFT data generation, benchmarks, testing
• Placeholder tokens are created for the Atropos pipeline (since real token IDs aren’t available from the OpenAI API)

Phase 2: VLLM ManagedServer (Full RL)

Uses ManagedServer for exact token IDs + logprobs via /generate. A client-side tool call parser reconstructs structured tool_calls from raw output.

• Use for: full RL training with GRPO/PPO
• Real tokens, masks, and logprobs flow through the pipeline
• Set tool_call_parser in config to match your model’s format (e.g., "hermes", "qwen", "mistral")

Creating Environments

Training Environment

from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
from atroposlib.envs.server_handling.server_manager import APIServerConfig

class MyEnvConfig(HermesAgentEnvConfig):
    my_custom_field: str = "default_value"

class MyEnv(HermesAgentBaseEnv):
    name = "my-env"
    env_config_cls = MyEnvConfig

    @classmethod
    def config_init(cls):
        env_config = MyEnvConfig(
            enabled_toolsets=["terminal", "file"],
            terminal_backend="modal",
            max_agent_turns=30,
        )
        server_configs = [APIServerConfig(
            base_url="https://openrouter.ai/api/v1",
            model_name="anthropic/claude-sonnet-4.6",
            server_type="openai",
        )]
        return env_config, server_configs

    async def setup(self):
        from datasets import load_dataset
        self.dataset = list(load_dataset("my-dataset", split="train"))
        self.iter = 0

    async def get_next_item(self):
        item = self.dataset[self.iter % len(self.dataset)]
        self.iter += 1
        return item

    def format_prompt(self, item):
        return item["instruction"]

    async def compute_reward(self, item, result, ctx):
        # ctx gives full tool access to the rollout's sandbox
        test = ctx.terminal("pytest -v")
        return 1.0 if test["exit_code"] == 0 else 0.0

    async def evaluate(self, *args, **kwargs):
        # Periodic evaluation during training
        pass

if __name__ == "__main__":
    MyEnv.cli()

Eval-Only Benchmark

For benchmarks, follow the pattern used by TerminalBench2, TBLite, and YC-Bench:

1. Create under environments/benchmarks/your-benchmark/
2. Set eval-only config: eval_handling=STOP_TRAIN, steps_per_eval=1, total_steps=1
3. Stub training methods: collect_trajectories() returns (None, []), score() returns None
4. Implement rollout_and_score_eval(eval_item) — the per-item agent loop + scoring
5. Implement evaluate() — orchestrates all runs, computes aggregate metrics
6. Add streaming JSONL for crash-safe result persistence
7. Add cleanup: KeyboardInterrupt handling, cleanup_all_environments(), _tool_executor.shutdown()
8. Run with evaluate subcommand

See environments/benchmarks/yc_bench/yc_bench_env.py for a clean, well-documented reference implementation.

Configuration Reference

HermesAgentEnvConfig Fields

Field	Type	Default	Description
enabled_toolsets	List[str]	None (all)	Which hermes toolsets to enable
disabled_toolsets	List[str]	None	Toolsets to filter out
distribution	str	None	Probabilistic toolset distribution name
max_agent_turns	int	30	Max LLM calls per rollout
agent_temperature	float	1.0	Sampling temperature
system_prompt	str	None	System message for the agent
terminal_backend	str	"local"	local, docker, modal, daytona, ssh, singularity
terminal_timeout	int	120	Seconds per terminal command
terminal_lifetime	int	3600	Max sandbox lifetime
dataset_name	str	None	HuggingFace dataset identifier
tool_pool_size	int	128	Thread pool size for tool execution
tool_call_parser	str	"hermes"	Parser for Phase 2 raw output
extra_body	Dict	None	Extra params for OpenAI API (e.g., OpenRouter provider prefs)
eval_handling	Enum	STOP_TRAIN	STOP_TRAIN, LIMIT_TRAIN, NONE

YAML Configuration

Environments can be configured via YAML files passed with --config:

env:
  enabled_toolsets: ["terminal", "file"]
  max_agent_turns: 60
  max_token_length: 32000
  agent_temperature: 0.8
  terminal_backend: "modal"
  terminal_timeout: 300
  dataset_name: "NousResearch/terminal-bench-2"
  tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B"
  use_wandb: true
  wandb_name: "my-benchmark"

openai:
  base_url: "https://openrouter.ai/api/v1"
  model_name: "anthropic/claude-sonnet-4.6"
  server_type: "openai"
  health_check: false

YAML values override config_init() defaults. CLI arguments override YAML values:

python my_env.py evaluate \
    --config my_config.yaml \
    --openai.model_name anthropic/claude-opus-4.6  # overrides YAML

Prerequisites

For all environments

• Python >= 3.11
• atroposlib: pip install git+https://github.com/NousResearch/atropos.git
• An LLM API key (OpenRouter, OpenAI, or self-hosted VLLM/SGLang)

For Modal-sandboxed benchmarks (TB2, TBLite)

• Modal account and CLI: pip install "hermes-agent[modal]"
• MODAL_TOKEN_ID and MODAL_TOKEN_SECRET environment variables

For YC-Bench

• pip install "hermes-agent[yc-bench]" (installs the yc-bench CLI + SQLAlchemy)
• No Modal needed — runs with local terminal backend

For RL training

• TINKER_API_KEY — API key for the Tinker training service
• WANDB_API_KEY — for Weights & Biases metrics tracking
• The tinker-atropos submodule (at tinker-atropos/ in the repo)

See RL Training for the agent-driven RL workflow.

Directory Structure

environments/
├── hermes_base_env.py          # Abstract base class (HermesAgentBaseEnv)
├── agent_loop.py               # Multi-turn agent engine (HermesAgentLoop)
├── tool_context.py             # Per-rollout tool access for reward functions
├── patches.py                  # Async-safety patches for Modal backend
│
├── tool_call_parsers/          # Phase 2 client-side parsers
│   ├── hermes_parser.py        # Hermes/ChatML <tool_call> format
│   ├── mistral_parser.py       # Mistral [TOOL_CALLS] format
│   ├── llama_parser.py         # Llama 3 JSON tool calling
│   ├── qwen_parser.py          # Qwen format
│   ├── deepseek_v3_parser.py   # DeepSeek V3 format
│   └── ...                     # + kimi_k2, longcat, glm45/47, etc.
│
├── terminal_test_env/          # Stack validation (inline tasks)
├── hermes_swe_env/             # SWE-bench training environment
│
└── benchmarks/                 # Evaluation benchmarks
    ├── terminalbench_2/        # 89 terminal tasks, Modal sandboxes
    ├── tblite/                 # 100 calibrated tasks (fast TB2 proxy)
    └── yc_bench/               # Long-horizon strategic benchmark