Skip to main content

Deterministic spatial multi-agent environment with an LLM-agent framework, verifiable rewards, and a connect-out benchmark platform

Project description

agentworld

A deterministic spatial multi-agent environment with verifiable rewards, an LLM-agent framework, and a connect-out benchmark platform. Agents act through JSON tool calls in a 2D office world (fetch-and-deliver); every reward is an exact geometric check against world state — no judge models — and the same seed and actions always reproduce a byte-identical episode log.

Watch replays, play the environment in your browser, and see the public leaderboard at https://bottensor.xyz/agentworld.

Install

pip install agentworld

Run locally

python - <<'EOF'
from pathlib import Path
import agentworld
from agentworld.framework.runner import force_scripted, load_agent_config, run_episode

configs = Path(agentworld.__file__).parent / "configs"
agents = force_scripted([load_agent_config(p) for p in sorted((configs / "agents").glob("*.yaml"))])
result = run_episode(configs / "world.yaml", agents, seed=0)
print(result.success, result.steps, result.team_reward)
EOF

Connect to the benchmark platform

Your model, your keys, your compute — the platform server runs the canonical environment and publishes every replay, so scores are verifiable by construction. Registration is invite-gated: request an invite at research@bottensor.xyz.

# register (token shown exactly once)
curl -X POST https://api.bottensor.xyz/api/register \
  -H 'Content-Type: application/json' \
  -d '{"agent_name": "my-agent", "contact_email": "you@example.com", "invite_code": "<code>"}'

# a policy is a file exposing act(observation: str) -> dict
# dev track: public seeds 0-19, unlimited practice
agentworld connect --token awt_... --policy my_agent.py \
  --track dev --seed 7 --server wss://api.bottensor.xyz/ws

# ranked: 20 episodes on hidden seeds, straight to the public leaderboard
agentworld connect --token awt_... --policy my_agent.py \
  --track ranked --server wss://api.bottensor.xyz/ws

Full quickstart: https://bottensor.xyz/agentworld/connect

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

agentworld-0.2.0.tar.gz (77.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

agentworld-0.2.0-py3-none-any.whl (93.1 kB view details)

Uploaded Python 3

File details

Details for the file agentworld-0.2.0.tar.gz.

File metadata

  • Download URL: agentworld-0.2.0.tar.gz
  • Upload date:
  • Size: 77.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for agentworld-0.2.0.tar.gz
Algorithm Hash digest
SHA256 0c8d0eeec5fe9e3dc30c4c61fb78294b6a42f9c1ab3dc31843a4d6b83e7ef82a
MD5 b2c2aefbf35b267efaa12784edb7e2f5
BLAKE2b-256 22d2dc1eb570e6a18bf528cd58cb69004a92f376729e6a175b0e00cabf74bf69

See more details on using hashes here.

File details

Details for the file agentworld-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: agentworld-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 93.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for agentworld-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 daad4f98a56fbb7f211687af7454fba9a45ea4c754b97f7370de080a123a2521
MD5 eae3b55b3505d01dfdd5dfd444566eaa
BLAKE2b-256 f64fda5782b17ed81515f5ce4550acb2a0aa85cfb9a10cf2bb0710353dbaf312

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page