Autonomous vision-language agent that plays Survival 3D on YouTube Playables — screenshots the game, reads goals from the UI, plans movement, and executes keyboard actions in a loop.
- Closed-loop agent on YouTube Playables: capture screenshot → read goal, progress, and inventory → plan 1–2 compass moves → send key presses → persist memory for the next cycle.
- GoalMakerLLM reads the game UI and picks the fastest heading; PlannerLLM turns that into concrete NORTH/SOUTH/EAST/WEST steps — each component can use OpenAI or local Ollama vision models.
- MemoryManager keeps JSON knowledge of mechanics, discoveries, and failed strategies; RunLogger saves per-cycle screenshots and run.json under outputs/.
- Playwright + Chrome with a persistent browser profile for one-time YouTube login; uv-based Python 3.11+ setup with configurable GAME_URL and model providers.
Black-box UI defect discovery for locally hosted web apps — Playwright exploration, LLM-assisted goals and verification, and an HTML findings report.
- Four-phase pipeline: BFS exploration in a real browser → LLM converts paths into plain-English test goals → parallel goal-driven executors → per-step LLM verifier that emits structured findings.
- Executors resolve instructions at runtime instead of replaying raw trajectories — app-agnostic design with Docker lifecycle management, DOM heuristics, and crash-detection fallbacks when the LLM is off.
- Single CLI (run_harness.py) with composable modes — full pipeline, planner-only, run-goals, multi-pass rollout, and report-only — default target is a buggy Memos Docker image, but the harness generalizes to any local web app.
Skill and installer that produce a rich, self-contained HTML report explaining any Prime Intellect verifiers environment.
- Walks the environment source and emits environment_overview.html—dataset and task samples, every reward/metric with weights and scoring range, rollout logic from prompt to final score, and a full config reference.
- Ships as an npm CLI (npx understanding-prime-env) with interactive setup plus flags for Claude Code, Cursor, Windsurf, GitHub Copilot, Zed, or all tools in one go.
- Meant to be used from inside a verifiers env folder so you can ask your assistant to “explain this environment” and get an overview styled to match the Prime Intellect platform.
RL environment where an LLM agent negotiates prices against an LLM-powered seller using real marketplace listings.
- Three difficulty levels (Easy / Medium / Hard) with varying zones of possible agreement — from a $480 ZOPA down to no ZOPA, testing walk-away discipline.
- Six-component reward system on a [−1, 1] scale: surplus capture, walk-away correctness, output compliance, closing speed, opening offer quality, and concession smoothness.
- Walk-away penalty weighted 5× — trains the agent to value discipline over forcing a bad deal.
- Pure Python arithmetic grader ensures reproducibility; stochastic LLM sampling on both sides creates diverse training signal across episodes.
- Deployable on Hugging Face Spaces, Docker, or locally via uv; ships with a browser playground showing real-time reward breakdowns.
Ice-sliding multi-player maze environment on OpenEnv for benchmarking planning and coordination in RL agents.
- Ice-sliding mechanics — agents slide until hitting a wall or another player, requiring multi-step lookahead over reactive movement.
- Simultaneous multi-player movement; solved only when every player reaches an exit cell in the same phase.
- Reward shaping penalises repeated actions (−1), reversals, and revisited board states scaled by prior visit count.
- Standard OpenEnv reset()/step() API — compatible with any RL training loop without environment-specific wrappers.
- Includes dataset validation tooling and GIF rendering from recorded rollouts for debugging agent trajectories.
CrewAI-powered bot that auto-syncs multilingual documentation across feature branches via a Git pre-push hook.
- 2nd Runner-Up at the CrewAI Fall Agentic AI Challenge.
- Detects English documentation changes and auto-generates synchronised translations (Korean, Brazilian Portuguese) with no manual intervention.
- Runs as a Git pre-push hook — fires before any feature branch push, prompting the developer to sync docs before code lands.
- Agent-based architecture with specialised CrewAI agents for change detection, translation, and cross-branch synchronisation.
- Minimal setup: Python 3.12+, uv, and an OpenAI key — clone + uv sync + one hook registration.
Full-stack interactive console for demonstrating and chatting with Reinforcement Learning models and agentic systems.
- Chat interface for real-time interaction with RL models, backed by smolagents for agent-based reasoning and step-by-step trace display.
- FastAPI backend (Python 3.12+) paired with a Next.js 15 / React 19 frontend — clean separation of model logic and UI.
- Integrates Hugging Face datasets directly; response caching via JSON files keeps repeated queries fast.
- Docker-first production deployment — single container exposes the full stack on port 7860, ready for Hugging Face Spaces.
- Environment-driven config (HF token, model name) makes swapping the underlying model a one-line change.