⚠️ LICENSE NOTICE ⚠️

IN ORDER TO USE THIS SOFTWARE, YOU MUST READ, UNDERSTAND AND AGREE TO THE GNU AGPL V3 LICENSE INCLUDED IN THIS REPOSITORY.

What is GrimmBot?

GrimmBot is an autonomous AI agent that runs inside a sandboxed Docker container with full desktop and browser control. It learns from its mistakes, builds its own tools, and gets better over time.

Core Features

• 🧠 Self-Learning from Mistakes — When the agent hits an error (blocked command, failed action, wrong approach), it writes a rule to data/adaptation.json so it never repeats the same mistake. Rules are retrieved using keyword matching — only rules relevant to the current task are loaded.
• 🛠️ Custom Tool Creation — If the agent's built-in tools can't handle a task, it writes and registers a new Python tool on the fly. Custom tools persist across sessions in data/custom_tools/.
• 👀 Zero-Token Monitoring: Grimmbot bypasses expensive LLM polling by dynamically writing custom Python tools that monitor changes locally, saving API tokens and requests during long-running observation tasks.
• 💾 Persistent RAG Memory — The agent remembers past tasks and outcomes using TF-IDF semantic search. It retrieves relevant past experiences to inform future actions (data/memory.json).
• ⏰ Task Scheduling — Supports one-time, daily, and interval-based background task scheduling with disk persistence.
• 👍 Feedback & Dataset Creation — Rate agent responses with thumbs up/down and export a JSONL dataset for fine-tuning by pressing the button in the UI. Feedback is restricted to local models only (Ollama, LM Studio) — the buttons are hidden when using cloud providers.
• 🌐 Browser Automation — Full Chromium control via CDP with DOM extraction. The agent reads webpage structure through tagged interactive elements, not screenshots.
• 🔒 Sandboxed Execution — All commands run inside Docker. Domain allowlists, command allowlists, and user approval gates prevent unintended actions.
• 👀 Live Desktop View — Watch the agent work in real-time through the noVNC panel in the web interface.

Architecture

Setup

Prerequisites: Docker and Docker Compose installed.

1. Clone and configure

git clone https://github.com/Grimm67123/GrimmBot.git
cd directoryforthegrimmbotfolder
cp .env.example .env

Edit .env with your model and API key:

# Local model example (Ollama)
LLM_MODEL=ollama/gemma3:4b
OLLAMA_API_BASE=http://host.docker.internal:11434

# Cloud model example (Gemini)
LLM_MODEL=gemini/gemini-2.5-flash
PROVIDER_API_KEY=your-api-key-here

Set your security allowlists:

ALLOWED_DOMAINS=github.com,wikipedia.org
ALLOWED_COMMANDS=ls,cat,echo,grep,find,python3,pip,node,npm,git,curl,wget

2. Start

docker compose up --build

3. Use

Open http://localhost:5000 in your browser in order to use the agent.

Gallery

GrimmBot's webUI at localhost:5000

GrimmBot improves itself by creating a rule that makes it avoid making the same mistake again.

Custom tool creation approval prompt.

Commands

Type these in the chat (no LLM call required):

Portability

All learning is stored in the data/ folder and is portable between machines:

• data/adaptation.json — Self-learned rules. Copy to another instance to transfer learned behaviors.
• data/memory.json — RAG memory (per-profile). Preserves context and knowledge.
• data/custom_tools/ — Agent-created Python tools. Fully portable.
• data/scheduler/ — Scheduled task definitions.
• data/feedback.json — Thumbs up/down feedback data (local models only).

FAQ

Which LLMs work? Any model supported by LiteLLM — Gemini, GPT, Claude, Ollama, LM Studio, Groq, Mistral, and others.

Can I use fully local/offline models? Yes. For example, for Ollama, set LLM_MODEL=ollama/your-model and GrimmBot connects to your host's Ollama via host.docker.internal. No internet needed for the agent itself. Right now, only Ollama, LocalAI, text gen, vLLM and LM Studio are supported.

How does the wormhole work? The wormhole/ folder is shared between your host and the container. Place files there for the agent to access, or retrieve files the agent creates.

What happens when the agent encounters an error? It analyzes the error, writes a rule to adaptation.json to avoid it in the future, and retries. Over time, the agent accumulates rules that make it more reliable.

How do I run tests?

pip install pytest
cd directoryforthegrimmbotfolder
pytest tests/ -v

Contribution Policy

Sorry, this project currently does not accept external pull requests. Bug reports and feature requests via GitHub Issues are welcome.

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