Enables offline AI agent automation with embedded local LLM (Qwen 2.5), sandboxed file operations through AgentFS, and dynamic skill loading. Exposes capabilities via MCP with tri-state safety guards for private, air-gapped environments without network connectivity or API costs.
Enables AI agents to offload mechanical, high-token work to local Ollama models through MCP, with role-based model discovery, batch processing, and file-aware inputs.
An all-in-one MCP server that enables AI agents with local system capabilities like filesystem navigation, command execution, desktop automation, and a continuity memory suite for persistent cross-session project context.
Enables multiple AI models to collaborate under a shared goal, architecture, plan, loops, sandbox, and acceptance criteria via a local-first MCP server.
Enables AI agents to operate a real, private, local Docker-based computer with durable files, terminal access, web research, and a persistent browser or desktop. It supports multiple agent clients via MCP or OpenAPI, with live viewing and human takeover.
Provides AI assistants with a local knowledge base and research library, enabling semantic and full-text retrieval, memory persistence, and multi-agent collaboration via 58 MCP tools.
