A local, auditable multi-model workflow engine that lets you define YAML graphs for orchestrating LLM agents across vendors, with MCP tools for validation, dry-runs, execution, and human approval, all fully observable in a local web interface.
Enables local-first LLM orchestration with persistent memory, knowledge management, routing, swarm patterns, API probing, tests, automation planning, and plugin discovery via a stdio MCP server, using SQLite for offline storage.
Autonomous spec-to-product coding-agent CLI. Its MCP server exposes 34 tools over stdio: project state and task-queue ops, memory retrieve/store, code search, quality and verification reports, repo hotspots/co-changes, and structured findings/learnings.
Self-hosted, source-available AI workflow automation platform. Build multi-agent, RAG, and tool-using pipelines on a visual canvas and publish any workflow as an MCP server (stdio/SSE/Streamable HTTP). Also an MCP client via the agent node.
MCP server that enables running and managing file-based AI Skills, Agents, and Flow workflows as DAGs, with tools for listing, executing, and resuming tasks via stdio JSON-RPC.
