A durable multi-agent orchestrator for software development with explicit run graphs, checkpoint/resume capabilities, and project memory exposed through MCP resources and tools. It enables coordinated agent workflows for coding, review, repair, CI, and approval with SQLite-backed memory retrieval and pluggable research backends.
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.
Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.
Enables AI assistants to orchestrate multi-step workflows by converting natural language into executable plans, coordinating tools, agents, and services via the Model Context Protocol.
Durable, agent-native AI runtime with native MCP client and server support. Rust core for performance with Python SDK for workflow authoring. Features graph-based workflows, durable execution, A2A protocol support, and multi-agent coordination.
