superagentx
Build multi-agent workflows with approvals, persistent state, and audit trails.
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- Updated Jun 24, 2026
Build multi-agent workflows with approvals, persistent state, and audit trails.
SuperAgentX coordinates modular agents that can carry out multi-step workflows across browsers, APIs, databases, conventional tools, and MCP integrations. Sensitive actions can pause for human approval, while execution state, memory, and audit logs are retained. It is aimed at applications that need agents to take actions without giving up governance or operator control.
Resource types
Use cases
Protocols & integrations
Route each conversation to the most suitable specialized AI agent.
Build collaborative multi-agent systems in Python or TypeScript.
Capabilities
Public GitHub facts last synced Jul 10, 2026.
Combine interchangeable models, data sources, and tools in LLM applications.
Trace, evaluate, monitor, and manage LLM, agent, and ML workflows.