langconfig-builder

LangConfig provides drag-and-drop visual workflow design using LangGraph nodes to build stateful multi-agent systems. You configure agents, define their tools and models, connect them with conditional logic, and orchestrate complex workflows—eliminating the need to write agent coordination code by hand.

Yes. LangConfig lets you configure agents with LLM model selection, system prompts, native tools, memory, pgvector-based RAG for knowledge retrieval, temperature, max_tokens, timeouts, and retry logic—all through visual configuration fields without manual code.

LangConfig supports deep agent hierarchies where specialized subagents delegate tasks to child agents. You visually define parent-child agent relationships, configure each agent's role and tools, and use conditional routing to direct queries to the appropriate subagent for task specialization.

Design a workflow in LangConfig with a classifier agent that routes incoming queries, connected to specialized agents like knowledge retrieval or human handoff. Configure each agent's tools and logic, add conditional branching between agents, and deploy—reducing development time from weeks to days.

Yes. LangConfig provides real-time monitoring and debugging capabilities so you can observe agent execution, tool calls, memory state, and RAG retrieval as workflows run, helping you validate multi-agent behavior and troubleshoot coordination issues.

LangConfig is designed for research, coding, data analysis, and content generation scenarios. Build agents with specialized tools and knowledge bases, orchestrate multi-step workflows across these domains, and manage complex agent hierarchies for each use case.