lionagi is a governed multi-agent orchestration framework. Build agent workflows in Python with typed, inspectable state, or run them straight from the terminal with the li CLI: single agents, parallel fan-outs, and DAG flows where an orchestrator plans
@ohdearquantSystematic debug workflow: research → orchestrate agents → escalate. Suggest when: stuck after 2-3 attempts, unfamiliar tooling,…
@ohdearquantFan one task out to multiple independent lionagi workers through `fanout.submit`, optionally synthesizing their answers. Use when…
@ohdearquantPlan and run a dependency-aware lionagi DAG through `flow.submit`, including dry runs and reactive follow-up capacity. Use when…
@ohdearquantPlan and execute multi-agent workflows through lionagi's orchestration MCP tool: flow.submit (DAG pipelines), fanout.submit…
@ohdearquantRun an existing lionagi playbook through the orchestration MCP server and retrieve its background result. Use when a saved…
@ohdearquantAuthor lionagi playbooks — reusable YAML workflow templates that define parametric agent tasks. Playbooks live at…
@ohdearquantAdversarial quality gate that hunts for flaws and returns a formal verdict. Use before shipping an artifact, when a result looks…
@ohdearquantDecomposes a goal into a multi-agent DAG, runs it, and synthesizes the results. Use when work needs several agents in a…
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