Orchestrate iterative refinement loops for autonomous agents using RalphRunner.
npx skills add https://github.com/midsphere-ai/exo --skill exo-ralph
Please help me install this Agent Skill. Skill: exo:ralph Source: https://github.com/midsphere-ai/exo/tree/main/skills/ralph Command: npx skills add https://github.com/midsphere-ai/exo --skill exo-ralph
Builds and orchestrates iterative refinement loops for autonomous agents using Ralph — RalphRunner, enabling a defined 5-phase cycle (Run/Analyze/Learn/Plan/Halt) with support for scorers, reflectors, stop conditions, and streaming events, plus integration points for Swarm via RalphNode.
Provide an Agent, create a RalphRunner via from_agent with scorers and a stop condition, then call run to commence iterative refinement.
High-intent search queries and answers about installing and using this skill.
You can wire an agent's run and stream methods into an iterative refinement loop using a factory function. This creates a runner that manages the 5-phase cycle and applies configured scorers and stop conditions for seamless integration.
Yes, you can embed refined loops into multi-agent swarm workflows using integration nodes. This allows the refinement cycle to operate within a broader swarm architecture while streaming iteration and stop events for observability.
Stop conditions and iteration limits control when the 5-phase cycle halts. You define scoring thresholds and maximum iteration counts to ensure the autonomous agent stops refining once quality goals are achieved.
Scorers evaluate output quality to gate improvements, while reflectors generate feedback for re-prompting during the Learn phase. Both are optional configurable components that enable autonomous agents to self-improve across iterations.
Visibility into refinement progress is achieved through streaming events. The loop emits iteration and stop events during execution, providing real-time observability of the Run, Analyze, Learn, Plan, and Halt phases as they occur.
A runner is required to execute the 5-phase cycle, while scorers, reflectors, and swarm integration nodes are optional. The runner can be created from an existing agent's run and stream methods without additional dependencies.
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