sub-pi-agent
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- AlicenseAqualityAmaintenanceEnables delegating coding tasks to a pi agent as a steerable background worker, allowing mid-run redirection, follow-ups, and keeping the delegate's context isolated from your main conversation.1225 npm19MIT
- AlicenseCqualityBmaintenanceEnables MCP hosts to delegate coding tasks to Pi CLI as a programmable sub-agent with session tracking and process management.74MIT
- FlicenseNot gradedqualityBmaintenanceEnables MCP hosts like Claude Code and Codex to spawn, manage, and interact with persistent, reusable Pi coding-agent sessions, supporting task dispatch, status checks, and session lifecycle control.2 npm-
- AlicenseNot gradedqualityCmaintenanceEnables an assistant to run pi coding-agent tasks in the background: dispatching a task, polling its status and tool trace, reading the final result, and cancelling it when no longer needed, all without pulling the delegate's context into its own conversation.MIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP clients such as Claude Code and Codex to delegate background Pi Coding Agent tasks, inspect and steer running work, and recover saved checkpoints after server restarts.291 npm12MIT
- AlicenseNot gradedqualityBmaintenanceEnables delegating tasks to agents you configure yourself through OpenAI-compatible Chat Completions backends, with automatic or explicit routing, synchronous calls, persistent asynchronous jobs, and multi-step reviewed workflows. It returns proposals and analyses only, leaving file changes, command execution, and approvals under the MCP client's control.MIT
TDQS
Scored across 11 tools
Most tools have clearly distinct purposes (spawn vs continue vs poll vs get_result vs terminate vs release). However, pi_steer_agent, pi_followup_agent, and pi_continue_agent all send messages to agents with subtly different timing; while descriptions clarify, an agent could still momentarily confuse which to use.
All tool names use a consistent snake_case pi_ prefix followed by verb_noun (pi_spawn_agent, pi_list_agents, pi_get_result, etc.). The pattern is predictable and readable throughout.
With 11 tools, the set is well-scoped for asynchronous agent orchestration. Each tool covers a distinct lifecycle operation (spawn, list, poll, wait, continue, steer, followup, terminate, release, get result, list models) without redundancy.
The surface covers the full agent lifecycle: creation, monitoring, interaction, termination, and release, plus model listing. The only minor gap is the absence of a permanent delete operation—release intentionally retains agents, so cleanup beyond unloading is not possible.