Reading
Summoner is an AI Agent orchestration framework that adds a process layer to AI coding workflows via checkpoint mechanisms, post-task reviews, and memory chain features, addressing issues like agents skipping diagnosis, forgetting reviews, and repeating mistakes.
Section 01
Summoner is an AI Agent orchestration framework that adds a process layer to AI coding workflows via checkpoint mechanisms, post-task reviews, and memory chain features, addressing issues like agents skipping diagnosis, forgetting reviews, and repeating mistakes.
Section 02
Section 03
Current AI coding agents (e.g., Claude Code, Cursor, Copilot) commonly face three core issues in practical use: skipping diagnosis, forgetting reviews, and repeating mistakes. When agents encounter errors, they often try to fix them directly without in-depth analysis of the root cause; after fixing, they rarely conduct systematic code reviews; and the same error patterns recur across different sessions.
Summoner's design philosophy targets these pain points—it is not a tool to replace existing agents, but rather adds a process layer to AI workflows. Through structured phase management and memory mechanisms, it makes agents' work more reliable and traceable.
Section 04
Summoner positions itself as the "Makefile for AI workflows". Just as Makefile defines the goals and dependencies of software building, Summoner defines the phases and flow rules of AI tasks. Its core features include:
The project uses the MIT license, with a code size of only about 7.5MB (63 files, 19 core files, compiled hooks), and zero external dependencies (only requires Go + SQLite3).
Section 05
Summoner provides five main commands covering full scenarios from bug fixing to feature development:
Section 06
This is the most commonly used command, executing a complete five-phase repair process:
Section 07
A complete new feature development process: Define → Plan → Implement → Test → Review.
Section 08
Fan-out review by 1-3 reviewers → Merge decision → Go/No-Go judgment.
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