Sprout

🌱 Sprout

A self-improving, multi-agent AI system that decomposes tasks and delegates to specialist agents. Sprout is an autonomous coding assistant built on a recursive agent architecture — where every action is a goal-directed delegation to a subagent.

Overview

Sprout operates on a simple but powerful loop:

Perceive → Recall → Plan → Act → Verify ─── loop
                                  │
                                  └──→ Learn (async)

A **root agent** receives your goal, breaks it into subgoals, and delegates each to a specialist. Specialists can be orchestrators themselves (delegating further) or leaf workers that execute primitives directly. The system learns from its mistakes — stumbles, failures, and inefficiencies are detected and fed into an asynchronous learning pipeline that mutates the agent genome over time.

Key Concepts

• Agents all the way down — Every action is a delegation to a subagent. The root never executes tools directly.
• Genome — A git-backed knowledge base storing agent definitions, memories, routing rules, and learned behaviors. Every mutation is committed for full auditability and rollback.
• Primitives — The immutable kernel operations: read_file, write_file, edit_file, apply_patch, exec, grep, glob, fetch. Only leaf agents execute these.
• Learn signals — Failures, timeouts, retries, and inefficiencies are detected automatically. When patterns emerge (≥3 repeated stumbles, ≥2 unresolved errors), the system triggers genome mutations to improve future performance.
• Multi-provider LLM — First-class support for Anthropic (Claude), OpenAI (GPT/o-series), OpenAI Codex, and Google (Gemini) with a unified adapter interface.

Architecture

root (best model — orchestrator)
  ├─ utility/reader          (fast — read-only file discovery)
  ├─ utility/editor          (balanced — file editing & creation)
  ├─ utility/command-runner  (fast — shell command execution)
  ├─ utility/