Shepherd

Shepherd is a Python substrate for LLM agents that records execution as a reversible, Git-like trace, enabling meta-agents to observe, fork, and revert agent runs.

Shepherd acts as a Python substrate where agent execution is a first-class object that meta-agents can inspect and manipulate. It logs every model action, tool call, and environment change as a structured event within a Git-like execution trace. This allows developers to revert agent states to any previous point and fork execution paths for testing different outcomes. Meta-agents can use these traces to intercept risky actions, repair failed workflows, or optimize long-horizon reinforcement learning. It supports executing agents as plain Python tasks, providing a way to reconstruct and operate over full execution states without requiring ad hoc tooling.

Shepherd at a glance

Best for: Software engineer, Researcher
Runs on: CLI
Pricing: Free
Released: June 2026
Category: AI agent tool
Website: shepherd-agents.ai

Key features

Reversible execution traces for agent runs
Git-like version control for agentic processes
Ability to fork and rewind agent states byte-for-byte
Intercepting of agent actions before environment impact
Support for defining agents as plain Python tasks
Performance optimized state reversion

Use it for

Preventing conflicts among parallel coding agents
Repairing agent workflows via counterfactual optimization
Improving credit assignment in long-horizon agentic reinforcement learning
Creating supervisor meta-agents to manage worker agents

Alternatives to Shepherd

GitAgent — Manage AI agents with familiar git workflows like branching, diffing, and reverting.
MyAgent — It features built-in permission controls, long-term memory, and skill self-evolution.
Agent Cockpit — It checks evidence chains deterministically so broken claims never slip by unnoticed.
Reflexio — It autonomously extracts lessons from interactions to permanently correct agent behavior.
Harness2 — It recursively refines agent execution structures using parallel and sequential search.
BrowserOS — Runs entirely locally and lets AI agents use real user logins to complete tasks.
Sentry — Improves agent reliability and learns reusable lessons from verified recoveries.
Agent Beacon — Retains coding agent knowledge across tools so errors do not repeat.
Raven — Provides a persistent, self-evolving environment for all-domain agent collaboration.
Agent Executor — Declarative runtime for scaling stateful agent workloads with isolation.
Guilds — Runs standing teams of AI agents with deep inspectability.
Webwright — Enables code-based web automation with reusable and rerunnable scripts.

All Shepherd alternatives