Your AI coding agent just rewrote 23 files. The build is broken, and git log shows nothing useful. This is the new debugging hell — and it is exactly what AgentTM (Agent Time Machine) was built to fix. Launched on Product Hunt on November 3 by developer Jyotishmoy Deka, AgentTM is a local, open-source tool that wraps your Codex sessions and records every file change as a replayable, restorable checkpoint — without touching your active workspace.
What AgentTM Does
When you run a Codex session through AgentTM, it captures sanitized session events into a SQLite ledger and takes SHA-256 content-addressed file snapshots at every operation boundary. The result is a complete, immutable timeline of everything the agent touched.
When something breaks — and it will — you open the local React UI, scroll back to the last known-good state, and restore those files directly into an isolated Git worktree. Your active workspace is never modified. You cannot make things worse by looking at what happened.
# Install AgentTM
go install github.com/Jyotishmoy12/AgentTM@latest
# Wrap your Codex session
agenttm run codex "refactor the auth module"
# Open the UI to inspect history
agenttm uiAll data lives in a .agenttm/ directory inside your project. No API keys. No cloud sync. No billing surprises.
The Problem Is Real and Getting Worse
The numbers from a recent survey make the case bluntly. Developers now spend 16.9 hours per week debugging versus 9.8 hours writing code — debugging accounts for 42% of the average work week. According to the survey, 45.2% of developers say debugging AI-generated code takes longer than writing it themselves, and 93% of engineering organizations report at least one bug that was misdiagnosed because of an AI hallucination. One developer publicly disclosed burning $6,000 in Claude credits overnight from an uncontrolled agent loop.
The issue is structural. AI coding agents modify files non-deterministically, run terminal commands silently, and can break things across a repository in ways that do not map to any git commit. Standard observability tooling was not designed for this failure mode.
Local-First Is the Differentiator
The existing AI agent observability category — AgentOps, LangSmith, Braintrust, Datadog LLM Observability — is almost entirely cloud-based. All of them require an account, API keys, and data leaving your machine. That is a real concern for teams working with proprietary code, or anyone who has experienced a cloud billing incident.
AgentTM runs entirely locally. Here is how it compares to the alternatives:
| Tool | Local? | Free? | AI Agent Focus? | File Time-Travel? |
|---|---|---|---|---|
| AgentTM | Yes | Yes | Yes | Yes |
| AgentOps | No | No | Yes | Partial |
| LangSmith | No | Freemium | Yes | No |
| Git only | Yes | Yes | No | No (commits only) |
It is the only tool in this category that is simultaneously local, free, and built specifically for AI coding agent session replay.
Built Fast, Iterating Faster
The technical foundation — Go backend, React UI, Wails desktop framework — runs as a native desktop app on macOS and Windows. Version 0.4.1 shipped on November 9, just six days after the initial Product Hunt launch. That release cadence suggests active development and a maintainer who is responding to feedback quickly.
The tool’s current scope is Codex-focused, but the architecture is generic enough that future support for Claude Code, Copilot Workspace, and other coding agents is a logical next step.
This Pattern Is the Future
AI agent adoption is accelerating fast. Uber reports 84% of engineers are now agentic. The bottleneck has shifted from writing code to understanding and debugging what agents did. The developer role has quietly become 70% reviewer and reviser.
What AgentTM establishes — checkpoint-based session recording with isolated worktree restoration — will likely become the standard audit trail for agentic workflows. Expect larger tools to build this pattern in. For now, the open-source version is worth pulling today, especially if you are running Codex sessions on any codebase you care about.
