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.
go install github.com/Jyotishmoy12/AgentTM@latest
agenttm run codex "refactor the auth module"
agenttm ui
All 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.