Hi OpenAI team and community, I’m using Codex quite actively for software development workflows, especially with GitHub issues, repo analysis, implementation tasks, testing, and deployment preparation. I can currently see the remaining percentage/credits of my Codex plan, but I cannot find a clear way in the Codex web/app interface to see how many tokens were consumed by a specific Codex conversation, task, or run. From the documentation, I understand that Codex usage is now based on token usage: input tokens, cached input tokens, and output tokens. I also see that Codex Settings → Usage shows remaining credit/limits, and that in Codex CLI the /status command can show current token usage during a session. However, what I would like to know is: Is there any way in Codex web/app to see token usage for a specific conversation, task, or run? Can I see how much of the context window is being used in a current Codex session? Can I see whether a task is becoming expensive because Codex has read too many files, kept too much previous conversation context, or generated too much output? Is there a recommended workflow to decide when to continue in the same Codex conversation versus starting a fresh one? Is there any log, export, or debug view that shows: input tokens, cached input tokens, output tokens, total credits consumed, files read, tool calls, and context size per task? The reason this matters is that many of us are trying to build disciplined agentic development workflows. For example, I’m trying to use one Codex conversation per GitHub issue, with small verifiable tasks, tests, and evidence comments. But without clear per-task token visibility, it’s difficult to know whether a conversation is becoming inefficient or whether the prompt/context should be reduced. A useful improvement would be something like a “Task usage breakdown” panel in Codex showing: Task / conversation usage: - Input tokens - Cached input tokens - Output tokens - Estimated credits consumed - Context window used - Files read - Largest context contributors - Recommendation: continue / start fresh / summarize context Even a rough estimate would be very helpful. The current remaining-credit percentage is useful, but it does not help me understand which conversations or workflows are consuming the most tokens. Is this currently possible somewhere, or is it only available through CLI/API/enterprise analytics? Thanks.

First off I am not an OpenAI employee. I used ChatGPT and gave it some information and while I knew it would not be able to break a Codex sesssion or usage back to eact token count, it could give you a clearer picture than tokens were here now they are gone. Here is part of the prompt used read: https://help.openai.com/en/articles/20001106-codex-rate-card read: https://community.openai.com/t/codex-rate-limits-discussion-thread/1378553/370 read: https://openai.com/index/unrolling-the-codex-agent-loop/ read: https://developers.openai.com/codex/pricing#what-are-the-usage-limits-for-my-plan The .codex directory should hold the details that can be used with the read documents to stitch together cost breakdowns. You can add to the prompt but hopefully this will allow you to explore more on your own. HTH Note: I use Codex with VS Code via the Codex extension which creates and updates the .codex directory.

Most of your questions can be answered by inspecting and studying the session details inside the codex home folder ~/.codex/sessions.In this folder you have details about each of your threads. If you don’t feel like manually exploring these files, you can ask an agent to do it for you. About your 4th question: In my case, I use to start new threads when the next context that I’ll work is in another direction. It is also good to keep things organized. Also, one of the reasons that sub-agents is useful is to prevent context rot issues. Actually you can develop a custom ui dashboard to render data from the sessions files inside that folder. I am doing it, but it’s still in progress.

It is possible to peek the local cache for token usage. Basically, there is a cache folder where codex sessions are stored. It is either under your user folder on win/mac or in the user folder under WSL if you are using it. Here is a small python snippet to help you get started, that will list the latest 5 thread IDs and the usage progression for the latest thread ID (or to peek an specific ID): #!/usr/bin/env python3 import json, os, re, sys from datetime import datetime from pathlib import Path thread_id = "" #inform a thread id to peek specific usage UUID = re.compile(r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}", re.I) def session_root(): return Path(os.environ.get("CODEX_HOME", Path.home() / ".codex")) / "sessions" def read_jsonl(path): with path.open(encoding="utf-8") as f: for line in f: try: yield json.loads(line) except json.JSONDecodeError: pass def id_from_file(path): for obj in read_jsonl(path): if isinstance(obj.get("id"), str): return obj["id"] payload = obj.get("payload") if obj.get("type") == "session_meta" and isinstance(payload, dict) and isinstance(payload.get("id"), str): return payload["id"] match = UUID.search(path.stem) return match.group(0) if match else path.stem def usage_item(obj): payload = obj.get("payload") return obj.get("type") == "event_msg" and isinstance(payload, dict) and payload.get("type") == "token_count" def main(): root = session_root() files = sorted(root.rglob("*.jsonl"), key=lambda p: p.stat().st_mtime, reverse=True) threads = [(id_from_file(p), p) for p in files] target = thread_id.strip() if not target: print("latest 5 threads:", file=sys.stderr) for tid, path in threads[:5]: ts = datetime.fromtimestamp(path.stat().st_mtime).isoformat(timespec="seconds") print(f"{ts} {tid} {path}", file=sys.stderr) target = threads[0][0] if threads else None if not target: raise SystemExit(f"no .jsonl sessions found under {root}") matches = [p for tid, p in threads if tid == target or tid.startswith(target) or p.stem.startswith(target)] if not matches: raise SystemExit(f"thread not found: {target}") print(f"\nusage items for {id_from_file(matches[0])}:", file=sys.stderr) for obj in read_jsonl(matches[0]): if usage_item(obj): print(json.dumps(obj, indent=2, ensure_ascii=False)) if __name__ == "__main__": main() It looks similar to this: { "timestamp": "2026-01-01T00:00:00.000Z", "type": "event_msg", "payload": { "type": "token_count", "info": { "total_token_usage": { "input_tokens": 120000, "cached_input_tokens": 100000, "output_tokens": 2500, "reasoning_output_tokens": 800, "total_tokens": 122500 }, "last_token_usage": { "input_tokens": 15000, "cached_input_tokens": 14000, "output_tokens": 150, "reasoning_output_tokens": 30, "total_tokens": 15150 }, "model_context_window": 200000 }, "rate_limits": { "limit_id": "example_limit_id", "limit_name": null, "primary": { "used_percent": 0.0, "window_minutes": 300, "resets_at": 1700000000 }, "secondary": { "used_percent": 1.0, "window_minutes": 10080, "resets_at": 1700600000 }, "credits": null, "plan_type": "example_plan", "rate_limit_reached_type": null } } }

Thanks everyone for the replies and pointers. After digging a bit deeper, I found something very useful that I had not fully understood before: the Codex session files stored under the .codex/sessions directory. That was especially valuable. The JSONL session files contain much more useful context than I expected for auditing agentic development workflows. In my case, they included information such as: model used; context window; input tokens; cached input tokens; output tokens; reasoning output tokens; total tokens; token usage per turn; tool calls; shell commands; session/task events; working directory / repo context. This does not fully solve the “official per-task billing breakdown in the Codex web UI” question, and it still does not directly give me the exact final billing/credit consumption in the same way a native usage panel would. But it is already extremely useful for building local observability around Codex workflows. For context, my current workflow is something like this: I use ChatGPT to validate the problem, product direction, task scope, GitHub issue content, acceptance criteria, model choice, and whether a task should be handled by ChatGPT or Codex. I create or refine GitHub issues based on that analysis. Codex then works against those issues in a more bounded way. After Codex completes a task, I review the implementation, tests, evidence, and whether the task should be continued, split, summarized, or started fresh. The .codex/sessions files now look like a very useful missing layer in that workflow. They could help audit not only token usage, but also agent quality, task complexity, tool-call behavior, context pressure, and whether my prompts/issues are too broad. One idea I am considering is having an agent periodically upload selected .codex/sessions JSONL files to the Drive folder linked to my ChatGPT project, so they can become dynamic project sources for later debugging, instruction improvement, and workflow analysis. Of course, this would need careful filtering/redaction, since those files may contain local paths, prompts, code snippets, tool outputs, or other sensitive context. I am also thinking out loud about maintaining a living index-session.md file that links Codex session JSONL files to the corresponding GitHub issues, PRs, branches, commits, and code changes. Something like: Issue #17 → Codex session file → branch → PR → commit → tests run → outcome Issue #18 → Codex session file → failed attempt → reason → improved prompt/instruction Issue #19 → Codex session file → context too large → recommendation to split task I am not completely sure yet whether this is the right abstraction, but it feels promising. It could turn Codex sessions into an auditable trail of agent work, not just hidden logs. Based on this, I started building a small local audit tool for my repos that imports these JSONL files and generates a task/session report with things like: model used; total input/output/cached tokens; cache ratio; max observed context usage; number of tool calls; estimated credits using a local rate card; recommendation: continue, summarize, start fresh, or split the task. For disciplined agentic software engineering, this is actually a very promising workaround. It lets me evaluate whether a Codex conversation is becoming too large, whether a task is too broad, whether too many files/tool calls are involved, and whether I should continue in the same session or start a fresh one. So the main discovery for me was: The .codex/sessions JSONL files are a very useful source of operational telemetry for auditing agent quality, context usage, task design, and workflow efficiency. I still think a native Codex “Task Usage Breakdown” panel would be extremely valuable, especially in the web/app UI. But these local session logs already provide a strong foundation for building custom audit workflows around GitHub issues, PRs, prompts, and agent performance.

[image] luisdemiguel: building a small local audit tool FYI I know several programers who have done the same once they learn of the /.codex/sessions directory and folders. The first thing even before doing any GUI or such is to create a daemon to backup the files and changes as there is no guarentee they will not be deleted, have their structure changed, etc.

If you need to hardwire things to that level, I’d recommend using the app-server in the Codex SDK. It will give much more information you don’t see in the IDE/App and some that don’t even go to the .jsonl, plus you can control the model, get usage per request, etc.

I find this github repository and could be interesting, for this codex .sessions files [image] GitHub - Pratiyush/llm-wiki: LLM-powered knowledge base from your Claude Code,... LLM-powered knowledge base from your Claude Code, Codex CLI, Copilot, Cursor & Gemini sessions. Karpathy's LLM Wiki pattern — implemented and shipped.

I may have something for this. I discovered recently that the Codex Security scan reports token usage upon completion so i used codex to help me design a skill that can report token telemetry from internal tools on a per message basis [image]

Something I find helpful is setting goals for all my tasks All my skills incorporate the relatively new goal function in some way When a workflow uses update_goal to mark a goal complete, the response includes the updated goal object. Skills can read that returned object immediately and report token usage if goal.tokensUsed exists. Example rule: After any update_goal call with status: complete, inspect the returned goal object. If goal.tokensUsed exists: - Report: Tokens used: If goal.tokenBudget also exists: - Report: Tokens used: / If goal.timeUsedSeconds > 0: - Also report elapsed time in a concise human-readable form. Do not require tokenBudget to exist before reporting tokensUsed. The important bit is that tokensUsed by itself is enough. Budgeted goals show used/budget; unbudgeted goals still show usage. Relevant: developers.openai(dot)com/cookbook/examples/codex/using_goals_in_codex

How can we clearly see token usage per Codex conversation/task, especially in Codex web?

Codex

EricGT May 21, 2026, 7:25pm 5

Have you considered using hooks?

Yes, I am trying to get you to do the work while I just drop suggestions.

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How can we clearly see token usage per Codex conversation/task, especially in Codex web?

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