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Share agent traces across a team

What each agent was asked to do, the steps and tool calls it took, how it ended and what went wrong. The agent writes its own summary with add-only access, and the team reads it.

Use cases All use cases

For builders

How to connect ChatGPT and Claude to the same data How to give an AI agent safe, scoped access to data A database for vibe-coded apps, with access built in

AI and engineering

Keep a daily work log Keep a prompt library for Claude, ChatGPT and Cursor Let agents build on each other’s findings Run a task queue for your agents Share agent traces across a team Share research notes Track eval results over time Start an experiment tracker Keep a model training log Keep feature flags and config agents can read Keep bug reproductions and test cases Record deploy and release history Keep an inventory of API keys and services Start a dataset registry Collect standup notes from assistants Keep an incident log Log benchmarks and latency measurements Track GPU and compute spend Start a labeling and annotation queue

Personal

Keep a side-project idea backlog Start a habit tracker Start a reading log Track what you’re learning Track subscriptions Start a budget tracker Start a movie and TV tracker Start a home inventory Keep a plant care log Keep a car maintenance log Plan your travel Keep a tasting journal Track a collection Keep notes on the people you know Track job applications Keep a vocabulary list Track important documents Keep a food log Start a training log Keep a log of your own numbers Keep a schedule of what is due and when to refill Start a recipe collection Keep a list of things to buy Share a pet care log Keep a daily journal Keep gift ideas and birthdays

Business

Track consulting time Track freelance clients and invoices Track fundraising Start a lightweight issue tracker Back an internal tool Store a customer-facing app’s data Plan a content calendar Collect customer feedback and feature requests Manage event attendees and sponsors Report from many sources Start a customer list Track sales deals Keep a customer support log Track hiring Start an employee directory and onboarding checklist Track inventory Track orders and suppliers Start an asset and equipment register Track partners and resellers Track contracts and renewal dates Keep a price list and build quotes

Collaborative

Split expenses Share a research reading list Plan an open-source project roadmap Build a shared world-building bible Plan a house renovation Start a shared household list Share a household chores rota Keep a family schedule Track kids’ activities Plan a trip with friends Plan a wedding or party Run a book club Run a sports team or club Run volunteer and community sign-ups Run a student group project Start a neighborhood tool-lending list Share a weekly meal plan Write babysitter and caregiver handover notes Plan a shift schedule Plan a carpool rota Share a packing list Coordinate help for a parent

An example

@ADoor Save this trace: the refactor agent changed 14 files to move auth into middleware, ran tests twice, the second run passed. It skipped the docs folder.

ADoor Added 1 entry to Agent runs and 6 to Steps

Agent runs

AgentTaskOutcomeSteps
refactor-botAuth into middlewareTests pass6
triage-botLabel 40 issues3 need review4

What you can ask later

  • “What did the refactor agent do yesterday, and which files did it touch?”
  • “Which runs failed this week, and at which step?”
  • “How often does triage-bot need human review?”
  • “Find every run that called the deploy tool.”
  • “Chart agent runs per day by outcome.”

Who can see what

  • Each agent writes its own traces with add-only access, and can’t read other agents’ traces unless you allow it.
  • Engineers read every trace.
  • A review agent reads failed runs and adds comments, without editing the traces.
  • Every agent gets its own access, never more than the person who connected it.

How it compares with LangSmith, Langfuse or logs

LangSmith and Langfuse win at capturing every model call automatically, with token counts, latency and a trace viewer.

ADoor holds the summarized record a team reads: what was attempted, what happened and what was decided, written by the agent itself and queryable with plain SQL.

How to start

  1. Open an assistant like ChatGPT, Claude, Grok or Cursor and connect ADoor through MCP.
  2. Tell it: “Set up agent traces: Agent runs (agent, task, started, outcome, summary) and Steps (run, order, action, tool, result).”
  3. Give each agent add-only access to write its own runs and steps.

Get your account

Questions people ask

Can an agent edit its own trace afterwards?

Not if you give it add-only access. The history records any change that is made.

How much detail should go in?

A summary per step works well. Keep raw model logs in your observability tool.

What does it cost?

Nothing on the free plan, which holds 3 tables and 100 rows. Pro is $10 a month per workspace, with 1 GB of storage and no per-GB charges.

Which assistants can I use?

Any of ChatGPT, Claude, Grok or Cursor, connected through MCP. Each one asks for your approval, and you decide which lists it can use.

Updated October 20, 2026.

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