OpenAdapt: your agent decides, OpenAdapt enters it and checks it saved

Name: OpenAdapt: your agent decides, OpenAdapt enters it and checks it saved
Author: OpenAdaptAI

by OpenAdaptAIversion 2.2.0i★ –iRuns on your computer

Registry nameio.github.OpenAdaptAI/openadapt-agent

Install

Overview

Description from the maker (original English)

Your AI agent decides what to enter. OpenAdapt enters it in the app and checks that it saved.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other
You need to fill in: OPENADAPT_BUNDLE_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: OPENADAPT_BUNDLE_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "openadapt-agent": {
      "command": "uvx",
      "args": [
        "openadapt-agent",
        "serve",
        "--bundles"
      ],
      "env": {
        "OPENADAPT_BUNDLE_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add openadapt-agent --env OPENADAPT_BUNDLE_KEY=your-value -- uvx openadapt-agent serve --bundles

Fill in the empty values before running it.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "openadapt-agent": {
      "command": "uvx",
      "args": [
        "openadapt-agent",
        "serve",
        "--bundles"
      ],
      "env": {
        "OPENADAPT_BUNDLE_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "openadapt-agent": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "openadapt-agent",
        "serve",
        "--bundles"
      ],
      "env": {
        "OPENADAPT_BUNDLE_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

OPENADAPT_BUNDLE_KEY secret env
Decryption key for encrypted-at-rest bundles (workflow.json.enc). Omit for plaintext bundles.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account OpenAdaptAI, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32464 servers in this category
Published to the registry more recently than 65% of the other 32464 servers in this category
Basic checks equal to or better than 100% of the other 32464 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is OpenAdapt: your agent decides, OpenAdapt enters it and checks it saved safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: OPENADAPT_BUNDLE_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.2.0) reached the registry 20 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, yontu

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

OpenAdapt Agent

This repository has moved to the OpenAdapt monorepo (packages/agent). It is archived and read-only; open issues and PRs there. The PyPI package name openadapt-agent is unchanged.

Your AI agent decides what to enter. OpenAdapt enters it in the app and checks that it saved.

Use it if your agent reads referrals, intake forms, or authorizations and works out what belongs in the record, but a person still types the result into an app with no usable API. Examples are an electronic medical record (EMR) and a payer portal. This package is the tool your agent calls to do that typing.

A person or your agent shows OpenAdapt the task in the real app. OpenAdapt replays those steps, and by default a run makes no AI model calls.
Before it types, OpenAdapt checks that the right record is open. After it saves, it reads the record back.
Your agent gets one of four outcomes: done and checked, stopped before saving, check the record, or didn't run. When OpenAdapt can't confirm a save, it says so, and a person checks the record.
It runs on your customer's computer, one that can already open the app. Your agent, or a small program acting for it, has to run on that computer too, since it starts OpenAdapt and talks to it directly. If your agent runs in your own cloud, ask us about a hosted route.

Pick one task your agent hands to staff today and talk to us about connecting it. Engineers can try the sandbox first.

Give your agent a program, not a mouse

Your agent doesn't send screenshots to a model and click. It calls a taught workflow as one typed tool, OpenAdapt runs the same steps every time, and by default your agent gets back an outcome and counts, not screens or recorded values.

openadapt-agent serve --mode production --bundles <bundle or directory of bundles>

openadapt-agent emit-skill --out ~/.claude/skills

Try it in three steps runs a sandbox with no account. Serve your own workflows and Write an Agent Skill cover both commands.

What changes for your customer's team

Step	Today	With OpenAdapt
Read the document and decide what to enter	Your agent	Your agent
Open the app and find the right record	Staff	OpenAdapt, which checks that it has the right record before it types
Type the values and save	Staff, retyping your agent's output	OpenAdapt, in the same screens staff use
Confirm the save	Staff, if they look again	OpenAdapt reads the record back before it reports done
Something doesn't match	Staff notice while typing, or someone finds it later	OpenAdapt stops before saving, and the run waits for a person
What staff handle	Every document	Runs that stopped and records to check. Nobody is notified, so someone checks the list of stopped runs.

For scale, two hospital time studies put keying one faxed referral into an electronic record at about 10 to 12 minutes (UCSF, JAMIA Open 2020; Calderdale and Huddersfield NHS Foundation Trust, HFMA 2024). We don't publish a time-saved number until a deployment measures one.

Who builds what

flowchart LR

A["Your agent reads the document and picks the values"] B["OpenAdapt enters them in the app on your customer's computer and reads the record back"] C["Your agent acts on the outcome"] D["A person at that computer decides or checks the record"] A -->|"workflow, inputs, request_id"| B B -->|"one outcome per request"| C B -.->|"needs_review or not_sure_if_saved"| D

You build	OpenAdapt provides
The agent that reads documents and decides what to enter	A workflow learned from one or more demonstrations in the real app, where a person or an agent shows the task
The call to `run_workflow`, with your own `request_id`	The run on your customer's computer, with a right-record check before it types and a record check after it saves
What your product does with each outcome	One result per request, and no second entry when your agent repeats a `request_id`
Your tests against the four outcomes	A sandbox that returns each outcome on demand

Your customer provides the computer and a person for runs that stop. A person or an agent shows the task, in web apps, native Windows apps, and over Citrix or remote desktop. A person accepts the result before it runs. See Record a first workflow, or talk to us about setting it up.

Try it in three steps

The sandbox is a practice server that needs no account. It has one workflow, add_triage_note, which adds a note to the first referral in MockMed, a test clinic app with made-up patients.

You need Claude Code (signed in) and uv. To use a different client or your own code, see Connect your own agent.

Add the sandbox server. Claude Code saves it for the current folder only.

   claude mcp add openadapt -- uvx --python 3.12 openadapt-agent serve

In the same folder, start Claude Code with claude. Claude Code starts the server.

Ask: "Use OpenAdapt to add the triage note 'Synthetic: recheck blood pressure in 2 weeks' with request id demo-0001."

Your agent should call list_workflows, then run_workflow with these arguments:

{"workflow": "add_triage_note", "inputs": {"note": "Synthetic: recheck blood pressure in 2 weeks"}, "request_id": "demo-0001"}

The result looks like this (some fields left out):

"outcome": "done", "label": "Done and checked", "safe_to_retry": false, "what_happened": "Simulated sandbox result (no app was opened): Saved the change and read the record back to confirm it.", "next_step": "none", "proof": "simulated", "run_id": "run-31ecb9d66db84f1aba8cb5f6", "model_calls": 0 }

This install simulates each run without opening MockMed, and every result says so. To watch the real engine do the work, see Drive MockMed with the real engine.

openadapt-agent installs from PyPI and requires openadapt-flow 2.1.0 or later, which is on PyPI and installs with it. To try unreleased changes, use --from git+https://github.com/OpenAdaptAI/openadapt-agent in place of the package name.

See every outcome

Ask for each case by adding sandbox_case to the inputs, with a new request_id each time. For example: "Now try the false_saved_banner case with request id demo-0002."

`sandbox_case`	`outcome`	What MockMed does
`normal` (default)	`done`	Saves the note, and the record check finds it.
`duplicate_record`	`needs_review`	Lists the same referral twice, so OpenAdapt can't tell which record to open. It stops before saving.
`false_saved_banner`	`not_sure_if_saved`	Shows a saved message, but the note wasn't stored.
`timeout_after_save`	`not_sure_if_saved`	Saves the note, then times out with an error.
`app_offline`	`did_not_run`	Doesn't answer, so nothing starts.

The sandbox remembers each request_id across sessions. Sending a used request_id with the same inputs returns the earlier result. Sending it with different inputs, such as a new sandbox_case, returns did_not_run with the reason request_id_conflict. To start over, delete ~/.openadapt/agent-sandbox, or set OPENADAPT_AGENT_SANDBOX_DIR to an empty folder.

Drive MockMed with the real engine

The tutorial extra makes openadapt-flow (Flow), the engine behind OpenAdapt, drive MockMed in a hidden browser. Replace the sandbox server with this one:

claude mcp remove openadapt

claude mcp add openadapt -- uvx --python 3.12 --from 'openadapt-agent[tutorial]' openadapt-agent serve

When the server starts, Flow records the workflow once and, if needed, downloads a browser (about 300 MB). The first request waits for that. In our test on a Mac, the first request took about 80 seconds. The call returned running after 60 seconds, and the agent called get_run for the result. If setup takes more than 15 minutes, the request ends did_not_run. With Flow, a done result has proof: "local" instead of "simulated".

The simulated sandbox suggests the same command in its list_workflows result.

What your agent gets back

outcome is one of four final values. While a run is still going, it's running.

`outcome` (label)	What happened	What your agent does next
`done` (Done and checked)	OpenAdapt saved the values and read the record back.	Mark the request complete and keep the `run_id`. Don't send it again.
`needs_review` (Stopped before saving)	It stopped before a save, and the run waits until someone at your customer's computer decides. Nothing was written, unless `record_changed` is `unknown`: while a run waits for a person to approve a save (label Waiting for approval), an earlier save in that run may have gone through.	Don't start the same work again. Call `get_run` later to see the decision.
`not_sure_if_saved` (Check the record)	A save may have gone through, but OpenAdapt couldn't confirm it.	Never retry. Have a person check the record in the app.
`did_not_run` (Didn't run)	This call wrote nothing. With `request_id_conflict`, an earlier request with the same `request_id` may have saved.	If `safe_to_retry` is true, fix the cause and resend the same request. Otherwise follow `next_action`.
`running` (Running)	The run didn't finish within `wait_seconds` (default 60).	Call `get_run` with the `run_id` until `outcome` changes.

Branch on outcome and safe_to_retry. next_step names the next move as a fixed code, so your code doesn't have to parse next_action: none, call_get_run, wait_for_person, check_record, fix_inputs, fix_setup, or retry.

Resend a request

request_id is your own id for the request, such as a referral id. Don't put patient details in it. A request is one request_id with one workflow and one set of inputs.

Resending the same request never enters the values twice. It returns the latest result, marked replayed: true.
If that result was did_not_run with safe_to_retry: true, resending starts a new attempt, up to 5 attempts per request_id.
After invalid_input, you can fix the inputs and resend with the same request_id. After any other retryable result, send the same inputs.
A different workflow or different inputs under a used request_id return did_not_run with request_id_conflict. Nothing new runs, and the earlier request may have saved. Call get_run with the result's first_run_id to see what happened to it.
No tool clears not_sure_if_saved. If the person who checks finds the work still needs doing, send it as a new request with a new request_id.

The check that stops a second entry covers one runs folder. See --runs-dir under Set server options.

Read the other fields

label: a short status you can display, such as "Done and checked".
reason: a code from a fixed list that names the cause, such as invalid_input or request_id_conflict. Every code is in REASONS in contract.py, and docs/DESIGN.md explains how each one is chosen.
`what_happen

Only the beginning is shown; read the rest in the repo

OpenAdapt: your agent decides, OpenAdapt enters it and checks it saved

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

yontu

README from the maker

OpenAdapt Agent

Give your agent a program, not a mouse

What changes for your customer's team

Who builds what

Try it in three steps

See every outcome

Drive MockMed with the real engine

What your agent gets back

Resend a request

Read the other fields