WorkerKitFor ChatGPT
AI workers for ChatGPT.
Connect WorkerKit to ChatGPT through developer mode and OAuth. Your workers run in the background with their own instructions, memory, schedules, budgets and app access, then return short reports with costs. Approve access on the WorkerKit consent page; no key goes into chat.
Start here
Add WorkerKit to ChatGPT in about a minute.
Open ChatGPT Plugins, copy the WorkerKit address below and connect with OAuth.
- 01
Turn on developer mode
In ChatGPT on the web: Settings, Security and login, Developer mode. OpenAI offers it on Pro, Plus, Business, Enterprise and Education, and on a workspace an owner enables it first.
- 02
Add WorkerKit
In Plugins, add a custom MCP server named WorkerKit with the address below. Choose OAuth, sign in and approve access as a WorkerKit account admin.
- 03
Ask ChatGPT to check it
Paste the prompt into a new chat. ChatGPT asks you to confirm anything that writes.
- WorkerKit address, OAuth
https://mcp.workerkit.ai/workers - Paste into a new ChatGPT chat
Use my WorkerKit connection. Call key_info and tell me which account and scopes you have. List my workers and recent runs. If there are none, ask what job I want done and suggest a ready-made kit for it. Ask me before you run, install or change anything.
- Open ChatGPT Plugins Where custom MCP servers are added, once developer mode is on.
The setup guide covers each screen, and what to do when developer mode is missing or the sign-in does not finish.
Why WorkerKit
Control your AI workforce from ChatGPT.
Ready-made, self-learning AI workers with only the access each job needs, a budget you set, and a record of every job and action.
- Put AI workers on jobs with real ROI. Install a kit for work your team already repeats, and a worker arrives with its instructions, app permissions and schedule already written and reviewed. Start with the job that costs the most time, or build your own kit.
- Turn your AI workers’ jobs and data into shared company knowledge. Each job’s instructions, apps and limits live in one kit your team keeps current, and workers that use the Records app keep what they find in shared tables and lists your team can open. Your team and every connected assistant work from the same process.
- Choose what each AI worker can access. Each worker holds its own grant for each app, read-only by default, and nothing it reads can widen it. An assistant's key carries only the scopes you choose and never an app credential.
- Track what your AI workers did and what they changed. Every run leaves a receipt with its result and its cost, and every tool call is logged with its outcome, so you can see what each worker did, when, and at what price.
- Keep AI spending within the limits you set. Each worker has a per-run and a daily spending limit, under a limit for the whole account. When one is reached, new runs stop before any money moves, and the receipt says why.
- Your AI workers learn as they work and keep getting better. A language worker can check every run against its job's quality criteria. A change that makes it better waits for your approval or, in Apply proven changes mode, applies on its own once it passes a comparison on your own examples. Its runs also propose what it should remember, and you approve what it keeps.
What ChatGPT is
Several surfaces, one door.
ChatGPT is OpenAI's assistant, on the web, on desktop and on mobile, across Free, Go, Plus, Pro, Business, Enterprise and Edu accounts. It extends itself through plugins, the current packaging of what were apps and, before that, connectors, and that layer is built on the Model Context Protocol: a plugin is an MCP server, optional UI, and the skills that teach ChatGPT how to use it. Developer mode is the setting that lets one person point ChatGPT at a remote MCP server of their own, by URL.
| What a fleet adds | Why it helps |
|---|---|
| One connection, many jobs | The fleet is a single connection rather than a connector per service. Each worker holds its own app grants underneath it, so the list in ChatGPT does not grow with the work. |
| Work that survives the tab | A conversation ends when you close it. A hosted worker keeps its schedule, runs at 6am, and has the answer waiting. |
| Effects you approve, with a receipt | Developer mode asks you to confirm write actions by default. WorkerKit puts a scope layer under that: the key holds no app credentials, workers reach apps under their own grants, and every run settles its exact cost. |
| A fleet that is not tied to this surface | The same workers answer any other MCP client, a terminal and a plain HTTPS API, so a plan change or a renamed menu does not strand the work. |
It is worth being precise about which surface, because ChatGPT has several and only one of them takes an arbitrary server. Deep research and company knowledge connectors accept a server that exposes exactly two read-only tools, search and fetch, which no general tool surface matches. Custom GPTs and their OpenAPI Actions were the old way in and are being retired: OpenAI has said new creation closes around 25 September 2026 and custom GPTs stop running on 11 December 2026, with plugins as the migration path. Developer mode is the door this page documents, and it is the one that is open.
Hired specialists
What a worker adds beside ChatGPT.
An AI worker is a background agent with its own instruction, memory, triggers, budget, model, app permissions and key. WorkerKit hosts the runs, so ChatGPT never has to hold a conversation open for them.
| Aspect | ChatGPT | A WorkerKit worker |
|---|---|---|
| Context | One conversation, plus what the account carries between them | Its own, sealed per run; only the digest returns |
| Model | The OpenAI model behind this conversation | Chosen per worker from any supported provider, or your own provider key |
| Identity | The plugins and connections on your account, acting as you | Its own, holding only the apps, tools and permissions its job needs |
| When it runs | While you are there, in a browser tab | Hosted around the clock: on demand, on a schedule in its own time zone, or on a signed inbound webhook |
| What comes back | The work itself, in the thread | A closing report of at most 8,000 characters, a typed digest beside it, and the settled cost |
| Memory | What the assistant remembers of your earlier conversations | The worker's own rules and facts, carried across runs, and approved by you before a run may use them |
| Taking it back | Remove the connection, or turn developer mode off | Disable or revoke one worker; nothing else moves |
The operator pattern
ChatGPT operates. Workers work.
| Step | Who | What happens |
|---|---|---|
| 1 | You | Ask in plain language. "Which workers ran today?", "Run the invoice chase now", "Pause the scanner." |
| 2 | ChatGPT | Reads its key's scopes first with key_info, picks the matching tool on the fleet connection, and follows the operating rules in the WorkerKit skill. |
| 3 | You again, for anything that writes | Developer mode asks you to confirm a write action by default, so starting, pausing or rewriting a worker is a decision you take rather than one you read about afterwards. |
| 4 | The worker | Executes in its own context, on its own runtime, on its schedule or on demand. |
| 5 | Both | ChatGPT reads the result with runs_feed and run_get: one connection covers the whole fleet, and the run's receipt carries the digest, the typed structure when the run produced one, and the settled cost. People receive it through the worker's delivery channels. |
ChatGPT becomes the fleet operator: it reads the fleet, says what it is about to change, and waits for the confirmation developer mode puts in front of you. The workers do the unattended work, and the interactive work workers are not for stays exactly where it is.
Why ChatGPT and WorkerKit
Each one covers what the other is not for.
Use ChatGPT for the work you want to watch and steer. Use a WorkerKit worker for run unattended against my apps, on a schedule, with its own grant, and deliver to Slack.
| ChatGPT strength | What WorkerKit adds beside it |
|---|---|
| Developer mode: full MCP client support, read and write | WorkerKit connects by URL through OAuth, with kit discovery included |
| Plugins that bring a service into the conversation as you work | Standing grants for the services that must stay reachable when nobody is watching, held per worker rather than per conversation |
| Write actions confirmed before they run | A scoped key underneath that confirmation: it holds no app credentials and reaches apps only through workers' own grants |
| The same assistant on the web, on desktop and on mobile | A fleet anyone on the account can operate from the surface they already use, with the same receipts whoever asked |
| Skills that teach a repeatable workflow, in SKILL.md | A skill file of our own, so the fleet arrives with its tool reference and operating rules already written |
What it unlocks
Background work that stays out of the chat.
Each run executes sealed inside its own context, settles its exact cost and returns a digest. The worker remembers its own state. Your conversation stays clean.
- Sealed jobs, small returns Every job runs in its own context and only the digest comes back. A long crawl becomes the few paragraphs that matter, so many workers fit in one conversation.
- The right model per job Inexpensive models scan, frontier models judge. Every run is priced to the token, with reserved-versus-settled tracking, so background work is never a black box on your bill.
- Money you can trust A run that never started costs nothing. A run that went silent mid-flight is absorbed by WorkerKit and labelled as an unbilled estimate. A failed run settles at actual usage with no minimum charge.
- Standing monitors and briefings Workers watch on a schedule and report only what is new, so you stop relying on remembering to ask.
- Two-way work A run that needs input does not guess or stall. It asks on every delivery channel and resumes as a linked follow-on run when you answer.
- Memory that compounds Rules and facts persist across runs, so the tenth run is smarter than the first. Anything a run proposes for memory waits for your approval before a future run may use it.
- Kits: install one or make your own Browse the catalog, install a ready-made worker, or author your own through the authoring guide, the validator and the publisher. Every path runs the same validators.
- Fleet operations Clone a tuned worker for the next client, set budgets, route deliveries, and start and stop workers, all from the assistant you already talk to.
- Delivery where people live Results land in email, Slack, Teams, Telegram, Discord, Notion, or SMS and WhatsApp, where their readers already are, instead of waiting in a chat nobody opens.
- Auditable trails Every run leaves a typed event stream, liveness heartbeats, owner and self scores, and the allow or redact outcome of each tool call underneath it.
MCP connections
WorkerKit for your fleet. Knowledge for your reports.
Use WorkerKit to discover kits, build workers and manage your fleet. Add Knowledge to search published worker reports. Both use the same sign-in or key.
| Mount | Address | What it carries |
|---|---|---|
| WorkerKit (default) | https://mcp.workerkit.ai/workers | Discover and install kits, build workers, and run and manage your fleet. Choose the full profile or a focused decision profile. Connect with OAuth or a bearer key. |
| Knowledge | https://mcp.workerkit.ai/knowledge | Company Knowledge: 6 tools to search and read published worker reports and track changes. Admins manage publication and reader access. Uses the same sign-in or key. |
| Area | Tools | What it covers |
|---|---|---|
| Continuous Learning | worker_quality_get, worker_quality_set, worker_quality_preview | Read criteria, three gates and availability reasons; save reviewed settings with expectedRevision; preview samples without a run. The worker dashboard offers owner-reviewed activation and a quoted AI draft when criteria are missing. Paid comparisons, diagnosis, monitoring, apply/rollback and company/shadow learning stay owner-only. Manager keys can disable any gate, but cannot enable automatic/shared learning. Kit install leaves all gates off. |
| Key | key_info | What your key is and exactly which scopes the other tools will honour. Call it first. |
| Fleet | workers_list, worker_get, worker_set_enabled | Every worker with live run state, one worker in full, and start or stop. |
| Apps and workers | worker_permissions_get, worker_permissions_set, worker_bindings_get, worker_bindings_set | What a worker may touch, in the same vocabulary a kit is written in, and its grants to call other workers: read both, replace its app set (account Admin) and fill, re-point or clear its worker grants. The writes need manageAppsAndWorkers; rules, identities and MCP tool bindings stay in the dashboard. |
| Runs | worker_run, run_bulk, worker_runs, run_get, run_events, run_transcript, run_cancel, run_score, run_clear_digest | Trigger a run with a prompt for that run, or one prompt across up to 20 workers; watch it step by step, read the receipt and the stored process log, cancel or grade it. On a decision worker it decides instead and acts on what it routes: sourceArgs and maxItems narrow and cap what is judged, and waitSeconds waits for the settled receipt, which carries the per-item decisions. |
| Fleet activity | runs_feed, fleet_pulse, fleet_health, account_usage, activity_feed, activity_summary, account_spend | Worker runs, app calls and configuration changes, activity counts, everything running now, fleet health, account headroom and spend by worker, day and model. |
| Two-way runs | run_question, run_answer | A run that needs an answer ends by asking; the answer starts a linked follow-on run. |
| Memory | memory_get, memory_add, memory_update, memory_delete | The rules and facts a worker carries into every run. |
| Schedules | schedules_list, schedule_create, schedule_update, schedule_delete, worker_timezone_set | When a worker starts on its own, and the time zone it works in: its local dates, its report times and the clock its daily schedules follow. |
| Instruction | instruction_get, instruction_set, instruction_versions, instruction_version_get, instruction_restore | The standing instruction, versioned on every change, with the history to compare against and roll back to. On a decision worker it reads the routing table and the install questions instead, and writes their answers. |
| Deliveries | delivery_list, delivery_channels, delivery_create, delivery_update, delivery_secret_rotate, delivery_delete | Where a run report goes when the worker finishes: email, Slack, Teams, Telegram, Notion, Discord, SMS or a signed webhook. |
| Deployment | models_list, deployments_list, deployment_get, worker_deploy, deployment_update, worker_undeploy | What makes an installed worker actually run: pick its model from the priced catalog, cap its spend, pause or resume it, take it off again. A worker with no deployment fires no schedule and refuses a run with not_deployed. |
| Budgets | budget_get, budget_set, fleet_budget_get, fleet_budget_set | A worker’s spend and run ceilings, and the account-wide fleet ceiling. Their own scope, because raising a dollar cap is the one management action that can cost money. |
| Creation | worker_clone_preview, worker_clone, worker_clone_bulk | Clone a worker into new ones, singly or in bulk, with a dry run first. A clone carries only permissions a person already approved on the source. |
| Deletion | worker_delete | Remove a worker for good, with its sub-workers. Permanent, where stopping one is not — which is why it is a scope of its own. |
| Kits | kit_install_preview, kit_install | Preview a directory kit against your account, then install it as a new worker — with deploy set, in the same call, which is what makes it run. |
| Kit authoring | kit_validate, kit_publish, kit_update, kit_replace, kit_unpublish, kit_relist, kit_make_private, kit_delete, kit_scan_get, my_kits_list, publisher_get_mine, publisher_set | Validate a kit in one dry run that reports every gate at once, publish it from content or from a worker you own, keep it private or list it, and keep your publisher profile. A private kit installed with kit_install is how an agent builds a worker from scratch. |
| Connected apps | apps_list, app_connect, app_disconnect, app_github_accounts, app_github_repos, app_github_branches, app_slack_workspaces, app_slack_conversations | Which apps an operator can use right now, in the kit vocabulary, and connecting the rest by credential: validated live, stored encrypted, never returned. Browser sign-ins stay on the Apps page. The GitHub and Slack lookups find repository and channel IDs for an install (discoverAppResources). |
| Model keys | model_keys_list, model_key_set, model_key_delete | Your own model-provider API keys, so runs bill your provider account instead of the wallet. |
| Onboarding and wallet | onboarding_get, wallet_get, wallet_checkout_create, wallet_checkout_get | Check model funding, hand off BYOK setup, inspect wallet balance, and request a human-confirmed Stripe checkout. A purchase is complete only when its status is credited. |
| Custom MCP servers | mcp_servers_list, mcp_server_get, mcp_server_create, mcp_server_discover, mcp_server_set_tools, mcp_server_delete | An app the platform does not offer, reached through its MCP server: register it as your own custom MCP app with its credential in the same call, enable the tools a job needs, and bind it in a kit. |
| API apps | api_apps_list, api_app_get, api_app_create, api_app_update, api_app_validate, api_app_preview, api_app_publish, api_app_version_get, api_app_version_revoke | Account-private API apps: validate and preview a definition offline, create and update drafts, publish immutable versions and revoke them. Authoring needs a live account Admin and managePrivateApiApps; connections and worker bindings are configured in the dashboard. |
| Decision authoring | decision_worker_create, worker_decision_set | Compile typed questions and a supported source recipe into a private kit and installed decision worker. Optional deploy; never starts a run. Requires createWorkers, plus manageDeployments when deploying. Retry the same requestId and body to recover the receipt. worker_decision_set attaches, replaces or removes a hybrid worker's classifier. |
| Action workers | action_worker_options, action_worker_create, worker_tools, worker_tool_call, worker_tool_invocation | Selected app tools with no model, called one at a time with exact inputs. Create with createWorkers; call and read receipts with invokeActions. A write needs an idempotencyKey; never resend an outcome_unknown call. |
| Support | support_request_create | Contact support about an error you cannot fix, a bug, an app request or a question. Returns a PE-XXXXXX reference. |
| Discovery and authoring reads | workerkit_about, directory_overview, kits_search, kit_get, kit_app_tools, kit_authoring_guide, kit_vocabulary | Public reads also available on the workers connection. Discover classification sources with kit_app_tools(purpose:"decision") and read kit_authoring_guide(section:"decision"). These reads never forward the manager bearer upstream. |
| Company Knowledge | knowledge_sources, knowledge_search, knowledge_get, knowledge_changes, knowledge_configuration_get, knowledge_configuration_set | On the separate mcp.workerkit.ai/knowledge mount, with the same key: discover, search and read published worker reports, reconcile changes, and configure sharing as an admin. Access follows publication and reader grants. |
The manager key holds no app credentials: workers reach apps under their own grants and firewall, and no prompt can widen that. With readRuns or invokeActions the key can read what those workers return, including an Action worker's tool results.
Your fleet is also available from other MCP clients and the REST API.
Classification on demand
Create a decision worker from your questions.
Categorize, score or triage connected data, then reuse and adjust the worker.
Reuse an existing worker or decision kit when its questions fit. For a custom classification task, creation compiles a supported source recipe and typed questions into a private kit and an installed decision worker. Optional deploy:true adds deployment; creation never starts a run or adds a schedule. The kit can be edited and published later through the normal kit lifecycle.
On the workers connection, call kit_app_tools(purpose:"decision") for recipes, argument schemas, permissions and examples, then kit_authoring_guide(section:"decision"). Check account connections with apps_list; public discovery does not check them. Send the request to decision_worker_create, follow nextCall, then use worker_run and run_get. Running needs runWorkers; results need readRuns. Use instruction_get / instruction_set for saved category or level answers, or answers on one run. Structural question/source changes need a revised kit and a replacement install. Check receipt status, coverage, omissions, warnings and cost before claiming completion.
Start with email-previews, calendar-events or sheets-rows. These recipes return judgments without app writes. Email previews do not include full threads; calendar events are invitation data, not transcripts; Sheets needs a Google file ID, a finite tab-qualified range and a columns map. A connected app alone does not make every tool a supported source. Source filters select evidence, not permissions: the Sheets recipe grants spreadsheet reads across the linked Drive account.
Supply 1–8 questions: choice with named options, score with ordered levels, or noul for the probability of a statement. Choice adds unclear automatically. Set an explicit confidenceFloor between 0 and 1; it routes uncertainty and does not promise accuracy. maxItems is 1–50, default 20. Creation requires createWorkers; optional deployment also needs manageDeployments.
Use a fresh requestId for each new worker. Retry with the same ID and identical body to recover the original receipt; a changed body returns 409. The response includes tokenId for MCP/CLI worker commands, stable workerId, kitSlug, readiness and nextCall. A deploymentError means the worker already exists: fix deployment on that worker. The receipt is a snapshot; check current worker readiness before running. No worker API secret is returned.
The workers connection includes discovery and authoring, so classification needs one MCP connection. Its full profile includes fleet management, app resource discovery, kit authoring and public directory reads. For a smaller list, a client that supports custom headers can send X-WorkerKit-Profile: decision on every request, including initialization, listing and calls, to select the smaller workflow tool set. A server operator can instead set MCP_WORKERS_PROFILE=decision. The endpoint and existing sign-in stay the same; a profile grants no additional permissions. Reconnect and relist after changing profiles. Use the advertised tool list: fleet administration, publishing, Action worker and event-stream tools require the full profile. Successful responses include structured content and a text fallback; creation declares an output schema.
App tools, no model
Call app tools through an Action worker.
One exact app operation per call, with the inputs you supply.
An Action worker (modelType: "none") exposes 1 to 64 selected app tools and has no model, instruction, schedule or deployment. Each call invokes one tool with the arguments the caller sends, under the worker's own app access, and readiness.canInvoke on worker_get says whether its tools can be called now. Run and deploy requests answer 400 action_worker_requires_invoke.
On the workers connection's full profile: action_worker_options lists apps with no tools selected; pass an explicit app for schemas and defaults (readWorkers). action_worker_create requires an explicit app or actionSpec and copies the selected app's default tools, or takes an actionSpec of 1 to 64, plus a slackConnectionId for Slack tools when the account has more than one Slack bot (createWorkers); worker_tools lists the saved tools (readWorkers); worker_tool_call calls one with arguments, plus an idempotencyKey on a write (invokeActions); worker_tool_invocation reads a write's receipt (invokeActions). Pass the returned tokenId. kits_search(modelType:"none") finds Action kits; install one with kit_install and no deploy.
An explicit actionSpec needs no app and can span native or reviewed MCP apps. Private API tools require a dashboard binding to a published version; they are not actionSpec refs.
A write tool (readOnly: false) needs an idempotency key. Repeating the key with the same arguments returns the stored receipt (a confirmation, not the app's response) without calling the app again; with other arguments it is a 409. A call answers with status succeeded, denied, failed, in_progress or outcome_unknown; branch on it, not on the HTTP code. On outcome_unknown the write may have happened: never resend it automatically, under any key; read the receipt and check the app. Each call counts toward the worker's daily request limit; a refusal marked retryable, such as usage_limit, is retried later under a new key. Only the owner changes the selected tools, in the dashboard.
Access and control
Scoped per worker, and scoped for ChatGPT too.
| Layer | What it holds |
|---|---|
| Per-worker grant | You connect each account yourself and choose what the worker may do there: per-app operations, read-only by default, narrowed by rules such as contact blocks, label and folder rules, customer-domain limits, time windows and IP allowlists. An account-level ceiling caps what any worker can be granted. |
| The app firewall | Every tool call is checked against the grant and the rules, field-masked, scanned for sensitive data, redacted, rate-limited and logged with its outcome. The worker's model never sees what the rules removed. |
| Prompt injection | A worker steered by content it reads still cannot exceed its grant. Proposed memory waits for a human. Publishing a kit to the directory runs a supply-chain and prompt-safety scan that fails closed. |
| The assistant's own access | A manager key carries explicit scopes, is shown once and stored hashed. No scope gives it app credentials, but invokeActions returns an Action worker's tool results, so grant it only where an agent should call those tools. An agent reads its scopes before planning, and an admin can revoke the key at any time without touching a worker. |
A worker is only as trustworthy as the access behind it, so the access is yours to give, to narrow and to take back, one worker at a time. You delegate without handing over the kingdom.
How it connects
Connect WorkerKit with developer mode.
Developer mode is the setting that lets ChatGPT add a remote MCP server by URL. OpenAI lists it as available to Pro, Plus, Business, Enterprise and Education accounts on the web, and on the three workspace plans an owner enables it first, so it is worth checking that before anything else.
ChatGPT uses OAuth to connect to WorkerKit. It opens the WorkerKit consent page, where an account admin approves access. ChatGPT stores the credential in the connection; it cannot use an API key pasted into chat.
The whole procedure, written for the agent doing it, is the setup guide. The human's part is three steps and about a minute.
If the OAuth mode does not complete, there is no key field to fall back on, so fall back to a different client instead.
The same fleet answers from anywhere else you work: any other MCP client, the wk CLI in a terminal, and the REST API for hosts that speak plain HTTPS. For a developer there is a fourth door, and it is OpenAI's own: the Responses API accepts a remote MCP server as a tool, with an authorization value alongside the server URL, which is where a manager key does belong. That is code you write, not the ChatGPT app.
Cost: WorkerKit has a Free plan with no card required. Runs bill model tokens at the provider list price with no markup, from a prepaid wallet or your own provider key, inside the budgets you set. Connecting ChatGPT is not a paid feature, and the plans are on the pricing page.
Questions
ChatGPT and WorkerKit, answered.
- Can ChatGPT connect to WorkerKit over MCP? Yes, through developer mode, which OpenAI describes as full MCP client support for all tools, both read and write. It is the one ChatGPT surface that takes an arbitrary remote MCP server by URL. OpenAI lists it as available to Pro, Plus, Business, Enterprise and Education accounts on the web, and notes that availability can depend on account and workspace policy. Neither Free nor Go is on that list.
- Which ChatGPT surface do I set this up on? ChatGPT on the web. Settings, then Security and login, then Developer mode; the connections themselves are added from the plugin list, with the plus button and a Connection. On Business, Enterprise and Education a workspace owner enables developer mode and custom MCP connectors first, and can grant it per person. The desktop app matters for one thing only: it is where a skill can be installed locally.
- Do I paste a WorkerKit key into ChatGPT? No. Use OAuth and approve access on the WorkerKit consent page. ChatGPT stores the credential in its connection and has no field for a custom Authorization header. Never put a key in chat or in the server URL.
- Can ChatGPT read my email or CRM through WorkerKit? Only through workers you set up. The connection holds a manager key with no app credentials, but run results (with readRuns) and an Action worker's tool results (with invokeActions) can include data from the apps that worker was granted. Choose the worker's app access and the key's scopes together; nothing said to ChatGPT in a conversation widens either.
- What does ChatGPT need to set this up? ChatGPT on the web with developer mode, plus a WorkerKit account admin to approve access. Add WorkerKit, complete OAuth, verify access with key_info, then follow the skill and smoke-test steps in the setup guide.
- What about custom GPTs and GPT Actions? Do not build on them. OpenAI has said new custom GPT creation closes around 25 September 2026 and custom GPTs stop running on 11 December 2026, with plugins named as the migration path. An Action over the WorkerKit REST API would also hold one builder-supplied key for everyone who used the GPT, which is the wrong shape for a fleet. Developer mode is the door, and it is the one this guide documents.
- How does ChatGPT get a worker's results? Two paths, and neither needs the tab open. ChatGPT pulls them over the fleet connection with runs_feed and run_get, so one connection covers every worker on the account and each receipt carries the closing report, the typed digest when the run produced one, and the settled cost. The people who need the result get it from the worker itself, through its delivery channels: Slack, Teams, email, Telegram, Discord, Notion, SMS and WhatsApp, or a signed webhook.
- I connected it and the fleet is empty. What now? New accounts start empty. Install a kit at workerkit.ai/kits, or let ChatGPT search the included catalog. With installKits access, it can show the setup and app permissions through kit_install_preview, then install the kit after you approve.
- What does it cost? WorkerKit has a Free plan and asks for no card: 5 workers, 500 tool calls a day, and the whole safety layer. Model tokens are billed at the provider list price with no markup, drawn from a prepaid wallet or from your own provider key, and capped by the per-worker and fleet budgets you set. Connecting ChatGPT costs nothing on our side; developer mode is the part that needs a paid ChatGPT plan, and that gate is OpenAI's rather than ours.
The setup guide is written for the agent doing the work, the MCP server page holds the surface behind it, and the wk CLI reaches the same fleet from a terminal.
To build a kit of your own, how to build a kit walks the procedure, and the kit-authoring skill is the file an agent keeps for it.