Yes—but not through a magic VS Code switch. A local Chat Model Relay can present one OpenAI-compatible endpoint to VS Code, then forward each request to an authenticated ChatGPT or Gemini browser session. That lets you use an existing web subscription from developer tools without creating separate provider API integrations, while introducing the fragility, privacy concerns and account-session risk of browser automation.
The project described here is a self-hosted, experimental relay. Its author, Onwuka Gideon, describes the goal as using “my existing ChatGPT and Gemini browser sessions from developer tools such as VS Code without managing separate API keys, model subscriptions, and provider-specific integrations.” Treat the relay’s feature list as project claims rather than independently verified guarantees.
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What the local gateway actually is
The relay sits between VS Code and a provider’s web application:
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VS Code → Chat Model Relay → logged-in ChatGPT or Gemini browser session
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To VS Code, the relay behaves like a model provider with familiar chat-completions-style requests. Behind that boundary, it drives a persistent browser session in which you are already signed in. The relay translates the IDE request into the provider’s web interaction, collects the response and returns it in the format VS Code expects.
This is different from calling an official model API. An API integration authenticates with an API key or OAuth flow and sends requests to a documented service endpoint. The relay instead depends on the behavior of a browser session, so changes to page structure, login state, anti-automation checks or account policy can break it without any change in your VS Code configuration.
Why use one endpoint for ChatGPT and Gemini?
A single local boundary solves a practical configuration problem. You can point VS Code’s model-provider setting at the relay and select or route requests to ChatGPT or Gemini there, rather than maintaining separate extension integrations for each provider.
- Existing subscriptions: the project’s purpose is to reuse sessions you already pay for on the web.
- One client configuration: VS Code talks to one local service instead of learning each provider’s request format.
- Provider switching: routing logic can select the ChatGPT or Gemini session for a conversation.
- Local control: the relay can remain bound to your computer instead of exposing a new public service.
These benefits do not mean the relay removes all authentication. Your browser sessions still have to be authenticated, and the local endpoint should have its own access control if anything besides your own VS Code process can reach it.
How the request flow works
1. VS Code sends a model request
VS Code supports bring-your-own-key (BYOK) connections to models from other providers, local models and custom endpoints. Its documented gateway-style configuration allows a chat client to call an endpoint you control. The relay uses that extension point rather than requiring a dedicated provider plug-in.
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2. The relay chooses a provider session
The gateway maps the requested model or session route to a ChatGPT or Gemini browser context. Persistent conversations and session routing are among the capabilities claimed by the project, so a route can preserve context instead of starting every request as a new chat. The exact model names and routing syntax depend on the relay implementation; the available project description does not establish a canonical configuration schema.
3. Browser automation performs the provider interaction
A persistent browser session carries the provider login and conversation state. This is the distinctive technique: the relay automates the web application rather than relying only on an official API. It may also translate IDE tool calls, attach files, submit vision requests and pass image-generation requests where the selected provider and session support them. Those are advertised capabilities, not independently tested results.
4. The relay returns an API-shaped response
VS Code receives a response in the format expected from an OpenAI-compatible chat endpoint. Streaming, tool-call translation and attachment handling therefore depend on two translation layers: the relay’s compatibility implementation and the provider’s current browser UI. A response that works for plain text may still fail for a tool call, a large attachment or a multimodal request.
What the project claims to support
| Capability | What it means for a VS Code user | Evidence status |
|---|---|---|
| OpenAI-compatible chat completions | VS Code can send familiar chat requests to one local endpoint. | Claimed by the project; compatibility should be tested with your client version. |
| ChatGPT and Gemini browser sessions | Requests can be routed to authenticated web sessions for the two providers. | Core project description; dependent on login state and browser automation. |
| Custom model endpoints in VS Code | The relay can fit VS Code’s BYOK/custom-provider architecture. | VS Code documents custom endpoints; relay-specific setup remains implementation-dependent. |
| Persistent conversations and session routing | Conversations can retain context and be directed to a chosen provider session. | Project claim, not independently verified. |
| File attachments and vision | Images or files may be sent through supported routes. | Project claim; limits vary by provider and relay translation. |
| Tool-call translation | Structured tool requests can be converted between VS Code and the browser session. | Project claim; particularly sensitive to schema mismatches. |
| Image generation where supported | An image request may be forwarded when the selected web service exposes that function. | Conditional project claim, not a universal guarantee. |
| Long-prompt attachment fallback | Large instructions can be moved into an attachment-style flow when direct prompt length is problematic. | Project claim; verify behavior and limits in your deployment. |
| Docker Compose deployment | The relay can be run as a containerized local service. | Project claim; no canonical compose file or repository was established. |
How to connect the relay to VS Code
Because the available project information does not identify a canonical repository, release, license or security policy, the safe procedure is conceptual rather than a copy-and-paste installation recipe. Do not download an unverified image or script that asks for browser cookies or account credentials.
- Choose a trusted deployment source. Confirm the relay’s repository, maintainer, release version, license and security documentation before running it. Those details are not established in the project description.
- Run it locally. Use the documented native or Docker Compose method for the specific release. Bind the listening interface to localhost unless you have a deliberate reason to permit LAN access.
- Create the browser sessions. Open the supported browser contexts, sign in to ChatGPT and/or Gemini yourself, and verify that the relay can reuse those sessions. Never paste a password or session cookie into an unknown configuration file.
- Record the local endpoint. Use the base URL and chat-completions route printed by the relay’s own documentation. Do not assume a port, path or model identifier that is not documented for your version.
- Add a custom provider in VS Code. In VS Code’s language-model or BYOK settings, choose the custom endpoint/gateway option, enter the relay URL, and supply whatever local credential the relay requires. VS Code’s documentation shows the general custom-endpoint pattern; the relay determines the exact fields.
- Map model names or routes. Select the relay’s ChatGPT or Gemini model identifier, or configure the relay’s session-routing rule if it supports that option. Keep the mapping explicit so a request cannot silently go to the wrong account.
- Test plain text first. Send a short prompt and confirm that the response arrives. Then test streaming, a new conversation, an attachment, a vision request and a tool call separately. Record which features work before relying on the gateway for production tasks.
- Lock down the service. Keep the process on localhost, protect its administrative interface, restrict log contents and stop the relay when you are not using it.
Can you use a ChatGPT subscription inside VS Code?
With this browser-session design, the intended answer is yes: VS Code sends requests to your local relay, and the relay uses the authenticated ChatGPT web session. That is not the same as receiving an official API entitlement. Your web subscription, account limits, available models and browser session remain the controlling factors.
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Expect the arrangement to be more brittle than an API integration. A provider can change its page markup, add a verification step, expire a session or alter how attachments and conversations are represented. The relay may then need an update even though VS Code itself has not changed. Account terms and anti-automation rules also apply; review the provider’s current terms before automating a personal session.
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They can share the relay’s local endpoint if the relay’s routing layer supports both sessions. VS Code does not need to know whether the response came from ChatGPT or Gemini; it sends the request to the configured gateway, and the gateway selects the provider.
Provider differences still surface at the compatibility boundary. Model names, context limits, multimodal input, tool schemas, streaming events and image-generation behavior are not identical. A route that works for a text completion may reject a Gemini-native feature or a ChatGPT-specific tool schema. Keep provider-specific tests and document the model mapping used by your team.
Browser relay, official API and MCP: three different designs
| Design | Authentication | Network exposure | Strengths | Risks and trade-offs |
|---|---|---|---|---|
| Local browser-session relay | Existing authenticated browser session; local relay credentials may also apply. | Normally localhost. | Can reuse web subscriptions, unify providers and preserve browser conversations. | UI changes, session expiry, automation detection, privacy exposure and uncertain terms-of-use compatibility. |
| Official provider API | Provider API key or documented OAuth. | Requests leave the machine to the provider API. | Documented contract, predictable request formats and easier operational monitoring. | Separate API billing or quotas, provider-specific setup and potentially different model availability from a web subscription. |
| ChatGPT MCP connection | Authentication configured for the MCP server. | OpenAI’s MCP quickstart uses a publicly reachable HTTPS URL, commonly through a tunnel such as ngrok, with an /mcp endpoint. |
Structured tool connection for ChatGPT integrations. | Requires public exposure and authentication; it is not a localhost-only browser relay. |
Calling the local browser relay an “official ChatGPT MCP integration” would be inaccurate. MCP and browser-session automation solve different problems and have different exposure and authentication requirements.
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Marketplace listings show that the same general demand can be met without building your own relay, but listing text is not proof of maintenance or security.
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OAI2LMApi
Its listing advertises connecting OpenAI-compatible APIs to VS Code’s Language Model API, native Google Gemini support, streaming, automatic model loading and cached model metadata. The listing says Gemini uses its native Gemini API format rather than OpenAI compatibility. Verify the extension’s current publisher, permissions, update history and data handling before installation.
GitHub Copilot API Gateway listing
This listing advertises a local server, OpenAI-compatible usage, Gemini and Ollama support, automatic model discovery and a default server port of 3030. Treat the port and feature set as listing claims, not universal defaults. Check current maintenance and security before exposing it beyond localhost.
ChatGPT Copilot listing
This marketplace entry advertises GPT models, Gemini, Ollama, MCP, file and image chat, streaming answers and code assistance inside VS Code. Confirm which features use official APIs, browser sessions or another authentication method; the listing alone does not establish that distinction.
Security checklist for a browser-session gateway
- Use a dedicated account when appropriate. A relay that can drive a browser session may be able to read conversation history, uploaded files and account-visible data.
- Keep the listener private. Bind to localhost and use a firewall rule if the software offers no explicit access token. Anyone who can reach an unauthenticated local endpoint could submit prompts through your session.
- Protect session storage. Browser profiles, cookies and refresh data should be readable only by your user account. Do not commit them to a repository or mount them into a broadly shared container.
- Minimize logs. Prompt and response logs can contain source code, secrets and personal data. Disable verbose request-body logging unless diagnosing a failure.
- Review container boundaries. A Docker container may still need access to a browser profile or display server. Grant only the volumes and capabilities required by the documented deployment.
- Separate test and work data. Do not send proprietary code, credentials or regulated information until you understand where the relay stores prompts and how the browser session handles them.
- Audit updates. A relay update can change network behavior or credential handling. Review release notes and configuration diffs before upgrading.
Common failure modes and recovery
The endpoint works, but the provider returns a login page
The browser session has expired, opened the wrong profile or encountered a verification challenge. Stop requests, open the exact browser context manually, complete the provider’s sign-in flow yourself, then retry a short text request. Do not automate around a CAPTCHA or disable account security controls.
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Check the relay’s supported MIME types, file-size limits and provider route. Test a small image or text file first. A compatibility layer may support ordinary chat completions while lacking a complete multipart or multimodal translation.
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Streaming stops midway
Determine whether the break occurs in the browser session, the relay’s event translation or VS Code’s client. Retry with non-streaming output if the relay exposes that option, then test a short response to isolate timeout and connection-length issues.
Tool calls are malformed
Compare the tool schema emitted by VS Code with the schema the selected provider accepts. Disable tools for the failing request, verify plain text, and reintroduce one simple tool. Tool-call translation is a claimed feature and is especially vulnerable to changes in either side’s schema.
A model name is rejected
Use the identifier recognized by the relay, not necessarily the name shown in the provider’s web interface. Refresh model metadata if your deployment supports discovery, and check the route mapping after provider-side model changes.
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That is a security issue unless you intentionally designed a protected LAN service. Restrict the bind address, firewall the port, add authentication and rotate any exposed session credentials. A localhost design should not be casually converted into a public endpoint.
When this approach makes sense
- Choose a browser-session relay when you value one VS Code endpoint, already have suitable web subscriptions, accept experimental behavior and can keep the service private.
- Choose official APIs when reliability, documented contracts, automation at scale, auditable credentials and predictable operations matter more than reusing a web subscription.
- Choose an MCP deployment when you specifically need an MCP tool connection and can provide the required authenticated HTTPS endpoint.
- Choose a marketplace extension only after checking its current maintenance, permissions, authentication path, provider terms and data handling.
What remains unknown about this project
The available project description does not establish a canonical repository, release history, license, security policy, supported browser engines, default port, exact endpoint path or maintenance commitment. Those omissions matter because a gateway handling authenticated sessions is infrastructure, not merely a VS Code preference.
Before adopting it for daily development, verify the implementation source, inspect its dependency and container configuration, confirm how credentials are stored, and test recovery after a browser-session expiry. Until those checks are complete, treat the relay as an experimental personal tool rather than a production-grade provider service.
Bottom line
A local Chat Model Relay is a workable architectural answer to “Can I use ChatGPT and Gemini from VS Code without separate provider integrations?” It gives VS Code one API-shaped boundary while the relay drives authenticated browser sessions. The convenience is real, but so is the cost: browser automation is less stable and less clearly governed than an official API, and an MCP connection has a different public-HTTPS deployment model. Keep the gateway local, test every capability you need, protect session data and verify the project’s source and maintenance before trusting it with sensitive code.
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