Selfcoder — Local AI coding agent for VS Code

Run a coding agent or chat with local models in LM Studio or Ollama.

A Copilot-like experience - local, private, and subscription-free.

Overview

Selfcoder brings local AI coding workflows into Visual Studio Code without locking you into a hosted subscription. Connect it to LM Studio or Ollama, choose the model that fits your machine, and work from a dedicated sidepanel or VS Code native Copilot Chat.

It brings AI assistance into your development workflow while giving you control over where code, prompts, files, and workspace context are processed. By default, requests are routed to the backend endpoint you configure, typically a local server running on your machine.

Choose Your Workflow

Selfcoder gives you three ways to use local models in VS Code, each suited to a slightly different use case.

Hint: For most workflows, the sidepanel is the best place to let Selfcoder manage context for you, especially when working with smaller models or limited hardware. Use the Local Model Provider when you specifically want to run the native flow and your machine has enough resources for the larger context it sends.

Key Features

Sidepanel Chat Optimized For Local Models

The Selfcoder sidepanel is the richest workflow surface. It is built for repeated coding work with local models.

• Streamed markdown responses with code blocks, copy actions, tables, lists, and links.
• Model picker with metadata such as context length, parameter size, vision, tool use, reasoning support, and aliases.
• Token usage indicator and conversation pruning when the request payload approaches the model's context limit.
• Persistent chat history with global or per-repository filtering.
• File pins, manual text attachments, image attachments for vision-capable models, and clipboard image support.
• Reasoning/thinking display for supported models.

Agent Mode

For users who want deeper workspace automation, Selfcoder can drive an installed OpenCode CLI to run multi-step coding tasks across your workspace.

• Starts an agent session in the current workspace over the ACP protocol.
• Surfaces agent activity, plans, reads/searches/edits, terminal output, and permission requests in the sidepanel.
• Opens a side-by-side diff editor with gutter markers for each file the agent touches, so you can review every change before keeping or reverting it.
• Tracks files changed during a session and can summarize or revert those changes from the sidepanel.

Model-Aware Context Budgeting

Local models vary widely in context size, so Selfcoder does not blindly dump the whole workspace into every request. It builds a request-scoped context package based on the selected model, the current conversation, and your prompt intent.

Selfcoder can include:

• pinned files you explicitly choose
• active editor selection or active file
• diagnostics from the current file
• recently edited files
• git diff summaries or focused hunks
• repository search snippets when the question needs codebase discovery
• workspace instruction files such as local-instructions.md, copilot-instructions.md, or CLAUDE.md

The result is a local-model-friendly balance: enough project context to answer well, without wasting the limited window that smaller local models often have.

Native VS Code/Copilot Chat Integration

Use @Selfcoder in VS Code/Copilot Chat when you want local assistance without leaving the native chat surface.

• Streams answers directly into the native chat UI.
• Reuses the configured LM Studio or Ollama backend.
• Includes native chat history, workspace instructions, and Selfcoder's request context pipeline.

Local Models In The Native Model Picker

Selfcoder can register eligible local models as a VS Code LanguageModelChatProvider so they appear as Selfcoder models in native chat and agent-style flows.

• Shows local models with tool-calling support when reported by the backend.
• Maps model details such as context length, vision support, and tool support into VS Code's model metadata.
• Translates VS Code chat messages, tool calls, and tool results into OpenAI-compatible request shapes.
• Streams text and tool-call responses back through VS Code's native APIs.

Supported Backends

Getting Started

1. Install either LM Studio or Ollama.
2. Start a local chat model in your chosen backend.
3. Install Selfcoder in VS Code.
4. Open the Selfcoder sidepanel and choose your backend/model.

Useful install commands:

Why Selfcoder

Selfcoder is for developers who want a capable AI coding assistant but would rather not send their code to a hosted service or pay a subscription to use one. The model runs locally, through LM Studio or Ollama, so your code and prompts stay on your machine.

Running locally comes with trade-offs, and it's worth being upfront about them. Performance depends on your hardware: a capable machine can run larger models and respond faster, while a more modest setup is better suited to smaller, lighter ones. Context windows are smaller than those of hosted models, too, and they vary from one model to the next.

Selfcoder is built around those realities. You pick the model that fits your machine and your task, and rather than sending the whole workspace on every request, it selects the files and context relevant to your question and fits them to the model you're running.