Local Coding Agent

Name: Local Coding Agent
Author: pvnc228

by pvnc228version 1.2.0i★ –iRuns on your computer

Registry nameio.github.pvnc228/local-coding-agent

Install

Overview

Description from the maker (original English)

Delegate small coding tasks to local Ollama/llama.cpp models; patches gated by real test runs.

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

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).
The registry lists no key to fill in.

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": {
    "local-coding-agent": {
      "command": "uvx",
      "args": [
        "local-coding-agent",
        "serve-mcp",
        "--workspace",
        "your-value"
      ]
    }
  }
}

Run this in your terminal

claude mcp add local-coding-agent -- uvx local-coding-agent serve-mcp --workspace your-value

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": {
    "local-coding-agent": {
      "command": "uvx",
      "args": [
        "local-coding-agent",
        "serve-mcp",
        "--workspace",
        "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": {
    "local-coding-agent": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "local-coding-agent",
        "serve-mcp",
        "--workspace",
        "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.

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 pvnc228, 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 32461 servers in this category
Published to the registry more recently than 91% of the other 32461 servers in this category
Basic checks equal to or better than 100% of the other 32461 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 Local Coding Agent 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?

The registry does not list a required key, though some servers ask you to sign in on first use.

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 (1.2.0) reached the registry 3 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, dotnet-mcp

Alternatives

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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.

Local Coding Agent

Local execution and safety layer for delegating coding tasks from cloud agents to models on your own machine.

What is Local Coding Agent?

Local Coding Agent lets a cloud coding agent delegate a narrow task to a local model without giving that model unrestricted access to your repository. Connect Codex, Claude Code, Cursor, Windsurf, Cline, OpenCode, Antigravity, or another compatible harness through MCP, the reusable Agent Skill, or the local-agent CLI. The cloud agent stays in charge of the larger task; Local Coding Agent supplies the controlled local execution path.

The calling agent sends a focused goal, a workspace, an allowlist of files, and checks. Your local model returns a proposal for review. The local model proposes; the controller verifies. A change is only reported as applied after the declared file scope held, the patch passed static analysis, and a real external test runner confirmed the configured checks. If a post-apply check fails, the change is rolled back and the rollback is itself verified against recorded file digests. The local model does not receive unrestricted shell or write access to your workspace.

Use the Desktop Harness when you want a Windows interface for local chat, model selection, sessions, task status, and change review. Use the CLI for terminals, scripts, CI, or direct integration with a coding harness. Both use the same controller and safety rules.

You keep control of Git and delivery. Local Coding Agent does not create commits, push branches, publish releases, or download a model on its own.

What the gate refuses

The same gate backs apply_proposal, local-agent apply, and the desktop apply action:

A patch that touches a file outside the declared scope is rejected before the workspace is touched (error.kind: patch_outside_scope).
An apply with no targeted check is refused — no external evidence, no apply (error.kind: apply_requires_checks).
A patch that fails the post-apply checks is rolled back; the result is "rejected" with error.kind: post_apply_check_failed.
A rollback that cannot be confirmed against the recorded baseline is reported as workspace_modified: true with workspace_state: "unknown" and risk kind rollback_failed — never as a clean restore.

Choose your download

Current public release: v1.2.0

Choose the CLI release for terminal workflows, scripts, CI, or integration with another AI coding environment. It provides the local-agent command, MCP server, Agent Skill, diagnostics, sessions, model tools, and task delegation.

Download the CLI wheel or CLI source archive. Windows users can download the Desktop Harness installer and verify the published files with SHA256SUMS.txt.

The v1.2.0 release contains the CLI archives and the Windows Desktop Harness installer. Local inference remains local; install Ollama or run a compatible local model server before requesting live model responses.

Desktop Harness

Open the Harness, choose a workspace, select a local model, and start working. The desktop application brings the complete local coding workflow into one focused interface:

Interactive Chat for questions, explanations, and everyday coding work.
Chat, Build, Plan, and Auto modes for different kinds of requests.
Delegated Tasks for handing a focused coding change to a local model.
Workspace sessions so you can return to previous conversations and tasks.
Model discovery, model profiles, context settings, hardware telemetry, and diagnostics.
Clear status messages when the local backend is ready, unavailable, or needs attention.
Reviewable code changes and checks before changes are applied.

The 30-second tour below follows one delegated change: the local model proposes, the controller checks scope, a real test runner decides, and a failed check rolls the change back. Click it for the video with sound.

CLI installation

Requirements:

Python 3.10 or newer.
Git on PATH for workspace operations.
Ollama or an OpenAI-compatible local model server for live inference.

Install the CLI package:

pipx install local-coding-agent

Or install from a checkout:

git clone https://github.com/pvnc228/local-coding-agent.git

cd local-coding-agent python -m pip install -e .

Check the installation:

local-agent doctor

local-agent test-run --mock

Add --json where supported when the output will be consumed by another tool.

Connect another AI coding agent

Local Coding Agent can provide its capabilities to other AI coding environments through MCP or an Agent Skill:

local-agent init-mcp --auto --write

local-agent init-skill --write

Start the MCP server manually when a client needs an explicit command:

local-agent serve-mcp --workspace . --enable-tasks

See local_coding_agent/docs/MCP_INTEGRATION.md for client configuration examples and skills/local-coding-agent/SKILL.md for the reusable Agent Skill.

A typical coding request

Choose a workspace and a local model.
Ask a question or describe the change you want.
Inspect the suggested code change in the application.
Run the relevant checks and apply the change when it looks right.

The application keeps the workflow understandable: you can see what is being changed, which checks are being run, and whether the local backend is available. Commits, tags, pushes, and publishing remain under your control.

CLI commands

Command	Purpose
`local-agent delegate`	Give a focused coding task to a local model.
`local-agent decompose`	Break a larger request into smaller coding tasks.
`local-agent apply`	Apply a reviewed change and run its checks.
`local-agent chat`	Run a prompt or a persistent chat session.
`local-agent sessions`	List or inspect saved sessions.
`local-agent profiles`	Inspect configured model profiles.
`local-agent memory`	Inspect model memory and VRAM settings.
`local-agent calibrate`	Calculate safe worker bounds for a VRAM budget.
`local-agent benchmark`	Run the local coding benchmark.
`local-agent doctor`	Diagnose Python, Git, Ollama, RAM, VRAM, and models.
`local-agent monitor`	Serve the local metrics dashboard.
`local-agent test-run --mock`	Run deterministic local workflow checks.

Examples:

local-agent chat "Explain what context_manager.py does"

local-agent chat --repl --session-id refactor-session local-agent delegate --task task.json --model qwen2.5-coder:7b --json local-agent apply --patch-file patch.diff --workspace . --file src/foo.py --check "pytest tests/" local-agent monitor --port 8765

Open http://127.0.0.1:8765/dashboard to view queue load, latency, tokens per second, and change records.

The CLI also exposes preview surfaces that are not part of the stable contract and may change without notice: ui/app, lsp, serve-acp, skeletonize, lint-patch, scan-models, spill-read, and grep. The stable, tested set is the table above plus desktop, chat, serve-mcp, init-mcp, and init-skill.

Field results & limitations

The published numbers come from a 20-task suite, so treat them as directional rather than settled:

On that suite, qwen3.8-27b-q4 scored 100.0% correctness with a 95% Wilson interval of [83.9%, 100.0%], and qwen3-8b-q6k scored 90.0% [69.9%, 97.2%]. The wide intervals are the small sample size, not a rounding artifact.
Small models handle single-file work in this harness; cross-file coordination (Tier 3 in the capability ladder) needs the host agent to decompose the task into atomic envelopes first.
Results are model and hardware specific. Reproduce them with local-agent benchmark before relying on any figure.

See local_coding_agent/docs/BENCHMARK.md for the methodology and the full table.

Local models

Model profiles describe the provider, model, context window, generation limits, and keep-alive behavior. The Harness can discover installed Ollama models and local GGUF files, while the CLI exposes the same information for scripts and integrations.

local-agent profiles list --json

local-agent memory status --json

Documentation

Quickstart
FAQ
MCP integration
Security policy
Changelog
Architecture reference
Protocol reference
Benchmark methodology
Roadmap
Runtime setup tips: context, KV cache, thinking and CPU limits

The same pages open inside the Desktop Harness under Help, and local-agent help-docs prints them in a terminal.

License

Local Coding Agent is distributed under the MIT License.

Local Coding Agent

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

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

dotnet-mcp

README from the maker

Local Coding Agent

What is Local Coding Agent?

What the gate refuses

Choose your download

Current public release: v1.2.0

Desktop Harness

CLI installation

Connect another AI coding agent

A typical coding request

CLI commands

Field results & limitations

Local models

Documentation

License