Lingma IPC Proxy

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Lingma IPC Proxy exposes Tongyi Lingma's local IDE plugin capability as standard OpenAI-compatible and Anthropic-compatible HTTP APIs. It can be used as a CLI proxy service or as a cross-platform desktop app for macOS and Windows.

The project is designed for tools such as Claude Code, Cline, Continue, OpenCode, custom agents, and any client that can talk to OpenAI or Anthropic style APIs.

Current Version

The current desktop line is v1.2.1.

Release builds are produced by GitHub Actions for:

Asset	Platform	Purpose
lingma-ipc-proxy_<tag>_darwin_arm64.tar.gz	macOS	CLI proxy
lingma-ipc-proxy_<tag>_windows_amd64.zip	Windows	CLI proxy
lingma-ipc-proxy-desktop_<tag>_darwin_arm64.zip	macOS	Desktop app
lingma-ipc-proxy-desktop_<tag>_windows_amd64.zip	Windows	Desktop app
lingma-ipc-proxy_<tag>_sha256.txt	all	Checksums

Desktop App

The desktop app wraps the proxy with a native-feeling control panel:

• Start, stop, and restart the proxy.
• Inspect health, latency, recent requests, models, settings, and logs.
• View full request and response bodies with internal scrolling and hidden scrollbars.
• Copy endpoint URLs, model IDs, request logs, and response logs with visible feedback.
• Detect Lingma IPC paths automatically on macOS and Windows, with manual fallback settings.
• Follow system theme automatically, or switch light/dark mode manually.
• Keep the proxy running when the window is closed; quit explicitly from the app/menu.

Screenshots

Light mode:

Dark mode:

Narrow window layout:

Supported APIs

API	Endpoint	Support
Health	GET / and GET /health	supported
Models	GET /v1/models	supported
OpenAI Chat Completions	POST /v1/chat/completions	streaming and non-streaming
Anthropic Messages	POST /v1/messages	streaming and non-streaming

What This Fork Adds

Compared with the original protocol proof of concept, this repository focuses on making the proxy usable as a complete local product:

• Function Calling / Tools for both OpenAI and Anthropic clients.
• Tool result continuation for multi-step agent loops.
• Image input for OpenAI image_url and Anthropic image blocks.
• More request parameter compatibility so stricter clients can connect without custom patches.
• Full request and response recording in the desktop app for debugging 400/500 errors.
• macOS and Windows desktop app with start/stop/restart, settings, logs, model discovery, themes, and window lifecycle handling.
• Cross-platform release packaging for CLI and desktop builds.

OpenAI Compatibility

The proxy accepts common OpenAI request fields:

• model, messages, stream
• temperature, top_p, stop
• max_tokens, max_completion_tokens
• presence_penalty, frequency_penalty
• tools, tool_choice, parallel_tool_calls
• response_format, seed, user, reasoning_effort
• image input through image_url data URLs or HTTP URLs

Anthropic Compatibility

The proxy accepts common Anthropic request fields:

• model, system, messages, stream
• temperature, top_p, top_k, stop_sequences
• max_tokens, metadata
• tools, tool_choice
• image blocks through base64 sources
• tool result continuation blocks

Architecture

flowchart LR
  Client["OpenAI / Anthropic Client"] --> HTTP["HTTP API Layer"]
  Desktop["Desktop App"] --> AppBridge["Wails Go Bridge"]
  AppBridge --> Service["Proxy Service"]
  HTTP --> Service
  Service --> Session["Session Manager"]
  Service --> Tools["Tool Emulation"]
  Service --> Models["Model Discovery"]
  Service --> Transport["Lingma Transport"]
  Transport --> Pipe["Windows Named Pipe"]
  Transport --> WS["macOS / Windows WebSocket"]
  Pipe --> Lingma["Tongyi Lingma IDE Plugin"]
  WS --> Lingma

Module Layout

Path	Responsibility
cmd/lingma-ipc-proxy	CLI entrypoint, config loading, signal handling
internal/httpapi	OpenAI/Anthropic HTTP routes, streaming SSE responses, request recording
internal/service	request orchestration, sessions, model discovery, proxy lifecycle
internal/lingmaipc	Lingma JSON-RPC transport over Named Pipe and WebSocket
internal/toolemulation	tool definition injection, action block parsing, tool result projection
desktop	Wails desktop shell, native window commands, proxy control bridge
desktop/frontend	Vue UI for dashboard, requests, models, settings, and logs
.github/workflows/release.yml	CI release pipeline for macOS and Windows CLI/Desktop packages

Transport Detection

Platform	Default transport	Detection
macOS	WebSocket	reads Lingma SharedClientCache files under user application support paths
Windows	Named Pipe / WebSocket	scans Lingma named pipes and shared cache hints
Linux	WebSocket	manual --ws-url is recommended

If auto detection fails, set the path manually in the desktop Settings page or pass CLI flags:

lingma-ipc-proxy --transport websocket --ws-url ws://127.0.0.1:36510 --port 8095
lingma-ipc-proxy --transport pipe --pipe-name '\\.\pipe\lingma-ipc'

Quick Start

Desktop App

1. Install VS Code and the Tongyi Lingma extension.
2. Log in to Tongyi Lingma and verify the Lingma panel can chat normally.
3. Download the desktop asset from Releases.
4. Start Lingma IPC Proxy.
5. Click 探测模型 after the proxy is running.
6. Configure clients to use http://127.0.0.1:8095.

CLI

git clone https://github.com/Lutiancheng1/lingma-ipc-proxy.git
cd lingma-ipc-proxy
go build -o ./dist/lingma-ipc-proxy ./cmd/lingma-ipc-proxy
./dist/lingma-ipc-proxy --host 127.0.0.1 --port 8095 --session-mode auto

Windows:

.\scripts\build.ps1
.\dist\lingma-ipc-proxy.exe --host 127.0.0.1 --port 8095 --session-mode auto

Client Configuration

Claude Code

export ANTHROPIC_BASE_URL="http://127.0.0.1:8095"
export ANTHROPIC_API_KEY="any"

Then select a model in Claude Code:

/model Qwen3-Coder

Cline

• Provider: OpenAI Compatible
• Base URL: http://127.0.0.1:8095/v1
• API Key: any
• Model ID: Qwen3-Coder

Continue

{
  "models": [
    {
      "title": "Lingma Proxy",
      "provider": "openai",
      "model": "Qwen3-Coder",
      "apiKey": "any",
      "apiBase": "http://127.0.0.1:8095/v1"
    }
  ]
}

Models

The proxy reports the models exposed by the Lingma plugin. The desktop app does not force a global model switch; the calling client should specify the model field. Clicking a model in the desktop app copies its model ID.

Observed model IDs include:

• Auto
• Kimi-K2.6
• MiniMax-M2.7
• Qwen3-Coder
• Qwen3-Max
• Qwen3-Thinking
• Qwen3.6-Plus

For tool-heavy coding workflows, Qwen3-Coder is the recommended first choice.

Configuration

Default config file:

./lingma-ipc-proxy.json

Example:

{
  "host": "127.0.0.1",
  "port": 8095,
  "transport": "auto",
  "mode": "agent",
  "shell_type": "zsh",
  "session_mode": "auto",
  "timeout": 120,
  "cwd": "/Users/you/project",
  "current_file_path": ""
}

Priority order:

1. built-in defaults
2. JSON config file
3. environment variables
4. command-line flags
5. desktop Settings page updates

Concurrency

Older builds rejected concurrent chat requests with a rate_limit_error saying the proxy handled one request at a time. Current builds use a small execution pool instead:

• default max concurrent chat requests: 4
• override with LINGMA_PROXY_MAX_CONCURRENT
• allowed range: 1 to 16
• session_mode=auto uses fresh Lingma sessions so parallel editor requests do not share one sticky session

Example:

LINGMA_PROXY_MAX_CONCURRENT=8 lingma-ipc-proxy --port 8095

Function Calling / Tool Calling

Lingma does not expose a native public OpenAI/Anthropic tool-call protocol, so this proxy emulates tool calling:

1. Normalize OpenAI or Anthropic tool definitions.
2. Inject tool contracts into the Lingma prompt.
3. Parse model action blocks from the response.
4. Convert parsed actions back into OpenAI tool_calls or Anthropic tool_use.
5. Feed tool results back into Lingma for continuation.

This is most reliable with Qwen3-Coder.

Local Desktop Build

Install Wails:

go install github.com/wailsapp/wails/v2/cmd/wails@v2.12.0

Build macOS:

npm ci --prefix desktop/frontend
cd desktop
wails build -platform darwin/arm64 -clean

Build Windows on Windows:

npm ci --prefix desktop/frontend
cd desktop
wails build -platform windows/amd64 -clean

The desktop bundle name is always Lingma IPC Proxy.

Release Plan

The release workflow is triggered by:

• pushing a tag such as v1.2.1
• manually running the Release workflow with a tag input

Planned improvements:

• macOS signing and notarization
• Windows installer packaging
• configurable log retention
• request export/import
• richer model metadata display
• optional Linux desktop packaging after the Lingma transport story is stable

Acknowledgements

This project is based on the protocol insight and initial discovery work from coolxll/lingma-ipc-proxy. The core idea of connecting to Lingma's private local IPC protocol and exposing standard API endpoints came from that project. This fork extends the implementation with broader OpenAI/Anthropic compatibility, tool emulation, image handling, desktop app support, request/log inspection, cross-platform packaging, and release automation.