Initial commit: Codex Launcher — Any AI Provider

Multi-provider integration for OpenAI Codex CLI/Desktop. - Translation proxy: Responses API ↔ Chat Completions / Anthropic Messages - GTK launcher with endpoint management, provider presets, Desktop/CLI launch - Codex Default mode (built-in OAuth, zero config) - Browser UA injection for Cloudflare-protected providers - Streaming SSE, tool calls, reasoning content support - Profile backup/import, model auto-fetch, bulk import - Zero pip dependencies (pure Python stdlib + GTK)
2026-05-19 14:57:31 +04:00
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+*.bak
+*.pyc
+__pycache__
+.endpoints.json
+config.toml
+*.log
+.cache/
+.codex/
+*.swp
+*~
+.DS_Store
--- a/README.md
+++ b/README.md
@@ -0,0 +1,447 @@
+<p align="center">
+  <a href="https://z.ai/subscribe?ic=ROK78RJKNW">
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+<p align="center">
+  <strong>Get 10% OFF Z.AI coding plans</strong><br/>
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+</p>
+
+---
+
+<h1 align="center">Codex Launcher — Any AI Provider</h1>
+
+<p align="center">
+  <strong>Run OpenAI Codex CLI &amp; Desktop with <em>any</em> AI provider.</strong><br/>
+  OpenCode &bull; Z.AI &bull; Anthropic &bull; OpenRouter &bull; Crof.ai &bull; NVIDIA NIM &bull; Kilo.ai &bull; and more
+</p>
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Python-3.8+-blue?logo=python&logoColor=white" alt="Python 3.8+" />
+  <img src="https://img.shields.io/badge/GTK-3.0-green?logo=gtk&logoColor=white" alt="GTK 3.0" />
+  <img src="https://img.shields.io/badge/License-MIT-yellow" alt="MIT License" />
+  <img src="https://img.shields.io/badge/Zero_Pip_Dependencies-✓-brightgreen" alt="Zero pip dependencies" />
+</p>
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Responses_API-✓-success" /> 
+  <img src="https://img.shields.io/badge/Chat_Completions-✓-success" /> 
+  <img src="https://img.shields.io/badge/Anthropic_Messages-✓-success" /> 
+  <img src="https://img.shields.io/badge/Streaming_SSE-✓-success" />
+  <img src="https://img.shields.io/badge/Tool_Calls-✓-success" />
+</p>
+
+---
+
+## The Problem
+
+OpenAI's Codex CLI v2.0+ exclusively uses the **Responses API** — a protocol that is incompatible with virtually every other AI provider:
+
+| Provider | API | Works with Codex? |
+|----------|-----|:-:|
+| OpenAI | Responses API | ✅ |
+| Z.AI | Chat Completions | ❌ |
+| OpenCode | Chat Completions | ❌ |
+| Anthropic | Messages API | ❌ |
+| Ollama | Chat Completions | ❌ |
+| OpenRouter | Chat Completions | ❌ |
+| NVIDIA NIM | Chat Completions | ❌ |
+| Crof.ai | Chat Completions | ❌ |
+
+The protocols differ in **endpoint paths**, **message formats**, **tool-call structures**, **streaming events**, and **completion semantics**. You can't just swap a base URL.
+
+## The Solution
+
+A three-component system:
+
+1. **Translation Proxy** — translates Responses API ↔ Chat Completions / Anthropic Messages in real-time
+2. **Config Engine** — generates Codex config files on-the-fly per provider, with backup/restore
+3. **GTK Launcher** — manages endpoints, launches Desktop or CLI, handles the full lifecycle
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│                         Codex Launcher GUI                          │
+│                    (endpoint management + lifecycle)                │
+└──────────┬─────────────────┬──────────────────┬────────────────────┘
+           │                 │                  │
+    ┌──────▼──────┐  ┌──────▼──────┐  ┌────────▼─────────┐
+    │  Codex      │  │  Native     │  │  Translation     │
+    │  Default    │  │  OpenAI     │  │  Proxy           │
+    │  (remove    │  │  (direct    │  │  (port 8080)     │
+    │  config)    │  │  URL)       │  │                  │
+    └──────┬──────┘  └──────┬──────┘  └────────┬─────────┘
+           │                │                   │
+           ▼                ▼          ┌────────┴────────┐
+    ┌──────────────┐ ┌───────────┐    │                 │
+    │ Built-in     │ │ config.   │    ▼                 ▼
+    │ Codex OAuth  │ │ toml      │ ┌────────────┐ ┌───────────┐
+    └──────────────┘ └───────────┘ │ OpenAI     │ │ Anthropic │
+                                   │ Chat Comp. │ │ Messages  │
+                                   └────────────┘ └───────────┘
+```
+
+---
+
+## Features
+
+### Multi-Provider Support
+- **Native OpenAI** — direct connection, no proxy needed
+- **OpenAI-compatible** — Z.AI, OpenCode Zen/Go, Crof.ai, NVIDIA NIM, Kilo.ai, OpenRouter, Ollama, Together, Groq, and any provider with a Chat Completions endpoint
+- **Anthropic** — Claude models via the Messages API
+- **Codex Default** — built-in Codex OAuth with official models, zero config
+
+### Translation Proxy (`translate-proxy.py`)
+- Full Responses API ↔ Chat Completions / Anthropic Messages bidirectional translation
+- **Streaming SSE** support with proper event sequencing (`response.created` → `response.output_text.delta` → `response.completed`)
+- **Tool calls** — full function calling support including parallel tool calls
+- **Reasoning content** — forwards `reasoning_content` fields from providers that support it
+- **Browser UA injection** — bypasses Cloudflare bot detection for providers like OpenCode
+- **Smart URL construction** — prevents double-path bugs (`/v1/chat/completions/chat/completions`)
+- **Header forwarding** — preserves client identity headers while filtering hop-by-hop headers
+- Zero dependencies — pure Python stdlib
+
+### GTK Launcher (`codex-launcher-gui`)
+- **Endpoint manager** — add, edit, delete, set default providers
+- **Provider presets** — one-click setup for 10+ providers with pre-filled URLs and model lists
+- **Model auto-fetch** — pulls available models directly from provider APIs
+- **Bulk model import** — paste a comma/newline-separated list of model IDs
+- **Launch Desktop** — starts Codex Desktop with the selected provider and model
+- **Launch CLI** — opens Codex CLI in a terminal with the selected provider
+- **Codex Default** — launch with built-in OAuth, no proxy or custom config
+- **Profile backup/import** — export and import endpoint configurations as portable JSON bundles
+- **Threaded operations** — model refresh runs in background, UI stays responsive
+- **Process lifecycle** — stall detection, kill/cleanup, config backup/restore around sessions
+- **Config normalization** — automatically strips stale API path suffixes from URLs
+
+### Process Management
+- Kills stale electron/webview/app-server processes from previous sessions
+- Removes stale PID files and sockets
+- Manages proxy lifecycle (start, health-check, stop)
+- Config backup before launch, automatic restore after exit
+
+---
+
+## Technology Stack
+
+| Component | Technology | Why |
+|-----------|-----------|-----|
+| Translation Proxy | Python 3 stdlib (`http.server`, `urllib`, `json`) | Zero dependencies, runs anywhere |
+| GUI Launcher | Python 3 + GTK 3.0 (`PyGObject/gi`) | Native Linux desktop integration |
+| Config Engine | Python 3 (`toml` generation, `json` catalogs) | Dynamic, no hardcoded configs |
+| Process Mgmt | bash + `os.setsid`/`os.killpg` | Unix process group lifecycle |
+| Streaming | Server-Sent Events (SSE) | Required by Codex Responses API |
+| API Translation | Responses API ↔ Chat Completions / Anthropic Messages | Protocol bridging |
+
+**Zero pip dependencies.** Everything uses Python stdlib + system GTK bindings.
+
+---
+
+## Quick Start
+
+### Prerequisites
+
+- **Codex CLI** ≥ 2.0 (`npm install -g @openai/codex` or bundled with Codex Desktop)
+- **Codex Desktop** installed at `/opt/codex-desktop/` (optional, for Desktop mode)
+- **Python 3.8+** (stdlib only)
+- **python3-gi** for GTK (`sudo apt install python3-gi`)
+- bash, curl, lsof
+
+### Install
+
+```bash
+git clone https://github.rommark.dev/admin/Codex-Launcher---Any-AI-Porovider.git
+cd Codex-Launcher---Any-AI-Porovider
+./install.sh
+```
+
+### Run
+
+Open **Codex Launcher** from your app grid, or:
+
+```bash
+codex-launcher-gui
+```
+
+### First Launch
+
+1. Click **Manage Endpoints** → **Add**
+2. Select a provider preset (e.g., "OpenCode Zen (OpenAI-compatible)")
+3. Enter your API key
+4. Click **Fetch from API** to auto-discover models, or add them manually
+5. Click **Save**
+6. Select the endpoint and model from the dropdowns
+7. Click **Launch Desktop** or **Launch CLI**
+
+---
+
+## Development Journey
+
+### Phase 1: The Z.AI Proxy — Protocol Reverse Engineering
+
+**Problem:** Codex CLI v2.0 switched exclusively to the Responses API. Z.AI (and every other provider) uses Chat Completions. They are fundamentally incompatible.
+
+**Approach:**
+1. Captured Codex's HTTP traffic to understand the exact Responses API request/response shape
+2. Mapped the protocol differences:
+   - `/v1/responses` → `/chat/completions` (endpoint)
+   - `input` array with typed items → `messages` array with role/content (message format)
+   - `function_call` items → `tool_calls` array on assistant messages (tool format)
+   - SSE `response.output_text.delta` events → `delta.content` chunks (streaming)
+3. Built the initial `zai-proxy.py` — a 200-line HTTP server that translates in both directions
+4. Hit a critical bug: Codex hung in "thinking" state. Discovered that merely emitting `response.done` is insufficient — the `response.completed` event **must** contain the full output item array
+5. Added streaming SSE with proper event sequencing — this was the breakthrough that made it work
+
+**Testing:** Manual end-to-end testing with `curl`, Codex CLI `--profile zai`, and Codex Desktop. Verified streaming, tool calls, and reasoning content.
+
+### Phase 2: Multi-Provider Architecture — The Unified Proxy
+
+**Problem:** Maintaining separate proxies for each provider (Z.AI, Anthropic, OpenRouter, etc.) was unmaintainable.
+
+**Approach:**
+1. Abstracted the translation into a backend plugin architecture:
+   - `openai-compat` backend: translates Responses → Chat Completions (works for any OpenAI-compatible API)
+   - `anthropic` backend: translates Responses → Anthropic Messages API
+2. Unified config loading: JSON config file, CLI arguments, and environment variables
+3. Shared HTTP server, model serving, and SSE framework
+4. Per-backend: message conversion, tool conversion, response conversion, stream conversion
+
+**Key design decisions:**
+- Pure Python stdlib — no Flask, no aiohttp, no pip dependencies. The proxy must work on any system with Python 3.
+- `http.server.BaseHTTPRequestHandler` — simple, synchronous, but sufficient for single-user desktop use
+- Config via JSON file — the launcher writes a proxy config to `~/.cache/codex-proxy/` for each endpoint
+
+### Phase 3: The GTK Launcher — Desktop Integration
+
+**Problem:** Users had to manage config files, start proxies manually, and remember which wrapper script to use.
+
+**Approach:**
+1. Built a GTK 3.0 GUI with three layers: main window, endpoint manager dialog, edit endpoint dialog
+2. Implemented dynamic config generation: for each launch, generate `config.toml` with the right provider definition, profile, and model catalog
+3. Model catalog generation with dual field naming (`slug` + `model`, `supported_reasoning_levels` + `supportedReasoningEfforts`) — required because Codex CLI and Codex Desktop use different field names
+4. Process lifecycle: backup config → cleanup stale processes → start proxy → write config → launch Codex → wait → restore config → stop proxy
+
+**Threading challenges:**
+- GTK requires all UI updates on the main loop. Used `GLib.idle_add()` for all cross-thread communication
+- Model refresh was blocking the UI — moved to a background thread with idle_add completion callbacks
+- Proxy startup waits up to 15 seconds with health checks before proceeding
+
+### Phase 4: Endpoint Management & Provider Presets
+
+**Approach:**
+1. JSON-based endpoint storage (`~/.codex/endpoints.json`)
+2. Provider presets with pre-filled URLs and model lists for common providers
+3. Auto-fetch models from provider `/v1/models` endpoints
+4. Bulk model import (paste comma/newline-separated model IDs)
+5. Profile backup/import — portable JSON bundles with endpoints + config
+
+**URL normalization:** Discovered that saved URLs sometimes had `/chat/completions` appended from manual entry. Added `normalize_base_url()` that strips trailing API path suffixes to prevent double-path bugs.
+
+### Phase 5: Cloudflare Bot Detection — The OpenCode 403 Saga
+
+**Problem:** OpenCode Zen/Go returned 403 (Cloudflare error 1010) for all requests.
+
+**Investigation:**
+1. Tested direct `curl` requests — all returned 403 regardless of auth header type (Bearer, x-api-key, both, none)
+2. Examined Codex logs — found `turn.has_metadata_header=true` and `error code: 1010`
+3. Error 1010 is Cloudflare's "Access denied" — bot detection based on User-Agent and browser headers
+4. Python's `urllib` sends `User-Agent: Python-urllib/3.x` which triggers the block
+
+**Solution:**
+1. Added `_BROWSER_HEADERS` — a set of Chrome-like headers (User-Agent, Sec-Ch-Ua, Sec-Fetch-*)
+2. `forwarded_headers()` with `browser_ua=True` — injects browser headers while preserving incoming client headers and filtering hop-by-hop headers
+3. Applied only to `openai-compat` backend where Cloudflare is common
+4. This resolved the 403 — upstream now returns 401 (auth) instead of 403 (bot block), confirming the headers work
+
+### Phase 6: Codex Default Mode — OAuth Without Config
+
+**Problem:** Users wanted to quickly switch back to built-in Codex OAuth without maintaining a separate endpoint.
+
+**Approach:**
+1. Added "Codex Default (Desktop)" and "Codex Default (CLI)" buttons
+2. On launch: backup config → **delete** `config.toml` entirely → start Codex → restore config after exit
+3. Key insight: writing empty strings (`model = ""`, `model_provider = ""`) causes Codex to error with "Model provider `` not found". The config must not exist at all for Codex to fall back to built-in defaults.
+
+---
+
+## Architecture Deep Dive
+
+### Request Flow (OpenAI-compatible provider)
+
+```
+Codex CLI/Desktop
+    │
+    │ POST /responses (Responses API)
+    │ Body: { model: "glm-5.1", input: [...], stream: true }
+    ▼
+┌─────────────────────────────────┐
+│  translate-proxy.py (port 8080) │
+│                                 │
+│  1. Parse Responses API body    │
+│  2. Convert input → messages    │
+│  3. Convert tools format        │
+│  4. Inject browser UA headers   │
+│                                 │
+│  POST /v1/chat/completions      │──→  Upstream Provider
+│  Body: { model, messages, ... }│      (opencode.ai, z.ai, etc.)
+│                                 │
+│  5. Receive Chat Comp. response │  ←── SSE stream or JSON
+│  6. Convert response format     │
+│  7. Emit Responses API SSE      │
+│                                 │
+└─────────────────────────────────┘
+    │
+    │ SSE: response.created
+    │ SSE: response.output_text.delta  (streamed tokens)
+    │ SSE: response.output_text.done
+    │ SSE: response.completed
+    ▼
+Codex CLI/Desktop (receives Responses API events)
+```
+
+### Config Lifecycle
+
+```
+┌─────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌─────────┐
+│ Backup   │───→│ Cleanup  │───→│ Generate │───→│ Launch   │───→│ Restore │
+│ config   │    │ stale    │    │ config   │    │ Codex    │    │ config  │
+│ .toml    │    │ processes│    │ + models │    │ process  │    │ from    │
+│          │    │          │    │          │    │          │    │ backup  │
+└─────────┘    └──────────┘    └──────────┘    └──────────┘    └─────────┘
+```
+
+### Model Catalog Format
+
+The launcher generates model catalog JSON with dual field naming to satisfy both Codex CLI and Codex Desktop:
+
+```json
+{
+  "models": [{
+    "slug": "glm-5.1",          // CLI reads this
+    "model": "glm-5.1",         // Desktop reads this
+    "supported_reasoning_levels": [...],  // CLI
+    "supportedReasoningEfforts": [...],   // Desktop
+    ...
+  }]
+}
+```
+
+---
+
+## Provider Presets
+
+| Preset | Backend | Base URL |
+|--------|---------|----------|
+| OpenAI | Native | `https://api.openai.com/v1` |
+| Anthropic | Anthropic | `https://api.anthropic.com/v1` |
+| OpenCode Zen | OpenAI-compat | `https://opencode.ai/zen/v1` |
+| OpenCode Go | OpenAI-compat | `https://opencode.ai/zen/go/v1` |
+| Crof.ai | OpenAI-compat | `https://crof.ai/v1` |
+| NVIDIA NIM | OpenAI-compat | `https://integrate.api.nvidia.com/v1` |
+| Kilo.ai | OpenAI-compat | `https://api.kilo.ai/api/gateway` |
+| OpenRouter | OpenAI-compat | `https://openrouter.ai/api/v1` |
+| Z.AI | OpenAI-compat | `https://api.z.ai/api/coding/paas/v4` |
+| Custom | Any | User-defined |
+
+---
+
+## File Structure
+
+```
+src/
+├── translate-proxy.py            # Translation proxy (openai-compat + anthropic)
+├── codex-launcher-gui            # GTK launcher GUI
+├── cleanup-codex-stale.sh        # Stale process cleanup
+└── codex-launcher.desktop.template  # Desktop entry template
+
+install.sh                        # One-command installer
+README.md                         # This file
+```
+
+### Installed Locations
+
+```
+~/.local/bin/translate-proxy.py       # Proxy
+~/.local/bin/codex-launcher-gui       # Launcher
+~/.local/bin/cleanup-codex-stale.sh   # Cleanup
+~/.local/share/applications/codex-launcher.desktop  # App grid entry
+~/.codex/endpoints.json               # Endpoint storage
+~/.codex/config.toml                  # Codex config (auto-generated)
+~/.cache/codex-proxy/                 # Proxy configs + model catalogs
+```
+
+---
+
+## Troubleshooting
+
+| Issue | Cause | Fix |
+|-------|-------|-----|
+| Window opens then disappears | Stale processes from previous session | Click **Kill && Cleanup** |
+| Window never opens | Startup freeze | Kill && Cleanup, then retry |
+| "Model provider `` not found" | Empty strings in config | V2 deletes config entirely for Default mode |
+| 403 Forbidden from OpenCode | Cloudflare bot detection | Proxy injects browser UA headers |
+| 401 Unauthorized from OpenCode | Invalid key or no credits | Check API key and billing |
+| Double path in URL | Stale `/chat/completions` in base URL | `normalize_base_url()` strips suffixes |
+| Proxy stops when terminal closes | SIGHUP to subprocess | Launcher uses `os.setsid` process groups |
+| Models not showing in picker | Wrong model catalog format | Must have both `slug` + `model` fields |
+| Codex hangs in "thinking" | Missing `response.completed` | Proxy emits full SSE event sequence |
+
+---
+
+## Adding a New Provider
+
+1. Click **Manage Endpoints** → **Add**
+2. Choose a preset or set **Custom**
+3. Set backend type: `OpenAI-compatible`, `Anthropic`, or `Native`
+4. Enter base URL and API key
+5. Click **Fetch from API** or add models manually
+6. Save and launch
+
+For providers behind Cloudflare, the proxy automatically injects browser headers. For providers with non-standard APIs, add a new backend module to `translate-proxy.py` following the `oa_*` / `an_*` pattern.
+
+---
+
+## Manual Proxy Usage
+
+```bash
+# Start proxy for any OpenAI-compatible provider
+python3 src/translate-proxy.py \
+  --backend openai-compat \
+  --target-url https://api.your-provider.com/v1 \
+  --api-key YOUR_KEY \
+  --port 8080
+
+# Or use a JSON config
+python3 src/translate-proxy.py --config my-proxy-config.json
+
+# Then run Codex
+codex --profile my-profile -c model=my-model
+```
+
+---
+
+## Requirements
+
+- Python ≥ 3.8
+- python3-gi (`sudo apt install python3-gi`)
+- Codex CLI ≥ 2.0
+- Codex Desktop (optional, for Desktop mode)
+- bash, curl, lsof
+
+**No pip dependencies.** Zero. Pure stdlib + system GTK.
+
+---
+
+## License
+
+MIT
+
+---
+
+<p align="center">
+  <strong>Get 10% OFF Z.AI coding plans</strong><br/>
+  <a href="https://z.ai/subscribe?ic=ROK78RJKNW">
+    <img src="https://img.shields.io/badge/Claim_Your_Discount-10%25_OFF-6366f1?style=for-the-badge&labelColor=4f46e5" alt="Z.AI 10% OFF" />
+  </a>
+</p>
--- a/install.sh
+++ b/install.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+BIN_DIR="$HOME/.local/bin"
+APP_DIR="$HOME/.local/share/applications"
+
+mkdir -p "$BIN_DIR" "$APP_DIR"
+
+cp "$SCRIPT_DIR/src/translate-proxy.py" "$BIN_DIR/"
+cp "$SCRIPT_DIR/src/codex-launcher-gui" "$BIN_DIR/"
+cp "$SCRIPT_DIR/src/cleanup-codex-stale.sh" "$BIN_DIR/"
+
+chmod +x "$BIN_DIR/translate-proxy.py"
+chmod +x "$BIN_DIR/codex-launcher-gui"
+chmod +x "$BIN_DIR/cleanup-codex-stale.sh"
+
+USERNAME=$(whoami)
+sed "s/YOUR_USERNAME/$USERNAME/g" "$SCRIPT_DIR/src/codex-launcher.desktop.template" > "$APP_DIR/codex-launcher.desktop"
+
+update-desktop-database "$APP_DIR" 2>/dev/null || true
+
+echo "Installed."
+echo "  translate-proxy.py   -> $BIN_DIR/translate-proxy.py"
+echo "  codex-launcher-gui   -> $BIN_DIR/codex-launcher-gui"
+echo "  cleanup-codex-stale  -> $BIN_DIR/cleanup-codex-stale.sh"
+echo "  desktop entry        -> $APP_DIR/codex-launcher.desktop"
+echo ""
+echo "Open 'Codex Launcher' from your app grid, or run: codex-launcher-gui"
--- a/src/cleanup-codex-stale.sh
+++ b/src/cleanup-codex-stale.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+# Cleanup script for Codex Desktop - kills stale processes before launch
+
+echo "Cleaning up stale Codex processes..." >&2
+
+# Kill codex app-server processes
+for pid in $(ps aux 2>/dev/null | grep -E "codex .*app-server" | grep -v grep | awk '{print $2}'); do
+  kill -9 "$pid" 2>/dev/null || true
+  echo "  Killed app-server pid=$pid"
+done
+
+# Kill webview server
+for pid in $(ps aux 2>/dev/null | grep webview-server.py | grep -v grep | awk '{print $2}'); do
+  kill -9 "$pid" 2>/dev/null || true
+  echo "  Killed webview-server pid=$pid"
+done
+
+# Kill main electron process for codex-desktop
+for pid in $(ps aux 2>/dev/null | grep "/opt/codex-desktop/electron" | grep "class=codex-desktop" | grep -v grep | awk '{print $2}'); do
+  kill -9 "$pid" 2>/dev/null || true
+  echo "  Killed electron pid=$pid"
+done
+
+# Kill all remaining child processes of codex-desktop
+for pid in $(ps aux 2>/dev/null | grep "/opt/codex-desktop/" | grep -v grep | awk '{print $2}'); do
+  kill -9 "$pid" 2>/dev/null || true
+done
+
+# Kill zai proxy (if any)
+for pid in $(ps aux 2>/dev/null | grep zai-proxy.py | grep -v grep | awk '{print $2}'); do
+  kill "$pid" 2>/dev/null || true
+done
+
+# Kill unified translation proxy (if any)
+for pid in $(ps aux 2>/dev/null | grep translate-proxy.py | grep -v grep | awk '{print $2}'); do
+  kill "$pid" 2>/dev/null || true
+done
+
+# Remove stale socket and PID files
+rm -f "$HOME/.codex/.launch-action-socket" 2>/dev/null || true
+rm -f "$HOME/.codex/.codex-desktop-launch-action" 2>/dev/null || true
+rm -f "$HOME/.local/share/codex-desktop/.launch-action-socket" 2>/dev/null || true
+rm -f "$HOME/.cache/codex-desktop/.launch-action-socket" 2>/dev/null || true
+rm -f "$HOME/.local/share/codex-desktop/.codex-desktop-pid" 2>/dev/null || true
+rm -f "$HOME/.cache/codex-desktop/.codex-desktop-pid" 2>/dev/null || true
+rm -f "$HOME/.local/share/codex-desktop/.webview-pid" 2>/dev/null || true
+rm -f "$HOME/.cache/codex-desktop/.webview-pid" 2>/dev/null || true
+
+sleep 1
+
+# Verify no remaining process on port 5175 (webview)
+if lsof -ti :5175 2>/dev/null | grep -q .; then
+  echo "  Warning: Port 5175 still in use"
+  lsof -ti :5175 2>/dev/null | xargs kill -9 2>/dev/null || true
+fi
+
+echo "Cleanup complete"
--- a/src/codex-launcher-gui
+++ b/src/codex-launcher-gui
--- a/src/codex-launcher.desktop.template
+++ b/src/codex-launcher.desktop.template
@@ -0,0 +1,10 @@
+[Desktop Entry]
+Type=Application
+Name=Codex Launcher
+Comment=Launch Codex Desktop with any AI provider
+Exec=/home/YOUR_USERNAME/.local/bin/codex-launcher-gui
+Icon=/opt/codex-desktop/resources/app.asar.unpacked/images/icon.png
+Terminal=false
+StartupNotify=true
+Categories=Development;IDE;
+Keywords=ai;coding;launcher;
--- a/src/translate-proxy.py
+++ b/src/translate-proxy.py
@@ -0,0 +1,632 @@
+#!/usr/bin/env python3
+"""
+translate-proxy.py — Responses API → backend API translation proxy.
+
+Backends:
+  openai-compat — any OpenAI-compatible Chat Completions API
+  anthropic     — Anthropic Messages API
+
+Usage:
+  python3 translate-proxy.py --config proxy-config.json
+  python3 translate-proxy.py --backend openai-compat --target-url https://... --api-key sk-...
+"""
+
+import json, http.server, urllib.request, time, uuid, os, sys, argparse
+
+# ═══════════════════════════════════════════════════════════════════
+# Config
+# ═══════════════════════════════════════════════════════════════════
+
+DEFAULT_MODELS = {
+    "openai-compat": [
+        {"id": "gpt-4o-mini", "object": "model", "created": 1700000000, "owned_by": "custom"},
+    ],
+    "anthropic": [
+        {"id": "claude-sonnet-4-20250514", "object": "model", "created": 1700000000, "owned_by": "anthropic"},
+    ],
+}
+
+def load_config():
+    p = argparse.ArgumentParser(description="Responses API translation proxy")
+    p.add_argument("--config", help="JSON config file path")
+    p.add_argument("--port", type=int, default=None)
+    p.add_argument("--backend", default=None, choices=["openai-compat", "anthropic"])
+    p.add_argument("--target-url", default=None)
+    p.add_argument("--api-key", default=None)
+    p.add_argument("--models-file", default=None, help="JSON file with model list array")
+    args = p.parse_args()
+
+    cfg = {}
+    if args.config:
+        with open(args.config) as f:
+            cfg = json.load(f)
+
+    for ck, ak in [("port", "port"), ("backend_type", "backend"),
+                    ("target_url", "target_url"), ("api_key", "api_key")]:
+        v = getattr(args, ak, None)
+        if v is not None:
+            cfg[ck] = v
+
+    env_map = {
+        "port": ("PROXY_PORT", "ZAI_PROXY_PORT", int),
+        "backend_type": ("PROXY_BACKEND", None, str),
+        "target_url": ("PROXY_TARGET_URL", "ZAI_BASE_URL", str),
+        "api_key": ("PROXY_API_KEY", "ZAI_API_KEY", str),
+    }
+    for ck, (ev1, ev2, conv) in env_map.items():
+        if ck not in cfg:
+            v = os.environ.get(ev1) or (os.environ.get(ev2) if ev2 else None)
+            if v:
+                cfg[ck] = conv(v) if conv == int else v
+
+    cfg.setdefault("port", 8080)
+    cfg.setdefault("backend_type", "openai-compat")
+    cfg.setdefault("target_url", "http://localhost:11434/v1")
+    cfg.setdefault("api_key", "")
+
+    models = cfg.get("models", [])
+    if not models and args.models_file:
+        with open(args.models_file) as f:
+            models = json.load(f)
+    if not models:
+        models = DEFAULT_MODELS.get(cfg["backend_type"], [])
+    cfg["models"] = models
+
+    return cfg
+
+CONFIG = load_config()
+PORT = CONFIG["port"]
+BACKEND = CONFIG["backend_type"]
+TARGET_URL = CONFIG["target_url"].rstrip("/")
+API_KEY = CONFIG["api_key"]
+MODELS = CONFIG["models"]
+
+# ═══════════════════════════════════════════════════════════════════
+# Shared helpers
+# ═══════════════════════════════════════════════════════════════════
+
+_pool = uuid.uuid4().hex[:8]
+
+_HOP_BY_HOP_HEADERS = {
+    "connection",
+    "keep-alive",
+    "proxy-authenticate",
+    "proxy-authorization",
+    "te",
+    "trailers",
+    "transfer-encoding",
+    "upgrade",
+    "host",
+    "content-length",
+}
+
+def uid(prefix="id"):
+    return f"{prefix}-{_pool}-{uuid.uuid4().hex[:12]}"
+
+def emit(event, data):
+    return f"event: {event}\ndata: {json.dumps(data)}\n\n"
+
+def upstream_target(base_url, suffix):
+    base = base_url.rstrip("/")
+    if base.endswith(suffix):
+        return base
+    return f"{base}{suffix}"
+
+_BROWSER_HEADERS = {
+    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/137.0.0.0 Safari/537.36",
+    "Accept": "application/json, text/event-stream, */*",
+    "Accept-Language": "en-US,en;q=0.9",
+    "Sec-Ch-Ua": '"Chromium";v="137", "Not/A)Brand";v="99"',
+    "Sec-Ch-Ua-Mobile": "?0",
+    "Sec-Ch-Ua-Platform": '"Linux"',
+    "Sec-Fetch-Dest": "empty",
+    "Sec-Fetch-Mode": "cors",
+    "Sec-Fetch-Site": "same-origin",
+}
+
+def forwarded_headers(request_headers, extra=None, browser_ua=False):
+    headers = {}
+    if browser_ua:
+        headers.update(_BROWSER_HEADERS)
+    for key, value in request_headers.items():
+        if key.lower() in _HOP_BY_HOP_HEADERS:
+            continue
+        if browser_ua and key.lower() == "user-agent":
+            continue
+        headers[key] = value
+    if extra:
+        headers.update(extra)
+    return headers
+
+# ═══════════════════════════════════════════════════════════════════
+# OpenAI-compat backend
+# ═══════════════════════════════════════════════════════════════════
+
+def oa_input_to_messages(input_data):
+    msgs = []
+    if isinstance(input_data, str):
+        msgs.append({"role": "user", "content": input_data})
+    elif isinstance(input_data, list):
+        for item in input_data:
+            t = item.get("type")
+            if t == "message":
+                role = item.get("role", "user")
+                text = ""
+                for part in item.get("content", []):
+                    pt = part.get("type", "")
+                    if pt in ("input_text", "output_text"):
+                        text += part.get("text", "")
+                    elif pt == "input_image":
+                        img = part.get("image_url", part)
+                        msgs.append({"role": role, "content": [{"type": "text", "text": text},
+                                    {"type": "image_url", "image_url": img}]})
+                        text = None
+                        break
+                if text is not None:
+                    msgs.append({"role": role, "content": text})
+            elif t == "function_call":
+                msgs.append({"role": "assistant", "content": None, "tool_calls": [
+                    {"id": item.get("call_id", item.get("id", uid("tc"))),
+                     "type": "function",
+                     "function": {"name": item.get("name", ""),
+                                  "arguments": item.get("arguments", "{}")}}
+                ]})
+            elif t == "function_call_output":
+                msgs.append({"role": "tool", "tool_call_id": item.get("id", ""),
+                             "content": item.get("output", "")})
+    return msgs
+
+def oa_convert_tools(tools):
+    if not tools:
+        return None
+    out = []
+    for t in tools:
+        if t.get("type") != "function":
+            continue
+        fn = t.get("function", {})
+        if fn:
+            out.append(t)
+        else:
+            out.append({
+                "type": "function",
+                "function": {"name": t.get("name", ""), "description": t.get("description", ""),
+                             "parameters": t.get("parameters", {})}
+            })
+    return out or None
+
+def oa_resp_to_responses(chat_resp, model, resp_id=None):
+    choice = chat_resp["choices"][0]
+    msg = choice["message"]
+    content = msg.get("content") or ""
+    finish = choice.get("finish_reason", "stop")
+    fm = {"stop": "completed", "length": "incomplete", "tool_calls": "completed", "content_filter": "incomplete"}
+    status = fm.get(finish, "incomplete")
+    outputs = []
+    rc = msg.get("reasoning_content")
+    if rc:
+        outputs.append({"type": "reasoning", "id": uid("rsn"), "status": "completed",
+                        "content": [{"type": "text", "text": rc}]})
+    if content:
+        outputs.append({"type": "message", "id": uid("msg"), "role": "assistant", "status": "completed",
+                        "content": [{"type": "output_text", "text": content, "annotations": []}]})
+    for tc in msg.get("tool_calls") or []:
+        fn = tc.get("function", {})
+        outputs.append({"type": "function_call", "id": uid("fc"), "call_id": tc.get("id"),
+                        "name": fn.get("name"), "arguments": fn.get("arguments", "{}"), "status": "completed"})
+    usage = chat_resp.get("usage", {})
+    return {"id": resp_id or uid("resp"), "object": "response", "created": int(time.time()),
+            "model": model, "status": status, "output": outputs,
+            "usage": {"input_tokens": usage.get("prompt_tokens", 0),
+                      "output_tokens": usage.get("completion_tokens", 0),
+                      "total_tokens": usage.get("total_tokens", 0),
+                      "input_tokens_details": {"cached_tokens": usage.get("prompt_tokens_details", {}).get("cached_tokens", 0)}}}
+
+def oa_stream_to_sse(chat_stream, model, req_id):
+    resp_id = req_id or uid("resp")
+    msg_id = uid("msg")
+    text_buf = ""
+    tc_buf = {}
+    fr = None
+
+    yield emit("response.created", {"type": "response.created",
+        "response": {"id": resp_id, "object": "response", "model": model,
+                     "status": "in_progress", "created": int(time.time()), "output": []}})
+    yield emit("response.in_progress", {"type": "response.in_progress", "response": {"id": resp_id}})
+    yield emit("response.output_item.added", {"type": "response.output_item.added",
+        "item": {"type": "message", "id": msg_id, "role": "assistant", "status": "in_progress", "content": []}})
+    yield emit("response.content_part.added", {"type": "response.content_part.added",
+        "part": {"type": "output_text", "text": "", "annotations": []}, "item_id": msg_id})
+
+    for line in chat_stream:
+        line = line.decode("utf-8", errors="replace").strip()
+        if not line or line.startswith(":") or line == "data: [DONE]":
+            continue
+        if not line.startswith("data: "):
+            continue
+        try:
+            chunk = json.loads(line[6:])
+        except json.JSONDecodeError:
+            continue
+        choices = chunk.get("choices", [])
+        if not choices:
+            continue
+        delta = choices[0].get("delta", {})
+        fr = choices[0].get("finish_reason")
+
+        content = delta.get("content")
+        if content:
+            text_buf += content
+            yield emit("response.output_text.delta", {"type": "response.output_text.delta",
+                        "delta": content, "item_id": msg_id, "content_index": 0})
+
+        for tc in delta.get("tool_calls") or []:
+            idx = tc.get("index", 0)
+            if idx not in tc_buf:
+                fid = uid("fc")
+                tc_buf[idx] = {"id": fid, "call_id": tc.get("id", fid), "name": "", "args": ""}
+                yield emit("response.output_item.added", {"type": "response.output_item.added",
+                    "item": {"type": "function_call", "id": fid, "call_id": tc_buf[idx]["call_id"],
+                             "name": "", "arguments": "", "status": "in_progress"}})
+            fn = tc.get("function", {})
+            if "name" in fn and fn["name"]:
+                tc_buf[idx]["name"] = fn["name"]
+            if "arguments" in fn and fn["arguments"]:
+                tc_buf[idx]["args"] += fn["arguments"]
+                yield emit("response.output_text.delta", {"type": "response.function_call_arguments.delta",
+                            "delta": fn["arguments"], "item_id": tc_buf[idx]["id"]})
+
+        rc = delta.get("reasoning_content")
+        if rc:
+            yield emit("response.reasoning.delta", {"type": "response.reasoning.delta", "delta": rc})
+
+    if text_buf:
+        yield emit("response.output_text.done", {"type": "response.output_text.done",
+                    "text": text_buf, "item_id": msg_id, "content_index": 0})
+        yield emit("response.content_part.done", {"type": "response.content_part.done",
+                    "part": {"type": "output_text", "text": text_buf, "annotations": []}, "item_id": msg_id})
+        yield emit("response.output_item.done", {"type": "response.output_item.done",
+            "item": {"type": "message", "id": msg_id, "role": "assistant", "status": "completed",
+                     "content": [{"type": "output_text", "text": text_buf, "annotations": []}]}})
+
+    for idx in sorted(tc_buf):
+        t = tc_buf[idx]
+        yield emit("response.function_call_arguments.done", {"type": "response.function_call_arguments.done",
+                    "item_id": t["id"], "name": t["name"], "arguments": t["args"]})
+        yield emit("response.output_item.done", {"type": "response.output_item.done",
+            "item": {"type": "function_call", "id": t["id"], "call_id": t["call_id"],
+                     "name": t["name"], "arguments": t["args"], "status": "completed"}})
+
+    fm = {"stop": "completed", "length": "incomplete", "tool_calls": "completed", "content_filter": "incomplete"}
+    status = fm.get(fr, "incomplete")
+    final_out = []
+    if text_buf:
+        final_out.append({"type": "message", "id": msg_id, "role": "assistant", "status": "completed",
+                          "content": [{"type": "output_text", "text": text_buf, "annotations": []}]})
+    for idx in sorted(tc_buf):
+        t = tc_buf[idx]
+        final_out.append({"type": "function_call", "id": t["id"], "call_id": t["call_id"],
+                          "name": t["name"], "arguments": t["args"], "status": "completed"})
+    yield emit("response.completed", {"type": "response.completed",
+        "response": {"id": resp_id, "object": "response", "model": model,
+                     "status": status, "created": int(time.time()), "output": final_out}})
+
+# ═══════════════════════════════════════════════════════════════════
+# Anthropic backend
+# ═══════════════════════════════════════════════════════════════════
+
+def an_input_to_messages(input_data):
+    msgs = []
+    if isinstance(input_data, str):
+        msgs.append({"role": "user", "content": input_data})
+    elif isinstance(input_data, list):
+        for item in input_data:
+            t = item.get("type")
+            if t == "message":
+                role = item.get("role", "user")
+                text = ""
+                for part in item.get("content", []):
+                    pt = part.get("type", "")
+                    if pt in ("input_text", "output_text"):
+                        text += part.get("text", "")
+                if role == "assistant":
+                    msgs.append({"role": "assistant", "content": text})
+                else:
+                    msgs.append({"role": "user", "content": text})
+            elif t == "function_call":
+                msgs.append({"role": "assistant", "content": [
+                    {"type": "tool_use", "id": item.get("call_id", item.get("id", uid("tu"))),
+                     "name": item.get("name", ""),
+                     "input": json.loads(item.get("arguments", "{}"))}
+                ]})
+            elif t == "function_call_output":
+                msgs.append({"role": "user", "content": [
+                    {"type": "tool_result", "tool_use_id": item.get("id", ""),
+                     "content": item.get("output", "")}
+                ]})
+    return msgs
+
+def an_convert_tools(tools):
+    if not tools:
+        return None
+    out = []
+    for t in tools:
+        if t.get("type") != "function":
+            continue
+        fn = t.get("function", {})
+        if fn:
+            out.append({"name": fn.get("name"), "description": fn.get("description", ""),
+                        "input_schema": fn.get("parameters", {"type": "object", "properties": {}})})
+        else:
+            out.append({"name": t.get("name"), "description": t.get("description", ""),
+                        "input_schema": t.get("parameters", {"type": "object", "properties": {}})})
+    return out or None
+
+def an_resp_to_responses(anthro_resp, model, resp_id=None):
+    blocks = anthro_resp.get("content", [])
+    sr = anthro_resp.get("stop_reason", "end_turn")
+    sm = {"end_turn": "completed", "max_tokens": "incomplete", "stop_sequence": "completed", "tool_use": "completed"}
+    status = sm.get(sr, "incomplete")
+    outputs = []
+    for b in blocks:
+        bt = b.get("type", "")
+        if bt == "text":
+            outputs.append({"type": "message", "id": uid("msg"), "role": "assistant", "status": "completed",
+                            "content": [{"type": "output_text", "text": b.get("text", ""), "annotations": []}]})
+        elif bt == "tool_use":
+            outputs.append({"type": "function_call", "id": uid("fc"), "call_id": b.get("id", ""),
+                            "name": b.get("name", ""), "arguments": json.dumps(b.get("input", {})),
+                            "status": "completed"})
+        elif bt == "thinking":
+            outputs.append({"type": "reasoning", "id": uid("rsn"), "status": "completed",
+                            "content": [{"type": "text", "text": b.get("thinking", "")}]})
+    usage = anthro_resp.get("usage", {})
+    return {"id": resp_id or uid("resp"), "object": "response", "created": int(time.time()),
+            "model": model, "status": status, "output": outputs,
+            "usage": {"input_tokens": usage.get("input_tokens", 0),
+                      "output_tokens": usage.get("output_tokens", 0),
+                      "total_tokens": usage.get("input_tokens", 0) + usage.get("output_tokens", 0),
+                      "input_tokens_details": {"cached_tokens": 0}}}
+
+def an_stream_to_sse(stream, model, req_id):
+    resp_id = req_id or uid("resp")
+    completed = []
+    msg_id = uid("msg")
+    text_buf = ""
+    tc_id = None
+    tc_call_id = None
+    tc_name = ""
+    tc_args = ""
+    block_type = None
+    stop_reason = "end_turn"
+
+    yield emit("response.created", {"type": "response.created",
+        "response": {"id": resp_id, "object": "response", "model": model,
+                     "status": "in_progress", "created": int(time.time()), "output": []}})
+    yield emit("response.in_progress", {"type": "response.in_progress", "response": {"id": resp_id}})
+
+    for raw in stream:
+        line = raw.decode("utf-8", errors="replace").strip()
+        if not line:
+            continue
+        if line.startswith("event: "):
+            evt_type = line[7:]
+            continue
+        if not line.startswith("data: "):
+            continue
+        try:
+            data = json.loads(line[6:])
+        except json.JSONDecodeError:
+            continue
+
+        et = data.get("type", "")
+
+        if et == "message_start":
+            pass
+
+        elif et == "content_block_start":
+            cb_type = data.get("content_block", {}).get("type", "")
+            block_type = cb_type
+            if cb_type == "text":
+                msg_id = uid("msg")
+                yield emit("response.output_item.added", {"type": "response.output_item.added",
+                    "item": {"type": "message", "id": msg_id, "role": "assistant",
+                             "status": "in_progress", "content": []}})
+                yield emit("response.content_part.added", {"type": "response.content_part.added",
+                    "part": {"type": "output_text", "text": "", "annotations": []}, "item_id": msg_id})
+            elif cb_type == "tool_use":
+                cb = data.get("content_block", {})
+                tc_id = uid("fc")
+                tc_call_id = cb.get("id", tc_id)
+                tc_name = cb.get("name", "")
+                yield emit("response.output_item.added", {"type": "response.output_item.added",
+                    "item": {"type": "function_call", "id": tc_id, "call_id": tc_call_id,
+                             "name": tc_name, "arguments": "", "status": "in_progress"}})
+            elif cb_type == "thinking":
+                pass
+
+        elif et == "content_block_delta":
+            dd = data.get("delta", {})
+            dt = dd.get("type", "")
+            if dt == "text_delta":
+                txt = dd.get("text", "")
+                text_buf += txt
+                yield emit("response.output_text.delta", {"type": "response.output_text.delta",
+                            "delta": txt, "item_id": msg_id, "content_index": 0})
+            elif dt == "input_json_delta":
+                pj = dd.get("partial_json", "")
+                tc_args += pj
+                yield emit("response.output_text.delta", {"type": "response.function_call_arguments.delta",
+                            "delta": pj, "item_id": tc_id})
+            elif dt == "thinking_delta":
+                tk = dd.get("thinking", "")
+                yield emit("response.reasoning.delta", {"type": "response.reasoning.delta", "delta": tk})
+
+        elif et == "content_block_stop":
+            if block_type == "text":
+                yield emit("response.output_text.done", {"type": "response.output_text.done",
+                            "text": text_buf, "item_id": msg_id, "content_index": 0})
+                yield emit("response.content_part.done", {"type": "response.content_part.done",
+                    "part": {"type": "output_text", "text": text_buf, "annotations": []}, "item_id": msg_id})
+                yield emit("response.output_item.done", {"type": "response.output_item.done",
+                    "item": {"type": "message", "id": msg_id, "role": "assistant", "status": "completed",
+                             "content": [{"type": "output_text", "text": text_buf, "annotations": []}]}})
+                completed.append({"type": "message", "id": msg_id, "role": "assistant", "status": "completed",
+                                  "content": [{"type": "output_text", "text": text_buf, "annotations": []}]})
+                text_buf = ""
+            elif block_type == "tool_use":
+                yield emit("response.function_call_arguments.done", {"type": "response.function_call_arguments.done",
+                            "item_id": tc_id, "name": tc_name, "arguments": tc_args})
+                yield emit("response.output_item.done", {"type": "response.output_item.done",
+                    "item": {"type": "function_call", "id": tc_id, "call_id": tc_call_id,
+                             "name": tc_name, "arguments": tc_args, "status": "completed"}})
+                completed.append({"type": "function_call", "id": tc_id, "call_id": tc_call_id,
+                                  "name": tc_name, "arguments": tc_args, "status": "completed"})
+                tc_id = None
+                tc_args = ""
+            block_type = None
+
+        elif et == "message_delta":
+            stop_reason = data.get("delta", {}).get("stop_reason", "end_turn")
+
+        elif et == "message_stop":
+            sm = {"end_turn": "completed", "max_tokens": "incomplete",
+                  "stop_sequence": "completed", "tool_use": "completed"}
+            status = sm.get(stop_reason, "incomplete")
+            yield emit("response.completed", {"type": "response.completed",
+                "response": {"id": resp_id, "object": "response", "model": model,
+                             "status": status, "created": int(time.time()), "output": completed}})
+
+# ═══════════════════════════════════════════════════════════════════
+# HTTP Server
+# ═══════════════════════════════════════════════════════════════════
+
+class Handler(http.server.BaseHTTPRequestHandler):
+    protocol_version = "HTTP/1.1"
+
+    def do_GET(self):
+        if self.path in ("/v1/models", "/models"):
+            self.send_json(200, {"object": "list", "data": MODELS})
+        else:
+            self.send_error(404)
+
+    def do_POST(self):
+        if self.path in ("/v1/responses", "/responses"):
+            self._handle()
+        else:
+            self.send_error(404)
+
+    def _handle(self):
+        try:
+            clen = int(self.headers.get("Content-Length", 0))
+            body = json.loads(self.rfile.read(clen))
+        except Exception as e:
+            return self.send_json(400, {"error": {"message": f"Bad request: {e}"}})
+
+        model = body.get("model", MODELS[0]["id"] if MODELS else "unknown")
+        stream = body.get("stream", False)
+
+        if BACKEND == "anthropic":
+            self._handle_anthropic(body, model, stream)
+        else:
+            self._handle_openai_compat(body, model, stream)
+
+    def _handle_openai_compat(self, body, model, stream):
+        input_data = body.get("input", "")
+        chat_body = {"model": model, "messages": oa_input_to_messages(input_data)}
+        for k in ("temperature", "top_p", "max_output_tokens"):
+            if k in body:
+                chat_body["max_tokens" if k == "max_output_tokens" else k] = body[k]
+        tools = oa_convert_tools(body.get("tools"))
+        if tools:
+            chat_body["tools"] = tools
+        if body.get("tool_choice"):
+            chat_body["tool_choice"] = body["tool_choice"]
+        chat_body["stream"] = stream
+
+        target = upstream_target(TARGET_URL, "/chat/completions")
+        fwd = forwarded_headers(self.headers, {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {API_KEY}",
+        }, browser_ua=True)
+        print(f"[translate-proxy] POST {target} model={model} stream={stream} ua={fwd.get('User-Agent','')[:50]}", file=sys.stderr)
+        req = urllib.request.Request(
+            target,
+            data=json.dumps(chat_body).encode(),
+            headers=fwd,
+        )
+        self._forward(req, stream, model,
+            lambda r: oa_resp_to_responses(json.loads(r.read()), model),
+            lambda s: oa_stream_to_sse(s, model, body.get("request_id") or body.get("id")))
+
+    def _handle_anthropic(self, body, model, stream):
+        input_data = body.get("input", "")
+        an_body = {"model": model, "messages": an_input_to_messages(input_data),
+                   "max_tokens": body.get("max_output_tokens", 8192)}
+        for k in ("temperature", "top_p"):
+            if k in body:
+                an_body[k] = body[k]
+        tools = an_convert_tools(body.get("tools"))
+        if tools:
+            an_body["tools"] = tools
+        if body.get("tool_choice"):
+            tc = body["tool_choice"]
+            if isinstance(tc, str):
+                an_body["tool_choice"] = {"type": tc}
+            elif isinstance(tc, dict):
+                an_body["tool_choice"] = tc
+        an_body["stream"] = stream
+
+        target = upstream_target(TARGET_URL, "/messages")
+        req = urllib.request.Request(
+            target,
+            data=json.dumps(an_body).encode(),
+            headers=forwarded_headers(self.headers, {
+                "Content-Type": "application/json",
+                "x-api-key": API_KEY,
+                "anthropic-version": "2023-06-01",
+            }),
+        )
+        self._forward(req, stream, model,
+            lambda r: an_resp_to_responses(json.loads(r.read()), model),
+            lambda s: an_stream_to_sse(s, model, body.get("request_id") or body.get("id")))
+
+    def _forward(self, req, stream, model, nonstream_fn, stream_fn):
+        try:
+            upstream = urllib.request.urlopen(req)
+        except urllib.error.HTTPError as e:
+            err = e.read().decode()
+            return self.send_json(e.code, {"error": {"type": "upstream_error", "message": err}})
+        except Exception as e:
+            return self.send_json(500, {"error": {"type": "proxy_error", "message": str(e)}})
+
+        if stream:
+            self.send_response(200)
+            self.send_header("Content-Type", "text/event-stream")
+            self.send_header("Cache-Control", "no-cache")
+            self.send_header("Connection", "keep-alive")
+            self.end_headers()
+            for event in stream_fn(upstream):
+                self.wfile.write(event.encode("utf-8"))
+                self.wfile.flush()
+        else:
+            result = nonstream_fn(upstream)
+            self.send_json(200, result)
+
+    def send_json(self, status, data):
+        body = json.dumps(data).encode()
+        self.send_response(status)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+    def log_message(self, fmt, *args):
+        msg = fmt % args if args else fmt
+        print(f"[translate-proxy] {BACKEND} {msg}", file=sys.stderr)
+
+if __name__ == "__main__":
+    server = http.server.HTTPServer(("127.0.0.1", PORT), Handler)
+    print(f"translate-proxy ({BACKEND}) listening on http://127.0.0.1:{PORT}", flush=True)
+    print(f"Target: {TARGET_URL}", flush=True)
+    print(f"Models: {[m['id'] for m in MODELS]}", flush=True)
+    server.serve_forever()