Weclaw Wechat AI Bridge
Connect WeChat to AI agents (Claude, Codex, Gemini, Kimi, etc.) using the WeClaw bridge in Go.
- Skill ID
- reason-machines/trending-skills/weclaw-wechat-ai-bridge
- Publisher
- reason-machines
- Repository
- trending-skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
reason-machines/trending-skills/weclaw-wechat-ai-bridgeInstalls these files- SKILL.md
What this skill tells the agent
WeClaw — WeChat AI Agent Bridge
Skill by ara.so — Daily 2026 Skills collection.
WeClaw connects WeChat to AI agents (Claude, Codex, Gemini, Kimi, OpenClaw, etc.) via a Go-based bridge. It handles QR-code login, message routing, media conversion, and agent lifecycle. Supports three agent modes: ACP (JSON-RPC subprocess, fastest), CLI (new process per message), and HTTP (OpenAI-compatible REST).
Installation
# One-line installer
curl -sSL https://raw.githubusercontent.com/fastclaw-ai/weclaw/main/install.sh | sh
# Via Go toolchain
go install github.com/fastclaw-ai/weclaw@latest
# Via Docker
docker run -it -v ~/.weclaw:/root/.weclaw ghcr.io/fastclaw-ai/weclaw startFirst-Run Flow
weclaw start # Shows QR code → scan with WeChat → auto-detects agents → saves config
weclaw login # Add/re-authenticate a WeChat account
weclaw status # Show running state and active agent
weclaw stop # Stop the background daemon
weclaw start -f # Foreground mode (debug/verbose)Logs: ~/.weclaw/weclaw.log Config: ~/.weclaw/config.json
Configuration
{
"default_agent": "claude",
"agents": {
"claude": {
"type": "acp",
"command": "/usr/local/bin/claude-agent-acp",
"model": "sonnet"
},
"codex": {
"type": "acp",
"command": "/usr/local/bin/codex-acp"
},
"claude-cli": {
"type": "cli",
"command": "/usr/local/bin/claude",
"args": ["--dangerously-skip-permissions"]
},
"codex-cli": {
"type": "cli",
"command": "/usr/local/bin/codex",
"args": ["--skip-git-repo-check"]
},
"openclaw": {
"type": "http",
"endpoint": "https://api.example.com/v1/chat/completions",
"api_key": "$OPENCLAW_GATEWAY_TOKEN",
"model": "openclaw:main"
}
}
}Environment Variables
| Variable | Purpose |
|---|---|
WECLAW_DEFAULT_AGENT | Override default agent at runtime |
WECLAW_API_ADDR | Change local HTTP API address (default 127.0.0.1:18011) |
OPENCLAW_GATEWAY_URL | HTTP agent endpoint |
OPENCLAW_GATEWAY_TOKEN | HTTP agent API token |
Agent Modes
| Mode | Process model | Best for |
|---|---|---|
acp | Long-running subprocess, JSON-RPC over stdio | Claude, Codex, Kimi, Gemini — fastest, session reuse |
cli | New process per message, --resume for sessions | claude -p, codex exec |
http | OpenAI-compatible /v1/chat/completions | Any REST-accessible model |
Auto-detection prefers acp over cli when both binaries exist.
Chat Commands (send as WeChat messages)
| Command | Action |
|---|---|
hello | Send to default agent |
/codex write a sort function | Route to named agent |
/cc explain this code | Use alias (/cc → claude) |
/claude | Switch default agent to Claude (persisted) |
/status | Show active agent info |
/help | List available commands |
Built-in Aliases
| Alias | Agent |
|---|---|
/cc | claude |
/cx | codex |
/cs | cursor |
/km | kimi |
/gm | gemini |
/ocd | opencode |
/oc | openclaw |
Proactive Messaging — CLI
# Send plain text
weclaw send --to "user_id@im.wechat" --text "Hello from WeClaw"
# Send an image
weclaw send --to "user_id@im.wechat" --media "https://example.com/photo.png"
# Send text + media together
weclaw send --to "user_id@im.wechat" \
--text "Check this out" \
--media "https://example.com/photo.png"
# Send a file
weclaw send --to "user_id@im.wechat" --media "https://example.com/report.pdf"Proactive Messaging — HTTP API
The local API listens on 127.0.0.1:18011 while weclaw start is running.
# Send text
curl -X POST http://127.0.0.1:18011/api/send \
-H "Content-Type: application/json" \
-d '{"to": "user_id@im.wechat", "text": "Hello from WeClaw"}'
# Send image
curl -X POST http://127.0.0.1:18011/api/send \
-H "Content-Type: application/json" \
-d '{"to": "user_id@im.wechat", "media_url": "https://example.com/photo.png"}'
# Send text + media
curl -X POST http://127.0.0.1:18011/api/send \
-H "Content-Type: application/json" \
-d '{"to": "user_id@im.wechat", "text": "See this", "media_url": "https://example.com/photo.png"}'Supported media types: png, jpg, gif, webp, mp4, mov, pdf, doc, zip.
Change listen address:
WECLAW_API_ADDR=0.0.0.0:18011 weclaw startGo Integration Example
Call the WeClaw HTTP API from a Go service to send notifications:
package main
import (
"bytes"
"encoding/json"
"fmt"
"net/http"
"os"
)
type SendRequest struct {
To string `json:"to"`
Text string `json:"text,omitempty"`
MediaURL string `json:"media_url,omitempty"`
}
type SendResponse struct {
OK bool `json:"ok"`
Message string `json:"message,omitempty"`
}
func sendToWeChat(to, text, mediaURL string) error {
apiAddr := os.Getenv("WECLAW_API_ADDR")
if apiAddr == "" {
apiAddr = "127.0.0.1:18011"
}
req := SendRequest{To: to, Text: text, MediaURL: mediaURL}
body, err := json.Marshal(req)
if err != nil {
return fmt.Errorf("marshal: %w", err)
}
resp, err := http.Post(
fmt.Sprintf("http://%s/api/send", apiAddr),
"application/json",
bytes.NewReader(body),
)
if err != nil {
return fmt.Errorf("post: %w", err)
}
defer resp.Body.Close()
var result SendResponse
if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
return fmt.Errorf("decode: %w", err)
}
if !result.OK {
return fmt.Errorf("weclaw error: %s", result.Message)
}
return nil
}
func main() {
recipient := os.Getenv("WECHAT_RECIPIENT_ID") // e.g. "user_id@im.wechat"
if err := sendToWeChat(recipient, "Build succeeded ✅", ""); err != nil {
fmt.Fprintf(os.Stderr, "failed: %v\n", err)
os.Exit(1)
}
fmt.Println("Message sent.")
}Docker Setup
# Build image
docker build -t weclaw .
# Step 1: Interactive login (scan QR code)
docker run -it -v ~/.weclaw:/root/.weclaw weclaw login
# Step 2: Run daemon with HTTP agent
docker run -d --name weclaw \
-v ~/.weclaw:/root/.weclaw \
-e OPENCLAW_GATEWAY_URL=https://api.example.com \
-e OPENCLAW_GATEWAY_TOKEN="$OPENCLAW_GATEWAY_TOKEN" \
weclaw
# Expose the local API externally (bind carefully — no auth by default)
docker run -d --name weclaw \
-v ~/.weclaw:/root/.weclaw \
-e WECLAW_API_ADDR=0.0.0.0:18011 \
-p 18011:18011 \
-e OPENCLAW_GATEWAY_TOKEN="$OPENCLAW_GATEWAY_TOKEN" \
weclaw
docker logs -f weclawACP/CLI agents require the agent binary inside the container. Mount the binary or build a custom image. HTTP agents work out of the box.
System Service (Auto-start)
macOS (launchd):
cp service/com.fastclaw.weclaw.plist ~/Library/LaunchAgents/
launchctl load ~/Library/LaunchAgents/com.fastclaw.weclaw.plistLinux (systemd):
sudo cp service/weclaw.service /etc/systemd/system/
sudo systemctl enable --now weclaw
journalctl -u weclaw -fDevelopment
make dev # Hot reload
go build -o weclaw . # Build binary
./weclaw start -f # Run in foregroundReleasing:
git tag v0.1.0
git push origin v0.1.0
# GitHub Actions builds darwin/linux × amd64/arm64 and uploads release artifactsCommon Patterns
Pattern: Per-user agent routing
Send /claude or /codex as a WeChat message to switch the default agent. The choice persists in ~/.weclaw/config.json across restarts.
Pattern: CI/CD build notifications
After a build, call weclaw send or POST to the HTTP API to push results to a WeChat contact or group.
Pattern: Media from agent
If an agent reply contains , WeClaw auto-downloads, AES-128-ECB encrypts, uploads to WeChat CDN, and delivers as a native image message — no extra config needed.
More from reason-machines
Use this skill in a project
Open any RiverX project, search the Skills panel for reason-machines/trending-skills/weclaw-wechat-ai-bridge, and install it in one click.
