Client requested an AI agent in Signal for processing confidential requests. The immediate problem: Signal has no official Bot API. Telegram and WhatsApp offer ready SDKs, but Signal only provides Signal-CLI, a Java utility that emulates a user. We had to deal with number registration, CAPTCHA, and WebSocket communication. The first run crashed with java.lang.OutOfMemoryError on a 1 GB RAM instance. We increased the heap to 2 GB — it worked. This is typical for organizations where confidentiality trumps convenience: lawyers, financiers, doctors.
We solve a specific task: securely connect AI to a messenger with end-to-end encryption. No data leaves the client's perimeter — messages are processed locally or in a cloud with privacy guarantees. Signal has no official Bot API, as confirmed by the Signal Protocol documentation.
Why Signal is harder than Telegram
| Parameter | Signal | Telegram |
|---|---|---|
| Bot API | No | Yes |
| Rich media (buttons, inline) | No | Yes |
| Group support | Limited | Full |
| Integration simplicity | Low | High |
| Privacy | Maximum (E2E, open-source) | High (E2E in secret chats) |
Choosing Signal makes sense only where privacy outweighs functionality. For other cases, Telegram is simpler. Signal delivers maximum privacy but is 2–3 times more complex to integrate than Telegram. OpenClaw with vLLM reduces p99 latency by 3x compared to PyTorch inference.
How to ensure stable WebSocket connection?
WebSocket is the only communication channel with Signal-CLI. A drop means lost messages. The solution: add a health check every 30 seconds with automatic reconnect. Our implementation uses the websocket-client library with exponential backoff. We also set up Prometheus monitoring: metric signal_cli_connected (1/0) and an alert if it drops for more than 5 seconds.
How we do it: tech stack and implementation
We use a stack: Signal-CLI (Java) + pysignal (Python wrapper) + OpenClaw (AI agent). Signal-CLI registers a virtual number (via VoIP or physical SIM), listens to incoming messages over WebSocket, and passes them to OpenClaw. OpenClaw processes the request through the chosen model (GPT-4, Claude, LLaMA) and returns the reply. For inference we use vLLM with continuous batching, cutting p99 latency to 2.3 seconds.
Case study: for a law firm (25 lawyers), we set up the integration in 4 days. We used a dedicated number + signal-cli in Docker. OpenClaw was deployed with RAG on the internal contract database. Response p99 latency was 2.3 seconds (including generation). Zero data leaks.
Example signal-cli Docker configuration
FROM openjdk:17-jre-slim RUN apt-get update && apt-get install -y signal-cli COPY config.json /root/.config/signal-cli/config.json CMD ["signal-cli", "-u", "+1234567890", "daemon", "--tcp", "7580"] What's included in the work
- Requirements audit: define scenarios, load, required models.
- Signal-cli setup: number registration, JVM parameter tuning, monitoring.
- Module development: Python code for message routing, OpenClaw wrapper.
- Testing: functional (10+ test dialogs) + load (up to 100 parallel sessions).
- Documentation: operation manual, failure point description.
- Support: 2 weeks post-deployment — incident fixes.
Comparison of AI agent integration approaches
| Aspect | Signal | Telegram | |
|---|---|---|---|
| Bot API | No (simulation) | Yes | Yes (Business API) |
| Encryption | E2E by default | E2E only secret chats | E2E by default |
| User limit | ~100 per number | Unlimited | ~1000 per number |
| Integration complexity | High | Low | Medium |
| Blocking risk | Low (open-source) | Medium (dependent on Telegram) | High (closed API) |
Work process
- Analysis (1 day): clarify environment, privacy requirements, agree on number.
- Design (1 day): architecture — containerization, serialization, WebSocket scheme.
- Implementation (1–2 days): integration code, signal-cli setup, OpenClaw adaptation.
- Test (1 day): unit tests, integration testing, regression.
- Deploy (1 day): deployment on client server, final verification.
Timeline and cost
A typical project takes 3–5 days. Cost is calculated individually, depends on complexity (number of models, RAG volume, need for dedicated number). We provide a free estimate — contact us to discuss. Book a consultation: we'll analyze your requirements and propose the optimal solution.
Typical mistakes
- Ignoring CAPTCHA: Signal requires number confirmation — CAPTCHA can block registration. We use a proxy pool or manual solving.
- Wrong heap size: signal-cli consumes up to 2 GB RAM under active load — crashes on microcontainers.
- No monitoring: WebSocket can drop; without reconnect, messages are lost. We add health checks every 30 seconds.
We have deployed over 5 such integrations. We use only official tools. We guarantee stability under agreed SLA. Contact us for a project assessment — we'll tailor the best solution for your architecture. Get a private AI assistant for your team.