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OpenClaw Integrations

How to Connect Telegram to OpenClaw: Setup, Models, and Workflow Guide

·5 min read

If you're searching for "how to connect Telegram to OpenClaw", the real question is usually not just whether the connection is possible. It's how to make Telegram usable inside an OpenClaw workflow with the right model, the right context, and the right level of control.

That's the practical framing.

OpenClaw gives you the orchestration layer: connectors, skills, tools, prompts, approvals, and the ability to run workflows where your team already works. Telegram provides the domain context. The integration becomes valuable when those two pieces are connected cleanly.

What “Connect Telegram to OpenClaw” Actually Means

In practice, connecting Telegram to OpenClaw usually involves four layers:

  • Authentication so OpenClaw can securely access Telegram
  • Tooling or proxy endpoints that expose the right Telegram actions and data
  • Skills/instructions that tell OpenClaw how to reason over Telegram context
  • Model selection so the assistant uses the right LLM for the job

That last piece matters more than most people expect.

Which Models Can You Use?

OpenClaw is model-flexible, so a Telegram integration does not need to be tied to a single provider. Depending on your setup, teams commonly want to use:

  • OpenAI models like GPT-4o, GPT-4.1, and o3 for broad reasoning and tool use
  • Anthropic models like Claude 3.5 Sonnet, Claude Sonnet 4/4.5, and Claude Opus for strong writing, analysis, and long-context work
  • Google models like Gemini 1.5 Pro or newer Gemini models for multimodal and large-context workflows
  • Other model backends if your OpenClaw environment exposes them

The practical point: you can connect Telegram to OpenClaw once, then run different workflows with different models depending on the job.

For example:

  • Use Claude for nuanced summarisation or drafting
  • Use OpenAI for structured extraction, tool-heavy workflows, or general-purpose copiloting
  • Use Gemini when multimodal or very large context windows matter

A Good Integration Pattern for Telegram

A strong Telegram + OpenClaw setup usually looks like this:

  1. OpenClaw receives a request in chat or from an automation
  2. It calls the right Telegram endpoint or proxy
  3. The selected model reasons over the returned context
  4. OpenClaw returns an answer, draft, classification, or action
  5. High-risk actions stay behind approvals or structured guardrails

That is what makes the setup operational rather than just experimental.

Step-by-Step: Connect Telegram to OpenClaw

Step 1: Create a Telegram Bot with BotFather

Open Telegram and start a conversation with @BotFather. Use the /newbot command, give your bot a name and username, and you'll receive a bot token. This token is what OpenClaw uses to send and receive messages through the Telegram Bot API.

Step 2: Configure Webhook or Long Polling

Telegram bots receive messages in two ways: webhooks (Telegram pushes updates to your server over HTTPS) or long polling (your server pulls updates on a loop). For a server with a public HTTPS endpoint, webhooks are preferable — set the webhook URL via the setWebhook API call pointing to your OpenClaw instance. For local development or servers without HTTPS, long polling works but adds latency.

Step 3: Set the Token in OpenClaw and Test

Add the bot token to your OpenClaw configuration and configure the connector for Telegram. Send your bot a message in Telegram — you should get a response from OpenClaw. Skill files work the same way as with Slack; the Telegram connector just changes the input/output surface, not how skills are processed.

Model-Specific Workflow Ideas

Telegram + OpenAI

Use this when you want a strong general-purpose setup for extraction, classification, action planning, and tool-driven workflows around Telegram.

Telegram + Claude

Use this when you want better writing quality, clearer summaries, stronger nuance, and reliable long-context reasoning over Telegram data.

Telegram + Gemini

Use this when the workflow benefits from large context windows, multimodal inputs, or Google-native ecosystem alignment.

Common Mistakes

Most teams do not fail because the model is bad. They fail because:

  • the Telegram connection is too thin
  • the model lacks the right live context
  • prompts are vague
  • no structured outputs are enforced
  • permissions and approvals are skipped
  • one model is forced to do every job, even when another would be a better fit

The best setup is usually one integration layer, multiple model options, and clear guardrails.

Challenges and Caveats

Telegram Has No Concept of Workspaces or Channels in the Same Way

Unlike Slack, Telegram bots don't have persistent team workspaces with member lists and channel histories. A Telegram bot interacts with individual users or groups via their chat IDs. For team use you'll need to think carefully about authorization — who is allowed to talk to the bot.

Message Formatting Differs

Telegram uses its own markdown variant (MarkdownV2 or HTML) rather than Slack's mrkdwn format. Skill files and response formatting that work well in Slack may need adjustment for Telegram — code blocks, bold text, and links all have different syntax.

Want Telegram Connected to OpenClaw Without Building the Whole Stack Yourself?

Cody supports Telegram out of the box alongside Slack, with no server configuration required. Connect your personal Telegram account and start chatting with your AI assistant immediately.

Get started with Cody →

Related OpenClaw Guides

Looking for a more workflow-first angle? See: Telegram AI Automation and Telegram AI Assistant.

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