Clawdbot 2026.1.24: Multi-Platform AI Agent Gets LINE Integration, Telegram Topic Sessions, and More

Category: AI | Published: March 4, 2026

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Clawdbot just shipped its first major release of 2026, and it's a substantial one. Version 2026.1.24 brings a set of features that collectively push the platform toward serious multi-platform production deployments. If you're running AI agents across messaging channels, managing approval workflows, or experimenting with local and privacy-first LLM backends, this update has something concrete for you.

Here's a breakdown of what changed and what it means in practice.

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Multi-Platform Messaging: LINE Rich Replies and Telegram Topic Sessions

The two headline messaging updates in 2026.1.24 address distinct pain points, and both are worth understanding in detail.

LINE Channel with Rich Replies and Quick Actions

LINE is dominant in Japan, Thailand, and Taiwan, making it a critical integration target for anyone building AI agents for East and Southeast Asian markets. Prior to this release, LINE support in Clawdbot was functional but flat — text in, text out. The 2026.1.24 update introduces rich replies and quick actions, which means your agent can now respond with structured message templates, button menus, and carousel-style content.

In practical terms, this enables workflows like:

• A customer support agent that replies with a structured card showing order status, plus action buttons for "Refund," "Track Package," or "Speak to Human"
• A scheduling assistant that presents available time slots as tappable quick replies instead of asking the user to type a time
• A product recommendation agent that surfaces items in a visual carousel with a direct "Add to Cart" action

For developers configuring LINE channel behavior in an OpenClaw skill, quick actions are defined as part of the reply payload structure:

{
  "type": "template",
  "altText": "Choose an option",
  "template": {
    "type": "buttons",
    "text": "How can I help you today?",
    "actions": [
      { "type": "message", "label": "Check Status", "text": "status" },
      { "type": "message", "label": "Talk to Support", "text": "support" }
    ]
  }
}

This brings LINE parity much closer to what Clawdbot has offered on other channels, and it's a meaningful unlock for production deployments targeting those markets.

Telegram DM Topics as Separate Sessions

Telegram introduced "Topics" in group supergroups as a way to organize conversations by thread. The problem for AI agents was that Clawdbot previously treated the entire group as a single session context — meaning messages across different topics would bleed into one conversation history. That's a significant issue for any agent trying to maintain coherent, focused conversations.

Version 2026.1.24 fixes this by mapping each Telegram topic to its own independent session. From the agent's perspective, a message from Topic A and a message from Topic B are now entirely separate contexts, with separate memory, separate conversation history, and separate state.

This is particularly useful for:

• Team workspaces where different Telegram topics represent different projects or departments, each with their own AI assistant context
• Support groups where different topics handle different product lines or issue categories
• Community bots where topic separation keeps the agent's responses relevant and scoped

No configuration change is required for existing Telegram integrations — the session isolation is applied automatically based on the message_thread_id present in the incoming update payload.

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Prompt Approval Controls and the /approve Command

One of the more operationally significant additions is the /approve command for executing agent prompts. This is a governance feature, and it matters for anyone running Clawdbot in an environment where human oversight of AI actions is required — whether for compliance, internal policy, or simply because the agent is operating on sensitive systems.

The flow works like this: an agent queues an action or prompt execution, and instead of running it immediately, it waits for an authorized user to issue /approve. Only then does the execution proceed.

This is directly useful in scenarios like:

• Automated code deployment agents where a human must sign off before the agent runs a shell command or pushes a change
• Financial workflow agents that prepare a transaction or report but require explicit approval before sending
• Content generation pipelines where a reviewer needs to authorize publishing before the agent posts

The /approve mechanism integrates cleanly with the existing prompt execution model and doesn't require restructuring your agent logic. You define which prompt steps are approval-gated in the skill configuration, and Clawdbot handles the hold-and-notify pattern automatically, alerting the designated approver via whatever messaging channel is active.

This is a foundational feature for enterprise-grade deployments. Expect it to get expanded in future releases with role-based approval routing and audit logging.

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Ollama and Venice Backend Support, Plus a Refreshed Control UI

Local and Privacy-First LLM Backends

2026.1.24 adds native support for Ollama and Venice as LLM backends. These two additions serve different but related needs.

Ollama is the go-to tool for running open-weight models locally — Llama 3, Mistral, Gemma, Qwen, and others. Adding Ollama support means you can now point Clawdbot at a local model endpoint with no data leaving your infrastructure. For developers building agents that handle sensitive data, or for teams that need to keep inference costs predictable and offline-capable, this is a practical option that was previously missing.

Venice is a privacy-focused inference platform that offers API access to open-source models with a strong no-logging, no-training-on-your-data policy. It sits between a fully local setup and a standard cloud API — you get the convenience of a managed API with stronger privacy guarantees than most mainstream providers.

Configuring either backend is straightforward. In your Clawdbot agent configuration, you specify the backend and endpoint:

llm_backend:
  provider: ollama
  base_url: http://localhost:11434
  model: llama3.2

or for Venice:

llm_backend:
  provider: venice
  api_key: your_venice_api_key
  model: llama-3.3-70b

This makes Clawdbot meaningfully more flexible as a multi-model orchestration layer, especially for teams that want to route different agent tasks to different backends based on sensitivity, cost, or capability requirements.

Control UI Refresh

The Control UI has received a visual overhaul — described in the release notes with characteristic understatement as "a proper glow-up." The interface is now cleaner, better organized, and more usable for monitoring and managing active agents. While UI updates don't change what your agents can do, a well-designed control surface reduces operational friction when you're managing multiple channels and workflows simultaneously.

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What This Release Signals

Taken together, the 2026.1.24 features point in a consistent direction: Clawdbot is being built for production deployments that span multiple messaging platforms, require human-in-the-loop governance, and need flexible LLM backend options that don't lock teams into a single provider.

The LINE and Telegram improvements address real distribution gaps for international deployments. The /approve command is the kind of feature that makes it possible to hand real operational responsibility to an AI agent without losing human control. And Ollama and Venice support gives teams meaningful options for managing data privacy and inference costs.

If you're building on OpenClaw skills or evaluating Clawdbot for a multi-channel AI automation project, this release is worth a close look. The platform is maturing quickly, and 2026.1.24 lays groundwork for more sophisticated multi-agent and multi-platform architectures ahead.

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Source: @openclaw on X

Tags: Clawdbot, OpenClaw, AI agents, LINE integration, Telegram bots, Ollama, Venice AI, LLM backends, AI automation, multi-platform agents