Using Telegram Topics for Unlimited Parallel AI Agent Conversations
A developer on r/openclaw shared a practical approach for managing multiple AI agents with unlimited parallel conversations using Telegram's forum feature.
Previous Approach and Limitations
The developer previously gave one agent three Telegram bots sharing the same workspace, which worked for parallel tasks with that single agent. However, this approach didn't scale when managing multiple agents, as creating three bots for every agent became impractical.
Telegram Topics Solution
Converting a Telegram group to a forum enables each topic to function as an isolated session. OpenClaw treats each topic as a separate conversation with its own context. The setup now uses:
- One group per agent
- One bot per group
- New topics created whenever a fresh parallel conversation is needed
Technical Implementation
- Each topic gets its own session key, preventing conversations from bleeding into each other
- One bot identity per agent keeps the setup clean and simple
- Agents can create new topics themselves when they need a fresh thread
- Works with the existing Telegram Bot API - just enable topics in group settings
Benefits and Use Cases
This approach gives all agents unlimited parallel conversations without extra bots, tokens, or configuration. The developer specifically mentioned using this for:
- Coding agent
- Social media agent
- Manager agent
The developer noted that Telegram's Bot API is more automation-friendly than Discord or Slack for this use case.
Future Development
The developer is currently working on cross-topic context functionality, where conversations in one topic might need information from another topic. The goal is to enable agents to send messages to each other directly instead of requiring manual copy-pasting between threads.
📖 Read the full source: r/openclaw
👀 See Also
Onboarding AI agents like junior contractors: CLAUDE.md and production lessons
A store run entirely with AI agents treated onboarding like hiring a junior contractor, finding that clear constraints in a CLAUDE.md document consistently outperformed 'smarter' models with vague instructions.
Running Claude Code as a Pure Judgment Engine Across the Full SDLC
A developer shares their architecture for using Claude Code as a reasoning engine inside a multi-layer system: Python handles orchestration, Claude Code handles code writing and review, with isolated subagents and a persistent wiki layer.
Practical Lessons from Deploying RAG Bots in Regulated Industries
A developer shares hard-won lessons from deploying RAG-powered AI assistants for Australian workplace compliance across construction, aged care, and mining operations. Key insights include query expansion techniques, document title matching, prompt layering, and infrastructure decisions.
Using AI to Port a Wi-Fi Driver from Linux to FreeBSD: A Case Study
A developer used Claude Code and Pi agent to attempt porting the Linux brcmfmac driver for Broadcom BCM4350 Wi-Fi chips to FreeBSD, first through direct code translation and then by generating a detailed 11-chapter specification for clean-room implementation.