7 Open-Source Frameworks for Deploying AI Bots to Messaging Platforms in 2026
A practical comparison of open-source tools that connect LLMs to Discord, Telegram, Slack, WeChat, and other messaging apps.
I spent the last few weeks evaluating open-source frameworks for a project that needed an AI chatbot running on multiple messaging platforms simultaneously — specifically Discord, Telegram, and WeChat.
The existing "best chatbot framework" listicles are mostly outdated (still recommending Dialogflow and BotKit in 2026?), so I figured I'd share what I actually found useful.
What I Was Looking For
My requirements were pretty specific:
- Multi-platform: One codebase, multiple messaging apps (not just web chat)
- LLM-native: Built for connecting to GPT, Claude, DeepSeek, etc. — not NLU-era intent matching
- Self-hosted: Full control over data and deployment
- Actually maintained: Regular commits, active community, recent releases
Here's what made the cut, organized by use case.
1. Botpress — The Enterprise Visual Builder
GitHub: 14.5k ⭐ | Language: TypeScript | License: MIT
Botpress has been around since 2017 and has evolved significantly. It now offers a visual flow builder, built-in NLU, and native integrations with Slack, Telegram, Messenger, and Microsoft Teams.
Strengths:
- Polished visual editor — genuinely usable by non-developers
- Built-in knowledge base and RAG
- Large plugin ecosystem
- Good documentation
Weaknesses:
- No WeChat, QQ, LINE, or DingTalk support
- Cloud-first model — self-hosting is possible but clearly not the priority
- Some advanced features gated behind paid plans
Best for: Teams that want a visual builder and primarily target Western messaging platforms.
2. Rasa — The NLU Veteran
GitHub: 21k ⭐ | Language: Python | License: Apache 2.0
Rasa is the OG of open-source chatbots. It's battle-tested in enterprise environments and offers the most sophisticated NLU pipeline of any open-source tool.
Strengths:
- Most mature conversation management (stories, rules, forms)
- Strong NLU with entity extraction
- Extensive enterprise track record
Weaknesses:
- Designed for the pre-LLM era — bolting on GPT feels awkward
- Steep learning curve
- Recent pivot to Rasa Pro (commercial) has fragmented the open-source offering
- Multi-platform support requires custom connectors
Best for: Enterprise teams with existing Rasa deployments or complex NLU requirements.
3. Wechaty — The WeChat Specialist
GitHub: 22.5k ⭐ | Language: TypeScript | License: Apache 2.0
If your primary target is WeChat, Wechaty is the standard. It provides a clean RPA-style SDK for WeChat automation and has expanded to support WhatsApp, Lark, and a few other platforms.
Strengths:
- Best WeChat integration available
- Clean, developer-friendly API
- Strong community in the Chinese developer ecosystem
Weaknesses:
- WeChat-centric — other platform support is secondary
- No built-in AI/LLM integration (BYO everything)
- WeChat's anti-bot measures can cause issues
Best for: Projects where WeChat is the primary or only platform.
4. Flowise — Visual LLM Chains
GitHub: 49k ⭐ | Language: TypeScript | License: Apache 2.0
Flowise gives you a drag-and-drop UI for building LangChain flows. It was acquired by Workday in 2025, which gives it enterprise backing but raises questions about long-term open-source commitment.
Strengths:
- Beautiful visual builder for LLM chains
- Direct LangChain integration
- Easy to prototype RAG applications
Weaknesses:
- Not really a "messaging bot" framework — it's an LLM orchestrator
- Messaging platform integrations are limited and feel bolted-on
- Post-acquisition direction unclear
Best for: Prototyping LLM workflows and RAG applications, not multi-platform messaging bots.
5. LangBot — Multi-Platform IM + LLM Hub
GitHub: 15.4k ⭐ | Language: Python | License: MIT
This one surprised me. LangBot (formerly QChatGPT) focuses specifically on the gap between AI backends and messaging platforms. It supports 10+ IM platforms including QQ, WeChat, Discord, Telegram, Slack, LINE, Lark, and DingTalk — which is more than anything else I found.
Strengths:
- Widest messaging platform coverage (both Chinese and international)
- Native integration with Dify, n8n, Langflow, Coze as "runners" — so you can use visual workflow tools for AI logic
- Also supports direct OpenAI/Claude/Gemini connections
- Pipeline architecture — different bots can use different AI backends
- Cross-process plugin isolation (plugins can't crash the main process)
- WebUI for management
- Listed in Dify's official docs as the recommended way to connect Dify to messaging platforms
Weaknesses:
- Documentation is bilingual (Chinese/English) but English docs are thinner
- Newer project — smaller Western community compared to Botpress/Rasa
- Plugin ecosystem is still rebuilding after a major architecture change
Best for: Anyone who needs to deploy an AI bot to multiple messaging platforms, especially if you're using Dify, n8n, or Langflow for AI orchestration.
6. AstrBot — The Community-Focused Alternative
GitHub: 18.3k ⭐ | Language: Python | License: MIT
AstrBot is LangBot's closest competitor and actually has more GitHub stars. It supports QQ, WeChat, Telegram, and Feishu with a simpler setup process.
Strengths:
- Easy to get started
- Active Chinese developer community
- Good plugin ecosystem for entertainment use cases
- Dify integration
Weaknesses:
- Fewer international platform integrations (no Discord, Slack, LINE, DingTalk)
- More focused on consumer/entertainment than B2B
- Less modular architecture
Best for: Chinese IM platforms with a focus on community/entertainment bots.
7. n8n + Custom Connectors — The DIY Approach
GitHub: 177k ⭐ | Language: TypeScript | License: Sustainable Use License
n8n isn't a chatbot framework per se, but its AI Agent nodes combined with messaging triggers (Telegram, Slack, Discord) make it a legitimate option. You build the entire flow visually.
Strengths:
- Most flexible — literally any workflow logic
- 400+ integrations for business logic
- Strong AI Agent support with tool calling
- Huge community
Weaknesses:
- No native WeChat, QQ, or LINE support
- Each platform needs its own trigger setup
- Not designed for high-throughput chat scenarios
- Conversation memory management is manual
Best for: Teams already using n8n who want to add AI chat capabilities to a few platforms.
Comparison Matrix
| Feature | Botpress | Rasa | Wechaty | Flowise | LangBot | AstrBot | n8n |
| Discord | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ |
| Telegram | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
| Slack | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ |
| ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | |
| ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | |
| LINE | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
| Lark/Feishu | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ |
| DingTalk | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
| Visual Builder | ✅ | ❌ | ❌ | ✅ | via Dify/n8n | ❌ | ✅ |
| LLM-Native | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ |
| Self-Hosted | ⚠️ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Dify Integration | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
| Plugin System | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ |
My Takeaway
The chatbot landscape has split into two worlds:
Western-focused tools (Botpress, Rasa) have good docs and polished UIs but barely support Asian messaging platforms. They were built for a pre-LLM world and are retrofitting AI capabilities.
Asia-origin tools (LangBot, AstrBot, Wechaty) cover WeChat/QQ/DingTalk but are less known in Western developer circles. The newer ones (LangBot, AstrBot) are LLM-native from the ground up.
Workflow tools (n8n, Flowise) aren't chatbot frameworks but are increasingly used as AI backends — especially when paired with a dedicated messaging layer.
If I had to pick one today for a project spanning both Chinese and international platforms, I'd probably go with LangBot + Dify. The Dify integration is officially documented and supported on both sides, and the platform coverage is unmatched. For Western-only deployments, Botpress is the safe choice.
What's your setup? I'm curious what other people are using — drop a comment.
This comparison is based on my evaluation in February 2026. Stars, features, and project directions change fast in this space.