AstrBot vs Dify: Which AI Chatbot Platform Should You Choose in 2026?

Mar 3, 2026

Cowrie

Dev @ Bswen

Problem

I needed to build an AI chatbot for my team in early 2026. After some research, I found two popular options: AstrBot and Dify. Both claim to be AI chatbot platforms, both are open source, and both have active communities.

But here’s what confused me:

platform-descriptions.txt

AstrBot: "Open-source multi-platform LLM chatbot framework"
Dify: "Open-source LLMOps platform for AI application development"

They sound similar, but the descriptions hint at different purposes. I spent weeks testing both platforms to figure out which one I actually needed. Turns out, I was asking the wrong question.

What I Discovered

After building test projects with both platforms, I realized AstrBot and Dify are not competing tools. They serve different layers of the AI stack.

Think of it this way:

architecture-layers.txt

┌─────────────────────────────────────────────────────┐
│                  Your Users                         │
│         (QQ, WeChat, Telegram, Discord...)          │
└─────────────────────┬───────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────┐
│                    AstrBot                          │
│           "The Delivery Truck"                      │
│    Connects messaging platforms to AI backends      │
└─────────────────────┬───────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────┐
│                     Dify                            │
│           "The Manufacturing Plant"                 │
│       Builds and orchestrates AI workflows          │
└─────────────────────┬───────────────────────────────┘
                      │
                      ▼
┌─────────────────────────────────────────────────────┐
│               LLM Providers                         │
│        (OpenAI, Claude, Gemini, Local...)           │
└─────────────────────────────────────────────────────┘

AstrBot is the delivery mechanism. Dify is the AI orchestration engine. They can work together, or each can work independently with direct LLM connections.

AstrBot: The Multi-Platform Connector

When I tested AstrBot, I wanted to see how quickly I could deploy a bot to multiple messaging platforms.

Platform Support

AstrBot supports 15+ platforms out of the box:

supported-platforms.txt

Tencent Ecosystem:
  ├── QQ (official API, OneBot, personal)
  ├── QQ Channels
  └── WeChat (Enterprise, Public Account, personal via Gewechat)

Enterprise Tools:
  ├── Feishu (Lark)
  ├── DingTalk
  └── Slack

Social Platforms:
  ├── Telegram
  ├── Discord
  ├── KOOK
  └── Misskey (ActivityPub)

Others:
  ├── VoceChat
  └── Satori protocol

This was exactly what I needed. My team uses QQ internally, but some stakeholders prefer Telegram. AstrBot let me write the bot logic once and deploy to both.

LLM Integration

AstrBot connects to multiple LLM providers:

llm-providers.txt

Cloud Providers:
  ├── OpenAI (GPT-4, GPT-3.5)
  ├── Google Gemini
  ├── Claude
  ├── DeepSeek
  ├── ChatGLM
  └── Tongyi Qianwen

Local Models:
  ├── Ollama
  └── LLMTuner

Advanced Features:
  ├── Multi-model mixing
  ├── Load balancing
  └── Automatic failover

I tested with OpenAI and a local Ollama instance. The failover worked as expected when I intentionally broke my OpenAI API key.

Plugin Ecosystem

AstrBot has 800+ community plugins. The built-in ones cover common use cases:

• Weather queries
• Translation
• Knowledge Q&A
• Math calculations
• Image generation

I installed the weather plugin to test the system:

plugin-install.sh

# One-click installation via WebUI
# Or via command:
astrbot plugin install weather

Agent Capabilities

AstrBot includes agent features:

• Code Executor: Runs Python in a sandbox
• Web Search: Integrates search results
• Task Management: Handles multi-step tasks
• Memory System: Remembers conversation context
• Tool Calling: Uses external tools
• MCP Support: Model Context Protocol for extended capabilities

Dify: The Visual AI Builder

When I switched to Dify, the experience was completely different. Dify focuses on building AI workflows visually.

The Workflow Builder

Dify’s drag-and-drop interface caught my attention. I built a simple RAG (Retrieval Augmented Generation) workflow in about 10 minutes:

rag-workflow.txt

┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
│  Start   │ ──→│ Knowledge│ ──→│   LLM    │ ──→│  Answer  │
│  Node    │    │  Base    │    │  Node    │    │  Node    │
└──────────┘    └──────────┘    └──────────┘    └──────────┘

No coding required. I dragged nodes onto the canvas and connected them.

Key Features

Dify provides:

dify-features.txt

LLMOps Capabilities:
  ├── Backend-as-a-Service (BaaS)
  ├── Model management
  ├── Observability and monitoring
  └── Production deployment

Visual Tools:
  ├── Workflow builder (drag-and-drop)
  ├── Prompt orchestration
  └── Dataset management

AI Features:
  ├── RAG Pipeline
  ├── Agent framework
  ├── Multi-model support
  └── Conversation management

Use Cases for Dify

Dify works well for:

1. Rapid Prototyping: Build AI apps without coding
2. Knowledge Base Q&A: Upload documents, get intelligent answers
3. Content Generation: Create workflows for writing tasks
4. Data Analysis: Process and analyze data through AI

The learning curve is low. My non-technical colleague built a customer support bot in an afternoon.

Feature Comparison

Here’s how they stack up:

Feature	AstrBot	Dify
Primary Purpose	Multi-platform chatbot deployment	LLMOps & AI application development
Core Focus	Messaging platform integration	AI workflow orchestration
Learning Curve	Medium	Low (visual)
Platform Support	15+ IM platforms	API-based integration
Plugin Ecosystem	800+ plugins	Plugin framework
Visual Interface	WebUI management	Drag-and-drop workflow builder
Local Models	Ollama, LLMTuner support	Via providers
Knowledge Base	Basic integration	Native RAG pipeline
Code Execution	Built-in sandbox	Via agent framework
API Access	REST API	REST API
Open Source	Yes	Yes (60K+ stars)
Best For	Deploying bots to IM platforms	Building AI applications

When to Use Each Platform

Choose AstrBot If:

• You need to deploy to QQ, WeChat, Telegram, or other IM platforms
• Multi-platform reach is your priority
• You want rich plugin ecosystem
• Local model deployment matters
• Enterprise compliance features (rate limiting, content filtering) are needed

Choose Dify If:

• Visual AI development is your preference
• Complex RAG implementations are required
• Knowledge base management is central to your project
• Multi-step agent workflows are needed
• Non-technical team members will build AI apps
• Rapid prototyping without coding is the goal

Choose Both If:

• Enterprise-grade AI deployment to multiple IM platforms is required
• Visual AI development + multi-platform delivery is the combination you need
• Advanced RAG capabilities with broad reach is the objective
• Non-technical team building AI with technical team deployment is your structure

Using Both Together

I eventually settled on using both. Here’s my architecture:

integration-architecture.txt

User on QQ/WeChat/Telegram
         │
         ▼
    ┌─────────┐
    │ AstrBot │  Handles platform connections
    └────┬────┘
         │ API calls
         ▼
    ┌─────────┐
    │  Dify   │  Handles AI logic, RAG, workflows
    └────┬────┘
         │
         ▼
    ┌─────────┐
    │   LLM   │  OpenAI, Claude, or local
    └─────────┘

Configuration Example

AstrBot connects to Dify as a provider:

dify.yaml

# astrbot/config/adapter/dify.yaml
provider: dify
api_endpoint: "https://your-dify-instance.com/v1"
api_key: "your-dify-api-key"
app_type: "chatbot"
enable_streaming: true

Dify handles the AI logic, knowledge base, and workflow orchestration. AstrBot handles the messaging platform connections, user management, and platform-specific features.

Benefits of This Approach

1. Best of Both Worlds: Dify’s visual workflow builder + AstrBot’s multi-platform support
2. Team Separation: Non-technical team builds workflows in Dify, technical team manages AstrBot deployment
3. Scalability: Each component can scale independently
4. Flexibility: Swap out either component without affecting the other

Cost Considerations

Both platforms are open source and free to self-host. The costs come from:

cost-breakdown.txt

Infrastructure:
  ├── Server hosting (self-hosted)
  └── Cloud hosting (managed options)

LLM API Costs:
  ├── OpenAI: $0.002-$0.06 per 1K tokens
  ├── Claude: $0.003-$0.075 per 1K tokens
  └── Local models: Hardware + electricity

Hidden Costs:
  ├── Storage for knowledge bases
  ├── Bandwidth for messaging
  └── Monitoring and logging

For a small team project, I spent about $20/month on OpenAI API calls and $15/month on a basic VPS to host both platforms.

Getting Started

Starting with AstrBot

Docker is the recommended deployment:

deploy-astrbot.sh

# Create directories
mkdir -p astrbot/data

# Run AstrBot
docker run -d \
  --name astrbot \
  -p 6185:6185 \
  -v ./astrbot/data:/AstrBot/data \
  soulter/astrbot:latest

# Access WebUI at http://localhost:6185

From the WebUI, I connected my LLM provider and configured QQ and Telegram adapters.

Starting with Dify

Dify also uses Docker:

deploy-dify.sh

# Clone the repository
git clone https://github.com/langgenius/dify.git
cd dify/docker

# Copy environment file
cp .env.example .env

# Start services
docker compose up -d

# Access at http://localhost

I then created a new application, connected OpenAI, and built my first workflow.

Integration Setup

To connect AstrBot to Dify:

1. In Dify, create an API-based application
2. Copy the API endpoint and key
3. In AstrBot, add Dify as a provider
4. Configure the adapter with the Dify credentials

Summary

In this post, I compared AstrBot and Dify based on my hands-on testing. The key insight: AstrBot excels at multi-platform chatbot deployment, while Dify excels at visual AI workflow building. They serve different purposes and can work together effectively.

For my project, I use both: Dify for building AI workflows with RAG, and AstrBot for deploying to QQ, Telegram, and WeChat. This combination gives me the best of both worlds.

If you’re deciding between them, start with your primary need: multi-platform deployment (AstrBot) or visual AI development (Dify). If you need both, they integrate well together.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 AstrBot Official Website
• 👨‍💻 AstrBot Documentation
• 👨‍💻 AstrBot GitHub
• 👨‍💻 Dify Official Website
• 👨‍💻 Dify Documentation
• 👨‍💻 Dify GitHub

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

Comments

Tags:

• ai

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