🤖 Agents

Last Updated: 2025-01-19

A collection of frameworks and tools for building AI agents.

Comparison Matrix

Framework	Self-Hosting	AI Capabilities	Visual Builder	Integrations	Price	Overall Score
n8n	10/10	8/10	9/10	9/10	9/10	9.0/10
Flowise	9/10	8/10	9/10	7/10	10/10	8.5/10
LangChain	8/10	9/10	5/10	8/10	10/10	8.2/10
ElizaOS	7/10	8/10	6/10	7/10	10/10	7.6/10
AutoGPT	7/10	8/10	4/10	6/10	10/10	7.1/10
Zerepy	6/10	7/10	5/10	7/10	10/10	7.0/10
Haystack	8/10	7/10	4/10	7/10	9/10	7.0/10
Rasa	9/10	7/10	6/10	6/10	7/10	7.0/10

Scoring Categories

Overall scores are calculated using weighted averages across five key dimensions:

• Self-Hosting: 25% - Ability to deploy and manage on own infrastructure
• AI Capabilities: 25% - Range and depth of AI features and model support
• Visual Builder: 20% - Quality and usability of visual development tools
• Integrations: 20% - Number and quality of available integrations
• Price: 10% - Cost-effectiveness and pricing model flexibility

Final score = (Self-Hosting × 0.25) + (AI Capabilities × 0.25) + (Visual Builder × 0.20) + (Integrations × 0.20) + (Price × 0.10)

n8n

• GitHub: n8n-io/n8n
• Website: n8n.io
• Description: Fair-code workflow automation platform with native AI capabilities
• Hosting Options:
  • Self-hosted (Fair-code)
  • Cloud version (n8n.cloud)
  • Enterprise self-hosted
• Pricing:
  • Self-hosted: Free for personal use
  • Cloud Starter: $20/month
  • Cloud Pro: $45/month
  • Enterprise: Custom pricing
• Key Features:
  • 400+ integrations
  • Visual workflow builder
  • AI-native platform with LangChain support
  • Code nodes (JavaScript/Python)
  • Self-hosting option
  • Enterprise features (SSO, RBAC)
• Deployment Options:

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  -e N8N_BASIC_AUTH_ACTIVE=true \
  -e N8N_BASIC_AUTH_USER=admin \
  -e N8N_BASIC_AUTH_PASSWORD=secure_password \
  -e NODE_ENV=production \
  --restart unless-stopped \
  --network n8n-network \
  docker.n8n.io/n8nio/n8n

Key security considerations:

• Basic authentication enabled
• Production environment
• Persistent data storage
• Isolated network
• Automatic container restart
• Pros:
  • Extensive integration library
  • Visual workflow builder
  • Code flexibility when needed
  • Strong enterprise features
  • Active community (55k+ GitHub stars)
• Cons:
  • Learning curve for complex workflows
  • Resource intensive for self-hosting
  • Some features limited to enterprise

Flowise

• GitHub: FlowiseAI/Flowise
• Website: flowiseai.com
• Description: Open-source UI visual tool for building LLM flows with LangchainJS
• Hosting Options:
  • Self-hosted (Open Source)
  • Cloud version (Beta)
• Pricing:
  • Community Edition: Free, self-hosted
  • Cloud Beta: Free during beta
  • Enterprise: Custom pricing
• Key Features:
  • Drag-and-drop flow builder
  • LangchainJS integration
  • 100+ pre-built nodes
  • API endpoint generation
  • Docker deployment support
  • Credential management
  • Built-in authentication
  • API key management
  • Team collaboration (Enterprise)
• Deployment Options:

# NPM installation
npm install -g flowise
npx flowise start

# Docker deployment
docker run -d \
  --name flowise \
  -p 3000:3000 \
  -v ~/.flowise:/root/.flowise \
  --restart unless-stopped \
  flowiseai/flowise

• System Requirements:
  • Node.js 18 or higher
  • 2GB RAM minimum
  • 1GB storage space
• Pros:
  • Intuitive visual interface
  • Easy deployment options
  • Active development
  • Strong LangChain integration
  • Free and open-source
  • Regular updates
  • Growing marketplace
• Cons:
  • Newer project compared to alternatives
  • Limited enterprise features
  • Community still growing
  • Some advanced features require coding
  • Cloud version still in beta

ElizaOS

• GitHub: ElizaOS
• Description: An operating system designed for AI agents, focusing on autonomous operation and system integration.
• Hosting Options:
  • Self-hosted only
  • Custom deployment
• Pricing:
  • Open Source: Free
  • Commercial use: Contact team
• Key Features:
  • AI-first architecture
  • Built-in agent capabilities
  • System-level AI integration
  • TypeScript
• Pros:
  • Deep system integration
  • Built for AI-first workflows
  • TypeScript ecosystem
  • Modern architecture
• Cons:
  • Early development stage
  • Limited community resources
  • Steeper learning curve
  • Documentation gaps

Zerepy

• GitHub: blorm-network/ZerePy
• Demo: Replit Template
• Description: A Python framework for building zero-shot capable AI agents
• Hosting Options:
  • Self-hosted
  • Replit deployment
• Pricing:
  • Open Source: Free
  • No paid plans currently
• Key Features:
  • CLI for managing agents
  • Multiple LLM support (OpenAI, Anthropic, EternalAI, etc.)
  • Social platform integrations (Twitter/X, Farcaster)
  • Blockchain integration with Solana
  • Python
• Pros:
  • Zero-shot capabilities
  • Multiple LLM support
  • Social media integration
  • Blockchain compatibility
  • Simple Python interface
• Cons:
  • Limited enterprise features
  • Smaller community
  • Basic documentation
  • Early-stage project

LangChain

• GitHub: langchain-ai/langchain
• Documentation: Python Docs
• Description: Framework for developing applications powered by language models
• Hosting Options:
  • Self-hosted
  • LangSmith (Managed Platform)
  • Cloud deployment support
• Pricing:
  • Open Source: Free
  • LangSmith: Free during beta
  • Enterprise: Custom pricing
• Key Features:
  • Chains and agents
  • Document handling
  • Memory management
  • Model integration
  • Python
• Pros:
  • Extensive ecosystem
  • Strong community support
  • Flexible architecture
  • Rich documentation
  • Multiple language support
• Cons:
  • Complex learning curve
  • Rapid development pace
  • Version compatibility issues
  • Memory management overhead

AutoGPT

• GitHub: Significant-Gravitas/AutoGPT
• Description: Autonomous AI agents that can perform complex tasks
• Hosting Options:
  • Self-hosted only
  • Docker deployment
• Pricing:
  • Open Source: Free
  • No commercial version
• Key Features:
  • Task decomposition
  • Autonomous goal pursuit
  • Internet access capabilities
  • Python
• Pros:
  • Autonomous operation
  • Flexible goal setting
  • Active development
  • Strong community
  • Open source
• Cons:
  • High token consumption
  • Inconsistent results
  • Limited enterprise support
  • Resource intensive

BabyAGI

• GitHub: yoheinakajima/babyagi
• Description: Task-driven autonomous AI agent
• Hosting Options:
  • Self-hosted only
  • Local deployment
• Pricing:
  • Open Source: Free
  • No paid plans
• Key Features:
  • Task creation and prioritization
  • Autonomous execution
  • Memory management
  • Python
• Pros:
  • Simple architecture
  • Easy to understand
  • Good for learning
  • Customizable
• Cons:
  • Basic functionality
  • Limited scalability
  • Minimal enterprise features
  • Basic task handling

Haystack

• GitHub: deepset-ai/haystack
• Documentation: Haystack Docs
• Description: End-to-end framework for building NLP applications
• Hosting Options:
  • Self-hosted
  • Deepset Cloud
  • Custom deployment
• Pricing:
  • Open Source: Free
  • Cloud Starter: Free
  • Cloud Pro: Custom pricing
  • Enterprise: Contact sales
• Key Features:
  • Question answering
  • Semantic search
  • Document retrieval
• Pros:
  • Production-ready
  • Scalable architecture
  • Strong NLP capabilities
  • Active maintenance
• Cons:
  • Resource intensive
  • Complex setup
  • Limited UI tools
  • Steep learning curve

Rasa

• GitHub: RasaHQ/rasa
• Documentation: Rasa Docs
• Description: Framework for building conversational AI assistants
• Hosting Options:
  • Self-hosted
  • Rasa Enterprise
  • Cloud deployment
• Pricing:
  • Open Source: Free
  • Pro: Starting at USD 1000/month
  • Enterprise: Custom pricing
• Key Features:
  • Natural language understanding
  • Dialogue management
  • Custom actions
• Pros:
  • Enterprise-grade
  • Customizable NLU
  • Active community
  • Self-hosted option
• Cons:
  • Complex configuration
  • Resource heavy
  • Training requirements
  • Limited free features

SMOLAgent

• GitHub: smol-ai/agent
• Description: Minimal autonomous agent that focuses on being small, maintainable, and extensible
• Hosting Options:
  • Self-hosted only
  • Local deployment
• Pricing:
  • Open Source: Free
  • No paid plans
• Key Features:
  • Minimal codebase (less than 1000 lines)
  • Task decomposition
  • Multiple LLM support
  • Extensible architecture
  • Python
• Pros:
  • Simple implementation
  • Easy to understand and modify
  • Lightweight deployment
  • Good for learning
  • Clean architecture
• Cons:
  • Limited built-in features
  • Basic functionality
  • Minimal enterprise support
  • Manual configuration required