BE-4: AI Agent Frameworks Research (CrewAI, LangChain, AutoGPT Style Systems)

[tecnodeveloper]

Description:
Research AI agent frameworks used to build autonomous systems. Understand how agents plan tasks, use tools (like scraping + LLMs), maintain memory, and coordinate workflows. Focus on frameworks suitable for building Price WatchDog automation system.

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User Story

Given I want to build an autonomous AI scraping agent
When I choose an agent framework
Then I should understand which framework is best for planning, tool use, and automation

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Tasks

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AI Agent Basics

1. Understand What an AI Agent Is

   • Difference between chatbot vs agent
   • Planning + execution loop
   • Tool usage concept

2. Agent Components

   • Brain (LLM)
   • Tools (scraping, APIs)
   • Memory (context storage)
   • Planner (task breakdown)

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CrewAI Research

3. Study CrewAI

   • Multi-agent collaboration system
   • Role-based agents (researcher, executor)
   • Task delegation system

4. CrewAI Pros & Cons

   • Easy multi-agent setup
   • Good for workflows
   • Less control over low-level execution

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LangChain Research

5. Study LangChain

   • Chains and pipelines
   • Tool calling system
   • Memory modules

6. LangChain Features

   • Agent system
   • Retrieval-Augmented Generation (RAG)
   • Integration with APIs & tools

7. LangChain Pros & Cons

   • Very flexible
   • Large ecosystem
   • Can become complex

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AutoGPT Style Systems

8. Understand AutoGPT Concept

   • Fully autonomous task loops
   • Goal → subtask → execution cycle
   • Self-prompting system

9. Strengths & Weaknesses

   • Fully autonomous behavior
   • Hard to control outputs
   • Unstable for production use

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Agent Workflow Design

10. Define Agent Flow

• User goal input
• Task breakdown
• Tool selection
• Execution
• Result validation

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Tool Integration

11. Agent Tool Usage

• Web scraping tool
• Price extraction tool
• Database storage tool
• Notification tool

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Memory Systems

12. Agent Memory Types

• Short-term memory
• Long-term memory
• Session memory

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Planning Systems

13. Task Planning

• Break user goal into steps
• Prioritize tasks
• Handle retries

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Comparison Matrix

14. Framework Comparison

• CrewAI → multi-agent workflow
• LangChain → flexible tool framework
• AutoGPT → full autonomy

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Performance & Stability

15. Production Readiness

• LangChain (stable)
• CrewAI (stable for workflows)
• AutoGPT (experimental)

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Use Case Mapping

16. Best Fit for Price WatchDog

• Scraping agent
• Price monitoring agent
• Notification agent

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Architecture Thinking

17. Agent System Design

• Single agent vs multi-agent
• Tool orchestration
• Failure handling

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Integration with LLMs

18. LLM + Agent Connection

• Gemini / OpenAI as brain
• Tool execution layer
• Response validation

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Real-World Scenarios

19. E-Commerce Agent Use Cases

• Product price tracking
• Stock checking
• Variant selection

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Limitations

20. Agent Challenges

• Hallucinations
• Infinite loops
• API cost control

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Acceptance Criteria

• Major AI agent frameworks studied
• Pros/cons clearly understood
• Workflow design defined
• Best framework identified for project
• Tool integration strategy planned

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Testing Steps

1. Run simple LangChain agent
2. Test CrewAI multi-agent flow
3. Simulate AutoGPT loop
4. Compare outputs
5. Measure stability

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Definition of Done

• AI agent frameworks fully researched
• Best architecture direction selected
• Tool + LLM integration plan ready