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Autonomous Research Agent

An AI agent system that performs multi-step research tasks with tool use, state management, and error recovery.

πŸš€ Status: Planned

Starting development after Project 1 MCP server is complete.

πŸ“‹ Overview

This project demonstrates:

  • AI agent architecture and design
  • Multi-step reasoning and planning
  • Tool use and MCP server integration
  • State management across sessions
  • Error handling and retry logic
  • Prompt engineering best practices

πŸ› οΈ Tech Stack

Backend:

  • Python 3.11+
  • FastAPI
  • Anthropic Claude API
  • Custom MCP servers (from Project 1)

Frontend:

  • Next.js 14
  • TypeScript
  • Real-time updates via Server-Sent Events

Agent Framework:

  • Custom agent implementation
  • MCP protocol for tool integration
  • Conversation state management

πŸ€– Agent Capabilities

  • Multi-step task decomposition
  • Web search and content synthesis
  • Code analysis via MCP tools
  • Document generation
  • Self-correction and error recovery
  • Conversation history and context management

πŸ—οΈ Architecture

Agent Loop:

  1. Receive user task
  2. Plan steps to accomplish task
  3. Execute steps using available tools
  4. Evaluate results and re-plan if needed
  5. Return synthesized response

Tools Available:

  • Web search (via MCP)
  • Code repository analysis (via Project 1 MCP server)
  • Document creation
  • Data analysis

πŸ“Š Features

  • Natural language task input
  • Real-time progress updates
  • Transparent reasoning (show agent's thought process)
  • Session persistence
  • Retry logic with exponential backoff
  • Cost tracking (API usage)

πŸ§ͺ Example Tasks

"Research the latest developments in MCP servers and create a comparison document"
"Analyze my GitHub repos and suggest architectural improvements"
"Find and summarize recent papers on prompt engineering"

πŸ“ˆ Technical Highlights

  • State Management: Persistent conversation context with PostgreSQL
  • Error Handling: Graceful degradation, retry with backoff
  • Tool Orchestration: Dynamic tool selection based on task
  • Performance: Async execution, parallel tool calls where possible

πŸš€ Getting Started

[Installation instructions coming soon]

πŸ“ Documentation

🎯 Learning Goals

  • Understand AI agent design patterns
  • Master MCP protocol and tool use
  • Build robust error handling systems
  • Production-grade prompt engineering

Built as part of my software engineering portfolio | View Other Projects

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