langgraphgo-showcases

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README

LangGraph Go Showcases

License go.dev reference GitHub Actions Go Report Card Go Version Showcases

A collection of AI agent showcases and demonstrations built with LangGraph Go. This repository was extracted from the showcases directory of the main LangGraph Go project to provide standalone, production-ready examples of AI agent implementations.

Overview

This repository contains 9 comprehensive AI agent implementations showcasing different use cases and patterns for building intelligent systems with LangGraph Go and LangChain Go. Each showcase is a complete, runnable project with its own README, examples, and documentation.

Table of Contents

Showcases

BettaFish

Deep Public Opinion Analysis with Multi-Agent Architecture

A complete Go implementation of the BettaFish project, featuring:

  • QueryEngine with reflection loop for iterative search refinement
  • MediaEngine for relevant image search
  • ForumEngine with LLM-driven discussion between agents
  • ReportEngine for comprehensive Markdown reports

📂 View BettaFish →

Key Technologies: Tavily API, Multi-agent discussion, Reflection patterns

GPT Researcher

Autonomous Research Agent for Comprehensive Reports

A Go port of gpt-researcher that automates research tasks:

  • Generates focused research questions from queries
  • Gathers information from 20+ web sources
  • Produces detailed 2000+ word reports with citations
  • Supports multiple report types (research, outline, resource)

📂 View GPT Researcher →

Key Technologies: Tavily search, Web scraping, Multi-source aggregation

Health Insights Agent

AI-Powered Blood Report Analysis

Intelligent health analysis system that processes blood test reports:

  • Extracts parameters from text and PDF reports
  • Identifies potential health risks with severity levels
  • Provides personalized recommendations (diet, lifestyle, medical)
  • Generates structured analysis with confidence scores

📂 View Health Insights Agent →

Key Technologies: LLM-based extraction, Medical knowledge, PDF processing

PeopleHub

Automated Person Research Agent

A Go implementation of PeopleHub for researching individuals:

  • Fetches LinkedIn profiles via web search
  • Performs comprehensive Google searches
  • Scrapes and summarizes relevant web pages
  • Generates detailed person research reports

📂 View PeopleHub →

Key Technologies: Parallel execution, Web scraping, Profile aggregation

DeepAgents

Filesystem-Aware AI Agent with Task Management

A powerful agent framework with filesystem access:

  • Read, write, and manage files within workspace
  • Built-in todo list management
  • SubAgent delegation for complex tasks
  • Pattern-based file finding with glob support

📂 View DeepAgents →

Key Technologies: Filesystem tools, Task delegation, Hierarchical agents

Insight

Deep Research Agent with Web Interface

Go implementation of ByteDance DeerFlow:

  • Multi-agent architecture (Planner → Researcher → Reporter → Podcast)
  • Real-time progress updates via Server-Sent Events
  • Modern dark theme web interface
  • Research history and result caching
  • Optional podcast script generation

📂 View Insight →

Key Technologies: SSE streaming, Web UI, Podcast generation, Caching

LangManus

Multi-Agent AI Automation Framework

A Go implementation of the LangManus framework:

  • Layered architecture with 7 specialized agents
  • Coordinator → Planner → Supervisor → Workers → Reporter
  • Code execution (Python/Bash) with safety controls
  • Web search integration via Tavily
  • Streaming support for real-time updates

📂 View LangManus →

Key Technologies: Multi-agent orchestration, Code execution, Task planning

Profile

Digital Footprint Analyzer

An OSINT tool for analyzing digital presence across the internet:

  • Searches 1000+ social media platforms
  • AI-powered psychological profiling
  • Real-time streaming feedback via SSE
  • Beautiful paper-textured UI design
  • Privacy-focused (public data only)

📂 View Profile →

Key Technologies: OSINT, Social media search, Psychological analysis

Live Demo: http://profile.rpcx.io

AI PDF Chatbot

Intelligent Document Q&A System

A full-stack RAG (Retrieval-Augmented Generation) application:

  • Upload and ingest PDF documents
  • Vector-based semantic search
  • Conversational Q&A interface
  • Backend: Go with LangChain Go
  • Frontend: Modern web interface

📂 View AI PDF Chatbot →

Key Technologies: RAG, Vector database, PDF processing, Chat interface

Getting Started

Each showcase is self-contained with its own dependencies and setup instructions.

General Prerequisites
  • Go: Version 1.21 or higher
  • API Keys: Most showcases require:
    • OpenAI API key (or compatible provider)
    • Tavily API key (for web search features)
Quick Start
  1. Choose a showcase from the list above
  2. Navigate to its directory: 
    cd <showcase-name>
  3. Follow the README in that directory for specific setup instructions
  4. Set required environment variables: 
    export OPENAI_API_KEY="your-key-here"
export TAVILY_API_KEY="your-tavily-key"  # if needed
  5. Run the showcase: 
    go run *.go
# or
go build && ./<showcase-name>

Requirements

Common Dependencies

All showcases use:

API Services

Most showcases integrate with:

  • OpenAI API - For LLM capabilities (GPT-4, GPT-3.5, etc.)
  • Tavily API - For web search functionality
  • Alternative Providers - Many support OpenAI-compatible APIs (DeepSeek, Azure, Ollama, etc.)
Environment Configuration

Common environment variables:

# Required
OPENAI_API_KEY="sk-..."

# Optional
OPENAI_API_BASE="https://api.openai.com/v1"  # Custom endpoint
OPENAI_MODEL="gpt-4o"                        # Model selection
TAVILY_API_KEY="tvly-..."                    # For search features

Project Structure

langgraphgo-showcases/
├── BettaFish/              # Public opinion analysis
├── gpt_researcher/         # Autonomous research agent
├── health_insights_agent/  # Medical report analyzer
├── pepolehub/             # Person research agent
├── deepagents/            # Filesystem-aware agent
├── Insight/               # Deep research with web UI
├── langmanus/             # Multi-agent framework
├── profile/               # Digital footprint analyzer
├── ai-pdf-chatbot/        # PDF Q&A system
├── LICENSE                # MIT License
└── README.md              # This file

Features Across Showcases

Agent Patterns
  • ✅ Multi-agent orchestration
  • ✅ Hierarchical task delegation
  • ✅ Reflection and self-improvement loops
  • ✅ Parallel execution
  • ✅ State management with LangGraph
Capabilities
  • ✅ Web search and scraping
  • ✅ Document processing (PDF, text)
  • ✅ Code generation and execution
  • ✅ Report generation (Markdown, HTML)
  • ✅ Real-time streaming
  • ✅ Filesystem operations
  • ✅ Task planning and decomposition
Interfaces
  • ✅ Command-line interfaces (CLI)
  • ✅ Web interfaces with modern UI
  • ✅ Programmatic APIs
  • ✅ Streaming support (SSE)

Use Cases

These showcases demonstrate solutions for:

  • 📊 Research & Analysis: Automated research, data gathering, report generation
  • 🏥 Healthcare: Medical report analysis, health insights
  • 🔍 OSINT: Public data aggregation, digital footprint analysis
  • 📝 Document Processing: PDF analysis, Q&A systems
  • 💼 Automation: Task orchestration, code execution, workflow automation
  • 🌐 Web Intelligence: Search, scraping, content synthesis

Learning Path

Beginner: Start with simpler showcases

  1. PeopleHub - Basic multi-step workflow
  2. GPT Researcher - Classic research pattern

Intermediate: Explore more complex patterns 3. Health Insights Agent - Structured extraction 4. DeepAgents - Filesystem and tools 5. AI PDF Chatbot - RAG implementation

Advanced: Multi-agent systems 6. BettaFish - Reflection loops 7. Insight - Full-stack with UI 8. LangManus - Complex orchestration

Contributing

Contributions are welcome! Whether it's:

  • Bug fixes
  • New showcases
  • Documentation improvements
  • Performance optimizations
  • Test coverage

Please feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

These showcases are inspired by and port several excellent open-source projects:

Special thanks to:

Support

For questions, issues, or discussions:

Built with ❤️ using LangGraph Go and LangChain Go

Directories

Path Synopsis
forum_engine
insight_engine
media_engine
mind_spider
query_engine
report_engine
schema
sentiment_model
utils
ai-pdf-chatbot
backend command
Package main implements the AI PDF Chatbot - a Go port of the original TypeScript/Node.js project.
 Package main implements the AI PDF Chatbot - a Go port of the original TypeScript/Node.js project.
agent
tools
agents
cli
config
tools
graph

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