langgraph-agents
🥈SilverModular LangGraph framework for building composable AI agents that perform research, writing, web search, and RAG tasks with multi-agent team orchestration.
710Updated 3mo ago
Intermediate30min to implementautomation
Saves ~180 min per use
Quick InstallView Source
git clone https://github.com/nehalvaghasiya/langgraph-agents.gitWorks with:
Claude
Overview
About This Skill
langgraph-agents is a Python framework for building composable AI agents powered by LangGraph and LLM models. It provides pre-built agents for document writing, web research, web scraping, note-taking, chart generation, and retrieval-augmented generation (RAG). The framework uses ReAct-style reasoning with tool calling and supports supervisor-based multi-agent teams for complex workflows. Developers can create individual agents or orchestrate teams of agents to collaborate on tasks like paper writing or in-depth research. Built on LangChain and Pydantic, it integrates with Groq and OpenAI LLMs and includes tools for web search, document I/O, and Python execution.
How to Use
Install with `uv sync` (recommended) or `pip install -r requirements.txt`. Export your Groq or OpenAI API key. Run examples with `PYTHONPATH=src uv run python3 examples/<agent_name>.py`. Instantiate agents like `SearchAgent(llm)` or `PaperWritingTeamAgent(llm)` and invoke them with LangGraph's graph.invoke() method.
Use Cases
Generate comprehensive documents and reports with the DocWriterAgent
Conduct in-depth research with multi-agent teams performing web search and scraping
Build question-answering systems over documents using RAG
Create visual data representations with the ChartGeneratorAgent
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/nehalvaghasiya/langgraph-agentsRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Create a LangGraph agent that can automate [TASK] for [COMPANY] in the [INDUSTRY] sector. The agent should use [DATA] to perform its functions. Specify the modules and workflow steps required to complete the task.
Example Output
# LangGraph Agent Design for Market Analysis Automation ## Overview This agent automates market analysis for TechCorp in the technology sector using real-time industry data. It performs the following functions: - Data collection from multiple sources - Competitive analysis - Trend identification - Report generation ## Modules 1. **Data Collection Module**: Gathers data from APIs, web scraping, and internal databases 2. **Analysis Module**: Processes data to identify trends and competitive positioning 3. **Reporting Module**: Generates comprehensive market analysis reports 4. **Decision Support Module**: Provides actionable insights and recommendations ## Workflow 1. **Data Ingestion**: Collects and cleans data from specified sources 2. **Analysis**: Processes data to identify key trends and competitive insights 3. **Report Generation**: Compiles findings into a structured report 4. **Insight Delivery**: Presents actionable recommendations to stakeholders ## Expected Output - Comprehensive market analysis report - Identified trends and opportunities - Competitive positioning insights - Actionable recommendations for strategic decision-making
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