An opinionated development framework for building production-ready AI agents with LangGraph. It grounds AI coding assistants (Cursor, Windsurf, Cline) and guides them to use local, official documentation, ensuring reliable, secure, and observable agentic workflows.
git clone https://github.com/botingw/langgraph-dev-navigator.gitThe langgraph-dev-navigator is an opinionated development framework designed to facilitate the creation of production-ready AI agents using LangGraph. This skill empowers developers and AI practitioners by grounding AI coding assistants like Cursor, Windsurf, and Cline in local, official documentation. By ensuring that these agents utilize reliable and secure resources, the framework promotes observable workflows that enhance overall productivity and efficiency in AI automation tasks. One of the key benefits of the langgraph-dev-navigator is its ability to streamline the development process for AI agents. With an implementation time of just 30 minutes, this skill allows teams to quickly set up their workflows without extensive overhead. Although specific time savings are not quantified, the structured approach it offers can lead to significant reductions in development time by minimizing errors and enhancing the reliability of the coding assistants. This skill is particularly beneficial for developers, product managers, and AI practitioners who are involved in creating and deploying AI automation solutions. By utilizing langgraph-dev-navigator, teams can ensure that their AI agents are not only effective but also compliant with best practices in security and observability. The skill is well-suited for those looking to enhance their workflow automation processes and integrate AI agents into their existing systems seamlessly. In practical terms, the langgraph-dev-navigator can be used in various scenarios, such as developing chatbots, automating data analysis, or creating intelligent customer support systems. The intermediate complexity of this skill means that users should have a foundational understanding of AI development principles. As organizations increasingly adopt AI-first workflows, integrating this skill can significantly enhance the capabilities of teams, allowing them to leverage AI automation more effectively in their projects.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/botingw/langgraph-dev-navigatorCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
I'm building a production-ready AI agent for [COMPANY] in the [INDUSTRY] sector. I need to use LangGraph Dev Navigator to create a workflow that leverages [SPECIFIC_TOOL] and follows [SPECIFIC_DOCUMENTATION]. Can you guide me through the process and provide code snippets for key components?
# AI Agent Development Plan for GreenTech Solutions ## Overview - **Company**: GreenTech Solutions - **Industry**: Renewable Energy - **Primary Tool**: LangGraph - **Documentation Source**: Official LangGraph Documentation ## Workflow Components 1. **Data Ingestion Module**: - Connects to internal databases - Validates data sources - Implements rate limiting 2. **Processing Pipeline**: - Uses LangGraph for state management - Implements error handling - Logs all operations 3. **Output Module**: - Formats results for end-users - Implements security checks - Provides observability metrics ## Next Steps - Review the generated code snippets - Integrate with existing systems - Test thoroughly in a staging environment
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