ai-agentic

Integrate LLMs, embeddings, RAG, and agent orchestration with multiple AI providers.

3|1|Updated Feb 27, 2026

Search Tags:#embeddings #ai #llm #rag #agent #orchestration #vector search

Ready to run

npx skills add https://github.com/orchestra-mcp/framework --skill ai-agentic

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-agentic
Source: https://github.com/orchestra-mcp/framework/tree/main/.claude/skills/ai-agentic
Command: npx skills add https://github.com/orchestra-mcp/framework --skill ai-agentic

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the development and integration of AI-powered features within your applications, from simple chat interfaces to complex autonomous agents.

Core Features & Use Cases

AI Chat & Code Generation: Integrate LLM capabilities for conversational interfaces and code assistance.
Embeddings & Vector Search: Enable semantic search and retrieval-augmented generation (RAG) for knowledge retrieval.
Agent Orchestration: Build autonomous agents that can reason, plan, and execute tasks using tools.
Use Case: Develop a customer support chatbot that can access your product documentation (RAG), answer user queries, and even suggest code snippets for common issues.

Quick Start

Use the ai-agentic skill to generate a Python function that summarizes a given text using the Claude API.

Frequently Asked Questions about ai-agentic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a RAG application with vector search and LLM integration?▼

This skill provides tools for RAG applications by generating embeddings, storing them in vector databases like pgvector or chromem-go, and retrieving relevant context to augment LLM prompts. It orchestrates the entire pipeline for semantic knowledge retrieval.

What is agent orchestration and how does it work with LLMs?▼

Agent orchestration coordinates autonomous agents that reason, plan, and execute tasks using external tools. This skill provides the framework to build these agents, enabling LLMs to move beyond simple text generation to autonomously solving multi-step problems.

Can I use both cloud and local vector stores for semantic search?▼

Yes, you can use both cloud and local vector stores. The skill supports pgvector for cloud deployments and chromem-go for local vector storage, allowing you to choose the environment that best fits your application's scale and privacy requirements.

Does this tool support multiple AI providers like Anthropic and OpenAI?▼

Yes, this tool supports multiple AI providers including Anthropic and OpenAI. It integrates LLM capabilities from these providers to facilitate chat, code generation, and autonomous agent features within your applications.

How do I add a customer support chatbot that accesses product documentation?▼

To add a documentation-aware chatbot, you use the RAG features to embed your product docs into a vector store, then pass retrieved context to the LLM chat interface. This allows the bot to answer user queries and suggest code snippets based on your specific documentation.