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TaskingAI

Open source platform for AI-native application development with RAG, function calling, and multi-LLM support

★ 5,389 GitHub Apache-2.0 airagllmfunction-callingvector-database AI Agents

Overview

TaskingAI is an open source platform designed for building AI-native applications. It provides a comprehensive toolkit including RAG (Retrieval-Augmented Generation) for knowledge retrieval, function calling for tool integration, multi-LLM support across different providers, and a built-in vector store. With its modular architecture and RESTful API, developers can quickly prototype and deploy AI applications with features like conversation management, assistant orchestration, and retrieval pipelines. The platform supports popular LLM providers like OpenAI, Anthropic, Google, and open-source models via Ollama and llama.cpp. TaskingAI runs on Docker and requires minimal setup, making it ideal for teams looking to build production-ready AI applications without managing complex infrastructure.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  taskingai:
    image: taskingai/taskingai-server:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/taskingai:/data

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Guides & articles

TaskingAI Tutorial: Deploy and Build Your First AI Assistant in 10 Minutes

A step-by-step tutorial to deploy TaskingAI with Docker, create an AI assistant with RAG capabilities, upload documents, set up function calling tools, and start chatting.

Jul 4, 2026

TaskingAI Guide: Build AI-Native Apps with RAG and Function Calling

An in-depth guide to TaskingAI — the open-source platform for building AI-native applications with RAG pipelines, function calling, multi-LLM orchestration, and a built-in vector store.

Jul 4, 2026