TaskingAI provides a Backend-as-a-Service (BaaS) platform for developing and deploying LLM-based AI agents. It targets developers building AI-native applications, offering a unified API for hundreds of LLM models, integrated tools, RAG systems, and conversation management, simplifying the transition from prototyping to scalable production.

How It Works

TaskingAI adopts a BaaS-inspired workflow, decoupling AI logic from client-side product development. It offers a unified API for integrating diverse LLM providers (OpenAI, Anthropic, local models via Ollama) and built-in tools (Google Search, web scraping). This modular approach allows flexible combination of models, tools, and RAG systems, addressing limitations of frameworks like LangChain (statelessness) and OpenAI's Assistant API (tied functionalities, proprietary model restrictions).

Quick Start & Requirements

• Install/Run: Use Docker Compose for self-hosting. Clone the repo, navigate to docker/, copy .env.example to .env, configure, and run docker-compose -p taskingai --env-file .env up -d.
• Prerequisites: Docker, Docker Compose, Git. Python 3.8+ for client SDK.
• Access: Console available at http://localhost:8080 (default credentials: admin/TaskingAI321).
• Client SDK: pip install taskingai.
• Docs: Documentation

Highlighted Details

• Unified API for hundreds of LLMs and local models.
• Built-in tools and RAG systems for agent enhancement.
• One-click deployment and multi-tenant support.
• Asynchronous processing via FastAPI for scalability.
• Intuitive UI console for management and testing.

Maintenance & Community

• Official Discord community available for support and discussion.
• Contribution guidelines provided.
• Contact: support@tasking.ai.

Licensing & Compatibility

• Released under a specific "TaskingAI Open Source License".
• License details and restrictions require careful review for commercial or closed-source use.

Limitations & Caveats

The specific "TaskingAI Open Source License" may contain restrictions not typical of permissive licenses like MIT or Apache 2.0, requiring careful legal review before commercial adoption.