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Basic Information

TaskingAI is a Backend-as-a-Service platform for building, managing and deploying LLM-based agents and AI-native applications. It provides a unified backend to integrate hundreds of language models and to manage agent functional modules such as tools, plugins, Retrieval-Augmented Generation systems, assistants and conversation history. The repository includes a self-hostable community edition with Docker compose examples and a Python client SDK for programmatic interaction. It supports both stateful and stateless usage patterns and allows decoupled configuration of models, memories, tools and retrieval systems. The project targets developers and teams who need an end-to-end server side to prototype, test in a console UI, and scale AI agents to production environments while supporting multi-tenant deployments.

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Features
All-in-one LLM platform with unified APIs to access many model providers. Built-in, customizable tools and a RAG system to enhance agent performance. BaaS-inspired workflow that decouples AI logic on the server from client product development. One-click deployment to production with Dockerized services and image-based upgrades. Asynchronous high-performance server implemented with Python FastAPI for concurrent workloads. Intuitive web console for managing projects, testing workflows and viewing conversation history. Broad integrations including OpenAI, Anthropic and options for local models via Ollama, LM Studio and Local AI. Plugin ecosystem for search, web readers and domain-specific retrievals. Multi-tenant support and unified management of tools, RAGs and model configs.
Use Cases
The repo helps teams move from console-based prototyping to scalable production by providing a ready backend, deployment scripts and a client SDK. It reduces infrastructure burden by offering Docker quickstart, auto migration on upgrades and standardized RESTful APIs for assistants, chats and messages. Developers can compose and reconfigure tools, retrieval systems and models without rewriting application code. State management options support both sessionful agents and stateless chat calls depending on use case. Integrated UI and example client code accelerate testing and integration. Documentation, API reference and community channels are provided to support adoption and contributions. The platform is suited for building interactive demos, enterprise productivity agents and multi-tenant AI applications.

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