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nexent

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Basic Information

Nexent is a zero-code platform for auto-generating AI agents, aimed at letting users create, run and scale agents using natural language rather than orchestration or drag-and-drop UIs. Built on the MCP ecosystem, the repo contains the platform core, built-in agent templates for scenarios like travel, research and business, and integrations for tools and models. The README highlights capabilities for agent running control, multi-agent collaboration, data processing and knowledge tracing, multimodal dialogue, and batch scaling. The project provides quick start instructions using Docker Compose and lists modest system prerequisites. Documentation, a developer guide, contribution guidance and community channels are provided to help users try the platform, extend it with MCP-compliant Python plugins, and report issues. The project is released under an MIT-style license with additional conditions.

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App URL
https://github.com/ModelEngine-Group/nexent
Github Repository
https://github.com/ModelEngine-Group/nexent/blob/main/README.md

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App Details

Features
Nexent converts plain language to runnable agent prompts and automatically selects tools and action plans. It includes a scalable data processing engine that handles 20+ data formats with fast OCR and table extraction and supports single-process to large-batch pipelines. The platform offers a personal-grade knowledge base with real-time file import and automatic summarization, and agents can consult both personal and global knowledge while indicating source provenance. Internet knowledge search integrates multiple web search providers so agents can combine fresh web facts with private data. Knowledge-level traceability attaches precise citations to answers. Multimodal understanding supports voice, text, files and images and can generate images. The MCP tool ecosystem enables Python plug-ins, model and tool swaps, and extensibility without changing core code.
Use Cases
Nexent lowers the barrier to building intelligent agents by enabling non-technical users to define agent behavior in natural language and by providing ready-made agents and templates for common scenarios. It helps teams process diverse data formats at scale, extract structured tables and OCR text, and run batch pipelines to automate repetitive tasks. Its knowledge base and web search integrations allow agents to answer questions with verifiable citations, which improves trust and auditability. Multimodal support lets users interact via speech, text and images. Developers can extend functionality through the MCP plugin spec and build from source, while Docker Compose quick start and stated hardware minima make local deployment straightforward. Documentation, community channels and an issues tracker support adoption and troubleshooting.

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