--- title: "nexent" type: "tool" slug: "modelengine-group-nexent" canonical_url: "https://www.graphcanon.com/tools/modelengine-group-nexent" github_url: "https://github.com/ModelEngine-Group/nexent" homepage_url: null stars: 5521 forks: 686 primary_language: "Python" license: "MIT" categories: ["ai-agents"] tags: ["harness-engineering", "multi-agent", "llm", "agentic-framework", "agentic-workflow", "agentic-ai", "agentic-rag", "agent"] updated_at: "2026-07-07T18:39:48.849181+00:00" --- # nexent > A zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles Nexent enables users to develop advanced AI agents without writing code, focusing on unified tools, memory, and orchestration with built-in constraints and feedback loops. ## Facts - Repository: https://github.com/ModelEngine-Group/nexent - Stars: 5,521 · Forks: 686 · Open issues: 278 · Watchers: 242 - Primary language: Python - License: MIT - Last pushed: 2026-07-07T11:41:50+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) ## Tags harness-engineering, multi-agent, llm, agentic-framework, agentic-workflow, agentic-ai, agentic-rag, agent ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. 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Endless reach. # 🚀 Get Started Now > ⭐ Before you get started, please star us on [GitHub](https://github.com/ModelEngine-Group/nexent) — your support drives us forward! ## Option 1: Try Our Official Demo No installation required — jump right in with our **[online demo environment](http://60.204.251.153:3000/en)** to experience Nexent's capabilities instantly. ## Option 2: Deploy on Your Own If you need to run Nexent locally or in your private infrastructure, we offer two deployment options: ### System Requirements | Resource | Docker | Kubernetes | |----------|--------|-------------| | **CPU** | 4 cores (min) / 8 cores (rec.) | 4 cores (min) / 8 cores (rec.) | | **Memory** | 8 GiB (min) / 16 GiB (rec.) | 16 GiB (min) / 64 GiB (rec.) | | **Disk** | 40 GiB (min) / 100 GiB (rec.) | 100 GiB (min) / 200 GiB (rec.) | | **Architecture** | x86_64 / ARM64 | x86_64 / ARM64 | | **Software** | Docker 24+, Docker Compose v2+ | Kubernetes 1.24+, Helm 3+ | > **Note:** Recommended configurations ensure optimal performance in production environments. ### Docker Deployment (Recommended for Individuals/Small Teams) Quick and straightforward for most users. Prerequisites: Docker 24+ and Docker Compose v2+: ```bash git clone https://github.com/ModelEngine-Group/nexent.git cd nexent bash deploy.sh docker ``` The root `deploy.sh` only forwards to the target deploy script; the native Docker implementation is `bash deploy/docker/deploy.sh`. The Docker and Kubernetes deploy scripts share the same deployment configuration model. Interactive runs show Bash TUI menus for component selection, port policy, and image source. `infrastructure` is required; `application`, `data-process`, and `supabase` are selected by default and can be disabled when you want a smaller deployment. Use `b`/Backspace to return to the previous TUI step and `q` to quit. Non-interactive runs can pass the same choices with `--version`, `--components`, `--port-policy development|production`, and `--image-source general|mainland|local-latest`. Successful deployments save non-sensitive choices to each deploy directory's `deploy.options` for reuse on the next run. Docker and Kubernetes both use `deploy/env/.env` as the runtime configuration file. Existing `deploy/env/.env` is kept as-is. If it does not exist, the deploy scripts first reuse `docker/.env`, then fall back to `deploy/env/.env.example`. Docker uninstall is handled by `bash uninstall.sh docker`. It can preserve or delete data volumes: run it interactively, pass `--delete-volumes true|false`, or use `bash uninstall.sh docker delete-all` to remove containers and persistent data. Offline image packages can be built with `bash deploy/offline/build_offline_package.sh --target docker --compress true`. The package includes image tar files, `load-images.sh`, root deploy/uninstall entrypoints, deployment scripts, SQL files, `manifest.yaml`, and `checksums.txt`; deploy it with `bash deploy.sh --load-images docker ...` on the target host. For detailed deployment instructions, see [Docker Installation](https://modelengine-group.github.io/nexent/en/quick-start/installation.html). ### Kubernetes Deployment (For Enterprise Production) Ideal for enterprise scenarios requiring high availability and elastic scaling. Prerequisites: Kubernetes 1.24+ and Helm 3+: `` ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/modelengine-group-nexent`](/api/graphcanon/tools/modelengine-group-nexent) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_