About Nexent
Nexent is an MCP server published by ModelEngine-Group in the AI category: nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.
The repository has 6.0K stars and 742 forks, with the last commit 3 days ago. Recent commit activity is the strongest signal that reported issues will get attention.
Install
git clone https://github.com/ModelEngine-Group/nexentThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
Nexent is a zero-code platform for auto-generating production-grade AI agents, built on Harness Engineering principles. It provides unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want.
One prompt. Endless reach.
🚀 Get Started Now
⭐ Before you get started, please star us on GitHub — your support drives us forward!
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+:
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. Use --defaults to skip the TUI and deploy with saved deploy.options or built-in defaults. Non-interactive runs can also 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. Monitoring-specific settings are generated from deploy/env/monitoring.env.example into deploy/env/monitoring.env.
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 build.sh --package --target docker --compress true or bash deploy/offline/build_offline_package.sh --target docker --compress true. The package includes image tar files, load-images.sh, push-images.sh, root deploy/uninstall entrypoints, deployment scripts, SQL files, manifest.yaml, and checksums.txt. Package deploys use saved deploy.options or built-in defaults without opening the TUI; add --config to configure interactively. Deploy with bash deploy.sh --load-images docker ... on the target host, or use bash deploy.sh --push-images --image-registry-prefix registry.example.com/nexent docker ... to push loaded images to an internal registry and deploy with that image prefix. When --push-images is used without a prefix, deploy.sh asks for it before push-images.sh prompts for the registry username and password.
For detailed deployment instructions, see Docker Installation.
Kubernetes Deployment (For Enterprise Production)
Ideal for enterprise scenarios requiring high availability and elastic scaling. Prerequisites: Kubernetes 1.24+ and Helm 3+:
git clone https://github.com/ModelEngine-Group/nexent.git
cd nexent
bash deploy.sh k8s
The native Kubernetes implementation is bash deploy/k8s/deploy.sh. It installs two independent Helm releases: nexent-infrastructure for Elasticsearch, PostgreSQL, Redis, and MinIO, then nexent for application services after infrastructure and the Elasticsearch API key are ready. Use --release-scope all|infrastructure|nexent to operate both releases or either side independently. The script reads deploy/env/.env and renders explicit Helm ConfigMap and Secret overrides. Use --persistence-mode local|dynamic|existing, --storage-class/--sc, --local-path, --local-node-name, and --existing-claim-prefix to control PVC behavior.
Kubernetes uninstall is handled by bash uninstall.sh k8s. By default it removes nexent first and nexent-infrastructure second. The same --release-scope option supports application-only removal and dependency-protected infrastructure removal.
Kubernetes offline packages use the same builder with --target k8s or --target all. Run load-images.sh on every cluster node that needs the images, or use --push-images --image-registry-prefix registry.example.com/nexent to push the images to an internal registry before deploying with the same version, image source, and image registry prefix.
For detailed deployment instructions, see Kubernetes Installation.
✨ Core Features
Nexent provides a comprehensive feature set for building powerful AI agents:
| Feature | Description |
|---|---|
| ⚙️ Multi-Model Integration | OpenAI-compatible with any provider, full LLM/Embedding/VLM/STT/TTS coverage, supports domestic model switching |
| 🤖 Zero-Code Agent Generation | Describe requirements in natural language, generate executable agents instantly, what you think is what you get |
| 🤝 A2A Agent Collaboration | Agent-to-Agent protocol enables seamless multi-agent cooperation and distributed workflows |
| 🧠 Layered Memory Mechanism | Two-tier memory (user-level + user-agent-level) for persistent context across conversations |
| 📝 Progressive Skill Disclosure | Dynamically loads Skill into context, maximizing context window efficiency |
| 🗄️ Personal-Grade Knowledge Base | Real-time import and intelligent retrieval for 20+ document formats, auto summaries, fine-grained access control |
| 🔧 MCP Tool Ecosystem | Plug-and-play extension system with custom development and third-party MCP service support |
| 🌐 Internet Knowledge Integration | Multi-source search blending real-time information with private data |
| 🔍 Knowledge-Level Traceability | Precise citations and source verification, full transparency for every fact |
| 🎭 Multimodal Interaction | Voice, text, images, files — comprehensive natural dialogue |
| 🔢 Agent Version Management | Version iteration and history rollback, safe and controllable |
| 🏪 Agent Marketplace | Official and community curated agents, one-click install and use |
| 👥 Multi-Tenancy & RBAC | Multi-tenant isolation, role-based access control, fine-grained resource management |
🤝 Join Our Community
If you want to go fast, go alone; if you want to go far, go together.
We have released Nexent v2.0! A comprehensive upgrade from v1.0, featuring A2A protocol support, progressive Skill disclosure, layered memory mechanism, user management with multi-tenancy, agent version management, agent marketplace, and more.
- 🗺️ Check our Feature Map to explore current and upcoming features.
- 🔍 Try the current build and leave ideas or bugs in the Issues tab.
Rome wasn't built in a day.
If our vision speaks to you, jump in via the Contribution Guide and shape Nexent with us.
Early contributors won't go unnoticed: from special badges and swag to other tangible rewards, we're committed to thanking the pioneers who help bring Nexent to life.
Most of all, we need visibility. Star ⭐ and watch the repo, share it with friends, and help more developers discover Nexent — your click brings new hands to the project and keeps the momentum growing.
📖 What's Next
Ready to dive deeper? Here are the main documentation entry points:
- Quick Start — System requirements and deployment guide
- Core Features — Comprehensive feature documentation
- User Guide — Agent development and usage
- Developer Guide — Build from source and customization
- FAQ — Common questions and troubleshooting
📄 License
Nexent is licensed under the MIT License.
README fetched from the repository 4 days ago. The original is authoritative.