nexent
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 Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Oct 2026
Nexent builds AI agents from a plain-language description. Say what the agent should do and it generates one, wired to MCP tools, a searchable knowledge base of your documents, memory and other agents. Huawei is behind it (the code's copyright is Huawei Technologies), it's MIT licensed, and it competes with Dify, Coze and Flowise. Self-hosting is free.
This is not a weekend toy. The Docker minimum is 4 cores, 8 GiB of RAM and 40 GiB of disk, and the stack brings Elasticsearch, PostgreSQL, Redis, MinIO and a slimmed-down Supabase for auth. bash deploy.sh docker walks you through it; Helm covers Kubernetes. You bring your own OpenAI-compatible models, so the LLM bill is yours.
There is no paid tier or hosted version; the website the README links to doesn't resolve. Solo: free, with a spare 16 GB box. Small teams: free, with multi-tenancy and role-based access built in. Larger teams: free, but you are the support desk.
The catch is security. An October 14 CVE lets any signed-in user read other tenants' knowledge-base files through version 2.7.0, and the access check on main still allows it. Two unauthenticated file-deletion bugs hit the 1.x line in May. Keep it single-tenant and off the public internet.
Free vs Self-Hosted vs Paid
fully freeFree: All of it. MIT-licensed code with no paid edition, no feature gates and no hosted plan found. Agent generation, MCP tools, knowledge base, two-tier memory, A2A multi-agent, agent marketplace, multi-tenancy and RBAC all ship in the repo.
Self-hosted: Docker Compose (bash deploy.sh docker, minimum 4 cores / 8 GiB RAM / 40 GiB disk, 8 cores / 16 GiB recommended) or Kubernetes 1.24+ with Helm 3 (16 GiB minimum, 64 GiB recommended). The infrastructure release runs Elasticsearch, PostgreSQL, Redis and MinIO; offline image packages exist for air-gapped installs.
Paid: No paid tier. The real ongoing cost is your model provider (any OpenAI-compatible LLM, embedding, vision and speech APIs) plus the server. The nexent.tech site linked from the README does not resolve, and an older license page in the docs still describes a commercial license for multi-tenant SaaS that the current MIT LICENSE file no longer contains.
Free under MIT with no paid tier; budget for a 16 GB server and your own LLM API usage, not a license.
What to do by team size
- Solo
- free, if you have a 16 GB machine to dedicate to it
- Small team
- free; multi-tenancy and RBAC are built in
- Medium team
- free, but patch for the October cross-tenant file-read CVE before giving more than one team access
- Large team
- free; no vendor support exists, so budget your own platform team
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