GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Nexent offers a zero-code platform for generating production-grade AI agents with Harness Engineering principles built into its framework.
Good fit when
- When you require unified tools and skills management through its Harness Engineering principles.
- For projects needing built-in constraints, feedback loops, and control planes to ensure agent reliability and performance.
Avoid when
- When a simpler, non-production grade solution is sufficient since Nexent's robust infrastructure might add unnecessary complexity.
- For environments lacking the recommended system resources such as 8 cores CPU, 16 GiB Memory, and 100 GiB Disk (for Kubernetes deployment), which can limit its performance and reliability.
- Requirements:
- Min 8 GB RAM; Requires Docker
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Aug 18, 2026
- Provenance
- Not a fork · Organization account
- As of Aug 18, 2026
- Security (OSV)
- No lockfile
- As of Jul 26, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install nexentHow it fits your stack(5)
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Evidence and technical details
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Overview
Nexent is a platform designed to automatically generate production-grade AI agents using principles from Harness Engineering, supporting unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes. The platform can be deployed via Docker or Kubernetes.
Capability facts
- Languages
- python
Source: github.language · Aug 18, 2026
Categories
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README
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 /...
For agents
This page has a .md twin and JSON over the API.