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Network-AI — Enterprise Evaluation Guide

This document exists so an engineer or architect can evaluate Network-AI in under 30 minutes without a sales call.

Quick Evaluation Checklist

Question Answer
Can I run it fully offline / air-gapped? Yes. Core orchestration, blackboard, permissions, FSM, budget, and compliance monitor require no network. Only the OpenAI adapter calls an external API — it is opt-in.
Do I control all data? Yes. All state lives in your data/ directory on your own infrastructure. Nothing is transmitted.
Is the source auditable? Yes. MIT-licensed, fully open source, no obfuscated code, no telemetry.
Does it have an audit trail? Yes. Every permission request, grant, denial, and revocation is appended to data/audit_log.jsonl with a UTC timestamp. See AUDIT_LOG_SCHEMA.md.
Can I plug in my own LLM / provider? Yes. The adapter registry supports LangChain, AutoGen, CrewAI, LlamaIndex, Semantic Kernel, OpenAI Assistants, Haystack, DSPy, Agno, MCP, OpenClaw, A2A, Codex, MiniMax, NemoClaw, APS, and a CustomAdapter for anything else.
Does it work with our existing agent framework? Yes. It wraps around your framework — you keep what you have and add guardrails on top.
Is there a security review? Yes. CodeQL scanning on every push, Dependabot auto-merge, Socket.dev supply chain score A, OpenSSF Scorecard. See SECURITY.md.
What does it cost to operate? Zero licensing cost. MIT license. Infrastructure cost = your own compute.
Is there a compliance module? Yes. ComplianceMonitor enforces configurable violation policies with severity classification and async audit loop.
Can I restrict which agents access which resources? Yes. AuthGuardian evaluates justification quality + agent trust score + resource risk score before issuing a grant token.

What It Does (One Paragraph)

Network-AI is a TypeScript/Node.js orchestration layer that sits between your agents and your shared state. It enforces: atomic blackboard writes (no race conditions when two agents write simultaneously), permission gating (agents must request access to sensitive resources and provide a scored justification), budget ceilings (per-agent token limits; rogue agents get cut off mid-task), FSM-based workflow governance (agents are blocked from skipping pipeline stages), and real-time compliance monitoring (tool abuse, turn-taking violations, response timeouts). It ships as an npm package with a companion Python skill bundle for OpenClaw/ClawHub environments.

Architecture Summary

Your agents
    │
    ▼
┌─────────────────────────────────────────────────────┐
│  Network-AI Orchestration Layer                     │
│                                                     │
│  LockedBlackboard  ──── atomic propose/commit       │
│  AuthGuardian      ──── permission scoring          │
│  FederatedBudget   ──── per-agent token ceilings    │
│  JourneyFSM        ──── FSM state governance        │
│  ComplianceMonitor ──── real-time violation policy  │
│  BlackboardValidator─── content quality gate        │
│  QAOrchestratorAgent── scenario replay & regression │
│  ProjectContextManager─ Layer-3 persistent memory   │
└─────────────────────────────────────────────────────┘
    │
    ▼
data/ (local filesystem — you own it)
  ├── audit_log.jsonl
  ├── active_grants.json
  ├── project-context.json
  └── blackboard state files

Full architecture: ARCHITECTURE.md

Security & Supply Chain

Check Status
CodeQL (GitHub Advanced Security) ✅ All alerts resolved
Dependabot ✅ Auto-merge enabled, dependency graph active
Socket.dev supply chain ✅ No high-severity flags
OpenSSF Scorecard ✅ SHA-pinned CI actions, provenance publishing
npm provenance ✅ Published with --provenance since v4.0.0
Secret scanning ✅ Enabled on repository
Vulnerability disclosure SECURITY.md — 48h acknowledgment, 7-day response

Stability & Support Expectations

Versioning

Network-AI follows Semantic Versioning:

  • Patch (4.0.x): bug fixes and security patches — safe to auto-update
  • Minor (4.x.0): additive features, backward-compatible — upgrade at your pace
  • Major (x.0.0): breaking API changes — migration guide provided in CHANGELOG

Security Fix Policy

Version Policy
4.10.x (current) Full support — bugs + security fixes
4.9.x Security fixes only
4.0.x – 4.8.x Security fixes only
< 4.0 No support

Response Times (GitHub Issues)

Severity Target
Security vulnerability (private) 48h acknowledgment, 7 days remediation
Bug with reproduction Best effort, typically < 7 days
Feature request Triaged on rolling basis

Stability Signals

  • 1,684 passing assertions across 21 suites
  • Deterministic scoring — no random outcomes in permission evaluation or budget enforcement
  • CI runs on every push and every PR
  • All examples ship with the repo and run without mocking

Integration Entry Points

Use case Starting point
Wrap existing LangChain agents INTEGRATION_GUIDE.md § LangChain
Add permission gating AuthGuardian in QUICKSTART.md
Add budget enforcement FederatedBudget in QUICKSTART.md
Add FSM workflow governance JourneyFSM in ARCHITECTURE.md
MCP server (model context protocol) npx network-ai-mcp — see QUICKSTART.md
Inject long-term project context into agents context_manager.py inject — see QUICKSTART.md § Project Context
Use with Claude API / Codex (tool-use schema) claude-tools.json — drop into tools array
Use as a Custom GPT Action openapi.yaml — import in GPT editor
Use as a Claude Project claude-project-prompt.md — paste into Custom Instructions
Inspect / manage state from terminal network-ai bb CLI — see QUICKSTART.md § CLI
Full working example (no API key) npx ts-node examples/08-control-plane-stress-demo.ts
Full working example (with API key) npx ts-node examples/07-full-showcase.ts

Known Adopters

See ADOPTERS.md.

License

MIT — LICENSE. No CLA required for contributions.