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 29 adapters: LangChain, AutoGen, CrewAI, LlamaIndex, Semantic Kernel, OpenAI Assistants, Haystack, DSPy, Agno, MCP, OpenClaw, A2A, Codex, MiniMax, NemoClaw, APS, Copilot, LangGraph, Anthropic Computer Use, OpenAI Agents SDK, Vertex AI, Pydantic AI, Browser Agent, Hermes (any OpenAI-compatible endpoint), Orchestrator, RLM (any RLM-compatible HTTP endpoint), 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). v5.0 adds: approval inbox (web-accessible approval queue), job queue (persistent priority FIFO with crash recovery), transport layer (JSON-RPC 2.0 with HMAC auth), agent VCR (record/replay for testing), comparison runner (side-by-side adapter evaluation), and 9 new adapters. v5.1.4 adds: RLMAdapter (recursive language model / any RLM-compatible HTTP endpoint), FederatedBudget child spending, blackboard metadata API, PhasePipeline compaction, semaphore-based fan-out, HookContext depth, and sub-goal recursion. 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 filesFull 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 |
|---|---|
| 5.2.x (current) | Full support — bugs + security fixes |
| 5.1.x | Security fixes only |
| 5.0.x | Security fixes only |
| 4.15.x | Security fixes only |
| 4.0.x – 4.13.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
- 2,834 passing assertions across 27 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.