Network-AI

Network-AI

TypeScript/Node.js multi-agent orchestrator — shared state, guardrails, budgets, and cross-framework coordination

Network-AI is a TypeScript/Node.js multi-agent orchestrator that adds coordination, guardrails, and governance to any AI agent stack.

• Shared blackboard with locking — atomic propose → validate → commit prevents race conditions and split-brain failures across parallel agents
• Guardrails and budgets — FSM governance, per-agent token ceilings, HMAC / Ed25519 audit trails, and permission gating
• Context signal-over-noise (v5.15) — ContextComposer assembles token-budgeted, relevance-ranked context packs (semantic/lexical × recency decay × scope affinity, position-aware layout); context_pack + blackboard_search MCP tools let agents pull curated state instead of dumping the whole board into their window
• 32 adapters — LangChain (+ streaming), AutoGen, CrewAI, OpenAI Assistants, OpenAI Responses (Assistants successor), LlamaIndex, Semantic Kernel, Haystack, DSPy, Agno, MCP, Custom (+ streaming), OpenClaw, A2A, Codex, MiniMax, NemoClaw, APS, Copilot, LangGraph, Anthropic Computer Use, Claude Agent SDK (agentic loops), OpenAI Agents SDK, Vertex AI, Gemini (Developer API), Pydantic AI, Browser Agent, Hermes (NousResearch Hermes / any OpenAI-compatible endpoint), Orchestrator (hierarchical multi-orchestrator), and RLM (Recursive Language Model / any RLM-compatible HTTP endpoint) — no glue code, no lock-in
• Persistent project memory (Layer 3) — context_manager.py injects decisions, goals, stack, milestones, and banned patterns into every system prompt so agents always have full project context
• v5.0 modules — Agent VCR (record/replay), comparison runner, coverage reporter, goal DSL, approval inbox, job queue, gRPC/HTTP transport, playground REPL, adapter test harness, and more
• Model-interaction lifecycle governance (v5.13) — GovernedModelGateway absorbs the model refusal → fallback → billing chain (cross-model fallback, fallback-credit repricing, effort governance, thinking-block handoff) behind one governed, budgeted, audited call

The silent failure mode in multi-agent systems: parallel agents writing to the same key use last-write-wins by default — one agent's result silently overwrites another's mid-flight. The outcome is split-brain state: double-spends, contradictory decisions, corrupted context, no error thrown. Network-AI's propose → validate → commit mutex prevents this at the coordination layer, before any write reaches shared state.

Use Network-AI as:

• A TypeScript/Node.js library — import { createSwarmOrchestrator } from 'network-ai'
• An MCP server — npx network-ai-server --port 3001
• A CLI — network-ai bb get status / network-ai audit tail
• A Claude Code plugin — /plugin install network-ai@network-ai
• A Gemini CLI extension — gemini extensions install https://github.com/Jovancoding/Network-AI
• An OpenClaw skill — clawhub install network-ai

5-minute quickstart →  |  Architecture →  |  All adapters →  |  Benchmarks →

---

🛡️ Model-Interaction Lifecycle Governance

Most governance tools stop at the agent boundary — they police which tools an agent may call before it acts. Network-AI also governs the layer underneath: how an agent talks to the model. When a frontier model declines a request with a classifier refusal, Network-AI absorbs the refusal → fallback → billing chain and presents one governed, budgeted, audited call.

• GovernedModelGateway — detect stop_reason:"refusal", audit which classifier fired, route to a fallback model, and redeem the fallback-credit token so the retry is repriced as a cache read.
• ModelBudget — per-model USD accounting with fallback-credit repricing; never sums tokens across models.
• RefusalTelemetry — a refusal is an HTTP 200, invisible to error-rate monitoring; emitted as a discrete non-error signal with an unservedRefusalCount gap to alert on.
• EffortPolicy — turn the effort cost dial into a policy object: cap sub-agents at low, require justification for xhigh/max.
• ThinkingBlockManager — keep thinking blocks unchanged on the same model; strip them on a cross-model fallback; guard prompts against reasoning_extraction refusals.
• Per-sub-agent fallback — FanOutFanIn steps and TeamRunner tasks each carry their own fallback agent and per-request retry budget (RetryBudget), because a turn can refuse independently across an agent and its sub-agents.

import { AnthropicMessagesAdapter, ModelBudget, RefusalTelemetry } from 'network-ai';

const adapter = new AnthropicMessagesAdapter();
await adapter.initialize({});
adapter.registerModelAgent('analyst', {
  client,                              // bring your own Anthropic client
  model: 'claude-fable-5',
  fallbackModels: ['claude-opus-4-8'], // classifier refusals fall through here
  budget: new ModelBudget({
    ceilingUsd: 5,
    pricing: {
      'claude-fable-5': { inputPerMTok: 10, outputPerMTok: 50 },
      'claude-opus-4-8': { inputPerMTok: 5, outputPerMTok: 25 },
    },
  }),
  telemetry: new RefusalTelemetry(),
});
const result = await adapter.executeAgent('analyst', { action: 'Summarize Q3 results', params: {} }, { agentId: 'cli' });
// result.data: { servedModel, servedByFallback, refused, refusalCategories, attempts, totalCostUsd }

OWASP Agentic AI Top 10 (2026) — engine coverage

Verify programmatically with verifyOwaspCoverage() (exported from network-ai):

| Risk | Status | Primary