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 audit trails, and permission gating
• 12 framework adapters — LangChain, AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Semantic Kernel, and more in one orchestrator — no glue code, no lock-in

Use Network-AI as:

• A TypeScript/Node.js library — import { createSwarmOrchestrator } from 'network-ai'
• An MCP server — npx network-ai-server --port 3001
• An OpenClaw skill — clawhub install network-ai

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

Why teams use Network-AI

Problem	How Network-AI solves it
Race conditions in parallel agents	Atomic blackboard: propose → validate → commit with file-system mutex
Agent overspend / runaway costs	FederatedBudget — hard per-agent token ceilings with live spend tracking
No visibility into what agents did	HMAC-signed audit log on every write, permission grant, and FSM transition
Locked into one AI framework	12 adapters — mix LangChain + AutoGen + CrewAI + custom in one swarm
Agents escalating beyond their scope	AuthGuardian — scoped permission tokens required before sensitive operations

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Your Application                        │
└──────────────────────────┬──────────────────────────────────┘
                           │  createSwarmOrchestrator()
┌──────────────────────────▼──────────────────────────────────┐
│                  SwarmOrchestrator                          │
│  ┌──────────────┐  ┌───────────────┐  ┌─────────────────┐  │
│  │ AdapterRegistry│  │ AuthGuardian  │  │ FederatedBudget │  │
│  │ (route tasks) │  │ (permissions) │  │ (token ceilings)│  │
│  └──────┬───────┘  └───────────────┘  └─────────────────┘  │
│         │                                                    │
│  ┌──────▼──────────────────────────────────────────────┐   │
│  │            LockedBlackboard (shared state)           │   │
│  │   propose → validate → commit  (file-system mutex)  │   │
│  └──────────────────────────────────────────────────────┘   │
│         │                                                    │
│  ┌──────▼───────────────────────────────────────────────┐  │
│  │  Adapters (plug any framework in, swap out freely)   │  │
│  │  LangChain │ AutoGen │ CrewAI │ MCP │ LlamaIndex │…  │  │
│  └──────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘
                           │
              HMAC-signed audit log

→ Full architecture, FSM journey, and handoff protocol

Install

npm install network-ai

No native dependencies, no build step. Adapters are dependency-free (BYOC — bring your own client).

Two agents, one shared state — without race conditions

The real differentiator is coordination. Here is what no single-framework solution handles: two agents writing to the same resource concurrently, atomically, without corrupting each other.

import { LockedBlackboard, CustomAdapter, createSwarmOrchestrator } from 'network-ai';

const board   = new LockedBlackboard('.');
const adapter = new CustomAdapter();

// Agent 1: writes its analysis result atomically
adapter.registerHandler('analyst', async () => {
  const id = board.propose('report:status', { phase: 'analysis', complete: true }, 'analyst');
  board.validate(id, 'analyst');
  board.commit(id);                           // file-system mutex — no race condition possible
  return { result: 'analysis written' };
});

// Agent 2: runs concurrently, writes to its own key safely
adapter.registerHandler('reviewer', async () => {
  const id = board.propose('report:review', { approved: true }, 'reviewer');
  board.validate(id, 'reviewer');
  board.commit(id);
  const analysis = board.read('report:status');
  return { result: `reviewed phase=${analysis?.phase}` };
});

createSwarmOrchestrator({ adapters: [{ adapter }] });

// Both fire concurrently — mutex guarantees no write is ever lost
const [, ] = await Promise.all([
  adapter.executeAgent('analyst',  { action: 'run', params: {} }, { agentId: 'analyst' }),
  adapter.executeAgent('reviewer', { action: 'run', params: {} }, { agentId: 'reviewer' }),
]);

console.log(board.read('report:status'));   // { phase: 'analysis', complete: true }
console.log(board.read('report:review'));   // { approved: true }

Add budgets, permissions, and cross-framework agents with the same pattern. → QUICKSTART.md

Demo — Control-Plane Stress Test (no API key)

Runs in ~2 seconds. Proves the coordination primitives without any LLM calls.

npm run demo -- --08

What it shows: atomic blackboard locking, priority preemption (priority-3 wins over priority-0 on same key), FSM hard-stop at 700 ms, live compliance violation capture (TOOL_ABUSE, TURN_TAKING, RESPONSE_TIMEOUT, JOURNEY_TIMEOUT), and FederatedBudget tracking — all without a single API call.

8-agent AI pipeline (requires OPENAI_API_KEY — builds a Payment Processing Service end-to-end):

npm run demo -- --07

Adapter System

12 frameworks, zero adapter dependencies. You bring your own SDK objects.

Adapter	Framework	Register method
CustomAdapter	Any function or HTTP endpoint	registerHandler(name, fn)
LangChainAdapter	LangChain	registerRunnable(name, runnable)
AutoGenAdapter	AutoGen / AG2	registerAgent(name, agent)
CrewAIAdapter	CrewAI	registerAgent or registerCrew
MCPAdapter	Model Context Protocol	registerTool(name, handler)
LlamaIndexAdapter	LlamaIndex	registerQueryEngine(), registerChatEngine()
SemanticKernelAdapter	Microsoft Semantic Kernel	registerKernel(), registerFunction()
OpenAIAssistantsAdapter	OpenAI Assistants	registerAssistant(name, config)
HaystackAdapter	deepset Haystack	registerPipeline(), registerAgent()
DSPyAdapter	Stanford DSPy	registerModule(), registerProgram()
AgnoAdapter	Agno (formerly Phidata)	registerAgent(), registerTeam()
OpenClawAdapter	OpenClaw	registerSkill(name, skillRef)

Extend BaseAdapter to add your own in minutes. See references/adapter-system.md.

Works with LangGraph, CrewAI, and AutoGen

Network-AI is the coordination layer you add on top of your existing stack. Keep your LangChain chains, CrewAI crews, and AutoGen agents — and add shared state, governance, and budgets around them.

Capability	Network-AI	LangGraph	CrewAI	AutoGen
Cross-framework agents in one swarm	✅ 12 adapters	❌ LangChain only	❌ CrewAI only	❌ AutoGen only
Atomic shared state (conflict-safe)	✅ propose → validate → commit	⚠️ Last-write-wins	⚠️ Last-write-wins	⚠️ Last-write-wins
Hard budget ceiling per agent	✅ FederatedBudget	⚠️ Callbacks only	❌	❌
Permission gating before sensitive ops	✅ AuthGuardian	❌	❌	❌
Tamper-evident audit trail	✅ HMAC-signed	❌	❌	❌
Encryption at rest	✅ AES-256-GCM	❌	❌	❌
Language	TypeScript / Node.js	Python	Python	Python

Testing

npm run test:all          # All suites in sequence
npm test                  # Core orchestrator
npm run test:security     # Security module
npm run test:adapters     # All 12 adapters
npm run test:priority     # Priority & preemption

1,184 passing assertions across 15 test suites (verified by counting assert() / pass() calls in each file):

Suite	Assertions	Covers
test-standalone.ts	83	Blackboard, auth, integration, persistence, parallelisation, quality gate
test-adapters.ts	142	All 12 adapters, registry routing, integration, edge cases
test-phase4.ts	133	FSM, compliance monitor, adapter integration
test-phase5d.ts	119	Pluggable backend
test-phase5f.ts	113	Phase 5f extended
test-phase5g.ts	106	Phase 5g extended
test-phase6.ts	122	Latest feature coverage
test-phase5c.ts	74	Named multi-blackboard
test-phase5e.ts	88	Phase 5e
test-phase5b.ts	56	Pluggable backend part 2
test-priority.ts	65	Priority preemption, conflict resolution, backward compat
test-security.ts	35	Tokens, sanitization, rate limiting, encryption, audit
test-phase5.ts	24	Named multi-blackboard base
test.ts	24	Full integration
test-phase4.ts (stubs)	4	FSM stub coverage

Documentation

Doc	Contents
QUICKSTART.md	Installation, first run, PowerShell guide, Python scripts CLI
ARCHITECTURE.md	Race condition problem, FSM design, handoff protocol, project structure
BENCHMARKS.md	Provider performance, rate limits, local GPU, max_completion_tokens guide
SECURITY.md	Security module, permission system, trust levels, audit trail
ENTERPRISE.md	Evaluation checklist, stability policy, security summary, integration entry points
AUDIT_LOG_SCHEMA.md	Audit log field reference, all event types, scoring formula
ADOPTERS.md	Known adopters — open a PR to add yourself
INTEGRATION_GUIDE.md	End-to-end integration walkthrough
references/adapter-system.md	Adapter architecture, writing custom adapters
references/auth-guardian.md	Permission scoring, resource types
references/trust-levels.md	Trust level configuration

Contributing

• Fork → feature branch → npm run test:all → pull request
• Bugs and feature requests via Issues
• If Network-AI saves you time, a ⭐ helps others find it

MIT License — LICENSE  ·  CHANGELOG  ·  CONTRIBUTING  · 

Keywords (for search)

ai-agents, agentic-ai, multi-agent, multi-agent-systems, multi-agent-system, agent-framework, ai-agent-framework, agentic-framework, agentic-workflow, llm, llm-agents, llm-agent, large-language-models, generative-ai, genai, orchestration, ai-orchestration, swarm, swarm-intelligence, autonomous-agents, agents, ai, typescript, nodejs, mcp, model-context-protocol, a2a, agent-to-agent, function-calling, tool-integration, context-engineering, rag, ai-safety, multi-agents-collaboration, multi-agents, aiagents, aiagentframework, plug-and-play, adapter-registry, blackboard-pattern, agent-coordination, agent-handoffs, token-permissions, budget-tracking, cost-awareness, atomic-commits, hallucination-detection, content-quality-gate, behavioral-control-plane, governance-layer, compliance-enforcement, fan-out-fan-in, agent-observability, permission-gating, audit-trail, OpenClaw, ClawHub, clawhub, AgentSkills, LangChain adapter, LangGraph, AutoGen adapter, AG2, CrewAI adapter, MCP adapter, LlamaIndex adapter, Semantic Kernel adapter, OpenAI Assistants adapter, Haystack adapter, DSPy adapter, Agno adapter, custom-adapter, AES-256 encryption, HMAC tokens, rate limiting, input sanitization, privilege escalation prevention, agentic-rag, deep-research, workflow-orchestration, ai-assistant, ai-tools, developer-tools, open-source