@ariaflowagents/core

AriaFlow core runtime and agent primitives for building structured, multi-agent conversations.

Install

npm install @ariaflowagents/core

Requirements

This package is built on top of the Vercel AI SDK v6.

• Peer deps: ai@^6 and zod@^3
• Provider packages (example): @ai-sdk/openai

Exports

This package exports:

• Agents: Agent, LLMAgent, FlowAgent, TriageAgent, CompositeAgent
• Runtime: Runtime, createRuntime
• Flows: FlowManager, FlowGraph, FlowNode, createFlowTransition
• Session: SessionManager, SessionStore, MemoryStore
• Tools: createTool, createToolWithFiller, createHandoffTool
• System Injections: InjectionQueue, commonInjections
• Prompts: PromptTemplateBuilder, PromptBuilder
• Hooks: HookRunner, loggingHooks, createMetricsHooks
• Guards: ToolEnforcer, StopConditions
• Utils: createDateParser, parseDate, parseDateRange, formatDateForSpeech, formatTimeForSpeech

Quick start

import { Runtime, createDateParser, type AgentConfig } from '@ariaflowagents/core';
import { openai } from '@ai-sdk/openai';

// Create date parsing tool for natural language dates
const dateParser = createDateParser();

const supportAgent: AgentConfig = {
  id: 'support',
  name: 'Support Agent',
  systemPrompt: 'You are a helpful support agent that can help book appointments.',
  model: openai('gpt-4o-mini') as any,
  type: 'llm',
  tools: {
    parse_date: dateParser,
  },
  // Production default: non-triage agents cannot hand off unless explicitly configured.
  canHandoffTo: [],
};

const runtime = new Runtime({
  agents: [supportAgent],
  defaultAgentId: 'support',
  defaultModel: openai('gpt-4o-mini') as any,
});

const run = async () => {
  for await (const part of runtime.stream({ input: 'Hello there' })) {
    if (part.type === 'text-delta') {
      process.stdout.write(part.text);
    }
  }
};

run();

Routing & Handoffs (Important Defaults)

AriaFlow supports invisible multi-agent routing via a handoff tool.

• A TriageAgent can route to specialists via handoff.
• Production default: non-triage agents only get the handoff tool if you explicitly set canHandoffTo.

Example:

const support: AgentConfig = {
  id: 'support',
  name: 'Support',
  type: 'llm',
  systemPrompt: 'General support agent.',
  model: openai('gpt-4o-mini') as any,
  // This agent may route to booking and billing specialists.
  canHandoffTo: ['booking', 'billing'],
};

Note: the runtime stream includes internal events like { type: 'handoff', ... }. If you are building a UI transcript, do not render these internal events directly to end users.

Built-in System Guardrails

The Runtime injects a small set of system-level instructions by default (e.g. “no secrets” and “invisible handoffs”) to reduce prompt-injection leakage and prevent user-visible routing language.

These are defense-in-depth guardrails. You should still treat tool inputs/outputs and webhook callbacks as sensitive, and filter what you expose to end users.

Guides

Guides live in packages/ariaflow-core/guides/:

• GETTING_STARTED.md
• RUNTIME.md
• FLOWS.md
• TOOLS.md
• GUARDRAILS.md

Related Packages

AriaFlow provides additional packages for specific deployment targets:

| Package | Description | Use When | |---------|-------------|----------| | @ariaflowagents/cf-agent | Cloudflare Durable Objects for Runtime and AgentFlowManager | Deploying to Cloudflare Workers | | @ariaflowagents/hono-server | Hono router for HTTP/WebSocket serving | Running a Node.js or Bun server |

Cloudflare Workers

Use @ariaflowagents/cf-agent for serverless deployment on Cloudflare:

npm install @ariaflowagents/cf-agent

Runtime (multi-agent):

import { AriaFlowChatAgent } from '@ariaflowagents/cf-agent';

export class MyChatAgent extends AriaFlowChatAgent {
  async createRuntime() {
    return {
      agents: [supportAgent],
      defaultAgentId: 'support',
    };
  }
}

Flow (structured conversation):

import { AriaFlowFlowAgent } from '@ariaflowagents/cf-agent';

export class ReservationAgent extends AriaFlowFlowAgent {
  async createFlowConfig() {
    return {
      initialNode: 'greeting',
      model: openai('gpt-4o-mini') as object,
      nodes: [...],
    };
  }
}

See @ariaflowagents/cf-agent for full documentation.

Hono Server

Use @ariaflowagents/hono-server for HTTP/WebSocket hosting:

npm install @ariaflowagents/hono-server

Runtime server:

import { Hono } from 'hono';
import { serve } from '@hono/node-server';
import { createNodeWebSocket } from '@hono/node-ws';
import { Runtime } from '@ariaflowagents/core';
import { createAriaChatRouter } from '@ariaflowagents/hono-server';

const runtime = new Runtime({ agents: [...] });
const app = new Hono();
app.route('/', createAriaChatRouter({ runtime }));

serve({ fetch: app.fetch, port: 3000 });

Flow server:

import { AgentFlowManager } from '@ariaflowagents/core';
import { createAriaFlowRouter } from '@ariaflowagents/hono-server';

const flowManager = new AgentFlowManager({ nodes: [...] });
app.route('/', createAriaFlowRouter({ flowManager, sessionId: 'my-flow' }));

See @ariaflowagents/hono-server for full documentation.

Core Concepts

Runtime (Multi-Agent)

The Runtime class orchestrates multiple agents with seamless handoffs:

• TriageAgent: Routes requests to the appropriate specialist
• Agent Handoffs: Transfer conversation context between agents
• Session Persistence: Maintains conversation state

AgentFlowManager (Single Flow)

The AgentFlowManager class manages structured, node-based conversations:

• Flow Nodes: Each node has a specific purpose and tools
• State Transitions: Tools drive transitions via createFlowTransition()
• Flow Hooks: Observe lifecycle events (onFlowStart, onTransition, etc.)
• Context Strategies: Control memory management (append, reset, summarize)

Date Parsing Utilities

Natural language date parsing for conversational agents using Chrono:

import { createDateParser, parseDate, formatDateForSpeech } from '@ariaflowagents/core';

// As a tool for agents
const dateParser = createDateParser();
const result = await dateParser.execute({ text: 'tomorrow at 3pm' });
// { success: true, startDate: '2026-01-18T15:00:00Z', ... }

// Standalone function
const parsed = parseDate('next Friday');
// { date: Date, text: 'next Friday', confidence: 1.0 }

// TTS-friendly formatting
formatDateForSpeech(new Date('2026-01-18')); // "Saturday, January 18, 2026"

Supported expressions:

• Relative: "tomorrow", "today", "yesterday", "in 3 days"
• Weekdays: "next Friday", "this weekend", "Monday morning"
• Specific dates: "March 15th", "December 25th, 2026"
• With time: "tomorrow at 3pm", "next Tuesday at 2:30pm"

Date Parser in Flows

Use the date parser within flow nodes for booking and scheduling:

import { createDateParser, createFlowTransition } from '@ariaflowagents/core';
import { tool } from 'ai';
import { z } from 'zod';

const dateParserTool = createDateParser();

const bookingFlow = {
  nodes: [
    {
      id: 'collect_date',
      prompt: 'What date would you like to book?',
      tools: {
        parse_date: tool({
          description: 'Parse the date from user input',
          inputSchema: z.object({
            dateText: z.string().describe('Natural language date'),
          }),
          execute: async ({ dateText }) => {
            const result = await dateParserTool.execute({ text: dateText });
            if (result.success) {
              return createFlowTransition('collect_time', { 
                date: result.startDate.split('T')[0] 
              });
            }
            return { error: 'Could not parse date' };
          },
        }),
      },
    },
    // ... more nodes
  ],
};