@vibe-agent-toolkit/agent-runtime

Runtime framework for building portable AI agents across multiple agent archetypes.

Features

• Agent Archetypes: Pre-defined patterns for common agent types
• Type-Safe: Full TypeScript support with Zod schema validation
• Framework Agnostic: Agents work with any LLM provider
• Declarative Configuration: Minimal boilerplate, maximum clarity

Agent Archetypes

1. Pure Function Tool

Stateless, deterministic functions with no external dependencies.

import { definePureFunction } from '@vibe-agent-toolkit/agent-runtime';
import { z } from 'zod';

export const haiku Validator = definePureFunction({
  name: 'haiku-validator',
  description: 'Validates haiku structure (5-7-5 syllables)',
  version: '1.0.0',
  inputSchema: z.object({ line1: z.string(), line2: z.string(), line3: z.string() }),
  outputSchema: z.object({ valid: z.boolean(), errors: z.array(z.string()) }),
}, (input) => {
  // Implementation
  return { valid: true, errors: [] };
});

2. LLM Analyzer

Single LLM call for classification or extraction tasks.

import { defineLLMAnalyzer } from '@vibe-agent-toolkit/agent-runtime';

export const photoAnalyzer = defineLLMAnalyzer({
  name: 'photo-analyzer',
  description: 'Analyzes cat photos using vision LLM',
  version: '1.0.0',
  inputSchema: PhotoInputSchema,
  outputSchema: CatCharacteristicsSchema,
  systemPrompt: 'You are an expert cat analyzer...',
}, async (input, ctx) => {
  const analysis = await ctx.callLLM(`Analyze this cat: ${input.imagePath}`);
  return JSON.parse(analysis);
});

3. Conversational Assistant

Multi-turn dialogue with conversation history.

import { defineConversationalAssistant } from '@vibe-agent-toolkit/agent-runtime';

export const chatAgent = defineConversationalAssistant({
  name: 'breed-advisor',
  description: 'Helps users find their perfect cat breed',
  version: '1.0.0',
  inputSchema: InputSchema,
  outputSchema: OutputSchema,
  systemPrompt: 'You are a friendly cat breed advisor...',
}, async (input, ctx) => {
  ctx.addToHistory('user', input.message);
  const response = await ctx.callLLM(ctx.history);
  ctx.addToHistory('assistant', response);
  return { reply: response };
});

3a. Two-Phase Conversational Assistant ⭐ NEW

Recommended pattern for conversational agents that gather information.

The two-phase pattern solves the "JSON every turn" anti-pattern by separating:

• Phase 1 (Gathering): Natural conversational text responses
• Phase 2 (Extraction): Structured data extraction when ready

import { defineTwoPhaseConversationalAssistant } from '@vibe-agent-toolkit/agent-runtime';

export const breedAdvisor = defineTwoPhaseConversationalAssistant({
  name: 'breed-advisor',
  description: 'Helps users find their perfect cat breed',
  version: '2.0.0',
  inputSchema: BreedAdvisorInputSchema,
  outputSchema: BreedAdvisorOutputSchema,

  gatheringPhase: {
    tone: 'enthusiastic',
    factors: [
      {
        name: 'musicPreference',
        type: 'enum',
        values: ['classical', 'jazz', 'rock', 'pop'],
        required: true,
        clarificationHint: 'Ask which category fits best for non-standard genres',
      },
      {
        name: 'livingSpace',
        type: 'enum',
        values: ['apartment', 'house', 'farm'],
        naturalLanguageMappings: {
          'flat': 'apartment',
          'big house': 'house',
        },
      },
    ],
    readinessCheck: (profile) => Object.keys(profile).length >= 4,
  },

  extractionPhase: {
    generateRecommendations: (profile) => matchBreeds(profile),
    useStructuredOutputs: false, // Set true for OpenAI gpt-4o-2024-08-06+
  },
});

Key Benefits:

• Declarative Configuration: 200+ lines of manual prompts → 50 lines of config
• Automatic Validation: Enum guidance generated from factor definitions
• Natural Language Mappings: Common phrases → formal values
• Priority Factors: Guide conversation flow

See docs/structured-outputs.md for detailed pattern comparison and best practices.

4. Agentic Researcher

ReAct pattern with tool calling and iterative reasoning.

import { defineAgenticResearcher } from '@vibe-agent-toolkit/agent-runtime';

export const researcher = defineAgenticResearcher({
  name: 'breed-historian',
  description: 'Researches cat breed history with web search',
  version: '1.0.0',
  inputSchema: ResearchInputSchema,
  outputSchema: ResearchOutputSchema,
  tools: {
    webSearch: async (query: string) => { /* ... */ },
  },
}, async (input, ctx) => {
  // ReAct loop: Thought → Action → Observation
  for (let i = 0; i < ctx.maxIterations; i++) {
    const thought = await ctx.callLLM(`Research: ${input.query}`);
    const result = await ctx.callTool('webSearch', thought);
    // Process result...
  }
});

5. Function Workflow Orchestrator

Deterministic multi-agent coordination.

import { defineFunctionOrchestrator } from '@vibe-agent-toolkit/agent-runtime';

export const orchestrator = defineFunctionOrchestrator({
  name: 'adoption-pipeline',
  description: 'Coordinates multi-step adoption process',
  version: '1.0.0',
  inputSchema: AdoptionInputSchema,
  outputSchema: AdoptionOutputSchema,
}, async (input, ctx) => {
  // Call multiple agents in sequence
  const analysis = await ctx.call('photo-analyzer', { image: input.photo });
  const validation = await ctx.call('name-validator', { name: input.name });
  const approval = await ctx.call('human-approval', { data: { analysis, validation } });

  return { approved: approval.approved };
});

6. LLM Intelligent Coordinator

LLM-based routing decisions in workflows.

import { defineLLMCoordinator } from '@vibe-agent-toolkit/agent-runtime';

export const coordinator = defineLLMCoordinator({
  name: 'smart-router',
  description: 'Routes submissions based on complexity',
  version: '1.0.0',
  inputSchema: SubmissionSchema,
  outputSchema: RoutingSchema,
  systemPrompt: 'Route submissions to the right handler...',
}, async (input, ctx) => {
  const decision = await ctx.callLLM(`Route: ${input.submission}`);

  return ctx.route(decision, {
    simple: async () => ctx.call('auto-approver', input),
    complex: async () => ctx.call('human-review', input),
  });
});

7. Function Event Consumer

Event-triggered execution without LLM.

import { defineFunctionEventConsumer } from '@vibe-agent-toolkit/agent-runtime';

export const processor = defineFunctionEventConsumer({
  name: 'pedigree-processor',
  description: 'Processes uploaded pedigree files',
  version: '1.0.0',
  inputSchema: FileEventSchema,
  outputSchema: ProcessingResultSchema,
}, async (input, ctx) => {
  const file = await parseFile(input.filePath);
  await ctx.emit('pedigree-processed', { result: file });
  return { status: 'processed' };
});

8. LLM Event Handler

Event-triggered with LLM processing.

import { defineLLMEventHandler } from '@vibe-agent-toolkit/agent-runtime';

export const handler = defineLLMEventHandler({
  name: 'triage-handler',
  description: 'Triages incoming submissions with LLM',
  version: '1.0.0',
  inputSchema: SubmissionEventSchema,
  outputSchema: TriageResultSchema,
  systemPrompt: 'Triage submissions by urgency...',
}, async (input, ctx) => {
  const priority = await ctx.callLLM(`Triage: ${input.submission}`);
  await ctx.emit('submission-triaged', { priority });
  return { priority };
});

9. External Event Integrator

Human-in-the-loop approval gates.

import { defineExternalEventIntegrator } from '@vibe-agent-toolkit/agent-runtime';

export const approvalGate = defineExternalEventIntegrator({
  name: 'human-approval',
  description: 'Waits for human approval',
  version: '1.0.0',
  inputSchema: ApprovalRequestSchema,
  outputSchema: ApprovalResultSchema,
  timeoutMs: 60000,
  onTimeout: 'reject',
}, async (input, ctx) => {
  await ctx.emit('approval-requested', input);
  const result = await ctx.waitFor('approval-response', ctx.timeoutMs);
  return result;
});

Agent Manifest

Every agent exposes a manifest describing its interface:

const agent = defineAgentType(config, handler);

console.log(agent.manifest);
// {
//   name: 'agent-name',
//   description: 'What this agent does',
//   version: '1.0.0',
//   archetype: 'archetype-name',
//   inputSchema: { /* JSON Schema */ },
//   outputSchema: { /* JSON Schema */ },
//   metadata: { /* Additional info */ }
// }

Runtime Adapters

Agents are framework-agnostic. Use runtime adapters to integrate with specific LLM frameworks:

• @vibe-agent-toolkit/runtime-openai - OpenAI SDK
• @vibe-agent-toolkit/runtime-vercel-ai-sdk - Vercel AI SDK
• @vibe-agent-toolkit/runtime-langchain - LangChain
• @vibe-agent-toolkit/runtime-claude-agent-sdk - Claude Agent SDK

See individual runtime packages for usage examples.

Examples

See @vibe-agent-toolkit/vat-example-cat-agents for complete examples across all archetypes.

License

MIT