Multi-Provider Agents

Build agents that work with OpenAI, Anthropic, Google, and 30+ LLM providers

Switch between any LLM provider — OpenAI, Anthropic, Google, and more — without changing your agent code.

Quick Start

1. Simple Usage

   npm install praisonai
   
2. With Configuration

   import { Agent } from 'praisonai';
   
   const agent = new Agent({
     instructions: 'You are a helpful assistant.',
     llm: 'openai/gpt-4o-mini'
   });
   
   const response = await agent.chat('Hello, how are you?');
   console.log(response);
   
3. Switch Providers with One Line

   import { Agent } from 'praisonai';
   
   const agent = new Agent({
     instructions: 'You are a helpful assistant.',
     llm: 'anthropic/claude-3-5-sonnet-latest'
   });
   
   const response = await agent.chat('Explain AI in one sentence');
   console.log(response);
   

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How It Works

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Model String Resolution

PraisonAI resolves a model string in three tiers: explicit prefix > prefix inference > OpenAI fallback.

llm string	baseURL set?	Resolved provider	Backend used
gpt-4o-mini	no	openai	OpenAI (native)
claude-3-5-sonnet-latest	no	anthropic	AI SDK
gemini-2.0-flash	no	google	AI SDK
openai/gpt-4o	no	openai	OpenAI (native)
anthropic/claude-3-5-sonnet-latest	no	anthropic	AI SDK
claude-3-5-sonnet-latest	yes	openai	OpenAI-compatible (baseURL)
anthropic/claude-3-5-sonnet-latest	yes	anthropic	AI SDK (explicit prefix wins)

A bare name plus a custom baseURL stays on the OpenAI-compatible path — see The baseURL exception.

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Supported Providers

Provider	Model String	Example
OpenAI	openai/model	openai/gpt-4o, openai/gpt-4o-mini
Anthropic	anthropic/model	anthropic/claude-3-5-sonnet-latest
Google	google/model	google/gemini-2.0-flash
Groq	groq/model	groq/llama-3.3-70b-versatile
Mistral	mistral/model	mistral/mistral-large-latest
Cohere	cohere/model	cohere/command-r-plus
DeepSeek	deepseek/model	deepseek/deepseek-chat
xAI	xai/model	xai/grok-2

---

Common Patterns

Agent with Tools

import { Agent } from 'praisonai';

function getWeather(city: string): string {
  return JSON.stringify({ city, temperature: 22, condition: 'sunny' });
}

const agent = new Agent({
  instructions: 'You help users check the weather.',
  llm: 'anthropic/claude-3-5-sonnet-latest',
  tools: [getWeather]
});

const response = await agent.chat('What is the weather in Paris?');
console.log(response);

Tools work identically on every provider — swap the llm string (openai/..., google/..., groq/..., ollama/...) with no other code change.

Structured Output

outputSchema takes a JSON Schema object (not a Zod schema) and constrains the model to matching JSON on every provider. OpenAI uses native response_format: json_schema; other providers (Anthropic, Google, Groq, Mistral, Ollama, …) route through the AI SDK backend’s generateObject({ schema }). See Structured Output.

import { Agent } from 'praisonai';

const agent = new Agent({
  instructions: 'Extract person information from text.',
  llm: 'openai/gpt-4o-mini',
  outputSchema: {
    type: 'object',
    properties: {
      name: { type: 'string' },
      age: { type: 'number' },
      city: { type: 'string' },
    },
    required: ['name', 'age', 'city']
  },
  outputSchemaName: 'Person'  // names the schema in the response_format payload
});

const result = await agent.chat('John is 30 years old and lives in Paris');
// '{"name":"John","age":30,"city":"Paris"}'

outputSchema works on every provider from PraisonAI PR #4412 onward. Reasoning families (gpt-5*, o1*, o3*, o4*) still omit temperature automatically. See Output → Schema-constrained output.

Multi-Agent Pipeline

import { Agent } from 'praisonai';

const researcher = new Agent({
  name: 'researcher',
  instructions: 'Research topics thoroughly.',
  llm: 'anthropic/claude-3-5-sonnet-latest'
});

const writer = new Agent({
  name: 'writer',
  instructions: 'Write engaging content based on research.',
  llm: 'openai/gpt-4o'
});

const research = await researcher.chat('Research the history of AI');
const article = await writer.chat(`Write an article based on: ${research}`);
console.log(article);

Environment Variables

export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...
export GROQ_API_KEY=gsk_...

# Or pass apiKey per-agent (as of PR #4483, honoured across all providers)

# Override backend resolver
export PRAISONAI_BACKEND=ai-sdk   # Force AI SDK for all providers
export PRAISONAI_BACKEND=native   # Force native providers only
export PRAISONAI_BACKEND=auto     # Auto-select (default)

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Best Practices

Use the provider/model string format

Always use "provider/model" strings ("openai/gpt-4o-mini") rather than provider-specific config objects. Switching providers is a one-line change.

Install provider packages only when needed

Core PraisonAI bundles OpenAI support. For other providers install @ai-sdk/anthropic, @ai-sdk/google, etc. only when you use them.

Match model to task

For quick tasks use openai/gpt-4o-mini or google/gemini-2.0-flash. For complex reasoning use anthropic/claude-3-5-sonnet-latest or openai/gpt-4o. Mix providers across agents in multi-agent workflows.

Store API keys in environment variables

Never hard-code API keys. Use environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.). For production deployments use a secrets manager.

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Related

Providers

Full provider reference and configuration

Structured Output

Type-safe JSON responses