# Universal Multi-Model AI Chat & Deep Reasoning API (`dev00/brave-leo-ai-chat-api`) Actor

High-performance, privacy-focused conversational AI gateway. Access Qwen 3.5, Llama 3.1/3.3, Claude 4.6 Sonnet, GLM 4.7 Flash, DeepSeek, and Mistral with web search citations, code generation, and zero user tracking.

- **URL**: https://apify.com/dev00/brave-leo-ai-chat-api.md
- **Developed by:** [dev00](https://apify.com/dev00) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$3.00 / 1,000 ai chat & reasoning queries

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## ⚡ Universal Multi-Model AI Chat & Deep Reasoning API (Real-Time Web Citations & Zero Logs)

> 🚀 **The #1 Most Complete, Low-Latency Privacy-First Conversational Engine, Multi-Model Routing, Web Search Citations & Technical Synthesis Scraper on Apify.**

Looking for private, ultra-fast conversational AI and reasoning without logging, IP tracking, or paying expensive monthly per-seat enterprise fees?

The **Universal Multi-Model AI Chat & Deep Reasoning API** delivers sub-second conversational synthesis backed by 28+ leading AI foundation models (Qwen 3.5, Llama 3.1/3.3, GLM 4.7 Flash, DeepSeek V3.2, Claude 4.6 Sonnet, GPT-5.5, Mistral Large) with real-time web search citation extraction, conversation memory (`conversation_id`), and zero user profiling.

***

### 🌟 Institutional Comparison: Why Choose Universal Multi-Model AI Chat?

| Feature | ChatGPT Plus ($20/mo) | Claude Pro ($20/mo) | Perplexity Pro ($20/mo) | ⚡ Universal AI Chat Actor |
| :--- | :---: | :---: | :---: | :---: |
| **Pricing Model** | 💸 $20.00 / user / mo | 💸 $20.00 / user / mo | 💸 $20.00 / user / mo | ⚡ **Pay-Per-Event ($0.003 / query)** |
| **Privacy & Anonymity** | ❌ Full User Tracking | ❌ Account Required | ⚠️ History Logged | ✅ **Zero Data Retention / No Logs** |
| **Multi-Model Access** | ⚠️ GPT models only | ⚠️ Claude models only | ⚠️ Fixed models | ✅ **28+ Models (Meta, Alibaba, Z.ai, Anthropic)** |
| **Real-Time Web Search** | ✅ Yes | ⚠️ Limited | ✅ Yes | ✅ **Live Web Search Synthesis** |
| **Conversation Memory** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ **Stateful `conversation_id` Support** |
| **Latency / Response Time** | ~1.5s | ~2.0s | ~1.8s | ⚡ **Sub-800ms Edge Gateway** |

***

### ⚡ Comprehensive Feature Breakdown

#### 1. 🤖 Multi-Model Foundation Selector (`model`)

Choose from 28+ top foundation models across Meta (Llama 3.1, Llama 3.3, Llama 4), Alibaba (Qwen 3.5 122B, Qwen 3 Coder 480B), Anthropic (Claude 4.6 Sonnet, Claude 4.5 Haiku, Claude Opus 4.8), DeepSeek, OpenAI (GPT-5.5, GPT OSS), xAI (Grok 4.3), and Mistral AI.

#### 2. 💬 General Conversational Chat (`CHAT`)

Execute real-time conversational question answering on any general, historical, technical, or creative topic with zero IP retention and dynamic model selection.

#### 3. 🧠 Deep Multistep Reasoning (`REASONING`)

Tackle complex multi-variable logic problems, financial derivations, statistical breakdowns, and architectural trade-offs.

#### 4. 💻 Code Generation & Technical Synthesis (`CODE_GEN`)

Generate production-ready code in Python, JavaScript, TypeScript, Go, Rust, C++, and SQL with inline documentation and algorithmic optimizations.

#### 5. 📋 Dynamic Models Directory (`MODELS`)

Query the live registry of 28+ supported foundation models with capability flags, token context limits, and provider metadata.

***

### 📥 Input Parameters & Schema

| Parameter | Type | Required | Default | Options / Valid Values | Description |
| :--- | :---: | :---: | :---: | :--- | :--- |
| `model` | `string` | No | `automatic` | `automatic`, `qwen-3-235b`, `llama-3-8b-instruct`, `glm-4-7-flash`, `deepseek-v3-2`, `claude-3-sonnet`, `mistral-large`, `qwen-3-coder-480b` | **AI Model Architecture**: Select your preferred reasoning engine. |
| `mode` | `string` | No | `CHAT` | `CHAT`, `REASONING`, `CODE_GEN`, `MODELS` | **Primary Operation Mode**: Selects the task routing pipeline. |
| `query` | `string` | **Yes** | — | Any search query, question, or prompt | The prompt or task to process. |
| `conversation_id` | `string` | No | `null` | Any string identifier (e.g. `user-chat-101`) | **Conversation ID**: Maintains context across multiple turns. |
| `system_language` | `string` | No | `en_US` | `en_US`, `en_GB`, `es_ES`, `fr_FR`, `de_DE`, `it_IT`, `pt_BR`, `ja_JP`, `zh_CN`, `zh_TW`, `ar_SA`, `ru_RU`, `hi_IN`, `ko_KR`, `nl_NL`, `tr_TR`, `pl_PL`, `sv_SE`, `id_ID`, `vi_VN` | **Output Language Locale Dropdown**: 20+ supported language locales. |

***

### 📥 Input Examples

#### Example 1: Multi-Turn Conversational Q\&A

```json
{
  "model": "automatic",
  "mode": "CHAT",
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points.",
  "conversation_id": "session-101",
  "system_language": "en_US"
}
```

#### Example 2: High-Performance Python Code Generation

```json
{
  "model": "qwen-3-235b",
  "mode": "CODE_GEN",
  "query": "Write a high-throughput Python async web crawler using aiohttp with connection pooling and retry logic.",
  "system_language": "en_US"
}
```

#### Example 3: Analytical Reasoning & Mathematics

```json
{
  "model": "glm-4-7-flash",
  "mode": "REASONING",
  "query": "Derive the Black-Scholes partial differential equation and explain the financial intuition behind delta hedging.",
  "system_language": "en_US"
}
```

#### Example 4: List Available Models Directory

```json
{
  "mode": "MODELS"
}
```

***

### 📤 Output Dataset Example

```json
{
  "mode": "CHAT_RESULT",
  "success": true,
  "conversation_id": "session-101",
  "turn_count": 1,
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points.",
  "model": "qwen-3-235b",
  "answer": "1. Basic Unit of Information: Classical computers use bits (0 or 1), whereas quantum computers use qubits, which can exist simultaneously as 0 and 1 via quantum superposition.\n2. Computational Scaling & Parallelism: Thanks to quantum entanglement and superposition, quantum computers can evaluate exponentially large solution spaces simultaneously rather than calculating sequentially.\n3. Problem Domain Optimization: Classical computers excel at everyday sequential computing, while quantum systems are built for complex simulations like molecular modeling, cryptography, and combinatorial optimization.",
  "citations_count": 0,
  "citations": [],
  "usage": {
    "completion_tokens": 142,
    "prompt_tokens": 28,
    "total_tokens": 170
  }
}
```

***

### 💻 Multi-Language Code Integration Suite

#### Python (`apify-client`):

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "model": "automatic",
    "mode": "CHAT",
    "query": "What are the latest breakthroughs in fusion energy in 2026?",
    "conversation_id": "user-chat-99",
    "system_language": "en_US"
}

run = client.actor("dev00/brave-leo-ai-chat-api").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['model']}] Answer:\n{item['answer']}")
```

#### JavaScript / Node.js (`apify-client`):

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const input = {
  model: "qwen-3-235b",
  mode: "CODE_GEN",
  query: "Write a high-performance Redis cache layer in TypeScript.",
  conversation_id: "dev-session-42",
  system_language: "en_US"
};

const run = await client.actor("dev00/brave-leo-ai-chat-api").call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log("Generated Code:", items[0].answer);
```

#### cURL:

```bash
curl -X POST "https://api.apify.com/v2/acts/dev00~brave-leo-ai-chat-api/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model": "automatic", "mode": "CHAT", "query": "Explain General Relativity in simple terms.", "conversation_id": "session-1"}'
```

***

### 💰 Pay-Per-Event (PPR) Pricing Model

This Actor operates under the transparent **Pay-Per-Event** monetization model:

- **Price**: **$0.003 per AI chat / reasoning execution** ($3.00 per 1,000 queries).
- **No Monthly Minimums**: Only pay for the exact queries you run.
- **Includes 28+ Foundation Models**: Zero price difference between Qwen, Llama, GLM, and Claude models.

***

### 🎯 High-Growth Industry Use Cases

1. **AI Customer Support Agents**: Embed private, low-latency conversational AI without storing customer PII.
2. **Automated Code Assistants & Review Bots**: Generate optimized snippets and review pull requests programmatically.
3. **Academic & Research Synthesis**: Perform literature summaries and mathematical derivations.
4. **Market & Competitive Intelligence**: Extract real-time factual summaries across industry sectors.
5. **Multi-Agent Systems**: Deploy fast, cost-effective reasoning workers across AutoGPT, CrewAI, and LangChain pipelines.

***

### ❓ Frequently Asked Questions (FAQ)

##### Q: How does this API ensure privacy?

All requests are routed with anonymized headers and zero session tracking or query logging.

##### Q: What models are supported?

Over 28 foundation models including Qwen 3.5 122B, Llama 3.1 8B, Llama 3.3 70B, Claude 4.6 Sonnet, GLM 4.7 Flash, DeepSeek V3.2, and GPT-5.5.

##### Q: How does conversation history work?

Simply pass a `conversation_id` string across multiple requests, and the engine automatically tracks and appends conversation context across turns.

***

### 🏷️ Keywords & SEO Tags

`multi model ai api, private ai api, zero tracking llm, qwen 3.5 api, llama 3.1 api, llama 3.3 api, glm 4.7 flash api, deepseek api, claude 4.6 sonnet api, code generation api, ai reasoning api, conversational ai gateway, privacy first chatbot, chatgpt alternative, perplexity alternative, apify ai actor, multi model ai gateway`

# Actor input Schema

## `model` (type: `string`):

Select your preferred AI foundation reasoning engine. 'automatic' dynamically routes to the fastest high-accuracy model.

## `mode` (type: `string`):

Select the primary operational mode. CHAT (Conversational Q\&A), REASONING (Deep analytical deduction), CODE\_GEN (Code writing), or MODELS (List available LLMs).

## `query` (type: `string`):

Your question, research prompt, or coding task for the AI engine.

## `conversation_id` (type: `string`):

Optional conversation ID to maintain state across multiple turns. Use the conversation\_id returned in the previous response to continue the same chat thread.

## `system_language` (type: `string`):

Select the language locale for the AI response.

## Actor input object example

```json
{
  "model": "automatic",
  "mode": "CHAT",
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points.",
  "system_language": "en_US"
}
```

# Actor output Schema

## `results` (type: `string`):

Direct link to the dataset items containing generated AI answers and citations

## `keyValueStoreResult` (type: `string`):

Direct link to the key-value store output record

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points."
};

// Run the Actor and wait for it to finish
const run = await client.actor("dev00/brave-leo-ai-chat-api").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points." }

# Run the Actor and wait for it to finish
run = client.actor("dev00/brave-leo-ai-chat-api").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points."
}' |
apify call dev00/brave-leo-ai-chat-api --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,dev00/brave-leo-ai-chat-api"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/BEn6As5wLzME5HqlM/builds/KE9ksj0DauATIq2e2/openapi.json