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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Unified interface for AI chat, Agentic workflows and more ...
| Date | Stars |
|---|---|
| 2026-07-31 | 1071 |
| 2026-08-02 | 1070 |
| 2026-08-06 | 1070 |
Today
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Momentum
0.0
growth rate 0.00%/day
<img width="1512" alt="Screenshot 2025-04-14 at 9 13 25 PM" src="https://github.com/user-attachments/assets/b89d1343-7c6f-4685-8bcf-dbcc71ce2229" />
## Introduction
[LLMChat.co](https://llmchat.co) is a sophisticated AI-powered chatbot platform that prioritizes privacy while offering powerful research and agentic capabilities. Built as a monorepo with Next.js, TypeScript, and cutting-edge AI technologies, it provides multiple specialized chat modes including Pro Search and Deep Research for in-depth analysis of complex topics.
LLMChat.co stands out with its workflow orchestration system and focus on privacy, storing all user data locally in the browser using IndexedDB, ensuring your conversations never leave your device.
## Key Features
**Advanced Research Modes**
- **Deep Research**: Comprehensive analysis of complex topics with in-depth exploration
- **Pro Search**: Enhanced search with web integration for real-time information
**Multiple LLM Provider Support**
- OpenAI
- Anthropic
- Google
- Fireworks
- Together AI
- xAI
**Privacy-Focused**
- **Local Storage**: All user data stored in browser using IndexedDB via Dexie.js
- **No Server-Side Storage**: Chat history never leaves your device
**Agentic Capabilities**
- **Workflow Orchestration**: Complex task coordination via custom workflow engine
- **Reflective Analysis**: Self-improvement through analysis of prior reasoning
- **Structured Output**: Clean presentation of research findings
## Architecture
LLMChat.co is built as a monorepo with a clear separation of concerns:
```
├── apps/
│ ├── web/ # Next.js web application
│ └── desktop/ # Desktop application
│
└── packages/
├── ai/ # AI models and workflow orchestration
├── actions/ # Shared actions and API handlers
├── common/ # Common utilities and hooks
├── orchestrator/# Workflow engine and task management
├── prisma/ # Database schema and client
├── shared/ # Shared types and constants
├── ui/ # Reusable UI components
├── tailwind-config/ # Shared Tailwind configuration
└── typescript-config/ # Shared TypeScript configuration
```
## Workflow Orchestration
LLMChat.co's workflow orchestration enables powerful agentic capabilities through a modular, step-by-step approach. Here's how to create a research agent:
### 1. Define Event and Context Types
First, establish the data structure for events and context:
```typescript
// Define the events emitted by each task
type AgentEvents = {
taskPlanner: {
tasks: string[];
query: string;
};
informationGatherer: {
searchResults: string[];
};
informationAnalyzer: {
analysis: string;
insights: string[];
};
reportGenerator: {
report: string;
};
};
// Define the shared context between tasks
type AgentContext = {
query: string;
tasks: string[];
searchResults: string[];
analysis: string;
insights: string[];
report: string;
};
```
### 2. Initialize Core Components
Next, set up the event emitter, context, and workflow builder:
```typescript
import { OpenAI } from 'openai';
import { createTask } from 'task';
import { WorkflowBuilder } from './builder';
import { Context } from './context';
import { TypedEventEmitter } from './events';
// Initialize event emitter with proper typing
const events = new TypedEventEmitter<AgentEvents>();
// Create the workflow builder with proper context
const builder = new WorkflowBuilder<AgentEvents, AgentContext>('research-agent', {
events,
context: new Context<AgentContext>({
query: '',
tasks: [],
searchResults: [],
analysis: '',
insights: [],
report: '',
}),
});
// Initialize LLM client
const llm = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
```
### 3. Define Research Tasks
Create specialized tasks for each step of the research process:
#### Planning Task
```typescript
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matched fp:ab3d2ff53a964605, topic:llm