langchain-stream

title	demo/langchain-stream/readme
group	demo/langchain-stream

LangChain Stream

LangChain integration for streaming responses with support for multiple AI providers.

Purpose

Demonstrates capabilities:

• LangChain integration for streaming
• Real-time token streaming
• Multi-provider architecture
• Seamless integration with agent-swarm-kit

Key Features

• LangChain Streaming: Real-time token transmission
• Multi-Provider Support: Cohere, LMStudio, Ollama
• Pharmaceutical Sales: Thematic agent for sales
• Token-by-Token Response: Smooth response generation
• Provider Fallback: Automatic provider switching

Technology Stack

• Runtime: Bun
• Language: TypeScript
• AI Framework: agent-swarm-kit + LangChain
• Streaming: LangChain streaming API
• AI Providers: Cohere, LMStudio, Ollama

Project Structure

src/
├── logic/
│   ├── agent/         # Triage agent
│   ├── completion/    # Streaming providers
│   ├── enum/          # Enumerations
│   ├── swarm/         # Root swarm
│   └── tools/         # Add to cart tool
└── index.ts           # Entry point

Installation and Setup

# Install dependencies
bun install

# Start
bun run src/index.ts

Configuration

Create a .env file:

COHERE_API_KEY=your_cohere_api_key
LMSTUDIO_API_URL=http://localhost:1234
OLLAMA_API_URL=http://localhost:11434

Usage Examples

1. Pharma consultation: "What helps with headaches?"

   • Response is generated token-by-token in real-time

2. Add to cart: "Add aspirin"

   • Streaming confirmation of addition

3. Product questions: "What antibiotics do you have?"

   • Smooth generation of product list

LangChain Benefits

• Streaming Performance: Instant delivery of first tokens
• User Experience: No delays in responses
• Provider Abstraction: Unified interface for different AI
• Error Handling: Graceful fallback when issues occur

Streaming Architecture

User Request → Agent Processing → LangChain → AI Provider
     ↓              ↓               ↓           ↓
Response ← Token Stream ← Completion ← Model Response

Use Cases

Critical for:

• Interactive chat applications
• Real-time customer support
• Live consultations
• Educational assistants
• Entertainment bots

Performance Benefits

• Perceived Speed: Responses start instantly
• Engagement: Users see generation progress
• Responsiveness: Better UX compared to batch responses
• Efficiency: Optimal network bandwidth usage