Introduction
Understanding the core concepts and architecture of Zypher Agent framework
What are Zypher Agents
Zypher Agents are autonomous AI systems that can reason, plan, and execute complex tasks by combining Large Language Models (LLMs) with external tools and services.
Core Concept
Think of a Zypher Agent as an AI assistant that can:
- Understand complex requests in natural language
- Plan multi-step approaches to solve problems
- Execute actions using external tools and APIs
- Learn from results and adapt its approach
- Communicate progress and results back to you
Unlike traditional chatbots that just generate text, Zypher Agents can actually do things in the real world.
Agents vs Traditional Software
Traditional software follows predetermined workflows - you write code that defines exactly what happens in each scenario. Zypher Agents are different:
| Traditional Software | Zypher Agents |
|---|---|
| Fixed workflows - Hard-coded logic for each task | Dynamic reasoning - LLM plans the approach based on context |
| Rigid execution - Same steps every time | Adaptive planning - Adjusts strategy based on results |
| Manual integration - Developer writes code for each tool | Automatic tool usage - Agent discovers and uses tools as needed |
| Explicit error handling - Pre-written code for each failure case | Intelligent recovery - Reasons about problems and finds solutions |
With Zypher Agents, you describe what you want accomplished, and the LLM brain figures out how to do it using available tools.
Quick Example
import { ZypherAgent, AnthropicModelProvider } from '@corespeed/zypher';
const agent = new ZypherAgent(
new AnthropicModelProvider({ apiKey: "your-key" })
);
await agent.init();
// Agent can understand complex requests and execute them
const result = await agent.runTask("Find the latest news about AI and summarize the top 3 articles");What Makes Zypher Agents Powerful
🧠 LLM-Powered Reasoning
Built on state-of-the-art language models like Claude and GPT-4, giving agents sophisticated reasoning capabilities.
🛠️ Tool Integration
Connect to any external service through the Model Context Protocol (MCP) - from web scraping to database queries.
🔄 Loop Interceptors
Control execution flow with interceptors for error handling, token management, and custom logic.
📝 Git Checkpoints
Track changes with automatic checkpointing - revert to previous states when needed.
⚡ Simple API
Just a few lines of code to create powerful agents. No complex configuration required.