The simplest Agentic AI library, specialized in LLM function calling.
@agentica is the simplest AI agent library specialized for LLM (Large Language Model) function calling. You can provide functions to call by Swagger/OpenAPI document or TypeScript class type, and it will make everything possible. Super AI Chatbot development, or Multi-Agent Orchestration, all of them can be realized by the function calling.
For example, if you provide Swagger document of a Shopping Mall Server, @agentica will compose Super AI Chatbot application. In the chatbot application, customers can purchase products just by conversation texts. If you wanna automate the counseling or refunding process, you also can do it just by delivering the Swagger document.
Also, the LLM function calling strategy is effective for the Multi-Agent Orchestration, and it is easier to develop than any other way. You don't need to learn any complicate framework and its specific paradigms and patterns. Just connect them through class, and deliver the TypeScript class type. @agentica will centralize and realize the multi-agent orchestration through function calling.
import { Agentica } from "@agentica/core";
const agent: Agentica = new Agentica({ ... });
await agent.conversate("I wanna buy MacBook Pro");shopping-chat.mp4
Demonstration video of Shopping AI Chatbot
Preparing detailed guide documents.
Until that, please satisfy with README document of each module.
- Core Library
- Benchmark Program
- WebSocket RPC
In here README document, @agentica/core is introducing its key concepts, principles, and demonstrating some examples.
However, this contents are not fully enough for new comers of AI Chatbot development. We need much more guide documents and example projects are required for education. We have to guide backend developers to write proper definitions optimized for LLM function calling. We should introduce the best way of multi-agent orchestration implementation.
We'll write such fully detailed guide documents until 2025-03-31, and we will continuously release documents that are in the middle of being completed.
https://nestia.io/chat/playground
I had developed Swagger AI chatbot playground website for a long time ago.
However, the another part obtaining function schemas from TypeScript class type, it is not prepared yet. I'll make the TypeScript class type based playground website by embedding TypeScript compiler (tsc).
The new playground website would be published until 2025-03-15.
As I've concenstrated on POC (Proof of Concept) development on the early stage level, internal agents composing @agentica/core are not cost optimized yet. Especially, selector agent is consuming LLM tokens too much repeatedly. We'll optimize the selector agent by RAG (Retrieval Augmented Generation) skills.
Also, we will support dozens of useful add-on agents which can connect with @agentica/core by TypeScript class function calling. One of them is @wrtnlabs/hive which optimizes selector agent so that reducing LLM costs dramatically. The others would be OpenAI Vector Store handler and Postgres based RAG engine.
With these @agentica/core providing add-on agents, you can learn how to implement the Multi-agent orchestration through TypeScript class function calling, and understand how @agentica/core makes the Multi agent system interaction super easily.
flowchart
Agent["<code>@agentica/core</code>"]
OpenAI("<b><u>OpenAI</u></b>")
Claude
Llama
Gemini
Agent==supports==>OpenAI
Agent-.not yet.->Claude
Agent-.not yet.->Llama
Agent-.not yet.->Gemini
Currently, @agentica/core supports only OpenAI.
It is because @agentica/core is still in the POC (Proof of Concept) and demonstration stage. However, even nthough OpenAI is the most famous model in the AI world, @agentica/core have to support much more models for broad users.
We're going to support much more models until 2025-04-30.