
agentica
TypeScript AI AI Function Calling Framework enhanced by compiler skills.
Stars: 932

Agentica is a specialized Agentic AI library focused on LLM Function Calling. Users can provide Swagger/OpenAPI documents or TypeScript class types to Agentica for seamless functionality. The library simplifies AI development by handling various tasks effortlessly.
README:
Agentic AI framework specialized in AI Function Calling.
Don't be afraid of AI agent development. Just list functions from three protocols below. This is everything you should do for AI agent development.
- TypeScript Class
- Swagger/OpenAPI Document
- MCP (Model Context Protocol) Server
Wanna make an e-commerce agent? Bring in e-commerce functions. Need a newspaper agent? Get API functions from the newspaper company. Just prepare any functions that you need, then it becomes an AI agent.
Are you a TypeScript developer? Then you're already an AI developer. Familiar with backend development? You're already well-versed in AI development. Anyone who can make functions can make AI agents.
import { Agentica, assertHttpController } from "@agentica/core";
import OpenAI from "openai";
import typia from "typia";
import { MobileFileSystem } from "./services/MobileFileSystem";
const agent = new Agentica({
vendor: {
api: new OpenAI({ apiKey: "********" }),
model: "gpt-4o-mini",
},
controllers: [
// functions from TypeScript class
typia.llm.controller<MobileFileSystem, "chatgpt">(
"filesystem",
MobileFileSystem(),
),
// functions from Swagger/OpenAPI
assertHttpController({
name: "shopping",
model: "chatgpt",
document: await fetch(
"https://shopping-be.wrtn.ai/editor/swagger.json",
).then(r => r.json()),
connection: {
host: "https://shopping-be.wrtn.ai",
headers: { Authorization: "Bearer ********" },
},
}),
],
});
await agent.conversate("I wanna buy MacBook Pro");
$ npx agentica start <directory>
----------------------------------------
Agentica Setup Wizard
----------------------------------------
? Package Manager (use arrow keys)
> npm
pnpm
yarn (berry is not supported)
? Project Type
NodeJS Agent Server
> NestJS Agent Server
React Client Application
Standalone Application
? Embedded Controllers (multi-selectable)
(none)
Google Calendar
Google News
> Github
Reddit
Slack
...
The setup wizard helps you create a new project tailored to your needs.
For reference, when selecting a project type, any option other than "Standalone Application" will implement the WebSocket Protocol for client-server communication.
For comprehensive setup instructions, visit our Getting Started guide.
Experience Agentica firsthand through our interactive playground before installing.
Our demonstrations showcase the power and simplicity of Agentica's function calling capabilities across different integration methods.
Find comprehensive resources at our official website.
https://github.com/user-attachments/assets/2f2a4cdc-6cf1-4304-b82d-04a8ed0be0dd
Tutorial Videos: https://www.youtube.com/@wrtnlabs
flowchart
subgraph "JSON Schema Specification"
schemav4("JSON Schema v4 ~ v7") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
schema2910("JSON Schema 2019-03") --upgrades--> emended
schema2020("JSON Schema 2020-12") --emends--> emended
end
subgraph "Agentica"
emended --"Artificial Intelligence"--> fc{{"AI Function Calling"}}
fc --"OpenAI"--> chatgpt("ChatGPT")
fc --"Google"--> gemini("Gemini")
fc --"Anthropic"--> claude("Claude")
fc --"High-Flyer"--> deepseek("DeepSeek")
fc --"Meta"--> llama("Llama")
chatgpt --"3.1"--> custom(["Custom JSON Schema"])
gemini --"3.0"--> custom(["Custom JSON Schema"])
claude --"3.1"--> standard(["Standard JSON Schema"])
deepseek --"3.1"--> standard
llama --"3.1"--> standard
end
Agentica enhances AI function calling by the following strategies:
- Compiler Driven Development: constructs function calling schema automatically by compiler skills without hand-writing.
- JSON Schema Conversion: automatically handles specification differences between LLM vendors, ensuring seamless integration regardless of your chosen AI model.
- Validation Feedback: detects and corrects AI mistakes in argument composition, dramatically reducing errors and improving reliability.
- Selector Agent: filtering candidate functions to minimize context usage, optimize performance, and reduce token consumption.
Thanks to these innovations, Agentica makes AI function calling easier, safer, and more accurate than before. Development becomes more intuitive since you only need to prepare functions relevant to your specific use case, and scaling your agent's capabilities is as simple as adding or removing functions.
In 2023, when OpenAI announced function calling, many predicted that function calling-driven AI development would become the mainstream. However, in reality, due to the difficulty and instability of function calling, the trend in AI development became agent workflow. Agent workflow, which is inflexible and must be created for specific purposes, has conquered the AI agent ecosystem.
By the way, as Agentica has resolved the difficulty and instability problems of function calling, the time has come to embrace function-driven AI development once again.
Type | Workflow | Vanilla Function Calling | Agentica Function Calling |
---|---|---|---|
Purpose | ❌ Specific | 🟢 General | 🟢 General |
Difficulty | ❌ Difficult | ❌ Difficult | 🟢 Easy |
Stability | 🟢 Stable | ❌ Unstable | 🟢 Stable |
Flexibility | ❌ Inflexible | 🟢 Flexible | 🟢 Flexible |
For support, questions, or to provide feedback, join our Discord community:
Agentica is open-source and available under the MIT License.
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