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sema-core

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sema-core

Event-driven core library for Sema AI assistant

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Sema Code Core Logo

An Event-Driven AI Coding Assistant Core Engine

Providing reliable and pluggable intelligent processing capabilities for building code assistant tools.

GitHub License Ask DeepWiki npm version Documentation arXiv

中文 | English

📖 Project Overview

Sema Code Core is an event-driven AI coding assistant core engine that provides reliable and pluggable intelligent processing capabilities for building code assistant tools. It supports core capabilities such as multi-agent collaboration, Skill extension, and Plan mode task planning, and can be quickly integrated into various AI programming tools.

View Documentation

✨ Core Features

FeatureDescription
Natural Language InstructionsDirectly drive programming tasks through natural language
Permission ControlFine-grained permission management ensures safe and controllable operations
Subagent ManagementMulti-agent collaboration with dynamic sub-agent scheduling based on task types
Skill Extension MechanismPlugin architecture to flexibly extend AI programming capabilities
Plan Mode Task PlanningDecomposition and execution planning of complex tasks
MCP Protocol SupportBuilt-in Model Context Protocol service to support tool extension
Multi-Model SupportCompatible with Anthropic, OpenAI SDK, and LLM APIs from major vendors

🎯 Scenarios

  • IDE / Editor Plugin Development — Provides low-level AI capability encapsulation for editors, allowing developers to focus on UI interaction without self-developing complex large model scheduling and tool calling logic.

  • Enterprise Internal R&D Tools — Private deployment + permission control, adapting to enterprise-owned models and security specifications. Out-of-the-box toolchain avoids building AI programming infrastructure from scratch.

  • Vertical Domain Intelligent Workflow — Decomposes complex engineering tasks (migration, refactoring, documentation) into automated processes. Multi-agent collaborative execution replaces manual processing of repetitive coding work.

  • Academic Research & Agent Prototype Verification — Provides a lightweight Agent experimental environment for academic institutions and independent researchers, supporting flexible combinations of toolchains and agent strategies, allowing researchers to focus on algorithmic innovation.

💼 Use Cases

IDE Extension (VSCode / JetBrains)

Sema Code Extension is an intelligent programming plugin based on the Sema Code Core engine, supporting both the VSCode extension and JetBrains plugins (IntelliJ IDEA / PyCharm / GoLand / WebStorm / CLion, etc.).

Sema Code VSCode Extension

SemaClaw

SemaClaw is a general-purpose engineering harness for building personal AI agents.

SemaClaw

Skill Web App

A Skill web application based on Sema Code Core, integrating Agent Skill Browser / Creator / Playground demo.

Skill Web App

🚀 Quick Start

1. Create a project and install dependencies

mkdir my-app && cd my-app
npm init -y
npm install sema-core

2. Download the example file

Download quickstart.mjs to the my-app directory, then modify the following two configurations:

const core = new SemaCore({
  workingDir: '/path/to/your/project', // Target repository path for the Agent to operate on
  ...
});

const modelConfig = {
  apiKey: 'sk-your-api-key', // Replace with your API Key
  ...
};

For more model configuration options, see Model Management

3. Run

node quickstart.mjs
miniCli

Cross-Language Integration Reference README.md

🛠 Development

# 1. Install dependencies
npm install

# 2. Build
npm run build

📜 License & Third-Party Dependencies

sema-core is released under the MIT License.

All production dependencies are pulled from the npm registry under permissive licenses (MIT / ISC / BSD / Apache-2.0 / BlueOak-1.0.0 / 0BSD); no copyleft (GPL / AGPL / LGPL) code is introduced. The full component list, license summary, and external service terms are tracked in THIRD_PARTY_NOTICES.md. Resolved versions are pinned via package-lock.json for reproducible installs and license traceability.

When calling third-party model or service APIs (Anthropic, OpenAI, OpenAI-compatible endpoints, MCP servers) through this library, the embedding application is responsible for complying with the corresponding provider's terms of service.

Keywords

ai

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Package last updated on 23 Jul 2026

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