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epsimo-agent

Build AI applications with multi-agent orchestration and persistent thread state.

Updated Jan 24, 2026
Search Tags:#multi-agent#cli-tool#agent-orchestration#python-sdk#ai-framework#virtual-database#conversation-ai
One-click install
npx skills add https://github.com/thierryteisseire/Epsimo-agent --skill epsimo-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: epsimo-agent
Source: https://github.com/thierryteisseire/Epsimo-agent/tree/main
Command: npx skills add https://github.com/thierryteisseire/Epsimo-agent --skill epsimo-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pyyaml, click, python-dotenv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the creation and management of sophisticated AI applications by providing a unified framework for multi-agent orchestration, persistent conversation state, and integrated tool usage.

Core Features & Use Cases

  • Unified CLI & SDK: Manage projects, agents, and data from your terminal or Python code.
  • Virtual Database: Store and retrieve structured data directly within conversation threads, eliminating the need for separate databases.
  • Streaming Conversations: Build real-time, interactive AI experiences.
  • React UI Kit: Quickly integrate pre-built chat components into your frontend.
  • Use Case: Develop a customer support chatbot that remembers user preferences and past interactions, uses web search tools for real-time information, and stores key customer data in its persistent thread state.

Quick Start

Use the epsimo agent skill to create a new AI application project named 'My First AI App'.

Frequently Asked Questions about epsimo-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a multi-agent AI application with persistent conversation state?▼

Multi-agent AI applications with persistent conversation state are built using a unified Python SDK and CLI that orchestrate agents and manage thread-based data via a Virtual Database. This eliminates the need for a separate database to store user interactions and preferences.

What is a Virtual Database for conversational AI and how does it work?▼

A Virtual Database for conversational AI directly stores and retrieves structured data within conversation threads. It works by maintaining persistent thread-based state management, allowing agents to remember past interactions and user preferences without requiring external database infrastructure.

Can I manage multi-agent orchestration projects entirely from a CLI tool?▼

Yes, you can manage multi-agent orchestration projects entirely from a CLI tool supported by Python libraries like click and python-dotenv. The unified CLI allows you to create projects, manage agents, and handle data directly from your terminal alongside the Python SDK.

Does this AI framework provide frontend components for streaming conversations?▼

This AI framework provides a React UI Kit featuring pre-built chat components for streaming conversations. This enables developers to quickly integrate real-time, interactive conversational AI experiences into their frontend applications.

What is the best way to store structured customer data in a chatbot without a separate database?▼

The best way to store structured customer data in a chatbot without a separate database is using a Virtual Database integrated into conversation threads. This approach allows a customer support chatbot to remember user preferences and persist key data across interactions natively.

What Python dependencies are required to use this multi-agent orchestration framework?▼

Required Python dependencies for this multi-agent orchestration framework include requests, pyyaml, click, and python-dotenv. These libraries support the unified CLI, Python SDK operations, and environment configuration needed to build and manage AI applications.

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