Meepo Platform

Meepo is a universal platform for integrating, managing, and orchestrating AI agents across different frameworks and providers. Our platform consists of three key components:

1. pymeepo: A universal Python SDK for integrating agents from any framework (LangChain, AutoGen, etc.)
2. Meepo Cloud: A managed platform for deploying, orchestrating, and monitoring agent workflows
3. Language SDKs: Native SDKs in multiple languages for interacting with Meepo Cloud

Core Features

• Universal Integration (pymeepo): One SDK to integrate and orchestrate agents from any framework
• Cloud Platform: Deploy, manage, and monitor agent workflows at scale
• Multi-Language Support: Native SDKs for Python, TypeScript, Go, and more
• Visual Workflow Builder: Create complex agent workflows through an intuitive drag-and-drop interface (Post-MVP)
• Enterprise Management: Comprehensive tools for deployment, monitoring, and scaling
• Marketplace: Share and discover pre-built workflows, tools, and integrations (Post-MVP)

MVP v0.1.0 Status

The current development focus is on delivering a robust MVP (v0.1.0) of the Meepo Platform. The goal is to provide an end-to-end "vertical slice" of functionality.

MVP Core Capabilities:

• Data Ingestion: Load data from public sources like YouTube, web pages, PDFs, and GitHub repositories.
• RAG & Query: Ask questions over ingested data via a powerful Retrieval-Augmented Generation pipeline.
• Agent Orchestration: Build and run agents with configurable prompts, tools, and LLMs (OpenAI, Claude, Gemini).
• Output Integrations: Push agent responses to external systems like Twitter, WordPress, Slack, or generic webhooks.
• Embeddable Chat UI: Interact with agents through a real-time, embeddable chat widget.

The project is currently in active development, following a detailed 22-week plan.

Development Timeline (22 Weeks)

The MVP is being developed across four distinct phases, with buffer weeks for stabilization and testing.

• Phase 1: Foundation & RAG (Weeks 1-4)

  • Project setup, database design, and core backend/frontend scaffolding.
  • Building the data ingestion pipeline, vector storage, and retrieval API.
  • Goal: A working RAG system.

• Phase 2: Agent System (Weeks 6-10)

  • Implementing user authentication, the core agent abstraction, and multi-provider LLM integration.
  • Adding streaming support, secret management, and responsible AI guardrails.
  • Goal: A functional, secure agent execution system.

• Phase 3: Integrations & UI (Weeks 12-15)

  • Developing output integrations, OAuth 2.0 flows, and a real-time chat UI.
  • Creating an embeddable widget and developer SDKs (Python/JS).
  • Goal: A complete user-facing application with external connections.

• Phase 4: Production Ready (Weeks 17-21)

  • Implementing comprehensive observability (logging, metrics, tracing) and security hardening.
  • Automating infrastructure (Terraform) and CI/CD pipelines for production deployment.
  • Goal: A stable, secure, and scalable production launch.

For a detailed breakdown, see the Full 22-Week Timeline.

Documentation

• MVP Requirements: Detailed requirements for the v0.1.0 release.
• Full Timeline: The complete 22-week development plan.
• Weekly Tasks: Granular, JIRA-style tasks for each week of development.
• Platform Requirements: The long-term vision and full platform specifications.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.