aws-samples

aws-samples / sample-multi-agent-orchestration-chat-on-agentcore

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Build & Share AI agents with your team. Full AgentCore, Full Serverless, Full TypeScript Sample

10
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69% credibility
Found May 21, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Multi-agent Orchestration Chat on AgentCore (MOCA) is a collaborative AI chat platform that lets teams create, customize, and share AI agents. Built on Amazon Bedrock AgentCore, it provides ready-to-use agents for software development, data analysis, and content creation, while also allowing users to build their own specialized assistants. The platform includes features like real-time chat, file storage, conversation memory, extensible tools (web search, code execution, browser automation), and enterprise security with user authentication. It's designed for teams who want to work alongside AI agents on complex tasks.

How It Works

1
💡 You discover a new AI chat platform

Your team is looking for a way to collaborate with AI agents that can help with coding, data analysis, and creative work.

2
🚀 You launch the platform in your cloud

With a few clicks, you deploy the multi-agent system to your organization's cloud account. Everything is automatically set up for you.

3
🔐 You create your account and log in

Your administrator sets up your user account. You sign in through a secure login page and gain access to your workspace.

4
🤖 You meet your AI assistants

You see a gallery of AI agents ready to help: a software developer, a data analyst, a physicist, and more. You can also create your own custom agents tailored to your needs.

5
You choose how to start
📦
Pick a preset agent

Select from ready-made assistants for coding, analysis, or creative tasks.

🛠️
Build a custom agent

Design your own AI assistant with specific instructions and tools.

6
💬 You chat and work together

You send messages, ask questions, and the AI assistant responds. It can search the web, write and run code, edit files, browse websites, and remember your conversation.

7
📁 Your work is automatically saved

Files you create, code you write, and your conversation history are stored securely in your personal cloud storage. Everything persists between sessions.

🎉 You accomplished your goal

Whether you debugged code, analyzed data, or generated content, your AI assistant helped you get things done. You can share your custom agents with teammates so they benefit too.

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AI-Generated Review

What is sample-multi-agent-orchestration-chat-on-agentcore?

This is a multi-agent AI chat platform built on Amazon Bedrock AgentCore that lets teams create, customize, and share AI agents across their organization. Built entirely in TypeScript with a serverless AWS architecture, it provides preset agents for software development, data analysis, and content creation, plus the ability to design custom agents tailored to specific needs. Users interact through a simple interface where agents can execute commands, search the web, analyze images, run Python code, and manage files with persistent conversation history and long-term memory.

Why is it gaining traction?

The main draw is the combination of multi-agent orchestration with team collaboration built on proven AWS infrastructure. The MCP server integration means you can extend agent capabilities with external tools like GitHub, AWS documentation, or filesystem access. Event-driven automation via EventBridge allows agents to run on schedules or external triggers. Real-time streaming through AppSync keeps responses snappy. For teams already invested in AWS, the Cognito-based authentication and DynamoDB/S3 storage provide enterprise-grade security with per-user data isolation.

Who should use this?

Development teams evaluating AI agent platforms for internal tooling will find the architecture straightforward to understand. Data analysts who need agents that can execute Python code and generate visualizations could benefit from the built-in code interpreter. Organizations exploring team-wide AI agent deployment should use this as a reference implementation to understand how Bedrock AgentCore integrates with shared storage and authentication. Avoid for production systems—maintainers explicitly flag this as a proof-of-concept not suited for hundreds of concurrent users.

Verdict

At 10 stars and clearly marked as a PoC, this repository scores 0.7% on credibility—it's an experiment, not a product. That said, the AWS samples provenance and comprehensive documentation (cost breakdowns, deployment guides, architecture diagrams) make it a solid learning resource for teams building similar systems. If you want to explore Bedrock AgentCore patterns or multi-agent orchestration, study the code. For production use, look elsewhere or build on these patterns with stronger testing and observability foundations.

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