Building an AI-Powered Open-Source Project Naming Agent using Strands on AWS Lambda

Introduction

Naming an open-source project is deceptively hard, it needs something creative, memorable, and unique that hasn’t already been taken on GitHub or as a domain name. This blog demonstrates building an AI-powered project naming agent using the Strands Agent framework, deployed on AWS Lambda. The agent uses Anthropic Claude 3.7 Sonnet on Amazon Bedrock to generate name suggestions and verifies availability in real-time with GitHub API lookups. The result is developers get ready-to-use project names with GitHub org and domain availability checks, all in a scalable, serverless package.

Why Strands Agents?

Strands Agents transforms large language models (LLMs) into tool-using AI agents. Instead of passively responding to prompts, agents can reason, call APIs, and return structured, actionable results.

The Agent Loop

Every agent follows a structured loop:

1. Input Processing – Parse and understand the user request.
2. Reasoning – Use the LLM to plan a response.
3. Tool Selection – Choose the right tool (e.g., HTTP request).
4. Tool Execution – Perform real-world actions (API calls).
5. Response Synthesis – Combine outputs into a coherent answer.

Ecosystem Components

• Core SDK – Base agent orchestration.
• Community Tools – Prebuilt tools (strands-agents-tools) for common tasks.
• Agent Builder – AI-powered assistant to generate new agents and tools.

Unlike simple chatbot wrappers, Strands provides multi-step reasoning, observability, and extensibility for production workloads.

Key Features of Project Naming Agent

• Intelligent Name Generation: Harnesses the creativity of Anthropic Claude 3.7 Sonnet to produce names that are not unique but also contextually relevant to the project’s domain, industry, or style guidelines. This ensures names align with branding and technical requirements.
• Real-Time Availability Checking: Directly integrates with GitHub’s API to confirm the organization’s name availability. A simple response validates usability, eliminating guesswork and reducing the risk of conflicts.
• Domain Suggestions: Goes beyond GitHub by suggesting catchy domain names, especially .com options, giving developers a complete branding package. These recommendations serve as a strong starting point for web presence.
• Serverless Architecture: Deployed on AWS Lambda, it benefits from pay-per-use pricing, automatic scaling, and built-in resilience. This makes it cost-effective for solo developers while still supporting enterprise-grade workloads.
• Flexible Integration: Designed to plug into various environments such as web apps, CLI tools, CI/CD pipelines, or IDE plugins, ensuring seamless adoption into existing workflows.

AWS Lambda Deployment Requirements

To successfully run Strands Agents inside AWS Lambda, package all dependencies into a Lambda layer. This layer ensures that the function has access to the required Python libraries.

Dependencies to include:

• strands-agents
• strands-agents-tools
• Any other required Python packages (e.g., boto3, requests)

Best Practices

• Allocate at least 512 MB of memory to the AWS Lambda function.
• Set the timeout to 30 seconds or higher, since AI model calls may take longer.
• Use Python 3.10+ runtime for compatibility with Strands SDK.

Technical WorkFlow

System Prompt

Model and Agent Setup

AWS Lambda Handler

Output

Use Cases and Applications

• Startup Founders – Quickly generate and validate project names across GitHub and domain platforms, saving valuable time during the early stages of product development. Instead of manually searching for availability, founders can instantly receive verified suggestions that speed up branding decisions.
• Development Teams – Integrate the agent into project initialization workflows or CI/CD pipelines to ensure consistent and unique naming conventions across repositories. This prevents conflicts and improves collaboration.
• Developer Tools – Perfect for embedding into IDE plugins, command-line tools, or project scaffolding scripts, enabling developers to automatically generate professional project names as part of their coding workflow.
• Educational Platforms – Students and educators can use the tool to create open-source projects with professional names, ensuring their repositories look polished and credible.
• Enterprise Initiatives – Large organizations can standardize internal and external project naming processes, ensuring compliance with branding guidelines and preventing duplication.

Conclusion

This AI-powered naming agent showcases how modern LLMs and real-world API integrations can solve a common developer challenge, securing unique, available project names. With Strands, developers gain a production-ready framework that streamlines tool orchestration, observability, and extensibility. Deployed on AWS Lambda, the solution delivers scalability, reliability, and cost efficiency, making it accessible for startups and enterprises.

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