Build AI agents in minutes with our AI agent builder

Building AI agents is exciting, but getting started can be overwhelming. You need to choose an LLM provider, set up vector search, implement conversation memory, handle data persistence, and write all the integration code before you can even test your first agent. What should be a quick prototype ends up taking days of setup and configuration.

We've built something to change that. Today, we're excited to announce our AI agent builder—an interactive code generator that creates production-ready AI agents powered by Redis in minutes, not days.

Generate complete AI agent code with a conversation

Our AI agent builder is a new interactive tool built directly into our documentation at redis.io/docs/latest/develop/ai/agent-builder/. Instead of starting from scratch or piecing together examples, you have a conversation with the builder about what you want to create, and it generates complete, working code tailored to your needs.

Here's what makes it different:

• Conversational interface: Tell the builder what you want in plain language

• Working code: Get complete, tested code you can deploy immediately

• Redis-optimized: Uses Redis data structures for optimal performance

• Multiple LLM providers: Support for OpenAI, Anthropic Claude, and Llama 2

• Copy or download: Get your code instantly and start building

Two types of intelligent agents

The builder currently supports two powerful agent types:

Recommendation engines

Build personalized recommendation systems that understand user preferences and deliver relevant suggestions. Perfect for:

• E-commerce product recommendations

• Content discovery platforms

• Personalized learning paths

• Media and entertainment suggestions

The generated code includes vector similarity search using Redis, user preference tracking, and real-time recommendation updates.

Conversational assistants

Create chatbots with memory and context awareness that can hold natural conversations. Ideal for:

• Customer support automation

• Virtual assistants

• Interactive documentation

• Educational tutors

The code includes conversation history management, context retention across sessions, and intelligent response generation using your chosen LLM.

How it works

Using the agent builder is simple:

1. Visit the builder: Go to the agent builder page
2. Choose your agent type: Select recommendation engine or conversational assistant
3. Answer a few questions: The builder asks about your use case and preferences
4. Get your code: Receive complete, production-ready code in Python (with more languages coming soon)
5. Copy or download: Use the built-in buttons to save your code
6. The entire process takes just a few minutes, and you end up with code that includes:

• Redis connection and configuration

• LLM integration (OpenAI, Anthropic, or Llama 2)

• Vector search setup for semantic similarity

• Conversation memory management

• Error handling and best practices

• Detailed setup instructions

Why Redis helps you build better AI agents

AI agents need three critical capabilities: fast data access, semantic search, and reliable memory. Redis delivers all three:

• Vector search:Redis provides high-performance vector similarity search for semantic understanding. Your agents can find relevant information, match user intent, and deliver personalized results in milliseconds.
• Conversation memory: Redis streams and data structures make it easy to store and retrieve conversation history, user preferences, and context across sessions.
• Real-time performance: In-memory speed means your agents respond instantly, creating natural, engaging user experiences.

The agent builder generates code that leverages these Redis capabilities automatically, so you get optimal performance without the complexity.

From code to working agent in minutes

After you generate your code, getting it running is straightforward:

1. Set up your environment: Install Redis (or use Redis Cloud) and the required dependencies
2. Configure your API keys: Add your LLM provider credentials to environment variables
3. Test locally: Start with simple interactions to verify everything works
4. Deploy and scale: Use Redis Cloud for production deployments with built-in scaling
5. The generated code includes detailed comments and setup instructions to guide you through each step. If you want to try a different configuration or agent type, just click "Start again" and generate new code.

What's coming next

We're starting with Python because it's the most popular language for AI development, but we're already working on support for:

• JavaScript (Node.js)

• Java

• C#

We're also expanding the types of agents you can build:

• RAG (retrieval-augmented generation) agents

• Multi-agent systems

• Tool-using agents

• Custom agent architectures

Each new agent type will have the same seamless experience: describe what you want, get production-ready code.

Try it yourself

Head over to our agent builder and create your first AI agent. Whether you're building a recommendation engine or a conversational assistant, you'll have working code in minutes.

We think this is going to dramatically lower the barrier to building intelligent apps with Redis. No more wrestling with integration code. No more piecing together examples. Just describe what you want and start building.

Learn more about AI agents

If you're new to AI agents or want to understand how they work, check out our “How agents work” guide. It explains the core concepts and why Redis is the perfect foundation for intelligent agents.

Have feedback on the agent builder? Want to see a specific agent type or language? Let us know in the Redis community forums or join our Discord server.

Happy building.

More resources

• Try our AI agent builder: https://redis.io/docs/latest/develop/ai/agent-builder/

• “How AI agents work” guide: https://redis.io/docs/latest/develop/ai/agent-builder/#how-agents-work

• Redis vector search docs: https://redis.io/docs/latest/develop/interact/search-and-query/query/

• AI notebooks collection: https://redis.io/docs/latest/develop/ai/notebooks-collection/

• Explore Redis AI ecosystem integrations: https://redis.io/docs/latest/develop/ai/ecosystem-integrations/

• Join the Redis Discord: https://discord.gg/redis