How Redis, Tavily, & IBM’s BeeAI supercharge AI apps

The next wave of AI apps demands more than just powerful models. Developers need a foundation that can orchestrate complex workflows, retrieve the right information instantly, and hold on to context without slowing down. No single tool delivers all of that, which is why open, modular stacks matter.

That’s the approach we showcased in a recent hands-on workshop with IBM Research’s BeeAI framework and Tavily: combining complementary technologies to build retrieval-augmented generation (RAG) apps that are fast, reliable, and production-ready. BeeAI serves as the AI agent framework and the essential glue that makes the rich context from Redis and Tavily seamlessly consumed by large language models, resulting in more accurate and contextually aware responses.

At the heart of the stack is Redis. With the Redis Vector Library (RedisVL), it moves beyond a simple database to serve as the memory and retrieval layer for AI. RedisVL brings vector search, semantic caching, and routing into one place, allowing developers to reduce both latency and cost. In other words, the same value Redis has always delivered for caching in apps now extends to the AI layer.

Explore the full workshop materials here.

What we built

Participants walked through building an end-to-end RAG workflow that integrated:

• BeeAI for orchestration: a production-ready agent framework for building AI agents.
• Tavily for retrieval: an API that provides high-quality, web-scale search results with relevance scoring, built for AI Agents and RAG pipelines.
• Redis with RedisVL as the memory layer for RAG and vector search: delivering low-latency ingestion, indexing, and querying of embeddings.

By nature, LLMs (the “brains” of AI Agents) are not deterministic, but the essence of the agent flow looked something like this:

1. A user query is orchestrated through a BeeAI agent (enabled with memory, instructions, and tools).
2. The LLM has access to a Tavily MCP tool if it needs to fetch up-to-date information from the internet.
3. The BeeAI Agent also has access to a RAG tool, which has synthetically created internal documents that were embedded, stored, and indexed in Redis’ vector database using RedisVL.
4. The LLM decides what tools need to be called, and the BeeAI framework orchestrates the tool calls and returns the tool results to the model. This, in turn, provides a more accurate, grounded response to the user’s original query.

Notably, the Redis database in the BeeAI workshop was created using the free Redis Cloud tier, which spins up in seconds and instantly provides access to all of Redis’ out-of-the-box capabilities. That ease of setup meant developers could focus entirely on building the RAG pipeline rather than managing infrastructure.

Why these technologies work better together

At the heart of this collaboration is a simple idea: For AI apps to be successful, orchestration, retrieval, and memory must work in harmony.

• BeeAI makes it easy to build production-ready AI Agents.
• Tavily ensures those workflows have access to accurate, fresh information at query time.
• Redis with RedisVL serves as the memory and retrieval layer for RAG, delivering persistence, real-time vector search, and hybrid querying at scale.

Next steps for devs

Want to try it yourself? Here’s where to begin:

1. Explore the workshop → Start with the hands-on tutorial.
2. Spin up Redis Cloud for free → Create a Redis Cloud database in seconds. The free tier includes vector similarity search, full Redis capabilities, and everything you need out of the box to get started with RAG.
3. Install RedisVL → Download RedisVL to add production-ready vector search, semantic caching, and memory to your own RAG pipelines.
4. Experiment with the stack → Orchestrate with BeeAI, extend context with Tavily, and persist or store in Redis Cloud.

Looking ahead

This collaboration between Redis, IBM Research’s BeeAI, and Tavily is just the beginning. Together, we’re proving that AI innovation happens faster when companies embrace modular, open components that work better together.

Future workshops and integrations will go deeper into:

• Scaling RAG pipelines across distributed Redis clusters.
• Combining BeeAI workflows with Redis streams for event-driven orchestration.
• Leveraging Tavily retrieval with Redis hybrid queries for multi-source grounding.

The future of AI is not about monolithic stacks—it’s about plugging best-in-class components into systems that are fast, reliable, and ready for production. Redis is proud to power the RAG memory and retrieval layer in this ecosystem, and we’re excited to see what developers build next.