AI Agents

Unlock the full potential of your AI agents with Qdrant’s powerful vector search and scalable infrastructure, allowing them to handle complex tasks, adapt in real time, and drive smarter, data-driven outcomes across any environment.

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AI Agents with Qdrant

AI agents powered by Qdrant leverage advanced vector search to access and retrieve high-dimensional data in real-time, enabling intelligent, Agentic-RAG driven, multi-step decision-making across dynamic environments.

Multimodal Data Handling

Qdrant enables AI agents to process and retrieve high-dimensional vectors from diverse data types (text, images, audio), supporting more comprehensive decision-making in multimodal environments.

Adaptive Learning

Qdrant supports continuous learning by enabling efficient vector retrieval and updates, allowing agents to learn and evolve based on real-time interactions and new data points.

Qdrant equips AI agents to adapt, learn, and collaborate efficiently.

Contextual Precision

Qdrant’s hybrid search combines semantic vector search, lexical search, and metadata filtering, enabling AI Agents to retrieve highly relevant and contextually precise information. This enhances decision-making by allowing agents to leverage both meaning-based and keyword-based strategies, ensuring accuracy and relevance for complex queries in dynamic environments.

Hybrid Search

Multi-Agent Systems

Qdrant’s scalability and multitenancy ensures that multiple agents can collaborate in distributed systems, enabling seamless coordination and communication - key for Agentic RAG workflows.

Multitenancy

Real Time Decision Making

Qdrant’s real-time, advanced vector search enables AI agents to act instantly on live data, which is crucial for time-sensitive, autonomous decision-making.

HNSW

Optimized CPU Performance for Embedding Processing

Qdrant’s architecture is optimized for high-throughput embedding processing, minimizing CPU load and preventing performance bottlenecks. This enables AI agents in Agentic RAG workflows to execute complex, multi-step tasks efficiently, ensuring smooth operation even at scale.

Distributed Deployment

Semantic Cache for Rapid Query Handling

Qdrant enhances AI agent efficiency with semantic caching, which preserves results of queries based on semantic equivalence rather than exact matches. This method reduces query processing times and system load by reusing previously computed answers, essential for high-throughput AI applications.

Semantic Cache

Qdrant integrates with the leading AI Agent frameworks

LangGraph

Framework for managing multi-LLM workflows with structured graphs for AI agents.

Swarm

Decentralized platform enabling collaboration among AI agents for task completion.

CrewAI

Team-based AI agent collaboration system orchestrating multi-agent workflows efficiently.

AutoGen

Automation tool for generating AI agent workflows and automating complex tasks.

On-demand Webinar

Building Agents with LlamaIndex & Qdrant

Ready to build more advanced AI agents? Watch this webinar to learn how to use LlamaIndex and Qdrant to create intelligent agents capable of handling complex, multi-modal queries in RAG-enabled systems.

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Learn how to get started with Qdrant for your AI Agent use case

Comparing AI Agent Frameworks: LangGraph, CrewAI, Swarm, and AutoGen

This guide offers a comparison of key AI agent frameworks, highlighting their strengths and ideal use cases for developers.

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Building OpenAI Swarm Agents with Qdrant

Learn how to build OpenAI Swarm agents using Qdrant for fast, scalable vector search and real-time actions.

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Building an AI Scheduler with Zoom, CrewAI, and Qdrant

Learn how to build an AI meeting scheduler with Zoom, LlamaIndex, and Qdrant, featuring a hands-on RAG recommendation engine code sample.

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QA.tech Case Study: AI Agents for Web Testing

QA.tech enhanced web app testing by deploying AI agents that mimic user interactions. To handle high-speed actions and make real-time decisions, they integrated Qdrant for scalable vector search, allowing for faster and more efficient data proc...

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On-demand Webinar

Building AI Agents for personalized recommendations with Qdrant and n8n

Learn in this video how to build an AI-powered recommendation system using Qdrant and n8n. It demonstrates how an AI agent retrieves data from Qdrant's vector database and leverages a large language model (LLM) to generate personalized recommendations based on user inputs.

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Building AI agents?

Apply for the Qdrant for Startups program to access a 20% discount to Qdrant Cloud, our managed cloud service, perks from Hugging Face, LlamaIndex, and Airbyte, and much more.

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