Memory and retrieval for AI agents
Build AI Agents With Memory That Scales
Give every AI agent fast, filtered long-term memory, whether it works alone or alongside other agents. Start free on Qdrant Cloud with no credit card.
Memory for intelligent agents
Give Your AI Agents Accurate Memory Across Every Session
A memory layer lets an agent pick up where its last session ended and reason over the right context at every step. Qdrant gives you long-term memory for AI agents with fast, accurate retrieval even under heavy filters, so memory keeps up with the agent.
Persistent AI agent memory across sessions.
Qdrant stores and indexes your vectors, so an agent's knowledge survives restarts and stays available across runs, users, and sessions.
Narrow memory to exactly what the agent needs.
Qdrant applies payload filters inside the HNSW search, so an agent can scope memory to one user, project, or time window and still get fast, accurate results.
Keep each tenant's memory separate.
Use multitenancy to give every user or agent its own slice of memory inside one collection.
Agentic retrieval
Let Autonomous AI Systems Adapt Retrieval at Query Time
In agentic RAG, the agent decides what to retrieve at each step. It might start with dense search, switch to hybrid search, tighten a filter, or re-weight scores based on what the last step returned. Qdrant exposes each retrieval primitive so a retrieval agent can combine them per query.
Combine hybrid search and filters in one query.
Hybrid search pairs semantic matching with keyword matching, and re-ranking improves the final order.
Run late-interaction models such as ColBERT.
Multi-vector support lets a retrieval agent use token-level models for higher precision.
Multi-agent systems
One Memory Layer for Multi-Agent Systems
In multi-agent systems, AI agents run in parallel and in series, and each one sends its own retrieval requests. In autonomous AI systems, a single workflow can run thousands of retrieval steps, so latency compounds and one slow result slows the whole chain.
Built for parallel retrieval.
Qdrant is written in Rust. At Lyzr, latency stays steady with 100+ concurrent agents sending over 1,000 queries per minute.
Give each agent only the access it needs.
Role-based access control (RBAC), SSO, and API keys scoped to individual collections keep agents inside their own data.
Deutsche Telekom built LMOS, a multi-agent platform, on Qdrant. It serves three countries and 2M+ conversations, and cut agent development time from 15 days to 2 days. Read the Deutsche Telekom case studyStart free with Qdrant Cloud
Deployment
Run Agent Memory Anywhere
Run agent memory where your data lives.
Cloud, hybrid, or on-prem.
Choose managed Qdrant Cloud, Hybrid Cloud, on-prem, or fully air-gapped.
Run it in your own VPC.
Keep agent memory inside your network and your jurisdiction.
Open source and portable.
The core engine is open source under the Apache 2.0 license.
Compliance built in.
Qdrant is an EU-based company, SOC 2 and GDPR compliant.
Plug into your agent framework.
Qdrant integrates with CrewAI, LlamaIndex, Haystack, LangChain, and Autogen.
Index on GPUs.
Qdrant Cloud supports GPU indexing, so large agent memories are searchable sooner.
Getting started
Build an AI Agent With Long-Term Memory
Create a cluster.
Start free on Qdrant Cloud with no credit card, or run Qdrant in a single Docker container.
Ingest your data.
Embed documents, conversation history, or images with your model of choice, and store them with payload fields your agent can filter on.
Connect your agent.
Point your [AI agent framework]() at the collection and query with filters or hybrid search as each step requires.
Common Questions
How do I get started with Qdrant Cloud?
How do I give my AI agents long-term memory?
Can every intelligent agent share one memory?
