krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-25 14:08:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

add links

Browse Source
This commit is contained in:
David Myriel
2026-09-14 09:06:08 +02:00
parent cfd76b8f23
commit 98fc925b77
2 changed files with 6 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/agentic-builders-guide.md
+2
View File
@@ -100,6 +100,8 @@ An agent is only as good as the tools it has access to. For the memory and retri
Agentic memory systems like [mem0](https://docs.mem0.ai/components/vectordbs/dbs/qdrant) or [Cognee](https://docs.cognee.ai/examples/documentation-intelligence#scenario-intelligent-documentation-assistant-to-built-with-qdrant) are designed to give agents out-of-the-box long-term and short-term memory capabilities. You can easily drop in Qdrant as the vector search engine to power these systems. Qdrant powers the indexing, storing, and retrieval of data, letting the memory system focus on edits, summaries, and deciding what information to remember and what information can be forgotten.
For a memory-system perspective on what to look for in the vector layer, the Cognee team wrote a guide to [choosing a vector database for search, RAG, and AI memory](https://www.cognee.ai/best-vector-database), including where Qdrant fits.
Agentic builders like [CrewAI](https://docs.crewai.com/en/tools/database-data/qdrantvectorsearchtool), [Dust](https://dust.tt/), [Voiceflow](https://www.voiceflow.com/), [Lyzr](https://www.lyzr.ai/), [n8n](https://n8n.io/), and [Cognigy](https://www.cognigy.com/) allow you to orchestrate your agentic flow. With these tools you can define multi-step, multi-agent workflows, and by adding Qdrant they gain a tool for grounding responses with your data, recalling past interactions, and searching multimodal knowledge. Qdrant provides the speed and precision needed for your workflow to be trustworthy as it scales.
### **Evaluation Frameworks**
qdrant-landing/content/articles/turboquant-quantization.md
+4
View File
@@ -216,3 +216,7 @@ TurboQuant gives Qdrant a new path on the compression ladder: 8x compression at
* [Interactive TurboQuant explainer](https://arkaung.github.io/interactive-turboquant/) by Arkar Min Aung — a hands-on, step-by-step walkthrough of the algorithm with interactive visualizations. The clearest high-level explanation of TurboQuant available, and a great place to build intuition before reading the paper.
* [Scalar Quantization in Qdrant](https://qdrant.tech/articles/scalar-quantization/) — the int8 baseline this post refers to.
* [Binary Quantization in Qdrant](https://qdrant.tech/articles/binary-quantization/) — the 1-bit baseline this post refers to.
**Qdrant in agent memory:**
* [Qdrant TurboQuant Cuts Cognee Vector Memory 8x](https://www.cognee.ai/qdrant-turboquant-vector-memory) — how [Cognee](https://www.cognee.ai/) uses TurboQuant in Qdrant to shrink the vector memory behind its agent memory layer.