Qdrant

Data & Analytics

Neotask on OpenClaw adds vector memory to your automation stack via Qdrant — storing knowledge and retrieving semantically similar content so your agents learn and recall like humans.

• Agent memory persists and compounds — your agent stores information in Qdrant and retrieves semantically relevant context on demand, giving every workflow access to accumulated knowledge
• Semantic search replaces brittle keyword lookups — your agent finds conceptually related content rather than exact matches, surfacing what matters not just what was typed
• Knowledge bases grow and stay useful — documents, decisions, and outputs get stored in Qdrant as your agents work, creating a searchable operational memory that improves over time

What You Can Do

The Qdrant integration gives Neotask 2 vector database actions for storing and retrieving information semantically.

`qdrant-store` — save any content to Qdrant as a vector embedding with metadata and collection assignment

`qdrant-find` — search Qdrant for content semantically similar to a query, returning the most relevant stored items

Every action runs autonomously or requires your approval — you decide.

Try Asking

"Store the output of today's strategy meeting in Qdrant under the 'decisions' collection"

"Find everything we've stored in Qdrant related to our pricing strategy decisions"

"Search our knowledge base for content similar to 'enterprise onboarding challenges'"

"Store this customer interview transcript in Qdrant and tag it with the customer segment"

Pro Tips

Pair Qdrant with every agent workflow that produces valuable output — store summaries, decisions, and research so future agents can retrieve them

Use `qdrant-find` as the memory layer for multi-agent teams: a research agent stores findings and a synthesis agent retrieves and combines them

Build semantic search into customer support workflows — your agent finds the most relevant past solutions before generating a new answer

Collections let you organize knowledge by domain: separate collections for product decisions, customer insights, engineering patterns, and marketing research