MANAGED DATABASES · FOR AI AGENTS
turbopuffer
Maintain vector and text retrieval over a hosted search index.
Store documents and vectors in turbopuffer namespaces and query them through its API or SDKs, combining search ranking with metadata filters.
Your integration checklist
- Capabilities
- What you need from your owner
- Create an account and API key, select a region and namespace, and define document identifiers, searchable fields and the embedding approach for your corpus.
- Authentication
- HTTP requests use an Authorization bearer API key. Keep credentials server-side and ensure each request targets the intended regional endpoint and namespace.
- Your first task
- Write a small authorized test corpus and query a known document using both its expected relevance and an access filter.
- Billing & payment
- Plans have minimum monthly usage commitments, with charges driven by stored, written and queried data. Features such as namespace pinning and additional vector indexes affect the calculation.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Cold queries can be slower than cached queries. Namespace and request limits apply. Retrieval quality depends on corpus coverage, schema and embeddings; search ranking is not a factual correctness guarantee.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Test access filters and cold retrieval before scaling the corpus
Editorial recommendation · Reviewed 2026-10-19
When to choose this service
Choose turbopuffer for a managed persistent search index when operating search infrastructure would distract from the agent workflow. Compare expected query patterns and latency needs with its storage-based design.
The dependency is an operated search index with durable storage and serving capacity. Computing a similarity score locally does not provide that maintained remote database across tasks and processes.
Suggested workflow
- Choose stable document IDs and metadata that separates projects or tenants. Load a small sample with representative text and vectors.
- Run known-match, unrelated and cross-tenant queries. Measure a first query separately from repeated queries so cache behavior is visible.
- Expand ingestion in bounded batches with checkpoints. Keep source versions and embedding configuration so updates and deletions can be reconciled.
Acceptance checks before real work
- Verify that unauthorized records cannot appear despite semantic similarity.
- Keep vector dimensions and model settings consistent for a query and its index.
- Estimate cost from data shape and access patterns, not only document count.
If the workflow fails
If retrieval misses a known source, inspect ingestion, filters and model configuration before rewriting the corpus. Back off on rate limits and resume from stable IDs rather than duplicating records.