turbopuffer
The official Python library for the turbopuffer API
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, uses a permissive MIT license, and provides a clean, type-safe interface to a vector database service. Install it if you need to query or manage vector embeddings via the turbopuffer API from Python.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Requires a turbopuffer API key (set via TURBOPUFFER_API_KEY environment variable or passed to the client).
- Low friction installation with a pure Python wheel.
License · maintenance · safety
MIT (permissive) — MIT license (permissive). You can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
last release 2026-08-04 (10 days) · last repo commit 2026-08-12 · 159 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,383,786 downloads/mo, #2,315 on PyPI
Alternatives
Verify before relying
pip install turbopuffer
import os
from turbopuffer import Turbopuffer
tpuf = Turbopuffer(
region="gcp-us-central1",
api_key=os.environ.get("TURBOPUFFER_API_KEY"),
)
ns = tpuf.namespace("example")
vector_result = ns.query(
rank_by=("vector", "ANN", [0.1, 0.2]),
top_k=10,
)
print(vector_result.rows)- Whether the aiohttp extra (turbopuffer[aiohttp]) is recommended for production async workloads or optional for most use cases.
- Performance characteristics and latency expectations for vector queries at scale.
- Rate limiting and quota behavior when exceeding API limits.
What it is and what it does
turbopuffer is the official Python client for the turbopuffer vector database API. It wraps HTTP endpoints with type-safe, auto-completing request and response objects built on pydantic, and offers both synchronous and asynchronous clients powered by httpx. The library is generated by Stainless and supports Python 3.9 and later.
You use it to query vectors by nearest-neighbor search (ANN), perform full-text search on indexed attributes, upsert and manage vector embeddings in namespaces, and handle pagination across result sets. Both sync and async clients have identical functionality; the async variant can optionally use aiohttp as its HTTP backend for improved concurrency. Error handling distinguishes between connection failures, rate limits, and API status errors.
Use it for
- Build a semantic search feature by querying nearest neighbors to an embedding vector.
- Implement hybrid search combining vector similarity with full-text BM25 ranking.
- Manage and upsert large batches of embeddings into isolated namespaces.
- Integrate vector search into async Python applications without blocking.
- Prototype or deploy AI/ML applications that rely on vector similarity at query time.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, installs with low friction, uses a permissive MIT license, and provides a clean, type-safe interface to a vector database service. Install it if you need to query or manage vector embeddings via the turbopuffer API from Python.
Install
turbopuffer on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance—last commit 2026-08-12, release 10 days old. Depends on httpx, pydantic, and several async/serialization utilities; all are stable, widely-used packages.
Requires Python 3.9 or later. Requires a turbopuffer API key (set via TURBOPUFFER_API_KEY environment variable or passed to the client).
License in practice
MIT license (permissive). You can use, modify, and distribute this package freely in commercial or private projects with minimal restrictions.
Quickstart
pip install turbopuffer
import os
from turbopuffer import Turbopuffer
tpuf = Turbopuffer(
region="gcp-us-central1",
api_key=os.environ.get("TURBOPUFFER_API_KEY"),
)
ns = tpuf.namespace("example")
vector_result = ns.query(
rank_by=("vector", "ANN", [0.1, 0.2]),
top_k=10,
)
print(vector_result.rows)Verify before relying
- Whether the aiohttp extra (turbopuffer[aiohttp]) is recommended for production async workloads or optional for most use cases.
- Performance characteristics and latency expectations for vector queries at scale.
- Rate limiting and quota behavior when exceeding API limits.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesaiohttpanyiodistrohttpxorjsonpybase64pydanticsniffiotyping-extensions |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,383,786 / month, #2,315 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: POSIXOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: turbopuffer-2.8.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “async vector database client”
- turbopufferProvides a Python client library for the turbopuffer vector database…
- pineconePinecone Python SDK provides a client for creating and managing…
- qdrant-clientPython client library for Qdrant vector search engine, supporting…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also supermemory · redisvl · upstash-vector · pagefind · pyobvector · voyager · google-cloud-vectorsearch · annoy · nano-vectordb · pinecone