llama-index-storage-kvstore-postgres
llama-index kvstore postgres integration
Decision gist · record as of 2026-08-14
Yes, if you are building a LlamaIndex application that requires persistent storage and already use or plan to use PostgreSQL. The low install friction, permissive license, and active maintenance make it a straightforward choice for that use case. Not necessary if you are prototyping with in-memory storage or using a different kvstore backend.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; PostgreSQL server must be accessible at connection time.
- Low install friction with three straightforward runtime dependencies.
- Active maintenance status as of March 2026, though repository metadata is not publicly available for independent verification.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most deployment contexts.
last release 2026-03-12 (155 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 151,031 downloads/mo, #10,946 on PyPI
Alternatives
Verify before relying
pip install llama-index-storage-kvstore-postgres
from llama_index.storage.kvstore.postgres import PostgresKVStore
kvstore = PostgresKVStore.from_connection_string(
"postgresql://user:password@localhost/dbname"
)- Whether this package is actively maintained by LlamaIndex maintainers or the broader community
- Performance characteristics and scalability limits for large-scale kvstore operations
- Whether asyncpg and psycopg2-binary can coexist without conflicts in the same environment
What it is and what it does
This package is a storage adapter that connects LlamaIndex to PostgreSQL, allowing you to persist key-value data (such as indexed documents and embeddings) in a relational database instead of memory or local files. It bridges LlamaIndex's kvstore abstraction layer with PostgreSQL, using asyncpg for async operations and psycopg2-binary for synchronous access.
Typically used when building LlamaIndex applications that need durable, queryable storage across application restarts or when sharing indexed data across multiple processes. The package handles the schema and connection logic, so you interact with it through LlamaIndex's standard kvstore interface rather than writing SQL directly.
Use it for
- Store and retrieve embeddings and indexed documents in PostgreSQL for production LlamaIndex applications
- Share indexed data across multiple LlamaIndex workers or services via a central PostgreSQL database
- Persist LlamaIndex caches and metadata durably across application restarts without rebuilding indexes
- Integrate LlamaIndex with existing PostgreSQL infrastructure in enterprise environments
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a LlamaIndex application that requires persistent storage and already use or plan to use PostgreSQL.
The low install friction, permissive license, and active maintenance make it a straightforward choice for that use case. Not necessary if you are prototyping with in-memory storage or using a different kvstore backend.
Install
llama-index-storage-kvstore-postgres on PyPI
Before you install
Low install friction with three straightforward runtime dependencies. Active maintenance status as of March 2026, though repository metadata is not publicly available for independent verification.
Requires Python 3.10 or later; PostgreSQL server must be accessible at connection time.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most deployment contexts.
Quickstart
pip install llama-index-storage-kvstore-postgres
from llama_index.storage.kvstore.postgres import PostgresKVStore
kvstore = PostgresKVStore.from_connection_string(
"postgresql://user:password@localhost/dbname"
)Verify before relying
- Whether this package is actively maintained by LlamaIndex maintainers or the broader community
- Performance characteristics and scalability limits for large-scale kvstore operations
- Whether asyncpg and psycopg2-binary can coexist without conflicts in the same environment
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesasyncpgllama-index-corepsycopg2-binary |
| Maintenance | Actively maintained 155 days since the last release |
| First released | |
| Downloads | 151,031 / month, #10,946 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_storage_kvstore_postgres-0.5.0-py3-none-any.whl
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See also llama-index-storage-docstore-postgres · tentaclio-postgres · llama-index-vector-stores-chroma · llama-index-vector-stores-redis · llama-index-vector-stores-lancedb · llama-index-vector-stores-qdrant · llama-index-vector-stores-milvus · llama-index-embeddings-langchain · beam-nuggets · pipestat