Add Infino integration (provider, vector store, registry)

packages.yml

@@ -797,3 +797,8 @@ packages:
   js: "n/a"
   downloads: 188
   downloads_updated_at: '2026-06-15T00:31:43.789982+00:00'
+- name: langchain-infino
+  repo: infino-ai/langchain-infino
+  js: "@infino-ai/langchain-infino"
+  downloads: 0
+  downloads_updated_at: "2026-06-15T00:31:43.789982+00:00"

src/oss/python/integrations/providers/infino.mdx

@@ -0,0 +1,70 @@
+---
+title: "Infino integrations"
+description: "Integrate with Infino using LangChain Python."
+---
+
+>[Infino](https://github.com/infino-ai/infino) is a retrieval engine that runs
+>SQL, full-text (BM25), vector, and hybrid (RRF) search over **one copy** of
+>your data in Apache Parquet on object storage, with no separate vector database
+>and search cluster to provision, sync, or keep consistent.
+
+The `langchain-infino` package surfaces that whole retrieval surface, not just
+`similarity_search`: semantic search *and* exact-keyword BM25 *and* their
+fusion, from a single in-process engine. Infino never embeds; you bring a
+LangChain `Embeddings` object and the integration supplies the vectors.
+
+## Installation and setup
+
+<CodeGroup>
+```bash pip
+pip install langchain-infino
+```
+
+```bash uv
+uv add langchain-infino
+```
+</CodeGroup>
+
+Infino runs in-process, so there are no credentials or API keys. A connection is
+a local path or an `s3://` URI for durable storage (`memory://` is ephemeral):
+
+```python
+import infino
+
+connection = infino.connect("./data")
+```
+
+## Vector store
+
+`InfinoVectorStore` wraps a single Infino table: the text, its embedding, the
+document id, declared metadata columns, and a JSON catch-all. Vector, filtered,
+MMR, and hybrid retrieval all run over that one table.
+
+```python
+from langchain_infino import InfinoVectorStore
+```
+
+For a detailed walkthrough, see the [InfinoVectorStore page](/oss/integrations/vectorstores/infino).
+
+## Retriever
+
+Beyond `as_retriever()` (vector), the store exposes lexical and fused
+retrievers, plus a self-query translator that lowers an LLM's structured query
+to a SQL `WHERE` over the declared metadata columns:
+
+```python
+from langchain_infino import (
+    InfinoBM25Retriever,
+    InfinoHybridRetriever,
+    InfinoTranslator,
+)
+```
+
+## LLM cache
+
+`InfinoSemanticCache` caches model responses keyed by prompt meaning, backed by
+one small Infino table:
+
+```python
+from langchain_infino import InfinoSemanticCache
+```

src/oss/python/integrations/vectorstores/index.mdx

@@ -578,6 +578,32 @@ vector_store = ElasticsearchStore(
 )
 ```
 
+</Accordion>
+<Accordion title="Infino">
+
+<CodeGroup>
+```bash pip
+pip install -qU langchain-infino
+```
+
+```bash uv
+uv add langchain-infino
+```
+</CodeGroup>
+```python
+import infino
+from langchain_infino import InfinoVectorStore
+
+connection = infino.connect("./data")  # local path, or "s3://my-bucket/kb"
+
+vector_store = InfinoVectorStore.from_texts(
+    [],
+    embeddings,
+    connection=connection,
+    table_name="support_kb",
+    dim=1536,
+)
+```
 </Accordion>
 <Accordion title="Milvus">