databricks-langchain
Support for Databricks AI support in LangChain
Decision gist · record as of 2026-08-14
Yes, if you are building LangChain applications on Databricks infrastructure. The package consolidates fragmented Databricks integrations and is actively maintained. The 11-dependency footprint is expected for an integration layer. No known vulnerabilities. Install only if you have a Databricks workspace and LangChain is your chosen framework; it adds no value as a standalone tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and valid Databricks workspace credentials to authenticate with Databricks endpoints.
- Low install friction; pure Python wheel.
- Active maintenance with recent release (65 days ago).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions, suitable for most production scenarios.
last release 2026-06-10 (65 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,527,468 downloads/mo, #2,592 on PyPI
Alternatives
Verify before relying
pip install databricks-langchain
from databricks_langchain import ChatDatabricks
llm = ChatDatabricks(endpoint="databricks-meta-llama-3-1-70b-instruct")
response = llm.invoke("Hello")- Whether Genie Agent features are available outside the private preview or require special enablement.
- Performance characteristics and latency when calling Databricks endpoints through LangChain.
- Compatibility matrix with specific Databricks workspace versions or LangChain versions beyond the stated requirements.
What it is and what it does
databricks-langchain bridges Databricks AI capabilities into LangChain applications, consolidating what were previously scattered across multiple packages. It provides LangChain-compatible wrappers for Databricks-hosted LLMs (like Llama and Mixtral via ChatDatabricks), vector search operations, embeddings generation, and experimental Genie integration for AI-powered analytics agents.
The package is designed for teams already using Databricks workspaces who want to build LangChain-based AI applications without managing separate authentication or API layers. It depends on langchain, databricks-sdk, and several Databricks-specific adapters (databricks-ai-bridge, databricks-mcp, databricks-openai, and others), making it a fairly heavy integration layer best suited for applications where Databricks is the primary AI infrastructure.
Use it for
- Build LangChain agents that query Databricks-hosted LLMs like Llama 3.1 without direct SDK calls.
- Create RAG pipelines using DatabricksVectorSearch for semantic search over Databricks-managed vector stores.
- Generate embeddings for documents or queries using Databricks embedding models within LangChain workflows.
- Prototype Genie-powered analytics agents that answer business questions over Databricks data (preview feature).
- Migrate existing langchain-databricks or langchain-community Databricks code to the consolidated package.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building LangChain applications on Databricks infrastructure.
The package consolidates fragmented Databricks integrations and is actively maintained. The 11-dependency footprint is expected for an integration layer. No known vulnerabilities. Install only if you have a Databricks workspace and LangChain is your chosen framework; it adds no value as a standalone tool.
Install
databricks-langchain on PyPI
Before you install
Low install friction; pure Python wheel. Active maintenance with recent release (65 days ago). Depends on 11 runtime packages including langchain, databricks-sdk, and several Databricks-specific bridges—a substantial dependency footprint typical of integration packages.
Requires Python >=3.10 and valid Databricks workspace credentials to authenticate with Databricks endpoints.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and redistribution with minimal restrictions, suitable for most production scenarios.
Quickstart
pip install databricks-langchain
from databricks_langchain import ChatDatabricks
llm = ChatDatabricks(endpoint="databricks-meta-llama-3-1-70b-instruct")
response = llm.invoke("Hello")Verify before relying
- Whether Genie Agent features are available outside the private preview or require special enablement.
- Performance characteristics and latency when calling Databricks endpoints through LangChain.
- Compatibility matrix with specific Databricks workspace versions or LangChain versions beyond the stated requirements.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesdatabricks-ai-bridgedatabricks-ai-searchdatabricks-mcpdatabricks-openaidatabricks-sdklangchain-mcp-adapterslangchainmlflowopenaipydanticunitycatalog-langchain |
| Maintenance | Actively maintained 65 days since the last release |
| First released | |
| Downloads | 3,527,468 / month, #2,592 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: databricks_langchain-0.20.0-py3-none-any.whl
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See also databricks-ai-bridge · langchain-databricks · unitycatalog-langchain · langchain-google-vertexai · databricks-openai · databricks-agents · databricks-vectorsearch · databricks-ai-search · langchain-elasticsearch · langchain-milvus