LLMApp
by Community
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, re
OSS
LLMApp
Added 1 June 2026
Overview
LLMApp provides cloud-ready templates for building RAG systems, AI pipelines, and enterprise search that sync live with external data sources. It connects to Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time APIs, keeping indexed data current without manual refresh. Docker-based deployment enables quick local or cloud setup.
Best for
Best for
Teams building enterprise search or RAG systems that need live data synchronization without custom connector development.
Use cases
- Building retrieval-augmented generation systems over live enterprise documents
- Creating search interfaces that stay synchronized with multiple data sources
- Deploying AI pipelines that ingest streaming data from Kafka or APIs
Notes
LLMApp provides cloud-ready templates for building RAG systems, AI pipelines, and enterprise search that sync live with external data sources. It connects to Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time APIs, keeping indexed data current without manual refresh. Docker-based deployment enables quick local or cloud setup.
59,487 stars on GitHub. Last updated 2026-01-07. Licensed MIT.
Use cases
- Building retrieval-augmented generation systems over live enterprise documents
- Creating search interfaces that stay synchronized with multiple data sources
- Deploying AI pipelines that ingest streaming data from Kafka or APIs
Pros
- Pre-built templates reduce setup time for common RAG and search patterns
- Native connectors to major enterprise and cloud storage systems
- Docker containerization simplifies deployment and local development
Cons
- Community project with 59k stars but no commercial support guarantee
- Limited to Jupyter Notebook as primary language, which may constrain production workflows
- Requires managing external data source credentials and connection maintenance
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Pre-built templates reduce setup time for common RAG and search patterns
- Native connectors to major enterprise and cloud storage systems
- Docker containerization simplifies deployment and local development
Cons
- Community project with 59k stars but no commercial support guarantee
- Limited to Jupyter Notebook as primary language, which may constrain production workflows
- Requires managing external data source credentials and connection maintenance
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