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Basic Information

LLMStack is a no-code platform and toolkit for building tailor-made generative AI agents, applications and chatbots by chaining multiple language models. It is designed to let users connect models to their own data sources and internal tools without writing code, and to deploy solutions either in the cloud or on-premise. The repo provides the core application, a no-code builder and components to import and preprocess data, create multi-step AI chains, and expose resulting apps via HTTP APIs or chat integrations. It supports triggering chains from collaboration platforms such as Slack and Discord and includes multi-tenant organization and user management. The project also documents local deployment, a pip-installable package, runtime dependencies like a background Docker jobs container, and default local admin credentials for initial setup.

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App Details

Features
LLMStack bundles a no-code agent builder that lets users chain multiple LLMs together and connect them to external tools and data. Data ingestion supports common formats such as CSV, TXT, PDF, DOCX and PPTX and sources including direct uploads, Google Drive, Notion and websites; the platform handles preprocessing and vectorization and provides a built-in vector database. It provides API access for apps and chatbots, Slack and Discord triggers, multi-tenant organization support and an admin panel for user and org management. Deployment options include local, on-premise and cloud hosting, with a cloud offering available. The repo notes a background Docker container requirement for jobs and offers installation via pip and a CLI that launches a local server on port 3000.
Use Cases
LLMStack helps teams and non-developers rapidly prototype and deploy generative AI workflows and agents for business use cases. Examples include AI SDRs for personalized outreach, research analyst agents that synthesize reports, RPA automations that fill forms and automate processes, text generation tools for marketing content, and chatbots trained on private documents. It simplifies operational tasks by handling data ingestion, preprocessing, vectorization and model chaining so organizations can focus on prompts and flows. Multi-tenant controls and on-premise deployment options support privacy and enterprise governance. API and messaging platform integrations let organizations embed agents into existing workflows, and the pip package with a local server aids testing and internal deployments.

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