Vector Retrieval and RAG · Public

LLMStack

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot2.3k stars

349 forks · 8 contributors

Doramagic.ai Last verification date: 2026-06-18 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-18

What is LLMStack?

LLMStack is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/trypromptly/LLMStack, https://github.com/trypromptly/LLMStack#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Repositorytrypromptly/LLMStack

2.3k stars · 349 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Overview and System Architecture

Related topics: Core Features: Apps, Agents, and Sheets, Deployment, Operations, and Troubleshooting

Source: https://github.com/trypromptly/LLMStack / Human Manual

Core Features: Apps, Agents, and Sheets

Related topics: Overview and System Architecture, AI Integrations and Data Processing

Source: https://github.com/trypromptly/LLMStack / Human Manual

AI Integrations and Data Processing

Related topics: Core Features: Apps, Agents, and Sheets, Deployment, Operations, and Troubleshooting

Source: https://github.com/trypromptly/LLMStack / Human Manual

Deployment, Operations, and Troubleshooting

Related topics: Overview and System Architecture, AI Integrations and Data Processing

Source: https://github.com/trypromptly/LLMStack / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/trypromptly/LLMStack, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars2.3k stars

Forks349 forks

Contributors8 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using LLMStack with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Feature: Governance and compliance layer for no-code agents
github / github_issue
02
Ich möchte meinen Verlag auf YouTube groß machen. Mein Verlag verkauft k
github / github_issue
03
Bug in file chat app
github / github_issue
04
llmstack doesn't start on a fresh install.
github / github_issue
05
very buggy
github / github_issue
06
Docker CLI fails on Windows due to NamedTemporaryFile lock — unable to s
github / github_issue
07
LLMStack fails to start when performing migration on fresh install
github / github_issue
08
https://docs.trypromptly.com/llmstack/development Getting error when run
github / github_issue
09
connection to server at "postgres" (172.18.0.3), port 5432 failed: FATAL
github / github_issue
10
Disallowed Host - invalid host header - you may need to add ... to ALLOW
github / github_issue
11
v0.2.6
github / github_release
12
v0.2.5
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install llmstack

Official start command · https://github.com/trypromptly/LLMStack#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

LLMStack Manual

No-code multi-agent framework to build LLM Agents, workflows and applications with your data

Open the full manual

https://github.com/trypromptly/LLMStack Project Manual
Table of Contents
Overview and System Architecture
Related Pages
1. Project Purpose and Scope
2. High-Level Runtime Architecture
3. Static Assets, Storage, and Documentation Layer
4. Deployment, Release Cadence, and Known Operational Issues

Overview and System Architecture

Related topics: Core Features: Apps, Agents, and Sheets, Deployment, Operations, and Troubleshooting

Source: https://github.com/trypromptly/LLMStack / Human Manual

Core Features: Apps, Agents, and Sheets

Related topics: Overview and System Architecture, AI Integrations and Data Processing

Source: https://github.com/trypromptly/LLMStack / Human Manual

AI Integrations and Data Processing

Related topics: Core Features: Apps, Agents, and Sheets, Deployment, Operations, and Troubleshooting

Source: https://github.com/trypromptly/LLMStack / Human Manual

Deployment, Operations, and Troubleshooting

Related topics: Overview and System Architecture, AI Integrations and Data Processing

Source: https://github.com/trypromptly/LLMStack / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.