LLMStack
No-code multi-agent framework to build LLM Agents, workflows and applications with your data
GraphCanon updated 3d · GitHub synced 3d
Decision brief
LLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise.
Good fit when
- Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models.
- Ideal for scenarios where quick development and deployment of generative AI applications are required without requiring significant programming knowledge.
Avoid when
- Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives.
- Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (612d since push)
- As of 3d
- Provenance
- Not a fork · Organization account
- As of 3d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install LLMStack PyPIHow it fits your stack(4)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Offers a no-code environment for creating AI agents, leveraging large language models to develop complex workflows and applications without coding.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 16, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 16, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Getting Started
Check out our Cloud offering at Promptly or follow the instructions below to deploy LLMStack on your own infrastructure.
LLMStack deployment comes with a default admin account whose credentials are admin and promptly. Be sure to change the password from admin panel after logging in.
Installation
Prerequisites
LLMStack depends on a background docker container to run jobs. Make sure you have Docker installed on your machine if want to use jobs. You can follow the instructions here to install Docker.
Install LLMStack using pip
pip install llmstack
If you are on windows, please use WSL2 (Windows Subsystem for Linux) to install LLMStack. You can follow the instructions here to install WSL2. Once you are in a WSL2 terminal, you can install LLMStack using the above command.
Start LLMStack using the following command:
llmstack
Above commands will install and start LLMStack. It will create .llmstack in your home directory and places the database and config files in it when run for the first time. Once LLMStack is up and running, it should automatically open your browser and point it to localhost:3000.
You can add your own keys to providers like OpenAI, Cohere, Stability etc., from Settings page. If you want to provide default keys for all the users of your LLMStack instance, you can add them to the
~/.llmstack/configfile.
For agents
This page has a .md twin and JSON over the API.