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llmflows

stoyan-stoyanov/llmflows

Simple, Explicit and Transparent LLM Apps

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

75views this month

708 stars35 forksLast push Feb 20, 2025 Python MIT

Decision brief

LLMFlows is a Python library designed for creating and managing LLM applications with a focus on simplicity and transparency.

Good fit when

When you need a straightforward and transparent approach to building LLM applications, LLMFlows offers a simple framework that prioritizes clarity in the development process.
If your project requires explicit management of LLM applications, LLMFlows provides a clear structure that can help maintain order and transparency in complex projects.

Avoid when

Avoid LLMFlows if you require advanced customization options that go beyond its simple and explicit design philosophy.
Do not use LLMFlows if your project demands a more complex framework that integrates a wide range of third-party services, as LLMFlows focuses on simplicity and may lack extensive integration features

Pricing:: freemium - The library is free to use, but additional services or support might incur costs.
Requirements:: Min 2 GB RAM; Python environment is required.

Observed Sep 18, 2026 · Source: enrich:decision_facts

llmflows alternatives Trust report

llmflows vs Flowise llmflows vs PocketFlow llmflows vs LLMStack

Verify the decision

Maintenance and security

Full trust report

Maintenance: Dormant (574d since push); As of Sep 18, 2026
Provenance: Not a fork · Personal account; As of Sep 18, 2026
Security (OSV): 20 low (20 low); As of Sep 18, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install llmflows

How it fits your stack(12)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

Both Flowise and LLMFlows aim to simplify development of AI agents, possibly serving similar use cases but through different methodologies.

PocketFlow is similar to LLMFlows as both aim at minimalist frameworks for building agentic AI applications.

LLMStack is a no-code platform for creating generative AI agents and applications. LLMFlows, on the other hand, offers a framework for developing straightforward and clear LLM apps. Both tools cater to building applications with large language models but differ in approach, with one being no-code and the other code-based.

Integrates

llmflows has an 'integrates with' relationship to vllm because llmflows is a framework for developing LLM applications that require efficient model serving, which vllm provides through its high-throughput and memory-efficient inference capabilities, thereby enhancing the performance of llmflows applications.

MLflow provides an open-source AI engineering platform that can integrate with LLMFlows for managing the lifecycle of machine learning models and large language model applications.

Txtai provides semantic search and language model workflows, which can integrate with LLMFlows to enhance its capabilities in building structured AI apps.

Depends on

LLMFlows likely depends on transformers for utilizing a wide variety of machine learning models including LLMs.

Related

Both are focused on making various large language models more accessible, but through different means.

Langchain-Chatchat

Local knowledge-based LLM RAG and Agent app with Langchain

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Similar tools

Same-category neighbours not already linked as typed edges.

awesome-llm-apps

Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.

semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps

agent-framework

Framework for building and deploying AI agents and multi-agent workflows

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A Python library for creating and managing LLM applications with a focus on simplicity and transparency.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 18, 2026

Categories

Inference & Serving LLM Frameworks

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 18, 2026)

pip install llmflows

Tags

ai chatgpt gpt-4 llm llm-inference llmops llms machine-learning

README

📦 Installation 📃 License LLMFlows is covered by the MIT license. For more information, check .

For agents

This page has a .md twin and JSON over the API.

/tools/stoyan-stoyanov-llmflows.md GET /api/graphcanon/tools/stoyan-stoyanov-llmflows GET /api/graphcanon/graph?tool=stoyan-stoyanov-llmflows

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