---
title: "llmflows"
type: "tool"
slug: "stoyan-stoyanov-llmflows"
canonical_url: "https://www.graphcanon.com/tools/stoyan-stoyanov-llmflows"
github_url: "https://github.com/stoyan-stoyanov/llmflows"
homepage_url: "https://llmflows.readthedocs.io"
stars: 706
forks: 35
primary_language: "Python"
license: "MIT"
categories: ["inference-serving", "evaluation-observability"]
tags: ["llms", "llm", "ai", "machine-learning", "gpt-4", "chatgpt", "openai", "prompt-engineering"]
updated_at: "2026-07-07T18:49:29.012507+00:00"
---
# llmflows
> Simple, Explicit and Transparent LLM Apps
LLMFlows is a framework that allows the creation of clear, transparent applications using large language models (LLMs), offering minimalistic abstractions for monitoring, maintenance, and debugging.
## Facts
- Repository: https://github.com/stoyan-stoyanov/llmflows
- Homepage: https://llmflows.readthedocs.io
- Stars: 706 · Forks: 35 · Open issues: 19 · Watchers: 10
- Primary language: Python
- License: MIT
- Last pushed: 2025-02-20T16:53:45+00:00
## Categories
- [Inference & Serving](/categories/inference-serving.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)
## Tags
llms, llm, ai, machine-learning, gpt-4, chatgpt, openai, prompt-engineering
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## README (excerpt)
```text
Documentation: https://llmflows.readthedocs.io
PyPI: https://pypi.org/project/llmflows
Twitter: https://twitter.com/LLMFlows
Substack: https://llmflows.substack.com
## 🤖 About LLM Flows
LLMFlows is a framework for building simple, explicit, and transparent LLM(Large Language Model) applications such as chatbots, question-answering systems, and agents.
At its core, LLMFlows provides a minimalistic set of abstractions that allow you to utilize LLMs and vector stores and build well-structured and explicit apps that don't have hidden prompts or LLM calls. LLM Flows ensures complete transparency for each component, making monitoring, maintenance, and debugging easy.
## 📦 Installation
```
pip install llmflows
```
## 🧭 Philosophy
### **Simple**
Our goal is to build a simple, well-documented framework with minimal abstractions that
allow users to build flexible LLM-powered apps without compromising on capabilities.
### **Explicit**
We want to create an explicit API enabling users to write clean and readable code while
easily creating complex flows of LLMs interacting with each other. LLMFlows' classes
give users full control and do not have any hidden prompts or LLM calls.
### **Transparent**
We aim to help users have full transparency on their LLM-powered apps by providing
traceable flows and complete information for each app component, making it easy to
monitor, maintain, and debug.
## ▶️ Live Demo
Check out [LLM-99](https://llm-99.vercel.app/) - a demo app that uses LLMs to explain
superconductors in simple terms. The app is built with LLMFlows, and FastAPI and uses
Pinecone to store document embeddings created from Wikipedia articles. You can find
the source code for this demo app and other examples in our
[examples folder](https://github.com/stoyan-stoyanov/llmflows/tree/main/examples/llmflows_in_fastapi/superconductor).
## 🧪 Getting Started
### LLMs
LLMs are one of the main abstractions in LLMFlows. LLM classes are wrappers around LLM
APIs such as OpenAI's APIs. They provide methods for configuring and calling these APIs,
retrying failed calls, and formatting the responses.
```python
from llmflows.llms import OpenAI
llm = OpenAI(api_key="")
result, call_data, model_config = llm.generate(
prompt="Generate a cool title for an 80s rock song"
)
```
### PromptTemplates
The `PromptTemplate` class allows us to create strings with variables that we can fill
in dynamically later on. Once a prompt template object is created an actual prompt can
be generated by providing the required variables.
```python
from llmflows.llms import OpenAI
from llmflows.prompts import PromptTemplate
prompt_template = PromptTemplate(
prompt="Generate a title for a 90s hip-hop song about {topic}."
)
llm_prompt = prompt_template.get_prompt(topic="friendship")
print(llm_prompt)
llm = OpenAI(api_key="")
song_title = llm.generate(llm_prompt)
print(song_title)
```
### Chat LLMs
Unlike regular LLMs that only require a prompt to generate text, chat LLMs require a
conversation history. The conversation history is represented
as a list of messages between a user and an assistant. This conversation history is
sent to the model, and a new message is generated based on it.
LLMFlows provides a `MessageHistory` class to manage the required conversation history
for chat LLMs.
You can build a simple chatbot by using the `OpenAIChat` and `MessageHistory` classes:
```python
from llmflows.llms import OpenAIChat, MessageHistory
llm = OpenAIChat(api_key="")
message_hist
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/stoyan-stoyanov-llmflows`](/api/graphcanon/tools/stoyan-stoyanov-llmflows)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_