Examples, recipes, and best-practice pipelines for the Pipelex LLM Pipeline framework.
If you just want to run an example, jump to Quick Start. If you'd like to share your own pipeline, head straight to Contributing. 📚 Check out the Pipelex Documentation for more information, in particular the Cookbook examples section.
.
├── pipelex_libraries/ # Main library code
│ ├── pipelines/ # Pipeline implementations
│ │ ├── base_library/ # Core pipeline components
│ │ ├── examples/ # Example pipelines
│ │ └── quick_start/ # Quick start tutorials
│ ├── llm_deck/ # LLM deck components
│ ├── llm_integrations/ # LLM provider integrations
│ └── templates/ # Prompt templates
├── examples/ # Advanced examples (PDF extraction, expense processing, etc.)
| ├── wip/ # Work in progress examples
└── quick_start/ # Getting started tutorials
git clone https://github.com/Pipelex/pipelex-cookbook.git
cd pipelex-cookbookmake installThis will install the Pipelex python library and its dependencies using uv.
cp .env.example .envEnter your API keys into your .env file. The OPENAI_API_KEY is enough to get you started, but some pipelines require models from other providers.
We highly recommend installing our own extension for PLX files into your IDE of choice. You can find it in the Open VSX Registry. It's coming soon to VS Code marketplace too and if you are using Cursor, Windsurf or another VS Code fork, you can search for it directly in your extensions tab.
python quick_start/hello_world.pydomain = "tech_tweet"
definition = "A pipeline for optimizing tech tweets using Twitter/X best practices"
[concept]
DraftTweet = "A draft version of a tech tweet that needs optimization"
OptimizedTweet = "A tweet optimized for Twitter/X engagement following best practices"
TweetAnalysis = "Analysis of the tweet's structure and potential improvements"
WritingStyle = "A style of writing"
[pipe]
[pipe.analyze_tweet]
type = "PipeLLM"
definition = "Analyze the draft tweet and identify areas for improvement"
inputs = { draft_tweet = "DraftTweet" }
output = "TweetAnalysis"
llm = "llm_for_writing_analysis"
system_prompt = """
You are an expert in social media optimization, particularly for tech content on Twitter/X.
Your role is to analyze tech tweets and check if they display typical startup communication pitfalls.
"""
prompt_template = """
Evaluate the tweet for these key issues:
**Fluffiness** - Overuse of buzzwords without concrete meaning (e.g., "synergizing disruptive paradigms")
**Cringiness** - Content that induces secondhand embarrassment (overly enthusiastic, trying too hard to be cool, excessive emoji use)
**Humblebragginess** - Disguising boasts as casual updates or false modesty ("just happened to close our $ 10M round 🤷")
**Vagueness** - Failing to clearly communicate what the product/service actually does
For each criterion, provide:
1. A score (1-5) where 1 = not present, 5 = severely present
2. If the problem is not present, no comment. Otherwise, explain of the issue and give concise guidance on fixing it, without providing an actual rewrite
@draft_tweet
"""
[pipe.optimize_tweet]
type = "PipeLLM"
definition = "Optimize the tweet based on the analysis"
inputs = { draft_tweet = "DraftTweet", tweet_analysis = "TweetAnalysis", writing_style = "WritingStyle" }
output = "OptimizedTweet"
llm = "llm_for_social_post_writing"
system_prompt = """
You are an expert in writing engaging tech tweets that drive meaningful discussions and engagement.
Your goal is to rewrite tweets to be impactful and avoid the pitfalls identified in the analysis.
"""
prompt_template = """
Rewrite this tech tweet to be more engaging and effective, based on the analysis:
Original tweet:
@draft_tweet
Analysis:
@tweet_analysis
Requirements:
- Include a clear call-to-action
- Make it engaging and shareable
- Use clear, concise language
### Reference style example
@writing_style
### Additional style instructions
No hashtags.
Minimal emojis.
Keep the core meaning of the original tweet.
"""
[pipe.optimize_tweet_sequence]
type = "PipeSequence"
definition = "Analyze and optimize a tech tweet in sequence"
inputs = { draft_tweet = "DraftTweet", writing_style = "WritingStyle" }
output = "OptimizedTweet"
steps = [
{ pipe = "analyze_tweet", result = "tweet_analysis" },
{ pipe = "optimize_tweet", result = "optimized_tweet" },
]Here is the flowchart generated during this run:
---
config:
layout: dagre
theme: base
---
flowchart LR
subgraph "optimize_tweet_sequence"
direction LR
FGunn["draft_tweet:<br>**Draft tweet**"]
EWhtJ["tweet_analysis:<br>**Tweet analysis**"]
65Eb2["optimized_tweet:<br>**Optimized tweet**"]
i34D5["writing_style:<br>**Writing style**"]
end
class optimize_tweet_sequence sub_a;
classDef sub_a fill:#e6f5ff,color:#333,stroke:#333;
classDef sub_b fill:#fff5f7,color:#333,stroke:#333;
classDef sub_c fill:#f0fff0,color:#333,stroke:#333;
FGunn -- "Analyze tweet" ----> EWhtJ
FGunn -- "Optimize tweet" ----> 65Eb2
EWhtJ -- "Optimize tweet" ----> 65Eb2
i34D5 -- "Optimize tweet" ----> 65Eb2
We ❤️ contributions! Before opening a pull request, please:
- Read
CONTRIBUTING.md. - Add your file under
examples/wip/<your-folder>; feel free to group related examples by topic. - Include a short README snippet at the top of your PLX describing purpose, inputs, and expected outputs.
- Verify the pipeline runs locally with a free/open LLM preset when possible, to lower the entry barrier for reviewers.
Tip: If you're unsure whether your idea fits, open a GitHub Discussion first—feedback is fast and public.([GitHub Docs][4])
| Channel | Use case |
|---|---|
| GitHub Discussions → "Show & Tell" | Share ideas, brainstorm, get early feedback. |
| GitHub Issues | Report bugs or request features. |
| Email (privacy & security) | [email protected] |
| Discord | Real-time chat — https://go.pipelex.com/discord |
This project is licensed under the MIT license. Runtime dependencies are distributed under their own licenses via PyPI.
Happy piping! 🚀