Pipelex is the reference Python runtime for executing methods, based on the MTHDS open standard.
Write business logic, not API calls.
pip install pipelex
pipelex initTo run pipelines with AI models, choose one of these options:
Get free credits with a single API key for LLMs, document extraction, and image generation across all major providers (OpenAI, Anthropic, Google, Azure, and more). New models added constantly.
- Get your API key at app.pipelex.com
- Add it to your
.envfile:PIPELEX_GATEWAY_API_KEY=your-key-here - Run
pipelex initand accept the Gateway terms of service
Migrating from pipelex_inference? The old
pipelex_inferencebackend is deprecated. Get your new Gateway key at app.pipelex.com.
Use your existing API keys from OpenAI, Anthropic, Google, Mistral, etc. See Configure AI Providers for setup.
Run models locally with Ollama, vLLM, LM Studio, or llama.cpp - no API keys required. See Configure AI Providers for details.
Create a complete AI method with a single command:
pipelex build pipe "Take a CV and Job offer in PDF, analyze if they match and generate 5 questions for the interview" --output results/cv_match.mthdsThis command generates a production-ready .mthds file with domain definitions, concepts, and multiple processing steps that analyzes CV-job fit and prepares interview questions.
cv_match.mthds
domain = "cv_match"
description = "Matching CVs with job offers and generating interview questions"
main_pipe = "analyze_cv_job_match_and_generate_questions"
[concept.MatchAnalysis]
description = """
Analysis of alignment between a candidate and a position, including strengths, gaps, and areas requiring further exploration.
"""
[concept.MatchAnalysis.structure]
strengths = { type = "text", description = "Areas where the candidate's profile aligns well with the requirements", required = true }
gaps = { type = "text", description = "Areas where the candidate's profile does not meet the requirements or lacks evidence", required = true }
areas_to_probe = { type = "text", description = "Topics or competencies that need clarification or deeper assessment during the interview", required = true }
[concept.Question]
description = "A single interview question designed to assess a candidate."
refines = "Text"
[pipe.analyze_cv_job_match_and_generate_questions]
type = "PipeSequence"
description = """
Main pipeline that orchestrates the complete CV-job matching and interview question generation method. Takes a candidate's CV and a job offer as PDF documents, extracts their content, performs a comprehensive match analysis identifying strengths, gaps, and areas to probe, and generates exactly 5 targeted interview questions based on the analysis results.
"""
inputs = { cv_pdf = "PDF", job_offer_pdf = "PDF" }
output = "Question[5]"
steps = [
{ pipe = "extract_documents_parallel", result = "extracted_documents" },
{ pipe = "analyze_match", result = "match_analysis" },
{ pipe = "generate_interview_questions", result = "interview_questions" },
]📄 Click to view the supporting pipes implementation
[pipe.extract_documents_parallel]
type = "PipeParallel"
description = """
Executes parallel extraction of text content from both the CV PDF and job offer PDF simultaneously to optimize processing time.
"""
inputs = { cv_pdf = "PDF", job_offer_pdf = "PDF" }
output = "Dynamic"
branches = [
{ pipe = "extract_cv_text", result = "cv_pages" },
{ pipe = "extract_job_offer_text", result = "job_offer_pages" },
]
add_each_output = true
[pipe.extract_cv_text]
type = "PipeExtract"
description = """
Extracts text content from the candidate's CV PDF document using OCR technology, converting all pages into machine-readable text format for subsequent analysis.
"""
inputs = { cv_pdf = "PDF" }
output = "Page[]"
model = "@default-text-from-pdf"
[pipe.extract_job_offer_text]
type = "PipeExtract"
description = """
Extracts text content from the job offer PDF document using OCR technology, converting all pages into machine-readable text format for subsequent analysis.
"""
inputs = { job_offer_pdf = "PDF" }
output = "Page[]"
model = "@default-text-from-pdf"
[pipe.analyze_match]
type = "PipeLLM"
description = """
Performs comprehensive analysis comparing the candidate's CV against the job offer requirements. Identifies and structures: (1) strengths where the candidate's profile aligns well with requirements, (2) gaps where the profile lacks evidence or doesn't meet requirements, and (3) specific areas requiring deeper exploration or clarification during the interview process.
"""
inputs = { cv_pages = "Page[]", job_offer_pages = "Page[]" }
output = "MatchAnalysis"
model = "$writing-factual"
system_prompt = """
You are an expert HR analyst and recruiter specializing in candidate-job fit assessment. Your task is to generate a structured MatchAnalysis comparing a candidate's CV against job requirements.
"""
prompt = """
Analyze the match between the candidate's CV and the job offer requirements.
Candidate CV:
@cv_pages
Job Offer:
@job_offer_pages
Perform a comprehensive comparison and provide a structured analysis.
"""
[pipe.generate_interview_questions]
type = "PipeLLM"
description = """
Generates exactly 5 targeted, relevant interview questions based on the match analysis results. Questions are designed to probe identified gaps, clarify areas of uncertainty, validate strengths, and assess competencies that require deeper evaluation to determine candidate-position fit.
"""
inputs = { match_analysis = "MatchAnalysis" }
output = "Question[5]"
model = "$testing-structured"
system_prompt = """
You are an expert HR interviewer and talent assessment specialist. Your task is to generate structured interview questions based on candidate-position match analysis.
"""
prompt = """
Based on the following match analysis between a candidate and a position, generate exactly 5 targeted interview questions.
@match_analysis
The questions should:
- Probe the identified gaps to assess if they are deal-breakers or can be mitigated
- Clarify areas that require deeper exploration
- Validate the candidate's strengths with concrete examples
- Be open-ended and behavioral when appropriate
- Help determine overall candidate-position fit
Generate exactly 5 interview questions.
"""View the pipeline flowchart:
flowchart TD
subgraph PAR["extract_documents_parallel (PipeParallel)"]
direction LR
EXTRACT_CV["extract_cv_text (PipeExtract)"]
EXTRACT_JOB["extract_job_offer_text (PipeExtract)"]
end
subgraph MAIN["analyze_cv_job_match_and_generate_questions (PipeSequence)"]
direction TB
PAR
CV_PAGES[["cv_pages: Page"]]
JOB_PAGES[["job_offer_pages: Page"]]
ANALYZE["analyze_match (PipeLLM)"]
MATCH[["MatchAnalysis"]]
GENERATE["generate_interview_questions (PipeLLM)"]
OUT[["Question"]]
end
CV_IN[["cv_pdf: PDF"]] --> EXTRACT_CV
JOB_IN[["job_offer_pdf: PDF"]] --> EXTRACT_JOB
EXTRACT_CV --> CV_PAGES
EXTRACT_JOB --> JOB_PAGES
CV_PAGES --> ANALYZE
JOB_PAGES --> ANALYZE
ANALYZE --> MATCH
MATCH --> GENERATE
GENERATE --> OUT
classDef default stroke:#1976D2,stroke-width:2px,fill:#E3F2FD,color:#0D47A1
style EXTRACT_CV stroke:#1565C0,fill:#BBDEFB,color:#0D47A1
style EXTRACT_JOB stroke:#1565C0,fill:#BBDEFB,color:#0D47A1
style PAR fill:#FFF9C4,stroke:#F57C00,stroke-width:2px
style CV_PAGES stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style JOB_PAGES stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style ANALYZE stroke:#1565C0,fill:#BBDEFB,color:#0D47A1
style MATCH stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style GENERATE stroke:#1565C0,fill:#BBDEFB,color:#0D47A1
style OUT stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style CV_IN stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style JOB_IN stroke:#2E7D32,fill:#C8E6C9,color:#1B5E20
style MAIN fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
Via CLI:
# Run with input file
pipelex run results/cv_match.mthds --inputs inputs.jsonCreate an inputs.json file with your PDF URLs:
{
"cv_pdf": {
"concept": "PDF",
"content": {
"url": "https://pipelex-web.s3.amazonaws.com/demo/John-Doe-CV.pdf"
}
},
"job_offer_pdf": {
"concept": "PDF",
"content": {
"url": "https://pipelex-web.s3.amazonaws.com/demo/Job-Offer.pdf"
}
}
}Via Python:
import asyncio
import json
from pipelex.pipeline.runner import PipelexRunner
from pipelex.pipelex import Pipelex
async def run_pipeline():
with open("inputs.json", encoding="utf-8") as f:
inputs = json.load(f)
runner = PipelexRunner()
response = await runner.execute_pipeline(
pipe_code="cv_match",
inputs=inputs
)
pipe_output = response.pipe_output
print(pipe_output.main_stuff_as_str)
Pipelex.make()
asyncio.run(run_pipeline())|
|
|
Pipelex is the reference Python runtime for executing repeatable AI methods, based on the MTHDS open standard. Instead of cramming everything into one complex prompt, you break tasks into focused steps, each pipe handling one clear transformation.
Each pipe processes information using Concepts (typing with meaning) to ensure your pipelines make sense. The mthds language (.mthds files) is simple and human-readable, even for non-technical users. Each step can be structured and validated, giving you the reliability of software with the intelligence of AI.
Learn More:
- Design and Run Methods - Complete guide with examples
- Kick off a Method Project - Deep dive into Pipelex
- Configure AI Providers - Set up AI providers and models
We highly recommend installing our extension for .mthds syntax highlighting in your IDE:
- VS Code: Install from the VS Code Marketplace
- Cursor, Windsurf, and other VS Code forks: Install from the Open VSX Registry, or search for "Pipelex" directly in your extensions tab
Running pipelex init will also offer to install the extension automatically if it detects your IDE.
Explore real-world examples in our Cookbook repository:
Clone it, fork it, and experiment with production-ready pipelines for various use cases.
The package supports the following additional features:
anthropic: Anthropic/Claude support for text generationgoogle: Google models (Vertex) support for text generationmistralai: Mistral AI support for text generation and OCRbedrock: Amazon Bedrock support for text generationfal: Image generation with Black Forest Labs "FAL" service
Install all extras:
Using pip:
pip install "pipelex[anthropic,google,google-genai,mistralai,bedrock,fal]"Pipelex supports two independent telemetry streams:
- Gateway Telemetry: When using Pipelex Gateway, telemetry must be enabled (tied to your hashed API key) to monitor service quality and enforce fair usage. Learn more
- Custom Telemetry: User-controlled via
.pipelex/telemetry.tomlfor your own observability systems (Langfuse, PostHog, OTLP). Learn more
We only collect technical data (model names, token counts, latency, error rates) - never your prompts, completions, or business data. Set DO_NOT_TRACK=1 to disable all telemetry (note: Gateway requires telemetry to function).
For more details, see the Telemetry Documentation or read our Privacy Policy.
We welcome contributions! Please see our Contributing Guidelines for details on how to get started, including development setup and testing information.
Join our vibrant Discord community to connect with other developers, share your experiences, and get help with your Pipelex projects!
- GitHub Issues: For bug reports and feature requests
- Discussions: For questions and community discussions
- Documentation
If you find Pipelex helpful, please consider giving us a star! It helps us reach more developers and continue improving the tool.
This project is licensed under the MIT license. Runtime dependencies are distributed under their own licenses via PyPI.
"Pipelex" is a trademark of Evotis S.A.S.
© 2025 Evotis S.A.S.
![@github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=64&v=4){kind=small}
