A method is a reusable, typed AI procedure — declared in a .mthds file and executed by Pipelex.
Each step is explicit, each output is structured, and every run is repeatable.
[pipe.summarize_article]
type = "PipeLLM"
inputs = { article = "Text", audience = "Text" }
output = "Text"
prompt = "Summarize $article in three bullet points for $audience."From here, Pipelex handles model routing across 60+ models, structured output parsing, and pipeline orchestration.
Declarative — Human-readable .mthds files that work across models |
Typed — Semantic types: AI understands what you mean, every input/output connects with purpose |
| Repeatable — Deterministic orchestration with controlled room for AI creativity | Composable — Chain pipes into sequences, nest methods inside methods, share with the community |
pip install pipelex
pipelex initInstall the mthds npm package:
npm install -g mthdsStart Claude Code:
claudeTell Claude to install the MTHDS skills marketplace:
/plugin marketplace add mthds-ai/skills
then install the MTHDS skills plugin:
/plugin install mthds@mthds-ai-skills
then you must exit Claude Code and reopen it.
/exit
Build your first method:
/mthds-build A method to summarize articles with key takeaways for different audiences
Run it:
/mthds-run
- Install the VS Code extension for
.mthdssyntax highlighting - Browse methods on the MTHDS Hub for inspiration
- Author your own
.mthdsmethods based on these examples - Validate with
pipelex validate bundle your_method.mthds - Run them with
pipelex run bundle your_method.mthds - View the flowchart in VS Code thanks to the extension
- Pipelex Gateway (Recommended) — Free credits, single API key for LLMs, OCR / document extraction, and image generation across all major providers. Get your key, add
PIPELEX_GATEWAY_API_KEY=your-key-hereto~/.pipelex/.env, runpipelex init. - Bring Your Own Keys — Use existing API keys from OpenAI, Anthropic, Google, Mistral, etc. See Configure AI Providers.
- Local AI — Ollama, vLLM, LM Studio, or llama.cpp — no API keys required. See Configure AI Providers.
A production method that takes a stack of CVs and a job offer PDF, extracts and analyzes each, then scores how well each candidate matches the role.
cv_batch_screening.mthds
[pipe.batch_analyze_cvs_for_job_offer]
type = "PipeSequence"
description = """
Main orchestrator pipe that takes a bunch of CVs and a job offer in PDF format, and analyzes how they match.
"""
inputs = { cvs = "Document[]", job_offer_pdf = "Document" }
output = "CandidateMatch[]"
steps = [
{ pipe = "prepare_job_offer", result = "job_requirements" },
{ pipe = "process_cv", batch_over = "cvs", batch_as = "cv_pdf", result = "match_analyses" },
]View concepts, supporting pipes, flowchart, and run commands
Concepts:
[concept.CandidateProfile]
description = "A structured summary of a job candidate's professional background extracted from their CV."
[concept.CandidateProfile.structure]
skills = { type = "text", description = "Technical and soft skills possessed by the candidate", required = true }
experience = { type = "text", description = "Work history and professional experience", required = true }
education = { type = "text", description = "Educational background and qualifications", required = true }
achievements = { type = "text", description = "Notable accomplishments and certifications" }
[concept.JobRequirements]
description = "A structured summary of what a job position requires from candidates."
[concept.JobRequirements.structure]
required_skills = { type = "text", description = "Skills that are mandatory for the position", required = true }
responsibilities = { type = "text", description = "Main duties and tasks of the role", required = true }
qualifications = { type = "text", description = "Required education, certifications, or experience levels", required = true }
nice_to_haves = { type = "text", description = "Preferred but not mandatory qualifications" }
[concept.CandidateMatch]
description = "An evaluation of how well a candidate fits a job position."
[concept.CandidateMatch.structure]
match_score = { type = "number", description = "Numerical score representing overall fit percentage between 0 and 100", required = true }
strengths = { type = "text", description = "Areas where the candidate meets or exceeds requirements", required = true }
gaps = { type = "text", description = "Areas where the candidate falls short of requirements", required = true }
overall_assessment = { type = "text", description = "Summary evaluation of the candidate's suitability", required = true }Click to view the supporting pipes implementation
[pipe.prepare_job_offer]
type = "PipeSequence"
description = """
Extracts and analyzes the job offer PDF to produce structured job requirements.
"""
inputs = { job_offer_pdf = "Document" }
output = "JobRequirements"
steps = [
{ pipe = "extract_one_job_offer", result = "job_offer_pages" },
{ pipe = "analyze_job_requirements", result = "job_requirements" },
]
[pipe.extract_one_job_offer]
type = "PipeExtract"
description = "Extracts text content from the job offer PDF document"
inputs = { job_offer_pdf = "Document" }
output = "Page[]"
model = "@default-text-from-pdf"
[pipe.analyze_job_requirements]
type = "PipeLLM"
description = """
Parses and summarizes the job requirements from the extracted job offer content, identifying required skills, responsibilities, qualifications, and nice-to-haves
"""
inputs = { job_offer_pages = "Page" }
output = "JobRequirements"
model = "$writing-factual"
system_prompt = """
You are an expert HR analyst specializing in parsing job descriptions. Your task is to extract and summarize job requirements into a structured format.
"""
prompt = """
Analyze the following job offer content and extract the key requirements for the position.
@job_offer_pages
"""
[pipe.process_cv]
type = "PipeSequence"
description = "Processes one application"
inputs = { cv_pdf = "Document", job_requirements = "JobRequirements" }
output = "CandidateMatch"
steps = [
{ pipe = "extract_one_cv", result = "cv_pages" },
{ pipe = "analyze_one_cv", result = "candidate_profile" },
{ pipe = "analyze_match", result = "match_analysis" },
]
[pipe.extract_one_cv]
type = "PipeExtract"
description = "Extracts text content from the CV PDF document"
inputs = { cv_pdf = "Document" }
output = "Page[]"
model = "@default-text-from-pdf"
[pipe.analyze_one_cv]
type = "PipeLLM"
description = """
Parses and summarizes the candidate's professional profile from the extracted CV content, identifying skills, experience, education, and achievements
"""
inputs = { cv_pages = "Page" }
output = "CandidateProfile"
model = "$writing-factual"
system_prompt = """
You are an expert HR analyst specializing in parsing and summarizing candidate CVs. Your task is to extract and structure the candidate's professional profile into a structured format.
"""
prompt = """
Analyze the following CV content and extract the candidate's professional profile.
@cv_pages
"""
[pipe.analyze_match]
type = "PipeLLM"
description = """
Evaluates how well the candidate matches the job requirements, calculating a match score and identifying strengths and gaps
"""
inputs = { candidate_profile = "CandidateProfile", job_requirements = "JobRequirements" }
output = "CandidateMatch"
model = "$writing-factual"
system_prompt = """
You are an expert HR analyst specializing in candidate-job fit evaluation. Your task is to produce a structured match analysis comparing a candidate's profile against job requirements.
"""
prompt = """
Analyze how well the candidate matches the job requirements. Evaluate their fit by comparing their skills, experience, and qualifications against what the position demands.
@candidate_profile
@job_requirements
Provide a comprehensive match analysis including a numerical score, identified strengths, gaps, and an overall assessment.
"""View the pipeline flowchart
flowchart LR
%% Pipe and stuff nodes within controller subgraphs
subgraph sg_n_8b2136e3fe["batch_analyze_cvs_for_job_offer"]
subgraph sg_n_91d5d6dc7c["prepare_job_offer"]
n_fde22777cb["analyze_job_requirements"]
s_f9f703fbb4(["job_requirements<br/>JobRequirements"]):::stuff
n_b8469c838f["extract_one_job_offer"]
s_d998350046(["job_offer_pages<br/>Page"]):::stuff
end
subgraph sg_n_f8d5afb7cd["process_cv_batch"]
subgraph sg_n_6e53e16369["process_cv"]
n_c18aded200["analyze_match"]
s_5c911f7e54(["match_analysis<br/>CandidateMatch"]):::stuff
n_a7ed00ac24["analyze_one_cv"]
s_c5ae714e89(["candidate_profile<br/>CandidateProfile"]):::stuff
n_d24f39aa60["extract_one_cv"]
s_427beb5195(["cv_pdf<br/>Document"]):::stuff
s_f1f80289df(["cv_pages<br/>Page"]):::stuff
end
subgraph sg_n_2cfb7a32c8["process_cv"]
n_f6a25d1769["analyze_match"]
s_ea99eee6ed(["match_analysis<br/>CandidateMatch"]):::stuff
n_f48b73fbee["analyze_one_cv"]
s_e1ffee913e(["candidate_profile<br/>CandidateProfile"]):::stuff
n_d16f2fe381["extract_one_cv"]
s_041bb18fb4(["cv_pdf<br/>Document"]):::stuff
s_5fbba7194a(["cv_pages<br/>Page"]):::stuff
end
subgraph sg_n_08a7186be9["process_cv"]
n_937e750ea4["analyze_match"]
s_bb41a103f0(["match_analysis<br/>CandidateMatch"]):::stuff
n_786a2969d5["analyze_one_cv"]
s_c47fe821d7(["candidate_profile<br/>CandidateProfile"]):::stuff
n_38f0cfd11c["extract_one_cv"]
s_2634ece93d(["cv_pdf<br/>Document"]):::stuff
s_44e253b325(["cv_pages<br/>Page"]):::stuff
end
end
end
%% Pipeline input stuff nodes (no producer)
s_9b7e74ac51(["job_offer_pdf<br/>Document"]):::stuff
%% Data flow edges: producer -> stuff -> consumer
n_a7ed00ac24 --> s_c5ae714e89
n_b8469c838f --> s_d998350046
n_f48b73fbee --> s_e1ffee913e
n_d16f2fe381 --> s_5fbba7194a
n_fde22777cb --> s_f9f703fbb4
n_d24f39aa60 --> s_f1f80289df
n_38f0cfd11c --> s_44e253b325
n_786a2969d5 --> s_c47fe821d7
n_c18aded200 --> s_5c911f7e54
n_f6a25d1769 --> s_ea99eee6ed
n_937e750ea4 --> s_bb41a103f0
s_c5ae714e89 --> n_c18aded200
s_9b7e74ac51 --> n_b8469c838f
s_d998350046 --> n_fde22777cb
s_e1ffee913e --> n_f6a25d1769
s_427beb5195 --> n_d24f39aa60
s_041bb18fb4 --> n_d16f2fe381
s_2634ece93d --> n_38f0cfd11c
s_5fbba7194a --> n_f48b73fbee
s_f9f703fbb4 --> n_c18aded200
s_f9f703fbb4 --> n_f6a25d1769
s_f9f703fbb4 --> n_937e750ea4
s_f1f80289df --> n_a7ed00ac24
s_44e253b325 --> n_786a2969d5
s_c47fe821d7 --> n_937e750ea4
%% Batch edges: list-item relationships
s_52d84618d0(["match_analyses<br/>CandidateMatch"]):::stuff
s_5c911f7e54 -."[0]".-> s_52d84618d0
s_ea99eee6ed -."[1]".-> s_52d84618d0
s_bb41a103f0 -."[2]".-> s_52d84618d0
%% Style definitions
classDef failed fill:#ffcccc,stroke:#cc0000
classDef stuff fill:#fff3e6,stroke:#cc6600,stroke-width:2px
classDef controller fill:#e6f3ff,stroke:#0066cc
%% Subgraph depth-based coloring
style sg_n_08a7186be9 fill:#fffde6
style sg_n_2cfb7a32c8 fill:#fffde6
style sg_n_6e53e16369 fill:#fffde6
style sg_n_8b2136e3fe fill:#e6f3ff
style sg_n_91d5d6dc7c fill:#e6ffe6
style sg_n_f8d5afb7cd fill:#e6ffe6
Via CLI:
pipelex run bundle cv_batch_screening.mthds --inputs inputs.jsonCreate an inputs.json file with your PDF URLs:
{
"cvs": {
"concept": "native.Document",
"content": [
{ "url": "https://pipelex-web.s3.amazonaws.com/demo/John-Doe-CV.pdf" },
{ "path": "inputs/Jane-Smith-CV.pdf" }
]
},
"job_offer_pdf": {
"concept": "native.Document",
"content": {
"url": "https://pipelex-web.s3.amazonaws.com/demo/Job-Offer.pdf"
}
}
}Via Python:
import asyncio
from pipelex.core.stuffs.document_content import DocumentContent
from pipelex.pipelex import Pipelex
from pipelex.pipeline.runner import PipelexRunner
# Generated by: `pipelex build structures bundle cv_batch_screening.mthds`
from structures.cv_batch_screening__candidate_match import CandidateMatch
async def run_pipeline() -> list[CandidateMatch]:
runner = PipelexRunner()
response = await runner.execute_pipeline(
pipe_code="batch_analyze_cvs_for_job_offer",
inputs={
"cvs": {
"concept": "Document",
"content": [
DocumentContent(url="https://pipelex-web.s3.amazonaws.com/demo/John-Doe-CV.pdf"),
DocumentContent(path="inputs/Jane-Smith-CV.pdf"),
],
},
"job_offer_pdf": {
"concept": "native.Document",
"content": DocumentContent(url="https://pipelex-web.s3.amazonaws.com/demo/Job-Offer.pdf"),
},
},
)
pipe_output = response.pipe_output
print(pipe_output)
return pipe_output.main_stuff_as_items(item_type=CandidateMatch)
Pipelex.make()
asyncio.run(run_pipeline())Claude Code builds your AI Method
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.
The same .mthds file runs from multiple execution targets:
| Target | How |
|---|---|
| CLI | pipelex run bundle method.mthds --inputs inputs.json |
| Python | PipelexRunner().execute_pipeline(...) |
| REST API | Self-hosted API server |
| MCP | Model Context Protocol — agents call methods as tools |
| n8n | Pipelex node for workflow automation |
| Description | Link | |
|---|---|---|
| MTHDS Standard | The open standard specification — language, package system, and typed concepts | mthds.ai |
| MTHDS Hub | Discover and share methods — browse packages, search by signature | mthds.sh |
| Skills Plugin | Claude Code plugin — 11 commands to build, run, edit, check, fix, and publish methods | github.com/mthds-ai/skills |
| Package System | Versioned dependencies, lock files with SHA-256 integrity, cross-package references via -> |
Packages docs |
| Know-How Graph | Typed discovery — "I have X, I need Y" — find methods or chains by typed signature | Know-How Graph |
View all 11 Skills Plugin commands
| Command | Description |
|---|---|
/mthds-build |
Build new AI method bundles from scratch |
/mthds-run |
Execute methods and interpret their JSON output |
/mthds-edit |
Modify existing methods — change pipes, update prompts, add steps |
/mthds-check |
Validate bundles for issues (read-only) |
/mthds-fix |
Auto-fix validation errors |
/mthds-explain |
Walk through execution flow in plain language |
/mthds-inputs |
Prepare inputs: templates, synthetic data, user files |
/mthds-install |
Install method packages from GitHub or local dirs |
/mthds-pkg |
Package management — init, deps, lock, install, update |
/mthds-publish |
Publish methods to the hub |
/mthds-share |
Share methods on social media |
Explore real-world examples in our Cookbook repository:
Clone it, fork it, and experiment with production-ready methods 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" servicelinkup: Web search with Linkupdocling: OCR with Docling
Install all extras:
pip install "pipelex[anthropic,google,google-genai,mistralai,bedrock,fal,linkup,docling]"Privacy & Telemetry — Pipelex Gateway collects only technical data (model names, token counts, latency) — never prompts or business data. If you want to avoid Gateway telemetry, disable pipelex_gateway and use your own provider keys or local AI instead. Learn more
Contributing — We welcome contributions! See our Contributing Guidelines.
Community —
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.
© 2025-2026 Evotis S.A.S.
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