AI Workflows
That Agents Build & Run

The open standard devtool for repeatable AI workflows.
Write business logic, not API calls.

pip install pipelex

pipelex build workflow "improve my YC application"

    domain = "cv_match"
    description = "Matching CVs with job offers and generating interview questions"
    main_pipe = "analyze_cv_job_match_and_generate_questions"

    [concept]
    Question = "A single interview question designed to assess a candidate."


    [concept.MatchAnalysis]
    description = "Analysis of alignment between a candidate and a position"

    [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 }

    [pipe.analyze_cv_job_match_and_generate_questions]
    type = "PipeSequence"
    description = "CV-job matching and interview question generation."
    inputs = { cv = "PDF", job_offer = "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" },
    ]

    [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", job_offer = "PDF" }
    output = "Dynamic"
    parallels = [
      { 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" }
    output = "Page[]"
    model = "extract_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" }
    output = "Page[]"
    model = "extract_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 = "llm_to_answer_hard_questions"
    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 = "llm_to_write_questions"
    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.
    """
  

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" Pipelex took us from idea to production in just three weeks. It’s so simple that even our intern was up and running in two days.
It made building our agentic flow feel effortless "

Aurélien Violette, CTO

" When you start measuring your AI assistant’s performance, two challenges dominate: reliability and accuracy. Pipelex helped us push toward 99%: the threshold for real automation "

Adrien Vesteghem, Chief AI Officer

" Gojob processes large volumes of CVs to match candidates with roles. With Pipelex, we set up a clear, deterministic workflow in minutes, accelerating delivery without added complexity. "

Yannick Tian, Staff AI Engineer

The Pipelex Viewpoint

Agents are great at solving new problems, terrible at doing the same thing twice.

We argue that repeatable AI workflows should complement agents: written in a declarative language that both humans and agents can understand, reuse, and compose.

These workflows become tools that agents can build, invoke, and share to turn repeatable cognitive work into reliable infrastructure.

At scale, this forms a Know-How Graph: a network of reusable methods that become shared infrastructure.

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Agent-First + Open Standard changes everything

Without Pipelex

❌ Manual glue code for every new workflow
❌ AI usage is technical, wrapping APIs
❌ Teams rebuild the same things
❌ Vendor Lock-in

With Pipelex

✅ Build workflows from natural language
✅ AI usage wrapping business logic
✅ Shared pipelines are your building blocks‍
✅ Open standard to run anywhere

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FAQs

Find answers to your most pressing questions about knowledge pipelines and AI workflows.

Why invent a new language?

AI workflows represent a new computing paradigm that demands a new approach. While natural language is great for human communication, it's too ambiguous for reliable automation. To collaborate with AI, we need a declarative language that captures domain expertise directly, preserving human intent while providing the required structure to orchestrate AI consistently. Think of it as the middle ground between vague English prompts and rigid code.

How does Pipelex compare to LangGraph, Mastra, Pydantic AI?

Those tools ask you to write code: they're dev tools for humans. Pipelex asks you to declare intent: it's a devtool for agents first. These frameworks lock you in. Pipelex gives you a portable language that non-tech people can write, any expert can read, and any platform can run. We're not competing on features: we're establishing the standard.

How does Pipelex compare to n8n, Workato, make.com?

Those are visual, drag-and-drop platforms built for humans clicking through GUIs. Pipelex is text-based and built for agents to write workflows autonomously. No-code workflows live trapped in their platforms. Pipelex workflows are portable files you can version control, share, and run anywhere. They focus on app connectors. We focus on AI-powered information processing with structured outputs.

What kind of language is Pipelex?

Pipelex is a declarative, domain-specific language (DSL) for AI workflows. It uses TOML syntax that reads like documentation, not code. Unlike traditional programming languages that express technical implementation, Pipelex operates at a conceptual level: you declare business intent like "extract buyer from invoice" rather than API calls. It's designed to be readable by domain experts, writable by agents, and executable anywhere.

Are you an agent framework or an “agent runtime”?

Neither. Pipelex provides deterministic AI workflows that agents can use as tools. Our workflows generate structured outputs reliably, making them perfect for agent tool use. Agents access Pipelex workflows via MCP (Model Context Protocol) or our API. Think of us as a tool provider for agents: we give them reliable, repeatable methods for information processing tasks.

When is Pipelex the right choice?

Pipelex is the right choice when you need repeatable, deterministic AI workflows for knowledge work. If you're processing invoices, analyzing contracts, or generating reports every week or a thousands of times a day, most of all you need consistent results every time, that's Pipelex. It's not for creative exploration or open-ended tasks, it's for when you've figured out the method and need to run it reliably at scale.

Is Pipelex open-source?

Yes. The core Pipelex language and Python runtime are fully open-source. You can find our repos at github.com/Pipelex/pipelex and our cookbook with examples at github.com/Pipelex/pipelex-cookbook. We follow an open-core model where enterprise features will require a commercial license, but the language itself will always remain open.

Can I extend Pipelex?

Yes, at three levels. First, pipelines are composable: they can call other pipelines as building blocks, so you can build on what others have created. Second, our codebase is modular with plugin systems for your own orchestrators, AI model APIs, cost reporters, and more. Third, it's open-source: fork it, extend it for your needs, and contribute back. The community shapes the standard.

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