Pipelex Roadmap

[lchoquel]

Last updated: 2026-04-11
This document outlines the direction and priorities for this project.
It is a living document and will be updated as plans evolve.

💡 Vision & design principles

Pipelex is a declarative language and Python runtime for AI workflows. It lets you define repeatable, multi-step AI transformations as simple, readable files made of modular “pipes” with structured inputs and outputs, so your workflows read like documentation instead of plumbing code.

We believe AI workflows should be:

• Declarative & conceptual – you describe what you want and the concepts involved, in natural language where needed, not low-level APIs.
• Repeatable, portable & open – workflows are just files that run anywhere (CLI, Python, FastAPI, MCP, n8n, …) without lock-in.
• Understandable by humans & agents – syntax carries semantic context so LLMs can generate/update workflows, while people can review them like specs.
• Modular & extensible – with adapters for plugins and dependency injections where it matters.
• Transparent & observable – logs, traces, and metadata make it easy to see what happened, and how to fix or improve, if needed.
• Not an agent framework – agents explore and improvise; Pipelex is where you write down the stable workflow so anyone (or any agent) can run it again.

🎯 Main tracks

• Easy to use
  → it just works, with minimal friction
• Robust workflow executions
  → if something fails, you get first-class traces to analyze and you can resume without restarting
• Fully leverage the conceptual aspects
  → validate concepts and assess operations automatically at each pipe input / output
• Connectivity
  Pipelex can already be used via Python, API, MCP, n8n and soon via all automation platforms.
  → Should we design an API to support Pipelex connectors?
  → Or should Pipelex become an MCP Client (in addition to being an MCP Server)?
• Community
  → Enable pipes to call other pipes across the Web and fulfill our vision of the Know-How Graph

⏱️ Near-term roadmap

🕺 Runtime features

This section is the most likely to change as we learn from users.

• Workflow reliability & tooling
  • Temporal.io
  • Delegate all content persistence to StorageProvider with references in payloads
  • Practical support for long jobs (several hours): we need a practical solution for notifying the client when we’re done or if a problem occurred
  • ♻️ Rock solid error handling
  • ♻️ Support Mistral Workflows as alternative to Temporal
  • Support cost reporting aggregation when execution is distrubuted
• Language improvements
  • Support defining a local model deck (aliases and presets) in a bundle.mthds
• Add capabilities
  • File search: ZeroEntropy, Gemini File search
  • More solutions to Web search / scrape: Gemini web search, Perplexity, Tavily, Parallel.ai, Firecrawl…
  • Misc: document anonymization with Hexagone AI
• Add modalities
  • Voice: MiniMax, pyannoteAl, Whisper, ElevenLabs…
  • Video: Gemini, Veo, Sora and other models dealing with video input/output

🤝 Integrations

• Agent Plugin & Skills
  • Claude Code plugin
  • Codex plugin
  • Tutorial & guide skill
  • Webapp integration skill
  • ♻️ Support recursive building to solve tasks of arbitrary depth
• IDE Extensions
  • Lint and Format
  • Enforce bundle blueprint schema
  • Run our validation tools from the extension’s Language Server
  • Pinpoint language errors (beyond linting) to the precise line & column
  • Run using local Pipelex API rather than CLI
  • Auto fix / complete code
  • Extension for IntelliJ in addition to the VS Code Extension
• MCP
  • ♻️ Local MCP
  • ♻️ Remote MCP Server based on API
• Automation
  • n8n community node
  • make.com module

👌 Examples and Howtos

We gather example workflows in our Cookbook is not a term reserved, but this section is about examples of deeper customization of Pipelex:

• Typescript project starter
• ♻️ Integrating evals
• Integrating custom AI models

🛠️ Refactoring & Chores

It’s hard to tell exactly when we’ll do these, but they must be done.

• Rewrite PipeLLM to be elementary, revamp ou trick for structuring with preliminary_text
• Rethink the ContentGeneratorProtocol

👐 Commercial services

These services are essentially infrastructure and they will remain closed source. But they are totally optional and they come with free tiers and free credits:

• Robust AI model routing to all models with a single API key: current service isn't robust enough, and lacking models
• ♻️ Hosted API for running workflows on-demand: coming soon

🙌 What to contribute to?

If you’d like to help, please do! 🙏🏻

The area where we need the most help is adding capabilities and integrations. In fact, there are so many possibilities, and our small team must focus on the Burning issues, the Visual Flowchart Generator, adding Temporal, and the various Refactorings: we’re already halfway through these.

To give some good options, we will add good-first-issue and help-wanted tags to issues. In any case, if you want to work on such topics, please give us a heads-up by adding a comment here or on the issue itself or, even better, contacting us on Discord. It’s important so that we have a chance to discuss details and, most of all, avoid double work.

🗣️ Feedback & discussion

We treat this roadmap as a conversation with the community.

• 💡 Do you have any feedback or suggestion to prioritize?

  Please comment in this thread.

• 🐞 Found a bug or a missing feature?

  Open an Issue or submit an Idea.