Welcoming OpenCrawling
OpenCrawling Kicks Off
I’m grateful to Piergiorgio Lucidi for maintaining the ManifoldCF over the past few years and more recently to kick starting the OpenCrawling project which I have agreed to participate in which I wanted to follow up with a short post that explains why this matters for the broader search and ingestion ecosystem.
OpenCrawling is not just another crawler project. It is an open-source effort to make scale-out ingestion reliable, composable, and easier to operate across modern cloud and hybrid environments.
That matters because ingestion is where most search and analytics projects fail, not because search is hard (it is!), but because collecting, normalizing, and delivering content at scale is Achilles heal over every search project.
Open-source ingestion is the long game
The OpenCrawling announcement is a welcome signal for anyone who cares about building reusable comprehensive infrastructure instead of proprietary silos.
Open-source ingestion gives teams a chance to:
- share battle-tested enterprise crawlers and connectors,
- adopt a common data model for web and enterprise content,
- inspect the pipeline instead of trusting a black box,
- and evolve the platform in public with real-world feedback.
That’s important for search, for retrieval-augmented AI, for compliance, and for any system that depends on timely, trustworthy content.
What I’m focused on as lead architect
My role is about more than writing code. It’s about shaping a platform that can survive the messy realities of production and enterprise content sources.
Here are the problems I want OpenCrawling to solve well:
Distributed crawling without brittle orchestration. Crawl jobs should scale across machines, recover from failures, and maintain an auditable state of what has been fetched.
Data hygiene and normalization. Ingestion pipelines need to deal with duplicate URLs, inconsistent metadata, compressed payloads, and source-specific quirks before anything useful can be indexed or consumed.
Extensible connector design. Everyone has a custom source. The platform should make it easy to add new connectors, not force a laundry list of vendor-specific adapters.
Observability and operational clarity. If an ingestion pipeline is dropping content, retrying too much, or stalling on one source, teams need clear signals and actionable remediation paths.
Open integration with downstream search and LLM systems. The project should be a clean input for both search indexes and generative retrieval stacks, not a separate black-box ingestion layer.
and lastly…it shoudl be dead simple to operate.
Why this is a good time for OpenCrawling
The broader landscape is changing fast:
- enterprise teams are realizing the value of their knowledge and need to get it accessible in RAG solutions,
- AI and vector retrieval raise the bar on the quality of source data,
- cloud-native architectures demand more resilient and observable pipelines,
- and open-source communities are the best place to align around shared infrastructure problems.
OpenCrawling is well positioned to bridge those trends because it starts with the hard part: getting data into the system reliably.
A personal note to the community
I’m looking forward to working with the team and with anyone who is solving crawling and ingestion at scale. If you are building connectors, operationalizing pipelines, or wrestling with content quality for search and retrieval, I want to hear what’s working for you.
The welcome post on OpenCrawling is a great introduction. This follow-up is the start of a conversation about what the platform should do next.
If you want to talk through ingestion architecture or migration strategy, I’m still available via my contact page.