Building a newsroom run by machines

You want a continuous information flow: collect sources, check what is true, summarise, turn it into video, and coordinate the whole thing. Five layers. Here is what exists at each one, what it costs, and where it breaks.

Layer 1: collect. ScrapeGraphAI (30,601 stars, MIT) reads a web page and hands back structured data. You give it an address and a sentence, it figures it out. But it does not read RSS feeds or X: for those two sources, you have to write or plug in your own collection. The hosted service costs from $20 a month, the library alone is free but the model calls are on you.

github.com/ScrapeGraphAI/…

Layer 2: verify. This is the hole. Feynman (8,871 stars, MIT) is often cited for this layer, but it is a scientific research agent: its sources are arXiv, PubMed, OpenAlex and ClinicalTrials.gov. It checks that citations exist and removes dead links, it does not check whether a news fact is true. No off-the-shelf tool does automated news verification. This layer remains to be written.

github.com/advaitpaliwal/feynman

Layer 3: summarise and translate. Kimi K3 (2.5 million downloads, house licence) swallows a 1,048,576-token window at once, about three quarters of a million words, and understands images. For summarising a large volume of sources, it is a serious candidate. But it does not run on your machine: the smallest light build weighs 466 GB. You need a hosting provider, which adds a subscription.

huggingface.co/moonshotai/Kimi-K3

Layer 4: turn into video. OpenMontage (56,292 stars, AGPL-3.0) chains script, voice, music and editing on its own. Its Clip Factory pipeline cuts one long source into a batch of ranked short clips, which is exactly the shape a news feed needs. The demos are priced between $1.33 and $5 per video, with a default cap at $10. The AGPL licence requires you to publish your modifications if you expose a service over a network.

github.com/calesthio/OpenMontage

Layer 5: coordinate the agents. This is the best-served layer, and all of it is open source. CrewAI (58,246 stars, MIT) makes agents work together by giving each one a role: one gathers, one checks, one summarises. LangGraph (41,266 stars, MIT) describes the same chain as a state graph, with a checkpoint at every node and a human hand possible before it moves on. Temporal (22,910 stars, MIT) knows nothing about agents, but knows how to make a long run survive a machine failure, which is the real problem for a feed that runs every day.

Limit All three are self-hosted: the machine, the queue and the retries stay on your side. The proprietary option exists (Raft, $8.80 per seat per month, raft.build), and it only earns its place if you want to run no machine at all.

The licence trap. n8n (203,744 stars) is quoted everywhere as the open orchestrator of the field. Its licence is not one: the "Sustainable Use License" restricts use to your own internal business purposes, and only lets you pass the software to others free of charge, for non-commercial use. A news pipeline you sell falls outside it.

github.com/crewAIInc/crewAI

github.com/langchain-ai/langgraph

github.com/temporalio/temporal

github.com/n8n-io/n8n/blob/master/…

The bottom line. Four layers out of five have a real tool, and coordination has three of them, all MIT. The hole is at the second: verifying a news fact has nothing off the shelf and remains to be written. So the bill does not come from the conductor, which costs nothing, but from what it conducts: the model for collection, the hosting for summarisation, the services for video. Before asking whether the pipeline works, you have to ask whether the bill holds.

ScrapeGraphAI for collection, OpenMontage for editing, CrewAI for coordination.

Your agent succeeds one time in three, and the model is not the reason.

An agent that fails one step in twenty fails two thirds of its tasks. That is not bad luck: a long task has to get every step right, and small errors multiply instead of adding up.

Let's keep one concrete case to the end: an agent that processes invoices arriving by email. Read the message, open the attachment, pull the amount, find the supplier, convert the currency, post the entry, acknowledge receipt. Around twenty steps. Out of thirty invoices this month, eleven make it through.

Switching models will not help, and you have no code to write. There are two kinds of failure, and confusing them costs days. Random failure trips sometimes, for different reasons: an automatic retry repairs it, and your eleven invoices become twenty-eight.

Systematic failure trips always, at the same step and in the same way: retrying changes nothing, you have to split the step in two or take it out of the model.

The move, today: open your agent's log and look at whether the same failure always comes back at the same place. That is the whole difference between an hour's repair and a lost week.

→ The four prompts that find where your agent fails, to paste one after the other into your assistant. They list the real steps, add a log, run the task ten times in dry-run mode, and hand back a table that separates the two kinds of failure.