Agently Daily News Collector v4

Agently Daily News Collector has been rewritten on top of Agently v4 and now uses:

• TriggerFlow for the end-to-end pipeline
• Agently v4 built-in Search and Browse tools
• structured output contracts instead of the old v3 workflow API

Version constraint: this project requires Agently v4.0.8.3 or newer. The current implementation uses TriggerFlow sub flow to organize per-column pipelines, so earlier v4 releases are not compatible with the workflow structure used here.

The previous Agently v3 project has been archived under ./v3.

Features

• Input a topic and generate a multi-column news briefing automatically
• Search, shortlist, browse, summarize, and assemble stories in one flow
• Save the final report as Markdown under ./outputs
• Keep prompt templates in ./prompts for easy editing
• Keep an independent ./tools layer so search/browse can be replaced without touching the main workflow
• Keep flow construction in ./workflow so orchestration can evolve independently from collector logic

Quick Start

1. Install dependencies:

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pip install -r requirements.txt

If you install Agently manually, make sure you use at least:

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pip install "agently>=4.0.8.3"

2. Edit SETTINGS.yaml:

• Keep the model block as environment placeholders
• Export the required environment variables:

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export AGENTLY_NEWS_BASE_URL="https://api.openai.com/v1"
export AGENTLY_NEWS_MODEL="gpt-4.1-mini"
export AGENTLY_NEWS_API_KEY="your_api_key"

• Or put them in a local .env file:

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AGENTLY_NEWS_BASE_URL=https://api.openai.com/v1
AGENTLY_NEWS_MODEL=gpt-4.1-mini
AGENTLY_NEWS_API_KEY=your_api_key

• Adjust language / search / concurrency settings if needed
• If your OpenAI-compatible endpoint does not require authentication, you can leave AGENTLY_NEWS_API_KEY unset and the project will skip auth.

3. Run:

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python app.py

Or pass a topic directly:

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python app.py "AI agents"

Project Structure

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.
├── app.py
├── news_collector/
├── tools/
├── workflow/
├── prompts/
├── outputs/
├── logs/
└── v3/

Important v3 -> v4 Changes

The business chain is still roughly:

outline -> search -> pick -> browse + summarize -> write column -> render markdown

What changed is the engineering shape around that chain.

Project-level changes

• The old v3 project used a main workflow plus a nested column workflow under ./workflows, with custom search.py / browse.py helpers and storage-style state passing.
• The v4 project separates responsibilities more clearly:
  • news_collector/: app/integration layer
  • workflow/: parent flow, column sub flow, and concrete chunk logic
  • tools/: search/browse adapter layer
  • prompts/: structured prompt contracts
• Model configuration is no longer hardcoded in Python. It now uses ${ENV.xxx} placeholders from SETTINGS.yaml, so deployment and local switching are simpler.
• Tool wiring is no longer buried inside workflow code. Search, browse, and logger are injected as TriggerFlow runtime resources, which makes the workflow easier to replace or test.
• The workflow plan is now closer to the business boundary:
  • parent flow: prepare_request -> generate_outline -> for_each(column) -> render_report
  • column sub flow: search -> pick -> summarize -> write_column
  • the summarize stage inside the column flow is further pushed down into a summary sub flow, where TriggerFlow handles fan-out and collection directly instead of leaving asyncio.gather in business code
  • this keeps the parent focused on report orchestration and the child focused on one column lifecycle
  • the immediate value of sub flow here is that the column pipeline becomes a reusable, independently evolvable workflow unit instead of staying buried inside one oversized parent chunk

Agently v4 features used here

• TriggerFlow orchestration
  • Replaces the old v3 workflow style with a more explicit flow graph (to, for_each, sub flow, branching-ready composition).
  • Unlike the old v3 Workflow chain, TriggerFlow here runs columns concurrently and also summarizes picked stories concurrently within each column.
  • Meaning for this project: the end-to-end news pipeline is easier to inspect, evolve, and split into chunks without mixing orchestration with business logic, while the parent report flow and the per-column pipeline can now be modeled directly as parent/child flows instead of one oversized chunk.
• Sub flow composition
  • The project can now extract a naturally repeated business pipeline, “build one column”, into its own TriggerFlow and invoke it repeatedly from the parent flow inside for_each(column).
  • Meaning for this project:
    • the parent flow stays focused on report-level orchestration
    • the column pipeline can be tested, visualized, and exported independently
    • future variants such as “briefing column”, “deep-dive column”, or “regional column” can reuse or derive from the child flow instead of cloning parent-flow nodes
    • capture / write_back makes the boundary between parent and child explicit for input, state, and resources
• Structured output contracts
  • YAML prompts now define output schema directly for outline generation, news picking, summarizing, and column writing.
  • Meaning for this project: much less handwritten parsing glue, clearer interfaces between steps, and easier prompt iteration.
• Built-in Search / Browse tools
  • The project now defaults to Agently v4 built-in tool implementations instead of the old project-local helpers.
  • Meaning for this project: less custom infrastructure code, and users can still swap implementations through ./tools without rewriting the workflow.
• Runtime resources and state namespaces
  • TriggerFlow runtime resources are used to inject logger/search/browse dependencies, while runtime state stores execution data such as request, outline, and intermediate results.
  • Meaning for this project: dependency wiring and execution state are separated cleanly, which keeps chunk code thinner and more maintainable.
• Environment-aware settings
  • Agently v4 set_settings(..., auto_load_env=True) works directly with ${ENV.xxx} placeholders.
  • Meaning for this project: model endpoint, model name, and API key can be switched by environment instead of editing code or committing secrets.

Overall effect on this project

• The core product behavior remains familiar to v3 users, but the project now has a cleaner app/workflow/tools/prompts split.
• More logic is expressed in Agently-native capabilities instead of project-specific glue code.
• True concurrency is now part of the default execution model. The v3 version was effectively serial, while the v4 version can process columns and per-column summaries in parallel through TriggerFlow.
• Replacing tools, adjusting prompts, or evolving workflow steps is now lower-risk than in the old v3 layout, and the overall orchestration shape is again aligned with the original “main flow + column flow” mental model.
• It also means workflow evolution can happen by layer: report-level changes stay in the parent flow, while column-level changes stay in the sub flow instead of forcing both to change together.

Notes

• Python >=3.10 is required because Agently v4 requires it.
• This project requires Agently >=4.0.8.3.
• Model settings now use Agently v4 auto_load_env=True with ${ENV.xxx} placeholders.
• tools/ defaults to Agently v4 built-in implementations, but you can replace the factories there with your own tools.
• workflow/ is now split by business boundary into the parent flow, the column sub flow, report-level chunks, and column-level chunks.
• news_collector/ acts as the app/integration layer for configuration, model wiring, and CLI entry support.
• The current sample SETTINGS.yaml enables BROWSE.enable_playwright: true by default because many news pages need a real browser to return usable content.
• If you do not want to install Playwright, set BROWSE.enable_playwright to false manually, but expect weaker browse quality on dynamic or protected sites.
• The settings loader keeps basic compatibility with the old v3 keys such as MODEL_PROVIDER, MODEL_URL, MODEL_AUTH, MODEL_OPTIONS, MAX_COLUMN_NUM, and USE_CUSTOMIZE_OUTLINE.