maestro-fetch 0.2.2

Fetch everything, for agents. Universal data acquisition with smart routing.

maestro-fetch

One interface. Any source. Agent-ready output.

Give it any URL -- web page, PDF, spreadsheet, cloud file, video, binary dataset -- and get back clean markdown or structured data. Smart routing picks the right adapter; pluggable browser backends handle anti-bot and authentication. No API key required.

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Quickstart

For AI Agents

# Claude Code -- install as a skill (Vercel skills ecosystem)
npx skills add maestro-ai-stack/maestro-fetch -y -g

# Claude Code -- install as a plugin (marketplace)
/plugin marketplace add maestro-ai-stack/maestro-fetch
/plugin install maestro-fetch@maestro-fetch

Works with: Claude Code | Cursor | Codex | Gemini CLI | OpenCode | Trae and any agent that speaks MCP or CLI tools.

For Developers

# Recommended (global command, no venv needed)
uv tool install maestro-fetch

# Or with all extras (PDF, media, browser, LLM, social)
uv tool install "maestro-fetch[all]"

# Classic pip
pip install maestro-fetch

mfetch "https://example.com"

Try it now:

$ mfetch "https://api.worldbank.org/v2/country/CN/indicator/NY.GDP.MKTP.CD?format=json&per_page=5"

## GDP (current US$) - China

| Year | GDP (USD)            |
|------|----------------------|
| 2024 | $17,794,782,410,032  |
| 2023 | $17,662,434,751,902  |
| 2022 | $17,963,170,547,847  |
| 2021 | $17,734,062,645,371  |
| 2020 | $14,687,674,437,370  |

$ mfetch "https://arxiv.org/pdf/2301.07041"

## Dissociating language and thought in large language models ...
(full paper text as clean markdown)

If you find this useful, consider giving it a star -- it helps others discover the project.

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Why maestro-fetch?

AI agents need data from the web. Most rely on built-in tools like WebFetch (Claude Code), curl, or requests. Here's why mfetch is better:

mfetch vs built-in agent tools

Dimension	mfetch	WebFetch (Claude Code built-in)
Speed	httpx direct — no LLM overhead	HTTP GET + small model processing (extra round-trip)
Token cost	Raw content → main model. Single pass.	Small model summarizes → main model reads summary. Double pass.
Content quality	Full raw markdown, tables as DataFrames, PDFs via Docling	Summarized by small model — large pages truncated, details lost
Recall rate	4-tier browser fallback (Extension → CDP → httpx → Playwright), anti-bot bypass, login session reuse	Plain HTTP GET only — no JS rendering, no auth, WAF blocks fail

mfetch vs other fetch tools

	mfetch	Firecrawl	Jina Reader	crawl4ai
Source types	7 adapters + community sources	Web only	Web only	Web only
PDF / Excel / CSV	Native (Docling + openpyxl)	Separate tool	No	No
Video transcription	yt-dlp + Whisper	No	No	No
Cloud storage	Google Drive, Dropbox, Baidu Pan	No	No	No
Binary datasets	GeoTIFF, NetCDF, Parquet, HDF5, Stata, ...	No	No	No
Browser backends	4 pluggable (Extension, CDP, httpx, Playwright)	Hosted only	Hosted only	Playwright only
Auth / login reuse	CDP reuses Chrome sessions, cookie import	No	No	No
Hosting	Local, no API key required	SaaS ($)	SaaS ($)	Local
Community adapters	Extensible (economics, climate, social, ...)	No	No	No
Cache	SQLite + content-addressed + TTL + LRU	No	No	No
Batch operations	Concurrent with configurable parallelism	API-based	No	No
Interactive sessions	session start/click/fill/screenshot/eval	No	No	No

maestro-fetch treats "fetch" as a universal problem -- not just web scraping. Give it any URI and it figures out the rest: route to the right adapter, pick a browser backend if needed, parse the content, return markdown or structured data.

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Benchmarks

Tested on macOS (Apple Silicon), Python 3.11, uv 0.11.2. March 2026.

Installation

Method	Time	Notes
uv tool install "maestro-fetch[all]"	~8s (200 packages)	Global command, no venv management
pip install "maestro-fetch[all]"	~45s	Requires manual venv setup

Fetch speed (single URL, public static page)

Tool	Pipeline	Latency
mfetch (httpx)	HTTP GET → html2text → raw markdown	~200ms
mfetch (Extension/CDP)	Chrome tab → extract → markdown	~500ms
WebFetch	HTTP GET → html2text → small LLM call → summary	~2-5s
curl + manual parse	HTTP GET → raw HTML (no processing)	~150ms

Token efficiency

Tool	Flow	Effective token cost
mfetch	Raw content → main model (Opus/Sonnet) processes it	1x
WebFetch	Small model processes content (hidden tokens) → summary → main model	~2x (double pass)

Content fidelity

Scenario	mfetch	WebFetch
10 KB HTML page	100% content preserved	~90% (minor summarization)
100 KB HTML page	100% content preserved	~60% (significant truncation)
PDF with tables	Tables as DataFrames, full text	Not supported
JS-rendered SPA	Full render via Extension/CDP	Fails (no JS engine)
Login-required page	CDP reuses Chrome session	Fails (no auth)

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Supported Sources

Adapter	Source types	Examples
web	HTML pages, APIs, SPAs	Any URL; falls back through Extension → CDP → httpx → Playwright
doc	Documents and spreadsheets	.pdf, .xlsx, .xls, .ods, .csv
binary	Archives, geospatial, data science	.zip, .parquet, .tif, .nc, .hdf5, .shp, .feather
cloud	Cloud storage	Google Drive, Google Docs/Sheets, Dropbox
media	Video and audio	YouTube, Vimeo (transcription via yt-dlp + Whisper)
baidu_pan	Baidu Pan	pan.baidu.com links via OAuth + PCS API
browser	Authenticated / JS-heavy pages	Playwright interactive sessions
source	Community adapters	World Bank, FRED, NOAA, academic datasets, ...

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CLI Usage

Fetch any URL

mfetch "https://example.com"                       # auto-detect, markdown output
mfetch "https://example.com/report.pdf"            # PDF -> markdown
mfetch "https://example.com" --output json         # JSON output
mfetch "https://example.com" --timeout 120         # custom timeout
mfetch "https://example.com" --batch urls.txt      # batch from file

Community source adapters

mfetch source update                               # pull latest adapters
mfetch source list                                 # show all adapters
mfetch source list --category economics            # filter by category
mfetch source info worldbank/gdp                   # show args and examples
mfetch source run worldbank/gdp CN                 # fetch World Bank GDP for China

Interactive browser sessions

mfetch session start "https://login-required.com"
mfetch session fill "#email" "[e-mail]"
mfetch session click "#submit"
mfetch session snapshot                            # current page as markdown
mfetch session screenshot                          # save screenshot
mfetch session end

Cache management

mfetch cache list                                  # show cached entries
mfetch cache clear                                 # clear all
mfetch cache clear --older-than 7d                 # evict old entries

Configuration

mfetch config init                                 # generate ~/.maestro-fetch/config.toml
mfetch config show                                 # display current config

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Python SDK

from maestro_fetch import fetch, batch_fetch

# Auto-detect and fetch
result = await fetch("https://example.com/data")
result.content       # markdown text
result.source_type   # "web" | "doc" | "cloud" | "media" | "binary"
result.tables        # list[pd.DataFrame] (if tabular data found)
result.metadata      # provenance dict
result.raw_path      # Path to cached raw file

# Batch with concurrency
results = await batch_fetch(urls, concurrency=10)

# LLM structured extraction (requires ANTHROPIC_API_KEY or OPENAI_API_KEY)
result = await fetch(
    "https://worldbank.org/report.pdf",
    schema={"country": str, "gdp": float},
    provider="anthropic",
)

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Installation

Recommended: uv (global command, no venv)

uv tool install maestro-fetch                # core only
uv tool install "maestro-fetch[all]"         # everything (PDF, media, browser, LLM, social)

pip

pip install maestro-fetch                    # core
pip install maestro-fetch[pdf]               # PDF + Excel (Docling, openpyxl)
pip install maestro-fetch[media]             # YouTube/audio (yt-dlp, Whisper)
pip install maestro-fetch[browser]           # Interactive sessions (Playwright)
pip install maestro-fetch[anthropic]         # Claude LLM extraction
pip install maestro-fetch[openai]            # GPT LLM extraction
pip install maestro-fetch[social]            # Twitter/Reddit API adapters
pip install maestro-fetch[all]               # Everything

Development setup

git clone https://github.com/maestro-ai-stack/maestro-fetch.git
cd maestro-fetch
uv sync --extra dev                          # or: python3.11 -m venv .venv && pip install -e ".[dev]"
pytest tests/ -v

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Works With

maestro-fetch integrates as a tool or skill in these AI agent environments:

• Claude Code -- via skills ecosystem or plugin marketplace
• Cursor -- as a CLI tool in agent mode
• OpenAI Codex -- as a shell tool
• Gemini CLI -- as an MCP tool
• OpenCode / Trae -- via CLI or MCP bridge

See the maestro-fetch skill definition for integration details.

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Architecture

CLI / SDK / MCP
       ↓
   Router (URL type detection via regex)
       ↓
   Adapter dispatch (priority: BaiduPan > Cloud > Binary > Doc > Web)
       ↓
   Web adapter fallback chain:
       Extension (real Chrome + opencli daemon, full auth)
           ↓ fail/unavailable
       CDP (Chrome DevTools Protocol, session reuse)
           ↓ fail/unavailable
       httpx (plain async GET, fastest for static pages)
           ↓ fail/WAF detected
       Playwright (headless Chromium, anti-bot stealth)
       ↓
   Optional: LLM extraction (--schema)
       ↓
   Cache (SQLite + content-addressed files, TTL)
       ↓
   FetchResult → markdown | json | csv | parquet

Router decision chain: (1) match community source adapter (@meta) → dispatch to source; (2) match built-in adapter by URL pattern → dispatch directly; (3) web fallback chain for everything else.

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Configuration

Config lives at ~/.maestro-fetch/config.toml. Generate with mfetch config init.

[cache]
max_size = "5GB"
default_ttl = 86400

[backends]
priority = ["extension", "cdp", "playwright"]

[backends.extension]
enabled = true
port = 19825

[backends.cdp]
endpoint = "http://127.0.0.1:9222"

Storage: ~/.maestro-fetch/ contains config.toml, cache.db, cache/, sources/, custom/, auth/.

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Roadmap

0.3.x — Polish

• Streaming output — yield chunks as they arrive for long pages and large PDFs
• MCP server — expose mfetch as an MCP tool for any agent (FastMCP)
• Retry with backoff — configurable retry policy per adapter
• mfetch pipe — stdin/stdout piping for Unix composability

0.4.x — Power

• Parallel batch with progress — tqdm progress bar, per-URL status reporting
• Diff mode — mfetch diff <url> compares cached vs live content, shows delta
• Schema library — pre-built extraction schemas for common pages (arXiv, PubMed, SEC filings, ...)
• Proxy rotation — SOCKS5/HTTP proxy support for high-volume scraping

1.0 — Fetch Anything

Any URI scheme → mfetch <uri> → clean structured output.

• Database — mfetch postgres://... / mfetch bigquery://... → DataFrame
• Cloud objects — mfetch s3://bucket/key / mfetch gs://... / mfetch az://...
• FTP/SFTP — mfetch sftp://host/path
• Email — mfetch imap://... → extract attachments and body
• Torrent — mfetch magnet:?xt=...
• IPFS — mfetch ipfs://Qm...
• Real-time feeds — mfetch ws://... / mfetch mqtt://...
• Plugin marketplace — mfetch plugin install <name>
• Watch mode — mfetch watch <url> --interval 5m with change detection

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Contributing

Core improvements -- open issues and PRs on this repo.

New source adapters -- contribute to maestro-ai-stack/maestro-fetch-sources. Each adapter is a single Python file with an @meta header and an async def run(ctx, ...) function.

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License

MIT

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Built by Maestro -- Singapore AI product studio.