Spawn thousands of sub-agents in parallel. Pass in a list of strings, files, or folders, get back structured data and generated artifacts.

Each subtask gets its own Modal sandbox and its own OpenAI Agents SDK agent. The agent has shell + file-edit + web-search tools plus a single submit(...) that ends the task. Results come back as JSON / CSV rows plus any files or folders the agent wrote.

npx skills add datacurve-ai/wide-research

This is the primary way to use wide-research from an agent. The skill teaches the agent when to fan out work, how to write job configs, and how to collect the outputs.

uv tool install wide-research
wide-research --help         # or the short alias: wr --help
wide-research doctor         # check credentials + paths

git clone https://github.com/datacurve-ai/wide-research ~/code/wide-research
cd ~/code/wide-research
uv sync --extra test
source .venv/bin/activate

wide-research doctor will tell you what's missing. If you use a custom env file, check it with wide-research doctor --env-file PATH. Short version:

• Modal: modal token set --token-id … --token-secret …
• OPENAI_API_KEY and optional OPENAI_BASE_URL — any of (first hit wins):
  1. --env-file PATH
  2. ./.env (current directory)
  3. ~/.wide-research/.env (recommended for global installs)
  4. shell environment
  5. repo .env (source checkouts only)

The OpenAI credential and base URL are used by the host-side Agent runner and are not injected into sandboxes. Use TOML secrets = [...] only for credentials that the sandbox itself should be able to read.

Everything the tool owns is under ~/.wide-research/:

• ~/.wide-research/.env — credentials
• ~/.wide-research/runs/ — one subdirectory per run

Override with WIDE_RESEARCH_HOME=/somewhere/else (whole base) or WIDE_RESEARCH_RUNS_DIR=/somewhere/else (runs only).

wide-research run defaults to spawn-detached + auto-tail: it starts a worker process, then follows the live merged log in your terminal. Ctrl+C only detaches the tail; the worker keeps running. Use --detach / -d to spawn and exit immediately, or --foreground / -f to run in-process for scripts/CI.

# 1. Create the smallest smoke-test config.
cat > echo.toml <<'EOF'
brief = "Echo each input."
name = "echo_test"
title = "Echo Test"
target_count = 3
inputs = ["alpha", "beta", "gamma"]

prompt_template = """
Return the input exactly, then call submit(success=true, output={"echo": "{{ input }}"}).
"""

[[output_schema]]
name = "echo"
type = "string"
title = "Echo"
description = "The echoed input."
EOF

# 2. Dry-run — validate a config without spawning sandboxes
wide-research run echo.toml --dry-run

# 3. Render just the first prompt
wide-research plan echo.toml --index 0

# 4. Smoke-test, then full run. Use -f / --foreground for scripts/CI:
wide-research run echo.toml --sample 1 --foreground
wide-research run echo.toml               # detaches, then auto-tails
wide-research run echo.toml --detach      # pure detached; wr wait to sync

# 5. Look at past runs
wide-research list
wide-research inspect ~/.wide-research/runs/<dir>
wide-research inspect ~/.wide-research/runs/<dir> --failed-only

If you cloned the repository, you can also run the checked-in examples under examples/.

TOML. Six required top-level keys + an output_schema array-of-tables:

brief = "Find the current CEO of each company."
name = "find_ceos"
title = "Find CEOs"
target_count = 3
inputs = ["Apple", "Microsoft", "Alphabet"]

prompt_template = """
Research the current CEO of {{ input }}.
Use web_search to verify, then call submit(success=true, output={"ceo": "..."}).
"""

[[output_schema]]
name = "ceo"
type = "string"
title = "CEO"
description = "Verified name of the current CEO."

See skills/wide-research/references/CONFIG.md for the full schema, and skills/wide-research/references/PRESETS.md for ready-to-paste [resources] / [image] blocks (research / coding / DinD / GPU).

• docs/agent-design.md — harness/compute split, the SDK-native tool set, and how the loop terminates.
• docs/file-io.md — how files and directories move in and out of sandboxes (<file> tags, mount_files, file / directory output fields, size limits).

For short configs you'd rather not leave on disk:

# Inline string
wr run --inline "$(cat <<'EOF'
brief = "tiny inline smoke"
name = "inline_smoke"
title = "Inline Smoke"
prompt_template = "Echo {{ input }}"
target_count = 2
inputs = ["hi", "there"]

[[output_schema]]
name = "echo"
type = "string"
title = "Echo"
description = "echoed"
EOF
)"

# Stdin via '-'
cat my-config.toml | wr run -
wr plan - < my-config.toml

wr run prints the run dir + pid and auto-tails by default. For a run started with --detach, or after Ctrl+C detaches the tail, follow along with:

wr tail ~/.wide-research/runs/<dir>        # watch what each subtask is doing
wr wait ~/.wide-research/runs/<dir>        # block silently; non-zero exit if any fail
tail -f ~/.wide-research/runs/<dir>/worker.log   # low-level worker log

# Stop a run early (tears down every live Modal sandbox first):
wr stop ~/.wide-research/runs/<dir>

# Or, if you'd rather have `run` block the current terminal:
wr run job.toml --foreground

Run summaries include token usage and best-effort cost estimates. By default, wide-research fetches the public OpenRouter model catalog from https://openrouter.ai/api/v1/models and caches it under ~/.wide-research/cache/ for a week. Set WR_DISABLE_PRICE_FETCH=1 to avoid that network request, or provide your own prices with WR_PRICE_TABLE_JSON.

MIT. See LICENSE.

Maintainers can build and validate the package locally with:

uv build
uvx twine check dist/*

Publish with uv publish after the package version has been bumped and the Modal/OpenAI example tests pass with live credentials.

See AGENTS.md.