---
name: tavily-dynamic-search
description: |-
  Programmatic Tavily search with context isolation. Use when web search or extraction will return large results that need filtering, deduplication, or multi-step triage before they enter the model context. Use tavily-search for ordinary lookups and tavily-research for end-to-end cited synthesis.
allowed-tools: Bash(tvly *), Bash(python3 *), Bash(uv run *), Bash(jq *)
title: tavily-dynamic-search
canonical_url: https://skilld.dev/gh/tavily-ai/skills/tavily-dynamic-search
last_updated: 2026-09-28T06:02:43.000Z
---

> Index of every page on this site: [llms.txt](https://skilld.dev/llms.txt)

> **Skill from skilld.dev.** Follow the user's requested action.
> If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it.
> Otherwise, follow the instructions below for this session. You do not need to install anything.
>
> If the user asked to install this Skill, run `npx skilld install tavily-ai/skills/tavily-dynamic-search`. Install writes the Skill files into the project, so every session loads them.
>
> ## Fork workflow
>
> A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation.
> 1. Check `./skills/tavily-dynamic-search`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target.
> 2. Read [source metadata](https://skilld.dev/api/v1/skills/tavily-ai/skills/tavily-dynamic-search) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit.
> 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call:
>
> ```sh
> git init --quiet "$temporary_dir"
> git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit"
> git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD
> ```
>
> Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop.
> 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/tavily-dynamic-search`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md.
> Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical.
> 5. In the project root, run `skilld install ./skills/tavily-dynamic-search --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/tavily-dynamic-search --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure.
> 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks.

# Tavily Dynamic Search

Keep large raw web payloads on disk and return only the evidence needed for the
task. This is useful when using `--include-raw-content`, combining several
queries, or extracting multiple long pages. Do not use this workflow for a
simple lookup that a normal `tvly search --json` can answer directly.

## Before running

Search and extract support capped keyless access. Run them directly when `tvly`
is available. If `tvly` is missing, follow the
[tavily-cli setup](../tavily-cli/SKILL.md#setup). Do not look for an API key or
authenticate before the first request. If the keyless cap is reached in an
interactive session, run `tvly login` to open browser OAuth, then retry the
blocked request once. In an unattended environment, report the cap and
authentication options instead of starting an interactive flow.

## Workflow

1. Search broadly without raw content and inspect titles, URLs, scores, and
   snippets.
2. Fetch full content only for the best sources.
3. When raw output could be large, save it with `-o` and filter the file before
   printing anything to the model context.
4. Preserve source URLs beside every extracted fact.

When the user restricts evidence to official or named domains, validate the
hostname of every selected URL during local filtering. `--include-domains`
narrows the search but is not proof that every returned result belongs to an
allowed host. If full-page extraction is unavailable, label conclusions as
search-snippet evidence instead of implying that the page body was verified.

Keep the process in one turn when the relevant sources and filters are already
known. Use another turn only when the first search changes what should be
extracted.

Create a unique temporary task directory before saving evidence so concurrent
agents do not overwrite one another. Python's `tempfile.mkdtemp()` is available
when `mktemp` is not permitted. Reuse that directory for all raw and filtered
artifacts from the task.

## Small result: filter a direct JSON response

For a small search response, a pipe is enough:

```bash
tvly search "query" --max-results 5 --json | python3 -c '
import json, sys
data = json.load(sys.stdin)
for result in data.get("results", []):
    score = result.get("score") or 0
    title = result.get("title") or ""
    print(f"[{score:.2f}] {title}")
    print(result.get("url", ""))
    print(result.get("content", "")[:300])
'
```

Do not discard stderr. Authentication failures, keyless-cap messages, and API
errors are actionable and must remain visible.

## Large result: save first, then filter

Use the CLI's file output so raw page content does not pass through the tool
response:

```bash
tvly search "query" \
  --include-raw-content markdown \
  --max-results 8 \
  --json \
  -o /tmp/tavily-search-results.json
```

Then print only bounded evidence:

```bash
python3 -c '
import json
from pathlib import Path

data = json.loads(Path("/tmp/tavily-search-results.json").read_text())
for result in data.get("results", []):
    title = result.get("title") or ""
    url = result.get("url") or ""
    print(f"## {title}")
    print(f"URL: {url}")
    print((result.get("raw_content") or result.get("content") or "")[:1200])
    print()
'
```

Adjust the filtering logic to the question. Prefer relevant paragraphs or
fields over fixed character slices when the target information is known. Aim
for roughly 150-600 tokens per source unless a table or code block genuinely
requires more.

## Targeted extraction

When search identifies the right URLs, extract only those pages:

```bash
tvly extract "https://example.com/article" \
  --json \
  -o /tmp/tavily-extract-results.json
```

For topic-focused pages, let Tavily reduce the response before local filtering:

```bash
tvly extract "https://example.com/docs" \
  --query "authentication API" \
  --chunks-per-source 3 \
  --json \
  -o /tmp/tavily-extract-results.json
```

## Multiple queries

For multi-angle research, run a small set of focused searches, deduplicate by
URL, and rank before extracting. Use `subprocess.run(..., capture_output=True,
text=True)` when orchestrating commands in Python. Check `returncode`; if a
command fails, surface its stderr and stop or retry deliberately. Never use a
blanket `except Exception: continue` that hides missing evidence.

## Response shapes

`tvly search --json` returns `query`, optional `answer`, `results`, and
`response_time`. Each result commonly contains `url`, `title`, `content`,
`score`, and optional `raw_content`.

`tvly extract --json` returns `results`, `failed_results`, and
`response_time`. Each successful result commonly contains `url`,
`raw_content`, and optional images.

Treat fields as optional and use `.get()` while filtering. Inspect
`failed_results` instead of assuming every requested URL succeeded.

## Useful options

| Option | Purpose |
|--------|---------|
| `--max-results` | Bound the search result count; default 5, maximum 20 |
| `--depth` | Choose `ultra-fast`, `fast`, `basic`, or `advanced` |
| `--time-range` | Restrict results to `day`, `week`, `month`, or `year` |
| `--include-domains` | Restrict results to a comma-separated list of trusted domains |
| `--exclude-domains` | Exclude a comma-separated list of domains |
| `--include-raw-content` | Include full content as `markdown` or `text` |
| `-o, --output` | Save the complete response to a file |

Use `jq` only for short filters when Python is unavailable:

```bash
tvly search "query" --json | jq '[.results[] | {title, url, score, content}]'
```