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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
125 lines
4.5 KiB
Plaintext
125 lines
4.5 KiB
Plaintext
---
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title: "Tavily Search Tool"
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description: "Perform comprehensive web searches using the Tavily Search API"
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icon: "magnifying-glass"
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mode: "wide"
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---
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The `TavilySearchTool` provides an interface to the Tavily Search API, enabling CrewAI agents to perform comprehensive web searches. It allows for specifying search depth, topics, time ranges, included/excluded domains, and whether to include direct answers, raw content, or images in the results.
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## Installation
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To use the `TavilySearchTool`, you need to install the `tavily-python` library:
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```shell
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uv add 'crewai[tools]' tavily-python
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```
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## Environment Variables
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Ensure your Tavily API key is set as an environment variable:
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```bash
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export TAVILY_API_KEY='your_tavily_api_key'
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```
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Get an API key at https://app.tavily.com/ (sign up, then create a key).
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## Example Usage
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Here's how to initialize and use the `TavilySearchTool` within a CrewAI agent:
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```python
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import os
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from crewai import Agent, Task, Crew
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from crewai_tools import TavilySearchTool
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# Ensure the TAVILY_API_KEY environment variable is set
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# os.environ["TAVILY_API_KEY"] = "YOUR_TAVILY_API_KEY"
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# Initialize the tool
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tavily_tool = TavilySearchTool()
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# Create an agent that uses the tool
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researcher = Agent(
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role='Market Researcher',
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goal='Find information about the latest AI trends',
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backstory='An expert market researcher specializing in technology.',
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tools=[tavily_tool],
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verbose=True
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)
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# Create a task for the agent
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research_task = Task(
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description='Search for the top 3 AI trends in 2024.',
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expected_output='A JSON report summarizing the top 3 AI trends found.',
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agent=researcher
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)
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# Form the crew and kick it off
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crew = Crew(
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agents=[researcher],
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tasks=[research_task],
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verbose=2
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)
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result = crew.kickoff()
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print(result)
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```
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## Configuration Options
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The `TavilySearchTool` accepts the following arguments during initialization or when calling the `run` method:
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- `query` (str): **Required**. The search query string.
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- `search_depth` (Literal["basic", "advanced"], optional): The depth of the search. Defaults to `"basic"`.
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- `topic` (Literal["general", "news", "finance"], optional): The topic to focus the search on. Defaults to `"general"`.
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- `time_range` (Literal["day", "week", "month", "year"], optional): The time range for the search. Defaults to `None`.
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- `days` (int, optional): The number of days to search back. Relevant if `time_range` is not set. Defaults to `7`.
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- `max_results` (int, optional): The maximum number of search results to return. Defaults to `5`.
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- `include_domains` (Sequence[str], optional): A list of domains to prioritize in the search. Defaults to `None`.
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- `exclude_domains` (Sequence[str], optional): A list of domains to exclude from the search. Defaults to `None`.
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- `include_answer` (Union[bool, Literal["basic", "advanced"]], optional): Whether to include a direct answer synthesized from the search results. Defaults to `False`.
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- `include_raw_content` (bool, optional): Whether to include the raw HTML content of the searched pages. Defaults to `False`.
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- `include_images` (bool, optional): Whether to include image results. Defaults to `False`.
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- `timeout` (int, optional): The request timeout in seconds. Defaults to `60`.
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## Advanced Usage
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You can configure the tool with custom parameters:
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```python
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# Example: Initialize with specific parameters
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custom_tavily_tool = TavilySearchTool(
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search_depth='advanced',
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max_results=10,
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include_answer=True
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)
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# The agent will use these defaults
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agent_with_custom_tool = Agent(
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role="Advanced Researcher",
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goal="Conduct detailed research with comprehensive results",
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tools=[custom_tavily_tool]
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)
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```
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## Features
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- **Comprehensive Search**: Access to Tavily's powerful search index
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- **Configurable Depth**: Choose between basic and advanced search modes
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- **Topic Filtering**: Focus searches on general, news, or finance topics
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- **Time Range Control**: Limit results to specific time periods
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- **Domain Control**: Include or exclude specific domains
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- **Direct Answers**: Get synthesized answers from search results
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- **Content Filtering**: Prevent context window issues with automatic content truncation
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## Response Format
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The tool returns search results as a JSON string containing:
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- Search results with titles, URLs, and content snippets
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- Optional direct answers to queries
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- Optional image results
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- Optional raw HTML content (when enabled)
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Content for each result is automatically truncated to prevent context window issues while maintaining the most relevant information. |