Added docs for TavilyGetResearch

docs/ar/tools/search-research/tavilyextractortool.mdx

@@ -12,7 +12,7 @@ mode: "wide"
 لاستخدام `TavilyExtractorTool`، تحتاج إلى تثبيت مكتبة `tavily-python`:
 
 ```shell
-pip install 'crewai[tools]' tavily-python
+uv add 'crewai[tools]' tavily-python
 ```
 
 تحتاج أيضاً إلى تعيين مفتاح Tavily API كمتغير بيئة:

docs/ar/tools/search-research/tavilyresearchtool.mdx

@@ -0,0 +1,125 @@
+---
+title: "Tavily Research Tool"
+description: "Run multi-step research tasks and get cited reports using the Tavily Research API"
+icon: "flask"
+mode: "wide"
+---
+
+The `TavilyResearchTool` lets CrewAI agents kick off Tavily research tasks, returning a synthesized, cited report (or a stream of progress events) instead of raw search results. Use it when an agent needs an investigative answer rather than a single web search.
+
+## Installation
+
+To use the `TavilyResearchTool`, install the `tavily-python` library alongside `crewai-tools`:
+
+```shell
+uv add 'crewai[tools]' tavily-python
+```
+
+## Environment Variables
+
+Set your Tavily API key:
+
+```bash
+export TAVILY_API_KEY='your_tavily_api_key'
+```
+
+Get an API key at [https://app.tavily.com/](https://app.tavily.com/) (sign up, then create a key).
+
+## Example Usage
+
+```python
+import os
+from crewai import Agent, Crew, Task
+from crewai_tools import TavilyResearchTool
+
+# Ensure TAVILY_API_KEY is set in your environment
+# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"
+
+tavily_tool = TavilyResearchTool()
+
+researcher = Agent(
+    role="Research Analyst",
+    goal="Investigate questions and produce concise, well-cited briefings.",
+    backstory=(
+        "You are a meticulous analyst who delegates web research to the Tavily "
+        "Research tool, then synthesizes the findings into short briefings."
+    ),
+    tools=[tavily_tool],
+    verbose=True,
+)
+
+research_task = Task(
+    description=(
+        "Investigate notable open-source agent orchestration frameworks released "
+        "in the last six months and summarize their differentiators."
+    ),
+    expected_output="A bulleted briefing with citations.",
+    agent=researcher,
+)
+
+crew = Crew(agents=[researcher], tasks=[research_task])
+print(crew.kickoff())
+```
+
+## Configuration Options
+
+The `TavilyResearchTool` accepts the following arguments — all can be set on the tool instance (defaults for every call) or per-call via the agent's tool input:
+
+- `input` (str): **Required.** The research task or question to investigate.
+- `model` (Literal["mini", "pro", "auto"]): The Tavily research model. `"auto"` lets Tavily pick; `"mini"` is faster/cheaper; `"pro"` is the most capable. Defaults to `"auto"`.
+- `output_schema` (dict | None): Optional JSON Schema that structures the research output. Useful when you want strictly typed results.
+- `stream` (bool): When `True`, the tool returns an iterator of SSE chunks emitting research progress and the final result instead of a single string. Defaults to `False`.
+- `citation_format` (Literal["numbered", "mla", "apa", "chicago"]): Citation format for the report. Defaults to `"numbered"`.
+
+## Advanced Usage
+
+### Configure defaults on the tool instance
+
+```python
+from crewai_tools import TavilyResearchTool
+
+tavily_tool = TavilyResearchTool(
+    model="pro",                # use Tavily's most capable research model
+    citation_format="apa",      # APA-style citations
+)
+```
+
+### Stream research progress
+
+When `stream=True`, the tool returns a generator (or async generator from `_arun`) of SSE chunks so your application can surface incremental progress:
+
+```python
+tavily_tool = TavilyResearchTool(stream=True)
+
+for chunk in tavily_tool.run(input="Summarize recent advances in retrieval-augmented generation."):
+    print(chunk)
+```
+
+### Structured output via JSON Schema
+
+Pass an `output_schema` when you need a typed result instead of a free-form report:
+
+```python
+output_schema = {
+    "type": "object",
+    "properties": {
+        "summary": {"type": "string"},
+        "key_points": {"type": "array", "items": {"type": "string"}},
+        "sources": {"type": "array", "items": {"type": "string"}},
+    },
+    "required": ["summary", "key_points", "sources"],
+}
+
+tavily_tool = TavilyResearchTool(output_schema=output_schema)
+```
+
+## Features
+
+- **End-to-end research**: Returns a synthesized, cited report rather than raw search hits.
+- **Model selection**: Trade off cost, speed, and depth via `mini`, `pro`, or `auto`.
+- **Streaming**: Stream incremental progress and results as SSE chunks for responsive UIs.
+- **Structured output**: Coerce results to a JSON Schema you define.
+- **Multiple citation styles**: Choose from numbered, MLA, APA, or Chicago citations.
+- **Sync and async**: Use either `_run` or `_arun` depending on your application's runtime.
+
+Refer to the [Tavily API documentation](https://docs.tavily.com/) for full details on the Research API.

docs/ar/tools/search-research/tavilysearchtool.mdx

@@ -12,7 +12,7 @@ mode: "wide"
 لاستخدام `TavilySearchTool`، تحتاج إلى تثبيت مكتبة `tavily-python`:
 
 ```shell
-pip install 'crewai[tools]' tavily-python
+uv add 'crewai[tools]' tavily-python
 ```
 
 ## متغيرات البيئة