Merge branch 'main' into cursor/simplify-filereadtool-docs-ac84

Bring locale pages in line with the updated English source of truth.

Co-authored-by: Rip&Tear <theCyberTech@users.noreply.github.com>

.github/CONTRIBUTING.md

@@ -103,8 +103,7 @@ chore(deps): bump pydantic to 2.11
 - Keep PRs focused — avoid bundling unrelated changes
 - PRs over 500 lines are labeled `size/XL` automatically
 - Title must follow the same conventional commit format
-- Link related issues where applicable (`#123`, `Fixes #123`, or the issue URL)
-- First-time contributors must open or pick an existing **open** issue first, then mention it in the PR title or body (for example `#123`). PRs without a linked open issue are closed automatically.
+- Link related issues where applicable
 
 ## Testing
 

.github/pull_request_template.md

@@ -1,23 +0,0 @@
-## Related issue
-
-Fixes #
-
-<!--
-First-time contributors must mention an existing open issue in this repo
-(for example #123). PRs without a linked open issue are closed automatically.
--->
-
-## Summary
-
-<!-- Explain the solution and why. -->
-
-## Verification
-
-<!-- List the automated and manual checks used to verify the change. -->
-
-- [ ] Tests added or updated for the changed behavior
-- [ ] Relevant tests and quality checks pass locally
-
-## Additional context
-
-<!-- Include screenshots, compatibility notes, follow-up work, or "None". -->

.github/workflows/ftc-require-issue.yml

@@ -1,121 +0,0 @@
-name: First-time contributor issue required
-
-on:
-  pull_request_target:
-    types: [opened, edited, reopened]
-
-permissions:
-  pull-requests: write
-  issues: read
-
-concurrency:
-  group: ftc-require-issue-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
-jobs:
-  require-issue:
-    # Allow-list returning contributors. FIRST_TIMER / FIRST_TIME_CONTRIBUTOR
-    # are often NONE on pull_request_target at opened time, which skipped the
-    # previous deny-list and left first-timer PRs open.
-    if: >
-      github.event.pull_request.user.type != 'Bot' &&
-      !contains(fromJSON('["MEMBER","OWNER","COLLABORATOR","CONTRIBUTOR"]'),
-                github.event.pull_request.author_association)
-    runs-on: ubuntu-latest
-    steps:
-      - name: Require an open issue
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REPO: ${{ github.repository }}
-          AUTHOR_ASSOCIATION: ${{ github.event.pull_request.author_association }}
-        run: |
-          python3 << 'PY'
-          import json
-          import os
-          import re
-          import subprocess
-          import sys
-
-          repo = os.environ["REPO"]
-          pr_number = os.environ["PR_NUMBER"]
-          owner, name = repo.split("/", 1)
-          print(
-              "author_association=",
-              os.environ.get("AUTHOR_ASSOCIATION", ""),
-              sep="",
-          )
-
-          patterns = (
-              re.compile(r"(?<![\w./-])#(\d+)\b"),
-              re.compile(rf"{re.escape(owner)}/{re.escape(name)}#(\d+)\b"),
-              re.compile(
-                  rf"https://github\.com/{re.escape(owner)}/{re.escape(name)}/issues/(\d+)\b"
-              ),
-          )
-
-          def gh_json(*args: str) -> dict:
-              return json.loads(
-                  subprocess.check_output(["gh", *args], text=True)
-              )
-
-          def is_open_repo_issue(number: int) -> bool:
-              result = subprocess.run(
-                  ["gh", "api", f"repos/{repo}/issues/{number}"],
-                  capture_output=True,
-                  text=True,
-              )
-              if result.returncode != 0:
-                  stderr = result.stderr or ""
-                  if "404" in stderr or "Not Found" in stderr:
-                      return False
-                  raise RuntimeError(
-                      f"GitHub API error looking up #{number}: {stderr}"
-                  )
-              payload = json.loads(result.stdout)
-              if "pull_request" in payload:
-                  return False
-              return (payload.get("state") or "").lower() == "open"
-
-          pr = gh_json(
-              "pr", "view", pr_number, "--repo", repo, "--json", "title,body,state"
-          )
-          text = f"{pr.get('title') or ''}\n{pr.get('body') or ''}"
-          candidates = {
-              int(match)
-              for pattern in patterns
-              for match in pattern.findall(text)
-          }
-          if any(is_open_repo_issue(number) for number in sorted(candidates)):
-              sys.exit(0)
-
-          if (pr.get("state") or "").upper() == "CLOSED":
-              sys.exit(0)
-
-          comment = f"""Thanks for the pull request.
-          
-          First-time contributors need an associated open issue before we can review a PR.
-          
-          1. Open an issue with a [template](https://github.com/{repo}/issues/new/choose), or pick an existing open one.
-          2. Open a new PR (or reopen this one) whose title or body mentions that issue, for example `#123`.
-          
-          See the [contributing guide](https://github.com/{repo}/blob/main/.github/CONTRIBUTING.md).
-          """
-          subprocess.run(
-              [
-                  "gh",
-                  "pr",
-                  "comment",
-                  pr_number,
-                  "--repo",
-                  repo,
-                  "--body",
-                  comment,
-              ],
-              check=True,
-          )
-          subprocess.run(
-              ["gh", "pr", "close", pr_number, "--repo", repo],
-              check=True,
-          )
-          PY

.github/workflows/vulnerability-scan.yml

@@ -86,27 +86,11 @@ jobs:
             --skip-editable
             --format json
             --output pip-audit-report.json
-            # chromadb <=1.5.9: Python HTTP server issues. No PyPI release beyond
-            # 1.5.9 yet. CrewAI only uses PersistentClient (embedded), not the
-            # HTTP server.
-            # GHSA-f4j7-r4q5-qw2c (CVE-2026-45829): pre-auth RCE. Fix merged in
-            # chroma-core/chroma#7237.
+            # chromadb <=1.5.9 (CVE-2026-45829 / GHSA-f4j7-r4q5-qw2c): pre-auth RCE in
+            # the Python HTTP server. Fix merged upstream in chroma-core/chroma#7237
+            # but no PyPI release beyond 1.5.9 yet. CrewAI only uses PersistentClient
+            # (embedded), not the HTTP server.
             --ignore-vuln GHSA-f4j7-r4q5-qw2c
-            # GHSA-2wm9-hf6c-p5cr (CVE-2026-45830): authenticated cross-tenant IDOR.
-            --ignore-vuln GHSA-2wm9-hf6c-p5cr
-            # GHSA-36p7-vc44-83pf (CVE-2026-45833): authenticated trust_remote_code
-            # injection on the collection-update endpoint.
-            --ignore-vuln GHSA-36p7-vc44-83pf
-            # GHSA-xph7-9rjv-w5fr (CVE-2026-45831): SimpleRBACAuthorizationProvider
-            # ignores tenant/database/collection scope.
-            --ignore-vuln GHSA-xph7-9rjv-w5fr
-            # nltk <=3.10.3: GHSA-8mgp-746c-j5xp (CVE-2026-81726): model-artifact
-            # APIs bypass pathsec and read/write outside allowed roots. No patched
-            # PyPI release yet (fixes are on nltk develop only). Transitive via
-            # crewai-tools[xml] -> unstructured; CrewAI does not call those APIs.
-            # TODO: drop this ignore when bumping nltk past 3.10.3 to a patched
-            # release; keep the ignore list in sync with .pre-commit-config.yaml.
-            --ignore-vuln GHSA-8mgp-746c-j5xp
           )
           uv run pip-audit "${pip_audit_args[@]}"
         continue-on-error: true

.pre-commit-config.yaml

@@ -29,7 +29,6 @@ repos:
       - id: pip-audit
         name: pip-audit
         # Keep this ignore list in sync with .github/workflows/vulnerability-scan.yml.
-        # TODO: drop --ignore-vuln GHSA-8mgp-746c-j5xp when bumping nltk past 3.10.3.
         entry: >-
           bash -c 'source .venv/bin/activate && uv run pip-audit --skip-editable
           --ignore-vuln PYSEC-2024-277
@@ -57,11 +56,7 @@ repos:
           --ignore-vuln PYSEC-2025-216
           --ignore-vuln PYSEC-2025-217
           --ignore-vuln PYSEC-2025-218
-          --ignore-vuln GHSA-f4j7-r4q5-qw2c
-          --ignore-vuln GHSA-2wm9-hf6c-p5cr
-          --ignore-vuln GHSA-36p7-vc44-83pf
-          --ignore-vuln GHSA-xph7-9rjv-w5fr
-          --ignore-vuln GHSA-8mgp-746c-j5xp' --
+          --ignore-vuln GHSA-f4j7-r4q5-qw2c' --
         language: system
         pass_filenames: false
         stages: [pre-push, manual]

AGENTS.md

@@ -14,23 +14,6 @@ Follow these guidelines when contributing:
 6. Follow software principles such as DRY and YAGNI.
 7. Keep diffs as minimal as possible.
 
-## Message Content
-
-`LLMMessage.content` is `str | list[dict[str, Any]] | None`; the list form is
-multimodal content parts. Never `str()` it — that puts a Python repr
-(`[{'type': 'text', 'text': 'hi'}]`) in front of the model and into memory.
-Collapse a message to text with the helper instead:
-
-```python
-from crewai.utilities.agent_utils import message_content_text
-
-text = message_content_text(msg)  # "" for None; joined text for a parts list
-```
-
-Parts arrive from a model and are typed `dict[str, Any]`, so a `text` key that
-is not a string is possible. `_content_parts_text` skips those blocks rather
-than raising, and names a list with no usable text `[multimodal content]`.
-
 ## Changing Docs
 
 1. Edit MDX under `docs/edge/en/*` and reference it from `docs/docs.json` if

README.md

@@ -91,7 +91,7 @@ intelligent automations.
   - [Learning Resources](#learning-resources)
   - [Understanding Flows and Crews](#understanding-flows-and-crews)
   - [Installation](#1-installation)
-  - [Setting Up Your Crew](#2-setting-up-your-crew)
+  - [Setting Up Your Crew](#2-setting-up-your-crew-with-the-yaml-configuration)
   - [Running Your Crew](#3-running-your-crew)
 - [Key Features](#key-features)
 - [Examples](#examples)
@@ -121,7 +121,7 @@ Four skills that activate automatically when you ask relevant CrewAI questions:
 
 | Skill | When it runs |
 |-------|--------------|
-| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.jsonc` / `main.py` |
+| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
 | `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
 | `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
 | `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
@@ -189,76 +189,47 @@ The true power of CrewAI emerges when combining Crews and Flows. This synergy al
 
 ### Getting Started with Installation
 
-To get started with CrewAI, follow these simple steps. The full walkthrough lives in the [installation guide](https://docs.crewai.com/en/installation).
+To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-CrewAI requires `Python >=3.10 and <3.14`. Check your version with:
+Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
-```bash
-python3 --version
-```
-
-CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling. If you haven't installed `uv` yet, install it first.
-
-**macOS/Linux:**
+First, install CrewAI:
 
 ```shell
-curl -LsSf https://astral.sh/uv/install.sh | sh
+uv pip install crewai
 ```
 
-If your system doesn't have `curl`, you can use `wget`:
+If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
 
 ```shell
-wget -qO- https://astral.sh/uv/install.sh | sh
+uv pip install 'crewai[tools]'
 ```
 
-**Windows:**
+The command above installs the basic package and also adds extra components which require more dependencies to function.
 
-```shell
-powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
-```
+### Troubleshooting Dependencies
 
-If you run into any issues, refer to [UV's installation guide](https://docs.astral.sh/uv/getting-started/installation/).
+If you encounter issues during installation or usage, here are some common solutions:
 
-Then install the CrewAI CLI:
+#### Common Issues
 
-```shell
-uv tool install crewai
-```
+1. **ModuleNotFoundError: No module named 'tiktoken'**
 
-If you encounter a `PATH` warning, run:
+   - Install tiktoken explicitly: `uv pip install 'crewai[embeddings]'`
+   - If using embedchain or other tools: `uv pip install 'crewai[tools]'`
 
-```shell
-uv tool update-shell
-```
+2. **Failed building wheel for tiktoken**
 
-If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/) with *Desktop development with C++*.
+   - Ensure Rust compiler is installed (see installation steps above)
+   - For Windows: Verify Visual C++ Build Tools are installed
+   - Try upgrading pip: `uv pip install --upgrade pip`
+   - If issues persist, use a pre-built wheel: `uv pip install tiktoken --prefer-binary`
 
-Verify the install:
+### 2. Setting Up Your Crew with the YAML Configuration
 
-```shell
-uv tool list
-```
-
-You should see something like:
-
-```shell
-crewai v0.102.0
-- crewai
-```
-
-To upgrade the global CLI later:
-
-```shell
-uv tool install crewai --upgrade
-```
-
-This upgrades the **global `crewai` CLI tool** only. To upgrade the `crewai` version inside a project's virtual environment, see [Upgrading CrewAI in a project](https://docs.crewai.com/en/guides/migration/upgrading-crewai).
-
-### 2. Setting Up Your Crew
-
-`crewai create crew` creates a JSON-first crew project. Agents live in `agents/*.jsonc`, tasks and crew-level settings live in `crew.jsonc`, and `crewai run` loads that JSON definition directly.
+To create a new CrewAI project, run the following CLI (Command Line Interface) command:
 
 ```shell
 crewai create crew <project_name>
@@ -269,126 +240,200 @@ This command creates a new project folder with the following structure:
 ```
 my_project/
 ├── .gitignore
-├── .env
-├── agents/
-│   └── researcher.jsonc
-├── crew.jsonc
-├── knowledge/
 ├── pyproject.toml
 ├── README.md
-├── skills/
-└── tools/
+├── .env
+└── src/
+    └── my_project/
+        ├── __init__.py
+        ├── main.py
+        ├── crew.py
+        ├── tools/
+        │   ├── custom_tool.py
+        │   └── __init__.py
+        └── config/
+            ├── agents.yaml
+            └── tasks.yaml
 ```
 
-If you need the older Python/YAML scaffold with `crew.py`, `config/agents.yaml`, and `config/tasks.yaml`, run:
-
-```shell
-crewai create crew <project_name> --classic
-```
-
-See [Using Annotations](https://docs.crewai.com/en/learn/using-annotations) for the classic pattern.
+You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks.
 
 #### To customize your project, you can:
 
-- Modify `agents/*.jsonc` to define each agent's role, goal, backstory, LLM, tools, and behavior.
-- Modify `crew.jsonc` to define tasks, process, and input defaults.
-- Add custom tools in `tools/` and reference them as `"custom:<name>"`.
-- Add optional knowledge files in `knowledge/` and skill files in `skills/`.
+- Modify `src/my_project/config/agents.yaml` to define your agents.
+- Modify `src/my_project/config/tasks.yaml` to define your tasks.
+- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
+- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
 - Add your environment variables into the `.env` file.
 
-Use `{placeholder}` values in agent and task text, then set defaults in `crew.jsonc` under `inputs`. When you run `crewai run`, the CLI prompts for any missing values.
-
 #### Example of a simple crew with a sequential process:
 
+Instantiate your crew:
+
 ```shell
 crewai create crew latest-ai-development
-cd latest_ai_development
 ```
 
-Then edit the generated files:
+Modify the files as needed to fit your use case:
 
-**agents/researcher.jsonc**
+**agents.yaml**
 
-```jsonc
-{
-  "role": "{topic} Senior Data Researcher",
-  "goal": "Uncover cutting-edge developments in {topic}",
-  "backstory": "You're a seasoned researcher who finds relevant information and presents it clearly.",
-  "llm": "openai/gpt-4o",
-  "tools": ["SerperDevTool"],
-  "settings": {
-    "verbose": true
-  }
-}
+```yaml
+# src/my_project/config/agents.yaml
+researcher:
+  role: >
+    {topic} Senior Data Researcher
+  goal: >
+    Uncover cutting-edge developments in {topic}
+  backstory: >
+    You're a seasoned researcher with a knack for uncovering the latest
+    developments in {topic}. Known for your ability to find the most relevant
+    information and present it in a clear and concise manner.
+
+reporting_analyst:
+  role: >
+    {topic} Reporting Analyst
+  goal: >
+    Create detailed reports based on {topic} data analysis and research findings
+  backstory: >
+    You're a meticulous analyst with a keen eye for detail. You're known for
+    your ability to turn complex data into clear and concise reports, making
+    it easy for others to understand and act on the information you provide.
 ```
 
-**agents/reporting_analyst.jsonc**
+**tasks.yaml**
 
-```jsonc
-{
-  "role": "{topic} Reporting Analyst",
-  "goal": "Create detailed reports based on {topic} data analysis and research findings",
-  "backstory": "You're a meticulous analyst who turns complex data into clear, concise reports.",
-  "llm": "openai/gpt-4o",
-  "settings": {
-    "verbose": true
-  }
-}
+````yaml
+# src/my_project/config/tasks.yaml
+research_task:
+  description: >
+    Conduct a thorough research about {topic}
+    Make sure you find any interesting and relevant information given
+    the current year is 2026.
+  expected_output: >
+    A list with 10 bullet points of the most relevant information about {topic}
+  agent: researcher
+
+reporting_task:
+  description: >
+    Review the context you got and expand each topic into a full section for a report.
+    Make sure the report is detailed and contains any and all relevant information.
+  expected_output: >
+    A fully fledged report with the main topics, each with a full section of information.
+    Formatted as markdown without '```'
+  agent: reporting_analyst
+  output_file: report.md
+````
+
+**crew.py**
+
+```python
+# src/my_project/crew.py
+from crewai import Agent, Crew, Process, Task
+from crewai.project import CrewBase, agent, crew, task
+from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
+
+@CrewBase
+class LatestAiDevelopmentCrew():
+	"""LatestAiDevelopment crew"""
+	agents: List[BaseAgent]
+	tasks: List[Task]
+
+	@agent
+	def researcher(self) -> Agent:
+		return Agent(
+			config=self.agents_config['researcher'],
+			verbose=True,
+			tools=[SerperDevTool()]
+		)
+
+	@agent
+	def reporting_analyst(self) -> Agent:
+		return Agent(
+			config=self.agents_config['reporting_analyst'],
+			verbose=True
+		)
+
+	@task
+	def research_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['research_task'],
+		)
+
+	@task
+	def reporting_task(self) -> Task:
+		return Task(
+			config=self.tasks_config['reporting_task'],
+			output_file='report.md'
+		)
+
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the LatestAiDevelopment crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)
 ```
 
-**crew.jsonc**
+**main.py**
 
-```jsonc
-{
-  "name": "Latest AI Development",
-  "agents": ["researcher", "reporting_analyst"],
-  "tasks": [
-    {
-      "name": "research_task",
-      "description": "Conduct thorough research about {topic}. Find recent, relevant information.",
-      "expected_output": "A list with 10 bullet points of the most relevant information about {topic}.",
-      "agent": "researcher"
-    },
-    {
-      "name": "reporting_task",
-      "description": "Review the research and expand each topic into a full section for a report.",
-      "expected_output": "A markdown report with the main topics, each with a full section of information. No fenced code blocks around the whole document.",
-      "agent": "reporting_analyst",
-      "context": ["research_task"],
-      "output_file": "output/report.md",
-      "markdown": true
+```python
+#!/usr/bin/env python
+# src/my_project/main.py
+import sys
+from latest_ai_development.crew import LatestAiDevelopmentCrew
+
+def run():
+    """
+    Run the crew.
+    """
+    inputs = {
+        'topic': 'AI Agents'
     }
-  ],
-  "process": "sequential",
-  "verbose": true,
-  "inputs": {
-    "topic": "AI Agents"
-  }
-}
+    LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
 ```
 
 ### 3. Running Your Crew
 
-Before running your crew, set the required keys in your `.env` file:
+Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
 
-- Your model provider API key — see [LLM setup](https://docs.crewai.com/en/concepts/llms#setting-up-your-llm)
-- A [Serper.dev](https://serper.dev/) API key if you use web search: `SERPER_API_KEY=YOUR_KEY_HERE`
+- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
+- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
 
-Then install dependencies and run from the project directory:
+Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:
 
 ```shell
-crewai install
+cd my_project
+crewai install (Optional)
+```
+
+To run your crew, execute the following command in the root of your project:
+
+```bash
 crewai run
 ```
 
-If you need additional packages, use `uv add <package-name>`.
+or
 
-You should see the output in the console, and `output/report.md` should be created in the project root.
+```bash
+python src/my_project/main.py
+```
+
+If an error happens due to the usage of poetry, please run the following command to update your crewai package:
+
+```bash
+crewai update
+```
+
+You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.
 
 In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/en/concepts/processes).
 
-For a Flow-first walkthrough, see the [Quickstart](https://docs.crewai.com/en/quickstart).
-
 ## Key Features
 
 CrewAI gives developers a practical foundation for building agentic systems that move from prototype to production: autonomous collaboration where it helps, explicit workflow control where it matters, and Python-native customization throughout.
@@ -656,13 +701,17 @@ A: CrewAI is a lean, fast Python framework built specifically for orchestrating
 
 ### Q: How do I install CrewAI?
 
-A: Install the CrewAI CLI with [UV](https://docs.astral.sh/uv/):
+A: Install CrewAI with [UV](https://docs.astral.sh/uv/):
 
 ```shell
-uv tool install crewai
+uv pip install crewai
 ```
 
-Then create a project with `crewai create crew <project_name>`, run `crewai install`, and start it with `crewai run`. See the [installation guide](https://docs.crewai.com/en/installation) for details.
+For additional tools, use:
+
+```shell
+uv pip install 'crewai[tools]'
+```
 
 ### Q: Is CrewAI a standalone framework?
 

docs/edge/ar/changelog.mdx

@@ -4,104 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
-<Update label="27 أغسطس 2026">
-  ## v1.15.18
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.18)
-
-  ## ما الذي تغير
-
-  ### الميزات
-  - ترقية تدفقات المحادثة إلى حالة مستقرة
-  - تسجيل نشر تم إنشاؤه مع UUID المعطى
-  - تحسين وثائق تدفقات المحادثة وواجهات برمجة التطبيقات
-  - السماح لإعلان بتسمية تنسيق استجابة الموجه
-  - السماح لتدفق الدردشة بإعلان شكل حالته الخاصة
-  - قبول إعدادات LLM على نمط الطاقم في إعلان المحادثة
-  - الإبلاغ عن إنشاء المشروع مع المعرف المُصنّع
-  - تسجيل ما إذا كانت العملية تحتوي على مدخلات، دون تسجيل المدخلات
-  - ملء معرف المشروع من كل أمر مشروع يتم استدعاؤه بواسطة المستخدم
-
-  ### إصلاحات الأخطاء
-  - الحفاظ على نتائج الأداة عندما تكون الإجابة النهائية فارغة
-  - ربط Claude Sonnet 4.6 الافتراضي بنافذة السياق 1M الخاصة به
-  - رفع الحد الأقصى الافتراضي لـ max_tokens من Anthropic لاستدعاءات الأدوات الكبيرة
-  - عرض أجزاء محتوى الرسالة كنص، وليس كتمثيل بايثون
-  - الاحتفاظ بأدوار الرسائل عندما يحصل Agent.kickoff على محادثة
-  - تخطي روابط الاعتراض على تدفقات crewai-internal
-  - تسجيل فشل المهام كفشلات، وليس نجاحات
-  - إصدار دورة حياة التدفق عند استئناف مكتوم
-  - فتح واجهة المستخدم النصية للمحادثة لتدفق دردشة إعلاني
-  - تسجيل crew_memory كسلسلة نصية، وليس كقيمة منطقية
-  - إصدار project_id دائمًا حتى تظل القيم الغائبة والفارغة متميزة
-
-  ### الوثائق
-  - توضيح وثائق المراقبة لـ Arize Phoenix
-
-  ## المساهمون
-
-  @Vidit-Ostwal, @arizedatngo, @joaomdmoura, @lorenzejay, @lucasgomide
-
-</Update>
-
-<Update label="19 أغسطس 2026">
-  ## v1.15.17
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.17)
-
-  ## ما الذي تغيّر
-
-  ### الميزات
-  - إضافة وثائق تدفقات المحادثة التصريحية
-  - توليف طرق المحادثة المدمجة للتصريحات
-  - تمكين التصريحات من قيادة وضع المحادثة
-  - جعل خيار الانضمام إلى المحادثة لا لبس فيه
-  - حمل شريحة AMP على الأدوات المستخرجة من مرجع الشريحة
-  - التعامل مع الرسائل الفردية الكبيرة أثناء تقسيمها
-
-  ### إصلاحات الأخطاء
-  - إصلاح استخدام اسم المضيف URL كاسم خادم MCP HTTP و SSE
-  - إغلاق نطاق الوكيل في كل محاولة فاشلة
-  - نسب أخطاء الأدوات إلى الأداة التي فشلت
-  - تثبيت فحوصات SSRF على كل خطوة إعادة توجيه وعنوان IP النظير
-  - حل المشكلات المتعلقة بالاستدعاءات الأصلية للأدوات المعطلة عبر واجهة برمجة تطبيقات استجابات OpenAI
-
-  ### الوثائق
-  - تحديث الوثائق مع لقطة وتغيير السجل للإصدار v1.15.16
-
-  ## المساهمون
-
-  @Copilot, @Vidit-Ostwal, @github-code-quality[bot], @joaomdmoura, @lorenzejay, @lucasgomide, @theCyberTech
-
-</Update>
-
-<Update label="13 أغسطس 2026">
-  ## v1.15.16
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.16)
-
-  ## ما الذي تغير
-
-  ### الميزات
-  - تقديم إدارة سياق التنفيذ مع دعم UUID
-  - تسجيل نوع الاستثناء الذي أنهى تدفق العمل
-  - تسجيل متى تم مشاركة دفعة تتبع مع AMP
-  - عد عمليات النشر من أي مصدر وتسجيل مكان بدايتها
-
-  ### إصلاحات الأخطاء
-  - تسجيل الإصدار الجاري على كل نطاق تم إصداره
-  - إصلاح التحقق من صحة اسم جدول البحث في MySQL
-  - منع فشل دورة من تحديد الدورة التالية على أنها فاشلة
-
-  ### الوثائق
-  - إضافة أدلة الواجهة الأمامية لـ CopilotKit و AG-UI
-
-  ## المساهمون
-
-  @joaomdmoura, @lorenzejay, @ranst91, @theCyberTech
-
-</Update>
-
 <Update label="11 أغسطس 2026">
   ## v1.15.15
 

docs/edge/ar/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Use Google Gemini
-memory = Memory(llm="gemini/gemini-3.7-flash")
+memory = Memory(llm="gemini/gemini-2.0-flash")
 
 # Pass a pre-configured LLM instance with custom settings
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/ar/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ mode: "wide"
 - **معالجة الأخطاء** – توجيه كيفية استجابة الـ Agents للإخفاقات والاستثناءات وحالات انتهاء المهلة.
 - **مطالبات خاصة بالأدوات** – تعريف تعليمات مفصلة لكيفية استدعاء الأدوات أو استخدامها.
 
-اطلع على [قوالب المطالبات الأصلية في مستودع CrewAI](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) لمعرفة كيفية تنظيم هذه العناصر. من هناك، يمكنك تجاوزها أو تكييفها حسب الحاجة لفتح سلوكيات متقدمة.
+اطلع على [قوالب المطالبات الأصلية في مستودع CrewAI](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) لمعرفة كيفية تنظيم هذه العناصر. من هناك، يمكنك تجاوزها أو تكييفها حسب الحاجة لفتح سلوكيات متقدمة.
 
 ## فهم تعليمات النظام الافتراضية
 

docs/edge/ar/guides/crews/first-crew.mdx

@@ -75,7 +75,7 @@ research_crew/
 }
 ```
 
-استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-3.7-flash`.
+استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `anthropic/claude-sonnet-4-6` أو `gemini/gemini-2.0-flash-001`.
 
 ## الخطوة 3: تعريف المهام وإعدادات الـ Crew
 

docs/edge/ar/guides/flows/conversational-flows.mdx

@@ -1,37 +1,35 @@
 ---
 title: تدفقات المحادثة
-description: أنشئ تطبيقات دردشة متعددة الجولات باستخدام handle_turn لكل جولة، وسجل الرسائل، وتوجيه النية، والتتبع، والبث المنظّم.
+description: أنشئ تطبيقات دردشة متعددة الجولات مع kickoff لكل جولة وسجل الرسائل وتوجيه النية والتتبع وجسور WebSocket.
 icon: comments
 mode: "wide"
 ---
 
 ## نظرة عامة
 
-تعامل التطبيقات المحادثية مع كل سطر من المستخدم كـ **تشغيل flow جديد** بنفس **معرّف الجلسة**. توفر CrewAI مساعدات لسجل الرسائل، وتوجيه النية الاختياري، وتأجيل التتبع، والبث المنظّم للجولات، إضافة إلى REPL محلي عبر `flow.chat()`.
+تعامل التطبيقات المحادثية مع كل سطر من المستخدم كـ **تشغيل flow جديد** بنفس **معرّف الجلسة**. توفر CrewAI مساعدات لسجل الرسائل وتصنيف النية الاختياري وتأجيل التتبع وجسور الواجهة، إضافة إلى REPL محلي `flow.chat()` للتدفقات المحادثية.
 
 | المفهوم | التنفيذ |
 |---------|---------|
 | معرّف الجلسة | `handle_turn(..., session_id=...)` → `kickoff(inputs={"id": ...})` → `state.id` |
 | سطر المستخدم | `handle_turn(message)` يضيف الرسالة إلى `state.messages` قبل تشغيل الرسم |
-| اكتمال الجولة | `conversation_turn_completed`؛ ومع تأجيل التتبع الافتراضي ينتظر `FlowFinished` استدعاء `finalize_session_traces()` |
-| تتبع الجلسة الكامل | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+| اكتمال الجولة | `FlowFinished` لهذا **التشغيل** فقط؛ تستمر المحادثة في `handle_turn` التالي |
+| تتبع الجلسة | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
 
 ## واجهات الجولات
 
 استخدم **`flow.handle_turn(message, session_id=...)`** لكل رسالة مستخدم من REST أو WebSocket أو الاختبارات أو الواجهات المخصصة. استخدم **`flow.chat()`** عندما تريد حلقة دردشة محلية في الطرفية لـ `Flow` محادثي.
 
-لا يقبل `Flow.kickoff()` الوسيطين `user_message=` أو `session_id=`. في التدفقات المحادثية، يخزن `handle_turn()` الرسالة المعلقة ويستدعي داخلياً `kickoff(inputs={"id": session_id})` بعد إعادة ضبط حالة التنفيذ الخاصة بالجولة.
+لا يقبل `Flow.kickoff()` الوسيطين `user_message=` أو `session_id=`. في التدفقات المحادثية، يخزن `handle_turn()` الرسالة المعلقة ويستدعي داخلياً `kickoff(inputs={"id": session_id})`.
 
 | API | الاستخدام |
 |-----|-----------|
 | `handle_turn(message, session_id=...)` | غلاف مريح لجولة واحدة في `Flow` محادثي |
-| `stream_turn(message, session_id=...)` | بث جولة محادثية واحدة كإطارات runtime مرتبة |
 | `chat()` | REPL محلي في الطرفية لـ `Flow` محادثي |
 | `kickoff(inputs={...})` | تشغيل متقدم للـ flow بدون معالجة جولة محادثية |
-| `ask()` | مطالبة حاجزة **داخل** خطوة واحدة (معالج إرشادي أو طلب توضيح) |
+| `ask()` | مطالبة حاجزة **داخل** خطوة واحدة |
 | `@human_feedback` | الموافقة/الرفض على **مخرجات خطوة** — وليس السطر التالي |
-
-ترفع `handle_turn()` و`stream_turn()` و`chat()` الخطأ `ValueError` ما لم يكن الوضع المحادثاتي مفعّلاً. يؤدي تطبيق `@ConversationConfig(...)` إلى تفعيله تلقائياً؛ وإلا فعيّن `conversational = True`.
+| `ChatSession.handle_turn(...)` | طبقة نقل فوق `handle_turn` |
 
 ## بداية سريعة
 
@@ -40,7 +38,7 @@ from uuid import uuid4
 
 from crewai import Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
 )
@@ -48,29 +46,31 @@ from crewai.flow import (
 
 @ConversationConfig(defer_trace_finalization=True)
 class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
     def route_turn(self, context):
         message = (self.state.current_user_message or "").lower()
-        if "order" in message:
+        if "طلب" in message or "order" in message:
             return "order"
-        if "bye" in message or "goodbye" in message:
+        if "وداع" in message or "goodbye" in message:
             return "goodbye"
         return "help"
 
     @listen("order")
     def handle_order(self):
-        reply = "Your order is on the way."
+        reply = "طلبك في الطريق."
         self.append_assistant_message(reply)
         return reply
 
     @listen("help")
     def handle_help(self):
-        reply = "How can I help?"
+        reply = "كيف يمكنني المساعدة؟"
         self.append_assistant_message(reply)
         return reply
 
     @listen("goodbye")
     def handle_goodbye(self):
-        reply = "Goodbye!"
+        reply = "وداعاً!"
         self.append_assistant_message(reply)
         return reply
 
@@ -79,141 +79,130 @@ session_id = str(uuid4())
 flow = SupportFlow()
 
 try:
-    flow.handle_turn("Where is my order?", session_id=session_id)
-    flow.handle_turn("What about returns?", session_id=session_id)
+    flow.handle_turn("أين طلبي؟", session_id=session_id)
+    flow.handle_turn("وماذا عن الإرجاع؟", session_id=session_id)
 finally:
-    flow.finalize_session_traces()  # one trace link for the whole chat
+    flow.finalize_session_traces()
 ```
 
-## بث جولة
-
-استخدم `stream_turn()` عندما تحتاج واجهة مستخدم أو بيئة تشغيل إلى أحداث منظّمة لجولة دردشة واحدة. يعيد جلسة بث تحتوي على إطارات مرتبة لتوجيه Flow، وأجزاء LLM، ونشاط الأدوات، ورسائل المحادثة.
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-result = stream.result
-```
-
-راجع [عقد بيئة البث](/edge/ar/learn/streaming-runtime-contract) للاطلاع على عقد الإطارات الكامل وقائمة القنوات.
-
 ## دورة حياة الجولة
 
-يشغّل كل `handle_turn` المسار التالي:
+كل `handle_turn` يشغّل:
 
-1. **إعداد الجولة** — يخزن رسالة المستخدم المعلقة، ويحل معرّف الجلسة، ويعيد ضبط تعقّب التنفيذ الخاص بالجولة، ثم يستدعي `kickoff(inputs={"id": session_id})`.
-2. **استعادة الحالة** — إذا وُجد `inputs["id"]` وكان `@persist` مهيّأً، تُحمّل أحدث لقطة.
+1. **`_configure_conversational_kickoff`** — دمج `session_id` / `user_message` في `inputs` وتطبيق `ConversationalConfig`.
+2. **استعادة الحالة** — عند وجود `inputs["id"]` و`@persist`.
 3. **`FlowStarted`** — في أول جولة للجلسة المؤجلة فقط.
-4. **ترطيب الجولة المعلقة** — تُضاف رسالة المستخدم إلى `state.messages`، وتُضبط `current_user_message` / `last_user_message`، ويُجرى التصنيف اختيارياً عند ضبط `intents` / `default_intents` مع `intent_llm`.
-5. **تنفيذ الرسم** — طرق `@start` التي يعرّفها المستخدم (إن وجدت) → `route_conversation` (نقطة البدء/الموجّه المدمجة) → معالج `@listen` المختار. تستدعي `route_conversation` أيضاً المساعد القابل للتجاوز `conversation_start()`.
-6. **نهاية التشغيل** — يُتخطى `flow_finished` لكل جولة وإنهاء التتبع عند تفعيل التأجيل؛ كما لا تغلق استدعاءات `Agent.kickoff()` المتداخلة أو crews دفعة الأب.
+4. **`prepare_conversational_turn`** — إضافة رسالة المستخدم و`last_user_message` وتصنيف اختياري.
+5. **تنفيذ الرسم** — `@start` → `@router` → معالجات `@listen`.
+6. **نهاية التشغيل** — يُتخطى `flow_finished` والتتبع لكل جولة عند التأجيل؛ `Agent.kickoff()` / crews لا تغلق دفعة الأب.
 
-استدعِ **`append_assistant_message(reply)`** عندما لا تطابق الرد الظاهر قيمة الإرجاع، أو عند قصّ التاريخ. تُسجَّل أيضاً سلسلة الإرجاع العامة كمساعد وتُضمَّن في لقطة `@persist`، فتستعيدها نسخة Flow جديدة. سطر المستخدم محفوظ عبر `handle_turn` — لا تُضفه مرة أخرى.
+استدعِ **`append_assistant_message(reply)`** في المعالجات. سطر المستخدم محفوظ عبر `handle_turn` — لا تُضفه مرة أخرى.
 
-## نظرة عامة على الإعداد
+## `ConversationalConfig` (افتراضيات على مستوى الصنف)
 
-يؤدي تزيين صنف فرعي من `Flow` بـ `ConversationConfig` إلى إرفاق افتراضيات الدردشة وتفعيل الوضع المحادثاتي معاً. راجع [مرجع الحقول الكامل](#conversationconfig) أدناه. ويمكنك تجاوز التصنيف المسبق لكل جولة عبر `handle_turn(..., intents=..., intent_llm=...)`.
+عيّن على صنف `Flow` كـ `conversational_config: ClassVar[ConversationalConfig | None]`.
 
-## مساعدات `ChatState` منخفضة المستوى
+| الحقل | الافتراضي | الغرض |
+|-------|-----------|--------|
+| `default_intents` | `None` | تسميات outcome للتصنيف التلقائي قبل kickoff |
+| `intent_llm` | `None` | نموذج التصنيف (مطلوب عند وجود intents) |
+| `interactive_prompt` | `"You: "` | مطالبة `kickoff(interactive=True)` |
+| `interactive_timeout` | `None` | مهلة لكل سطر في الوضع التفاعلي |
+| `exit_commands` | `exit`, `quit` | كلمات إنهاء الوضع التفاعلي |
+| `defer_trace_finalization` | `True` | إبقاء دفعة trace واحدة مفتوحة بين الجولات |
 
-تظل `ChatState` و`ConversationalConfig` القديمة ومساعدات `crewai.flow.conversation` قابلة للاستيراد للتنسيق المتقدم أو الاختبارات أو الأغلفة المخصصة. وهي منفصلة عن واجهتي `ConversationState` / `ConversationConfig`، ولا تضيف وسيطي `user_message=` أو `session_id=` إلى `Flow.kickoff()`.
+يمكن التجاوز لكل kickoff عبر `intents=` و`intent_llm=`.
+
+## `ChatState` (شكل الحالة الموصى به للحفظ)
 
 ```python
 from crewai.flow import ChatState
 
 
 class MyChatState(ChatState):
-    # Inherited: id, messages, last_user_message, last_intent, session_ready
+    # موروث: id, messages, last_user_message, last_intent, session_ready
     research_turn_count: int = 0
     custom_flag: bool = False
 ```
 
 | الحقل | الدور |
 |-------|------|
-| `id` | UUID الجلسة (نفس `inputs["id"]`) |
-| `messages` | `list` من `{role, content}` لسجل LLM |
+| `id` | UUID الجلسة (مثل `session_id` / `inputs["id"]`) |
+| `messages` | قائمة `{role, content}` لسجل LLM |
 | `last_user_message` | آخر سطر مستخدم في هذه الجولة |
 | `last_intent` | تسمية المسار بعد التصنيف (إن وُجد) |
-| `session_ready` | علم bootstrap لمرة واحدة (الصلاحيات، وذاكرات التخزين المؤقت، وغيرها) |
+| `session_ready` | علم bootstrap لمرة واحدة |
 
-`ConversationalInputs` هو `TypedDict` لمفاتيح `kickoff(inputs={...})` الاصطلاحية: `id` و`user_message` و`last_intent`.
-
-تخزن `ConversationState` رسائل `messages` ككائنات `ConversationMessage`، وتوفر أيضاً `current_user_message` و`ended` و`events` و`agent_threads`. استخدم `conversation_messages` عند تمرير سجلها القانوني إلى LLM.
+`ConversationalInputs` هو `TypedDict` لـ `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
 
 ## API المحادثة على `Flow`
 
-### معاملات `handle_turn`
+### معاملات `kickoff` / `kickoff_async`
 
 | المعامل | الغرض |
 |---------|--------|
-| `message` | نص هذه الجولة |
+| `user_message` | نص هذه الجولة (أو `{"role": "user", "content": "..."}`) |
 | `session_id` | UUID المحادثة → `inputs["id"]` / `state.id` |
-| `intents` | تسميات النتائج لـ `classify_intent` قبل kickoff |
+| `intents` | تسميات outcome لـ `classify_intent` قبل kickoff |
 | `intent_llm` | LLM للتصنيف (مطلوب مع `intents`) |
-| `**kickoff_kwargs` | تُمرر إلى `kickoff()` لخيارات مثل `input_files` و`from_checkpoint` و`restore_from_state_id` |
-
-### معاملات `kickoff`
-
-يقبل `Flow.kickoff()` كلاً من `inputs` و`input_files` و`from_checkpoint` و`restore_from_state_id`. مرر `inputs={"id": session_id}` عندما تحتاج إلى تنفيذ flow خام، لكن استخدم `handle_turn()` عندما يمثل الاستدعاء رسالة دردشة.
+| `interactive` | حلقة CLI عبر `ask()` (للعروض المحلية فقط) |
+| `interactive_prompt` | مطالبة الوضع التفاعلي |
+| `interactive_timeout` | مهلة `ask()` لكل سطر |
+| `exit_commands` | كلمات إنهاء الوضع التفاعلي |
+| `inputs` | حقول حالة إضافية |
+| `restore_from_state_id` | استنساخ من flow محفوظ آخر |
 
 ### سمات المثيل
 
 | السمة | الغرض |
 |-------|--------|
-| `conversational` | عيّنه على `True` لتفعيل الرسم المحادثاتي و`handle_turn()` |
-| `defer_trace_finalization` | تجاوز اختياري على مستوى المثيل. وإلا تقرأ `_should_defer_trace_finalization()` القيمة `ConversationConfig.defer_trace_finalization`. |
-| `suppress_flow_events` | يخفي لوحات flow في الطرفية ويمنع أحداث تنفيذ الطرق؛ وتظل أحداث بدء/انتهاء flow تصدر |
-| `stream` | علم البث العام لـ Flow. استخدم `stream_turn()` للجولات المحادثية بدلاً من جمع هذا العلم مع `handle_turn()`. |
+| `conversational_config` | افتراضيات `ConversationalConfig` على مستوى الصنف |
+| `defer_trace_finalization` | علم المثيل؛ يُضبط تلقائياً من config عند kickoff |
+| `suppress_flow_events` | يخفي لوحات console؛ **التتبع يُسجّل** |
+| `stream` | بث؛ مع `ChatSession.handle_turn(..., stream=True)` |
 
 ### طرق وخصائص
 
 | الاسم | الوصف |
 |------|--------|
-| `append_assistant_message(content)` | إضافة رد مساعد مرئي للمستخدم إلى `state.messages` |
 | `append_message(role, content, **extra)` | إضافة إلى `state.messages` |
 | `conversation_messages` | سجل للقراءة فقط لاستدعاءات LLM |
-| `classify_intent(text, outcomes, *, llm, context=None)` | تعيين النص إلى نتيجة واحدة (بنفس منطق الاختزال المستخدم في `@human_feedback`) |
-| `receive_user_message(text, *, outcomes=None, llm=None)` | إضافة رسالة مستخدم، وضبط `last_intent` اختيارياً |
+| `classify_intent(text, outcomes, *, llm, context=None)` | تعيين outcome |
+| `receive_user_message(text, *, outcomes=None, llm=None)` | إضافة رسالة مستخدم؛ `last_intent` اختياري |
 | `finalize_session_traces()` | إصدار `flow_finished` المؤجل وإنهاء دفعة trace |
-| `_should_defer_trace_finalization()` | hook متقدم/داخلي يحسم ما إذا كان إنهاء trace لكل جولة مؤجلاً |
+| `_should_defer_trace_finalization()` | هل يُؤجل إنهاء trace لكل جولة |
 | `input_history` | سجل تدقيق مطالبات وردود `ask()` |
 
 ### مساعدات الوحدة (`crewai.flow.conversation`)
 
-يمكن استيرادها من `crewai.flow.conversation` للاختبارات أو التنسيق المخصص. تستخدم هذه المساعدات بنية `ConversationalConfig` القديمة؛ كما تمسح `prepare_conversational_turn()` قيمة `last_intent`، بخلاف `handle_turn()` التي تحتفظ بها كسياق للموجّه.
-
 | الدالة | الوصف |
 |--------|--------|
-| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | دمج وسائط المحادثة في `inputs` |
+| `normalize_kickoff_inputs(...)` | دمج kwargs المحادثة في `inputs` |
 | `get_conversation_messages(flow)` | قراءة الرسائل من الحالة أو المخزن |
-| `append_message(flow, role, content, **extra)` | مثل طريقة المثيل |
-| `prepare_conversational_turn(flow, user_message=..., intents=..., intent_llm=..., config=...)` | ترطيب الجولة منخفض المستوى للأغلفة المخصصة |
-| `receive_user_message(flow, text, ...)` | مثل طريقة المثيل |
+| `append_message(flow, ...)` | مثل طريقة المثيل |
+| `prepare_conversational_turn(flow, ...)` | تهيئة الجولة (عادةً kickoff يستدعيها) |
+| `receive_user_message(flow, ...)` | مثل طريقة المثيل |
 | `set_state_field(flow, name, value)` | تعيين حقل dict أو Pydantic |
 | `get_conversational_config(flow)` | قراءة `conversational_config` |
 | `input_history_to_messages(entries)` | تحويل `input_history` لصيغة رسائل LLM |
 
 ## أنماط توجيه النية
 
-### أ. تصنيف مسبق عبر `ConversationConfig` (الأبسط)
+### أ. تصنيف مسبق عبر `ConversationalConfig` (الأبسط)
 
-عيّن `default_intents` و`intent_llm`. يصنّف كل `handle_turn()` الرسالة الحالية مسبقاً. تكون الأولوية لنتيجة غير فارغة يعيدها `route_turn()` مخصص؛ وإلا تستخدم `route_conversation` النية المصنّفة للجولة الحالية.
+عيّن `default_intents` و`intent_llm`. كل kickoff يصنّف قبل `@router`؛ اقرأ `self.state.last_intent` في `route()`.
 
-### ب. تصنيف داخل `route_turn` (مطالبات أغنى)
+### ب. تصنيف داخل `@router` (مطالبات أغنى)
 
-عيّن `default_intents=None` كي يضيف `handle_turn()` رسالة المستخدم فقط. داخل `route_turn()`، استدعِ `classify_intent` بمطالبة أو أوصاف مخصصة:
+عيّن `default_intents=None` ليضيف kickoff الرسالة فقط. في `route()` استدعِ `classify_intent`:
 
 ```python
-def route_turn(self, context):
+@router(bootstrap)
+def route(self):
     intent = self.classify_intent(
-        self._routing_prompt(self.state.current_user_message),
+        self._routing_prompt(self.state.last_user_message),
         ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
-        llm="gpt-4o-mini",
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
     )
     self.state.last_intent = intent
     return intent
@@ -223,59 +212,70 @@ def route_turn(self, context):
 
 ## عندما ينتهي الـ flow ويستمر المستخدم
 
-يُكمل كل `handle_turn()` تشغيل رسم واحد، وتستمر المحادثة عبر `handle_turn()` آخر يستخدم `session_id` نفسه. مع دورة حياة التتبع المؤجلة افتراضياً، يصدر ذلك التشغيل `conversation_turn_completed`، بينما يصدر `FlowFinished` مرة واحدة عندما تغلق `finalize_session_traces()` الجلسة. ويستعيد `@persist` الرسائل والأعلام والسياق.
+`FlowFinished` يعني أن **تنفيذ الرسم هذا** اكتمل. تستمر المحادثة بـ `kickoff` آخر ونفس `session_id`. `@persist` يستعيد `messages` والأعلام والسياق.
 
-**نمط الحفظ:** يُفضّل `@persist` على **خطوة نهائية واحدة** (مثل `finalize`) وليس على صنف `Flow` بالكامل. يحفظ الاستمرار على مستوى الصنف بعد كل طريقة؛ وتستخدم `load_state` أحدث صف، وقد يكون لقطة في منتصف التشغيل (مثلاً بعد `bootstrap` مباشرة) لا تتضمن تحديثات المعالج من الجولة نفسها.
+**نمط الحفظ:** يُفضّل `@persist` على **خطوة نهائية واحدة** (مثل `finalize`) وليس على صنف `Flow` بالكامل. الحفظ على مستوى الصنف بعد كل method قد يفقد تحديثات المعالجات في نفس الجولة.
 
 لا تستخدم `@human_feedback` لأسطر المتابعة في الدردشة إلا عند الحاجة لموافقة بشرية على مخرجات خطوة محددة.
 
-## `Flow` المحادثاتي
+## `Flow` المحادثاتي (تجريبي)
 
-اشترك في رسم الدردشة المحادثاتي بتعيين `conversational = True` على صنف فرعي من `Flow` أو بتطبيق `@ConversationConfig(...)`. يوفر `Flow` الأساسي عندئذٍ `route_conversation` كنقطة البدء/الموجّه المدمجة، إضافة إلى مستمعي `converse_turn` و`end_conversation`. يظل المستمع المهمل `answer_from_history_turn` متاحاً للتوافق. يدير الإطار `state.messages`، ويمكنه تشغيل LLM للموجّه، ويبقي دفعة trace مفتوحة عبر الجولات. أنت تكتب **المسارات المخصصة**؛ والإطار يتولى الباقي.
+<Warning>
+  **ميزة تجريبية.** سطح `Flow` المحادثاتي (`conversational = True`،
+  `handle_turn`، `ConversationConfig`، `RouterConfig`،
+  `ConversationState`، الرسم البياني المدمج والمساعدات) يقع تحت
+  `crewai.experimental` وقد يتغير شكله قبل التخرج. ثبّت إصدار CrewAI إذا
+  كنت تعتمد على سلوك محدد، وراقب changelog للتحديثات الكاسرة. الملاحظات
+  والمشاكل مرحب بها.
+</Warning>
+
+فعّل الرسم المحادثاتي بتعيين `conversational = True` على صنف فرعي من `Flow`. عندئذٍ يُظهر `Flow` الأساسي رسم `@start` / `@router` / `converse_turn` / `end_conversation` مدمجاً، ويدير `state.messages`، ويُشغّل LLM التوجيه، ويبقي دفعة trace مفتوحة عبر الجولات. أنت تكتب **المسارات المخصصة** فقط؛ والإطار يتولى الباقي.
 
 استخدمه عندما تريد دردشة متعددة الجولات مع موجّه قائم على LLM ومعالجات لكل مسار دون توصيل دورة الحياة يدوياً. استخدم `Flow[ChatState]` (النمط الأدنى مستوى في الأعلى) عندما تحتاج تحكماً كاملاً.
 
 ### مثال سريع
 
 ```python
-from crewai import Flow
+from crewai import LLM, Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
+    RouterConfig,
 )
 
 
-@ConversationConfig(defer_trace_finalization=True)
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # المسارات + الأوصاف تُكتشف تلقائياً من معالجات @listen
+)
 class SupportFlow(Flow[ConversationState]):
-    def route_turn(self, context: dict) -> str | None:
-        message = (self.state.current_user_message or "").lower()
-        if "search" in message or "news" in message:
-            return "INTERNET_SEARCH"
-        if "docs" in message or "crewai" in message:
-            return "CREWAI_DOCS"
-        return "converse"
+    conversational = True
 
     @listen("INTERNET_SEARCH")
     def handle_internet_search(self) -> str:
         """Fresh web research, current news, real-time lookups."""
-        reply = "I would run the web research route here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
     @listen("CREWAI_DOCS")
     def handle_crewai_docs(self) -> str:
         """Look up the CrewAI documentation for framework/API questions."""
-        reply = "I would look up the CrewAI docs here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
 
 flow = SupportFlow()
 try:
-    flow.handle_turn("What can you do?")              # routes to converse
-    flow.handle_turn("Search the web for AI news.")   # routes to INTERNET_SEARCH
-    flow.handle_turn("Check the CrewAI docs.")         # routes to CREWAI_DOCS
+    flow.handle_turn("ماذا يمكنك أن تفعل؟")                   # يوجَّه إلى converse (مدمج)
+    flow.handle_turn("ابحث في الويب عن أخبار الذكاء الاصطناعي.")  # يوجَّه إلى INTERNET_SEARCH
+    flow.handle_turn("لخص النتيجة الأولى.")                    # يعود إلى converse
 finally:
     flow.finalize_session_traces()
 ```
@@ -297,54 +297,27 @@ def kickoff() -> None:
 |-------|-----------|-------|
 | `system_prompt` | `slices.conversational_system_prompt` من i18n | رسالة system يستخدمها `converse_turn` المدمج. مرر `""` للتعطيل التام. |
 | `llm` | `None` | LLM المحادثة (يستخدمه `converse_turn` وكاحتياطي للموجّه). |
-| `router` | `None` | تجاوزات `RouterConfig` اختيارية. مع وجود مستمعين مخصصين وLLM قابل للحل، يُفعّل التوجيه تلقائياً حتى عند إغفال هذا الحقل. |
-| `answer_from_history_prompt` | افتراضي الإطار | **مهمل.** استخدم system prompt الخاص بـ `converse` أو تجاوز `converse_turn()`. |
-| `answer_from_history_llm` | `None` | **مهمل.** استخدم `llm`؛ إذ يتلقى `converse` السجل القانوني بالفعل. |
+| `router` | `None` | `RouterConfig` للتوجيه عبر LLM. بدونه، يسقط الـ flow دائماً إلى `converse`. |
+| `answer_from_history_prompt` | افتراضي الإطار | رسالة system للمسار الاختياري `answer_from_history`. |
+| `answer_from_history_llm` | `None` | يُفعّل الاختصار `answer_from_history` عند تعيينه. |
 | `intent_llm` | `None` | LLM لمسار التصنيف المسبق القديم `intents=`/`default_intents`. |
 | `default_intents` | `None` | تسميات النتائج للتصنيف المسبق القديم. |
 | `visible_agent_outputs` | `None` | `"all"` أو قائمة بأسماء الـ agents الذين تُرفع مخرجاتهم من `append_agent_result()` إلى رسائل عامة. |
 | `defer_trace_finalization` | `True` | يبقي دفعة trace واحدة مفتوحة عبر استدعاءات `handle_turn()`. |
 
-<Warning>
-  تم إهمال `answer_from_history_prompt` و`answer_from_history_llm` ومسار
-  `answer_from_history`، وستُزال في إصدار مستقبلي. فهي تكرر `converse`، الذي
-  يتولى بالفعل السجل القانوني، وتضيف استدعاء LLM للتحقق من أهلية الإجابة،
-  ويجري تجاوزها عندما يعيد الموجّه التلقائي المعتاد مساراً. تظل الإعدادات
-  الحالية تعمل وتُصدر `DeprecationWarning`.
-</Warning>
-
-عند عدم وجود مسارات مخصصة، تسقط الجولات إلى `converse`. ومع وجود مسارات مخصصة وLLM للمحادثة/الموجّه، ينشئ الإطار `RouterConfig` افتراضية؛ لا توفر واحدة صراحةً إلا لتخصيص المطالبة أو قائمة المسارات أو الأوصاف أو سلوك fallback. أما ضبط `default_intents` فيستخدم مسار التصنيف المسبق القديم.
-
-إذا لم يُهيأ LLM للمحادثة، يعيد `converse_turn` المدمج عنصراً نائباً للإعداد بدلاً من توليد إجابة.
-
 ### `RouterConfig` وفهرس المسارات المُولَّد تلقائياً
 
 ```python
-from typing import Literal
-
-from pydantic import BaseModel
-
-from crewai import LLM
-from crewai.flow import RouterConfig
-
-
-class MyRoute(BaseModel):
-    intent: Literal["INTERNET_SEARCH", "CREWAI_DOCS", "converse"]
-
-
-ROUTER_LLM = LLM(model="gpt-4o-mini")
-
-
-router_config = RouterConfig(
-    prompt="Optional domain framing (policy, voice, persona).",
-    response_format=MyRoute,        # optional; auto-generated otherwise
-    llm=ROUTER_LLM,                  # falls back to ConversationConfig.llm
-    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # optional; inferred from listeners
+RouterConfig(
+    prompt="تأطير اختياري للنطاق (سياسة، صوت، شخصية).",
+    response_format=MyRoute,        # اختياري؛ يُولَّد تلقائياً عند الإغفال
+    llm=ROUTER_LLM,                  # يسقط إلى ConversationConfig.llm
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # اختياري؛ يُستنتج من المستمعين
     route_descriptions={
-        "INTERNET_SEARCH": "Override the docstring for this one route.",
+        "INTERNET_SEARCH": "تجاوز الـ docstring لهذا المسار فقط.",
     },
-    default_intent="converse",       # used when LLM call fails or no LLM available
-    fallback_intent="converse",      # used when LLM returns an invalid route
+    default_intent="converse",       # يُستخدم عند فشل LLM أو غيابه
+    fallback_intent="converse",      # يُستخدم عندما يعيد LLM مساراً غير صالح
     intent_field="intent",
 )
 ```
@@ -352,17 +325,13 @@ router_config = RouterConfig(
 تُبنى رسالة الموجّه إلى LLM تلقائياً. لكل مسار يختار الإطار وصفاً بهذا الترتيب من الأولوية:
 
 1. `RouterConfig.route_descriptions[label]` — تجاوز صريح.
-2. `Flow.builtin_route_descriptions[label]` — نص جاهز من الإطار لـ `converse` و`end` ولمسار التوافق المهمل `answer_from_history` (مصاغ لـ LLM التوجيه).
-3. قيمة `description` المعلنة للطريقة (تستخدمها التدفقات التعريفية وإسقاطات DSL).
-4. أول سطر غير فارغ من docstring معالج `@listen(label)`.
-5. فارغ (المسار يظهر في الفهرس بلا وصف).
+2. `Flow.builtin_route_descriptions[label]` — نص جاهز من الإطار لـ `converse` و`end` و`answer_from_history` (مصاغ لـ LLM التوجيه).
+3. أول سطر غير فارغ من docstring معالج `@listen(label)`.
+4. فارغ (المسار يظهر في الفهرس بلا وصف).
 
 عملياً، **إضافة مسار جديد = `@listen("X")` + docstring من سطر واحد**:
 
 ```python
-from crewai.flow import listen
-
-
 @listen("INTERNET_SEARCH")
 def handle_internet_search(self) -> str:
     """Fresh web research, current news, real-time lookups."""
@@ -381,34 +350,13 @@ Routes:
 
 `RouterConfig.prompt` مخصص لـ **تأطير النطاق** (شخصية المساعد، قواعد العمل، النبرة). فهرس المسارات يُبنى تلقائياً — لا تُدرج المسارات في `prompt`؛ سيختل التزامن لحظة إضافة معالج جديد.
 
-### تسمية المعالجات
-
-السلسلة النصية في `@listen("…")` هي **تسمية مسار للموجّه** (اسم حدث)، وليست اسم طريقة Python. تتشارك تسميات المسارات وأحداث اكتمال الطرق مساحة مشغلات واحدة، ولذلك تؤدي تسمية المعالج باسم مساره نفسه إلى إعادة تشغيل المعالج في حلقة.
-
-استخدم اسماً مختلفاً للطريقة — تستخدم أمثلة التوثيق بادئة `handle_*`:
-
-```python
-@listen("create_video")
-def handle_create_video(self) -> str:
-    """User wants a new video."""
-    ...
-```
-
-لا تكرر تسمية المسار في اسم الطريقة:
-
-```python
-@listen("create_video")
-def create_video(self) -> str:  # rejected at flow instantiation
-    ...
-```
-
 ### المسارات المدمجة
 
 | المسار | المعالج | الغرض |
 |--------|---------|-------|
 | `converse` | `converse_turn` | معالج الدردشة الافتراضي. يستدعي `ConversationConfig.llm` بـ system prompt + التاريخ القانوني للرسائل. |
 | `end` | `end_conversation` | يضبط `state.ended = True` ويُصدر رد إنهاء. |
-| `answer_from_history` | `answer_from_history_turn` | **مسار توافق مهمل.** استخدم `converse`، الذي يتلقى السجل القانوني بالفعل. |
+| `answer_from_history` | `answer_from_history_turn` | اختياري. يُوجَّه إليه عندما يكون `ConversationConfig.answer_from_history_llm` مُعيَّناً ويمكن الإجابة على الرسالة من التاريخ فقط. |
 
 يمكنك تجاوز أي من هذه بتعريف معالج بنفس الاسم في الصنف الفرعي.
 
@@ -418,9 +366,9 @@ def create_video(self) -> str:  # rejected at flow instantiation
 
 1. يعيد ضبط تعقّب التنفيذ لكل جولة (`_completed_methods`, `_method_outputs`) ليُعاد تشغيل الرسم — بدون ذلك، استدعاءات `kickoff` المتكررة على نفس النسخة ستُحدث دائرة قصر من الجولة الثانية لأن `Flow.kickoff_async` يعتبر `inputs={"id": ...}` استعادة من نقطة تفتيش.
 2. يُلحق رسالة المستخدم بـ `state.messages` ويضبط `current_user_message` / `last_user_message`. يُحافَظ على `last_intent` **من الجولة السابقة** كي يستخدمها LLM التوجيه كإشارة.
-3. يُشغّل طرق `@start` التي يعرّفها المستخدم (إن وجدت)، ثم `route_conversation` كنقطة البدء/الموجّه المدمجة، ثم معالج `@listen` المختار. وتستدعي `route_conversation` المساعد القابل للتجاوز `conversation_start()`.
+3. يُشغّل `conversation_start` → `route_conversation` → معالج `@listen` المختار.
 4. يخزّن الموجّه قراره في `state.last_intent` (يكون مرئياً لسياق التوجيه في الجولة التالية).
-5. إذا أعاد معالجك سلسلة نصية ولم يستدعِ `append_assistant_message`، فإن `handle_turn` يُلحقها نيابةً عنك ويحفظ `state.messages` المحدَّث حتى تشمل استعادة `@persist` جولة المساعد.
+5. إذا أعاد معالجك سلسلة نصية ولم يستدعِ `append_assistant_message`، فإن `handle_turn` يُلحقها نيابةً عنك.
 
 استدعِ `handle_turn()` لرسائل الدردشة. استدعاء `kickoff(inputs={"id": ...})` مباشرةً يشغل الرسم بدون غلاف الجولة المحادثية.
 
@@ -441,8 +389,6 @@ flow.chat()
 4. يطبع نتيجة المساعد.
 5. ينهي traces الجلسة المؤجلة داخل كتلة `finally`.
 
-يُفعّل `chat(defer_trace_finalization=True)` مؤقتاً علم التأجيل على مستوى المثيل للـ REPL، ثم يعيد قيمته السابقة عند الخروج.
-
 خصص سلوك الطرفية عبر I/O قابل للحقن:
 
 ```python
@@ -461,12 +407,6 @@ flow.chat(
 لتشغيل آثار جانبية (إعداد ناقل أحداث، قياس عن بُعد) في كل قرار توجيه، تجاوز `route_turn`:
 
 ```python
-from typing import Any
-
-from crewai import Flow
-from crewai.flow import ConversationState
-
-
 class SupportFlow(Flow[ConversationState]):
     conversational = True
 
@@ -475,7 +415,7 @@ class SupportFlow(Flow[ConversationState]):
         return super().route_turn(context)
 ```
 
-لتجاوز موجّه LLM بالكامل واختيار مسار برمجياً، أعد سلسلة نصية غير فارغة من `route_turn`. لا يؤدي إرجاع قيمة falsy من التجاوز إلى استدعاء `_route_with_config()`؛ بل يسقط التوجيه إلى النية المصنّفة مسبقاً لهذه الجولة، ثم إلى مسار التوافق المهمل `answer_from_history` عند إعداده، وأخيراً إلى `converse`. تكون `last_intent` من الجولة السابقة متاحة في سياق الموجّه، لكنها لا تُعاد أبداً كـ fallback.
+لتجاوز موجّه LLM واختيار مسار برمجياً، أعد سلسلة نصية من `route_turn`؛ إعادة `None` تسقط إلى `_route_with_config(...)`.
 
 ### `append_assistant_message` و`append_agent_result`
 
@@ -486,76 +426,9 @@ class SupportFlow(Flow[ConversationState]):
 
 يمكن لـ `ConversationConfig.visible_agent_outputs` رفع النتائج الخاصة لـ agents محددين إلى عامة عالمياً (`"all"` أو قائمة بالأسماء).
 
-## تعريف تدفق محادثاتي بصيغة JSON/YAML
-
-يمكن لـ [التدفق التعريفي](/edge/ar/concepts/cli) أن يكون محادثاتيًا أيضًا. أضف كتلة `conversational` في المستوى الأعلى وعرّف مساراتك الخاصة كطرق تستمع (`listen`) إلى تسمية مسار:
-
-```yaml
-schema: crewai.flow/v1
-name: SupportFlow
-
-conversational:
-  system_prompt: You are a terse support assistant.
-  llm: gpt-4o-mini
-  router:
-    llm: gpt-4o-mini
-
-methods:
-  handle_order:
-    description: Order status, shipping and delivery questions.
-    listen: order
-    do:
-      call: agent
-      with:
-        role: Support specialist
-        goal: Answer order questions accurately
-        backstory: Knows the fulfilment pipeline.
-        input: "${state.current_user_message}"
-```
-
-تعريف الكتلة هو الاشتراك نفسه — القيمة الافتراضية لـ `enabled` هي `true`. اضبطها على `enabled: false` للاحتفاظ بالإعدادات مع إيقاف المحادثة. يؤدي ذلك أيضاً إلى تعطيل إنشاء الطرق المدمجة، ولذلك يجب أن توفر التعريفة رسماً عادياً غير محادثاتي.
-
-تُوفَّر لك ثلاثة أشياء:
-
-| المُوفَّر | التفاصيل |
-|----------|--------|
-| الرسم البياني المدمج | تُضاف `route_conversation` و`converse_turn` و`end_conversation` تلقائيًا. يُحتفظ بـ `answer_from_history_turn` المهملة للتوافق. عرّف طريقة بأحد هذه الأسماء لتجاوزها. |
-| حالة المحادثة | تُستخدم `ConversationState` عند عدم وجود كتلة `state`. وتُركّب حالة Pydantic ذات `ref` أو `json_schema` تلقائياً مع الحقول المحادثية؛ ولا يلزم أن ترث من `ConversationState`. |
-| كتالوج المسارات | يُستنتج من الطرق غير الموجّهة التي تحمل تسميات `listen`، مع استبعاد المسارات الداخلية. تتبع الأوصاف ترتيب الأولوية أعلاه، ويمكن لـ `router.routes` الصريحة تقييد الخيارات. |
-
-تقبل حقول `llm` و`router.llm` و`intent_llm` التعريفية إما معرّف نموذج أو خريطة إعدادات مثل `{model: openai/gpt-4o-mini, max_tokens: 512}`. وتدعم كتلة `conversational` أيضاً `default_intents` و`visible_agent_outputs` و`defer_trace_finalization` وحقول `RouterConfig` الموضحة أعلاه. تظل تعريفات `answer_from_history_prompt` / `answer_from_history_llm` المهملة مقبولة للتوافق.
-
-شغّله من Python بنفس واجهات الجولة المستخدمة مع تدفق محادثاتي معرّف بصنف:
-
-```python
-from crewai.flow import Flow
-
-flow = Flow.from_declaration(path="flow.yaml")
-
-try:
-    flow.handle_turn("Where is my order?", session_id="session-1")
-finally:
-    flow.finalize_session_traces()
-```
-
-### تسمية المسارات
-
-تتشارك تسميات المسارات وأسماء الطرق مساحة اسم واحدة للمشغّلات، لذا يجب ألا يحمل المعالج اسم المسار الذي يستمع إليه — يُرفض `create_video` الذي يستمع إلى `create_video` عند بناء التدفق. استخدم بادئة `handle_*`.
-
-### ما لا يمكن للتعريفة التعبير عنه
-
-| غير قابل للتعبير | استخدم بدلًا منه |
-|-----------------|-------------|
-| مثيل `LLM` حي أو `BaseLLM` مخصص | سلسلة معرّف نموذج أو خريطة إعدادات ثابتة |
-| `router.response_format` كصنف نموذج حيّ | سمِّ الصنف بمرجع python: `response_format: {python: my_project.schemas.ConversationRoute}`. احذفه ويولّد الإطار واحدًا |
-| تجاوز `route_turn()` | اكتب Flow بلغة Python، أو استبدل طريقة `route_conversation` التعريفية بإجراء `call: code` / expression |
-| تجاوز `can_answer_from_history()` | مهمل. استخدم `converse` أو تجاوز `converse_turn()` في Python. |
-
-يفتح `crewai run` واجهة المحادثة النصية للتدفق المحادثاتي التعريفي — نفس الواجهة التي يحصل عليها Flow محادثاتي مكتوب بلغة Python. تحتاج حلقة المحادثة إلى طرفية، ولذلك يخرج التشغيل بدون طرفية برمز غير صفري مع إرشادات بدلاً من تنفيذ جولة واحدة؛ شغّله من Python هناك عبر `handle_turn()` أو `stream_turn()`. وتعمل الطريقة التعريفية ذات كتلة `human_feedback:` (وفي Python: ‏`@human_feedback`) على REPL طرفي، لأن runtime يجمع الملاحظات بمطالبة حاجزة لا تستطيع TUI خدمتها. لا يُقبل `--inputs` مع Flow محادثاتي — فمدخل كل جولة هو الرسالة التي تكتبها — واستئناف جلسة حسب المعرّف غير موصول بواجهة CLI بعد؛ استخدم `flow.handle_turn(message, session_id=...)` من Python لذلك.
-
 ## التتبع عبر الجولات
 
-مع `defer_trace_finalization=True` (افتراضي في `ConversationConfig`):
+مع `defer_trace_finalization=True` (افتراضي في `ConversationalConfig`):
 
 - **دفعة trace واحدة** لجلسة الدردشة.
 - **`flow_started`** في الجولة الأولى فقط؛ **`flow_finished`** مرة في `finalize_session_traces()`.
@@ -566,30 +439,17 @@ finally:
 flow.chat(session_id=session_id)
 ```
 
-`flow.chat()` يستدعي `finalize_session_traces()` نيابةً عنك. عندما تملك الحلقة عبر `handle_turn()`، استدعِ `finalize_session_traces()` عند انتهاء الجلسة.
+`flow.chat()` يستدعي `finalize_session_traces()` نيابةً عنك. عندما تملك الحلقة عبر `handle_turn()` أو `kickoff(...)`، استدعِ `finalize_session_traces()` عند انتهاء الجلسة.
 
-يخفي `suppress_flow_events=True` لوحات Rich ويمنع أحداث تنفيذ الطرق. وتظل أحداث بدء/انتهاء Flow تصدر، فيبقى بالإمكان تتبع دورة حياة Flow الخارجية، بينما تُحذف spans الطرق الفردية.
+`suppress_flow_events=True` يخفي لوحات Rich فقط؛ أحداث trace والـ methods تُصدر.
 
 ### دورة حياة trace لـ `Flow` المحادثاتي
 
-يستخدم [`Flow` المحادثاتي](#flow-المحادثاتي) دورة حياة التتبع نفسها: القيمة الافتراضية لـ `defer_trace_finalization` هي `True`، ولذلك يبقي كل `handle_turn()` trace الجلسة مفتوحاً. تمنع الجولات المؤجلة أيضاً إصدار `flow_failed` لكل جولة؛ وعند حدوث خطأ في جولة أو إلغاء الجلسة، أنهِ الجلسة صراحةً. يغلق ذلك الدفعة بحدث `FlowFinished` على مستوى الجلسة بدلاً من حدث `FlowFailed` لكل جولة. لُف REPL/الحلقة دائماً بـ `try/finally` واستدعِ `flow.finalize_session_traces()` عند الخروج. بدون ذلك، تبقى دفعة trace مفتوحة وقد لا تُصدَّر المحادثة النهائية أبداً.
+يستخدم [`Flow` المحادثاتي](#flow-المحادثاتي-تجريبي) التجريبي نفس دورة حياة tracing: `defer_trace_finalization` افتراضياً `True`، فيبقي كل `handle_turn()` أثر الجلسة مفتوحاً. أنهِ دوماً عند نهاية الجلسة — لُف حلقتك بـ `try/finally` واستدعِ `flow.finalize_session_traces()` عند الخروج. بدون ذلك، تبقى الدفعة مفتوحة وقد لا تُصدَّر آخر محادثة أبداً.
 
 ## البث
 
-استخدم `stream_turn()` للواجهات المحادثية، وكرّر عبر كائنات `StreamFrame` المرتبة التي يعيدها:
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-reply = stream.result
-```
-
-بالنسبة إلى Flow غير محادثاتي، يؤدي ضبط `stream = True` إلى جعل `kickoff()` يعيد `StreamSession`. لا تضبط `flow.stream = True` عند استخدام `handle_turn()`؛ إذ تملك `stream_turn()` دورة حياة البث المحادثاتي.
+اضبط `stream = True` على صنف `Flow`. عندئذٍ يُصدر `kickoff(...)` أحداث `assistant_delta` (وما يرتبط بها) عبر ناقل الأحداث القياسي.
 
 ## الاستيراد
 
@@ -604,15 +464,10 @@ from crewai.flow import (
     router,
     start,
 )
-from crewai.flow.conversation import prepare_conversational_turn
-from crewai.flow import (
-    ConversationConfig,
-    ConversationState,
-    RouterConfig,
-)
 ```
 
 ## مراجع
 
 - [إتقان إدارة حالة Flow](/ar/guides/flows/mastering-flow-state)
 - [أنشئ أول Flow](/ar/guides/flows/first-flow)
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL بسيط مع `RESEARCH` ووكيل Exa

docs/edge/ar/guides/flows/first-flow.mdx

@@ -104,7 +104,7 @@ crewai flow add-crew content-crew
 }
 ```
 
-استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-3.7-flash` أو `anthropic/claude-sonnet-4-6`.
+استبدل `provider/model-id` بالنموذج الذي تستخدمه، مثل `openai/gpt-4o` أو `gemini/gemini-2.0-flash-001` أو `anthropic/claude-sonnet-4-6`.
 
 3. أنشئ `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 

docs/edge/ar/guides/frontend/channels.mdx

@@ -1,156 +0,0 @@
----
-title: القنوات
-description: شغّل نفس وكيل CrewAI كروبوت على Slack أو Teams باستخدام CopilotKit Channels SDK ومنصة Intelligence المُدارة.
-icon: messages
-mode: "wide"
----
-
-## قابل مستخدميك حيث هم بالفعل
-
-وكيل CrewAI الذي بنيته في [النظرة العامة](/edge/ar/guides/frontend/overview) لا يجب أن يعيش خلف تطبيق ويب فقط. يمكن لنفس الـ Crew أو الـ Flow أن يعمل كروبوت داخل منصة مراسلة. لا حاجة لإعادة البناء ولا لنسخة ثانية من منطق وكيلك: يبقى الوكيل مكشوفًا عبر [بروتوكول AG-UI](https://docs.ag-ui.com)، وتقوم **قناة** بتشغيله من Slack أو Microsoft Teams.
-
-يوفّر [Channels SDK](https://docs.copilotkit.ai/slack) من CopilotKit تلك القناة. تُعرّف `createChannel` في وقت تشغيل صغير، وتوجّهه إلى وكيل CrewAI الخاص بك، وتتولى منصة **Intelligence** المُدارة من CopilotKit التوسّط في الاتصال مع مزوّد المراسلة.
-
-<Note>
-على خلاف بقية هذا القسم، فإن Channels **ليست ذاتية الاستضافة**. تعمل من خلال **CopilotKit Intelligence** — وهي سطح مطلوب لـ Channels، بحكم التصميم (تتوفر طبقة مجانية). تحتفظ Intelligence باتصال المنصة وبيانات الاعتماد، وتستقبل كل حدث من المنصة، وتسلّم الدور إلى عملية قناتك؛ تشغّل عمليتك الوكيل وتبثّ الرد مرة أخرى. تقوم بإعداد Slack مرة واحدة في لوحة تحكم Intelligence، ولا تدخل بيانات اعتماد المنصة عمليتك أبدًا. يبقى وكيلك وأدواتك وحالتك ملكًا لك.
-</Note>
-
-## كيف تتكامل الأجزاء معًا
-
-لا يتغير أي شيء بخصوص خادم وكيل CrewAI الخاص بك. يستمر في تقديم الـ Crew أو الـ Flow عبر AG-UI تمامًا كما في النظرة العامة. ما تضيفه هو عملية Node منفصلة طويلة الأمد مبنية باستخدام `@copilotkit/channels`: تسجّل قناة على `CopilotRuntime`، وتتصل بـ Intelligence، وتشغّل وكيلك كلما وصلت رسالة.
-
-```
-Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-تحتفظ عملية القناة باتصال دائم مع بوابة Intelligence، لذا فهي تحتاج إلى مضيف طويل الأمد — لا يمكن لمعالج طلبات بلا خادم (serverless) أن يملك ذلك الاتصال. يمكن لخادم CrewAI الخاص بك أن يستمر في تقديم واجهة الويب الأمامية من النظرة العامة في الوقت نفسه: تطبيق الويب والقناة ما هما إلا عميلان لنقطة نهاية AG-UI واحدة.
-
-## دليل التكامل
-
-<Steps>
-
-<Step title="ثبّت حزم Channels">
-
-يأتي Channels SDK مكتمل العناصر — كل منصة تُشحن في الحزمة الواحدة، بلا محوّل خاص بكل منصة لتثبيته. أضفه إلى جانب وقت التشغيل الذي يستضيف القناة وعميل CrewAI AG-UI:
-
-```bash
-npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="أنشئ قناة في Intelligence">
-
-في [لوحة تحكم CopilotKit](https://docs.copilotkit.ai/slack)، أنشئ قناة واربط Slack — ترشدك Intelligence خلال إنشاء تطبيق Slack وتحتفظ ببيانات اعتماده. يترك ذلك متغيّري بيئة لعمليتك، كلاهما من لوحة التحكم:
-
-```bash
-export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
-export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
-```
-
-</Step>
-
-<Step title="عرّف القناة">
-
-تُعرّف `createChannel` القناة وتربط وكيلك بها. ابنِ الوكيل كمصنع لكل خيط (thread) بحيث تحصل كل محادثة على جلستها الخاصة، مستخدمًا نفس `CrewAIAgent` الذي تستخدمه النظرة العامة في وقت تشغيل الويب، موجّهًا إلى نقطة نهاية AG-UI الخاصة بك. تتيح `identifyUser: "platform"` لـ Intelligence ربط كل مستخدم من المنصة بهوية ثابتة.
-
-```ts
-// channel.ts
-import { createChannel } from "@copilotkit/channels";
-import { CrewAIAgent } from "@ag-ui/crewai";
-
-const channel = createChannel({
-  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
-  identifyUser: "platform",
-  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
-  agent: (threadId) => {
-    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
-    agent.threadId = threadId;
-    return agent;
-  },
-});
-
-// A mention subscribes the thread and runs the agent; afterwards every message
-// in a subscribed thread runs it without needing another mention.
-channel.onMention(async ({ thread }) => {
-  await thread.subscribe();
-  await thread.runAgent();
-});
-
-channel.onMessage(async ({ thread }) => {
-  if (await thread.isSubscribed()) await thread.runAgent();
-});
-
-export { channel };
-```
-
-</Step>
-
-<Step title="سجّل القناة على وقت التشغيل">
-
-أنشئ `CopilotRuntime` مع بوابة Intelligence وقناتك، ثم قدّمه باستخدام `createCopilotNodeListener`. تبقى خريطة `agents` فارغة — القناة توفّر وكيلها الخاص. انتظر حتى تكون القناة جاهزة كي يفشل بدء التشغيل بصوت عالٍ عند وجود إعداد معطوب.
-
-```ts
-// server.ts
-import { createServer } from "node:http";
-import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
-import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
-import { channel } from "./channel";
-
-const runtime = new CopilotRuntime({
-  agents: {}, // the channel supplies its own agent; no web-facing agents needed
-  intelligence: new CopilotKitIntelligence({
-    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
-  }),
-  channels: [channel],
-});
-
-const listener = createCopilotNodeListener({ runtime });
-await listener.channels?.ready({ timeoutMs: 15_000 });
-
-createServer(listener).listen(3123, () => {
-  console.log("Channels runtime listening on port 3123");
-});
-```
-
-</Step>
-
-<Step title="شغّل وقت تشغيل القناة">
-
-ابدأه إلى جانب خادم وكيل CrewAI الخاص بك:
-
-```bash
-uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-npx tsx server.ts                 # terminal 2 — Channels runtime
-```
-
-اذكر الروبوت في Slack أو Teams فيشغّل الـ Crew أو الـ Flow الخاص بك، ويبثّ الرد مرة أخرى داخل الخيط. يبقى الخيط مشتركًا، لذا تعمل رسائل المتابعة دون الحاجة إلى ذكر آخر.
-
-</Step>
-
-</Steps>
-
-## نموذج الأحداث
-
-تتفاعل القناة مع أحداث المنصة عبر معالِجات، ويستقبل كل معالِج خيطًا (`thread`) تديره بعدد قليل من الدوال:
-
-- **`channel.onMention`** يُطلَق عندما يذكر مستخدم الروبوت بـ @. استدعِ `thread.subscribe()` للانضمام إلى الخيط، ثم `thread.runAgent()` لتشغيل وكيل CrewAI الخاص بك عند الذكر.
-- **`channel.onMessage`** يُطلَق عند كل رسالة في خيط يمكن للروبوت رؤيته. قيّده بـ `thread.isSubscribed()` كي لا يستجيب الوكيل إلا حيث انضمّ، ثم `thread.runAgent()`.
-- **`thread.runAgent()`** يشغّل وكيل CrewAI المرفق للدور الحالي ويبثّ مخرجاته مرة أخرى داخل القناة. مرّر `{ prompt }` لتجاوز النص الذي يعمل عليه الوكيل.
-
-يستقبل وكيلك `RunAgentInput` عاديًا من AG-UI ويصدر أحداث AG-UI عادية؛ تبقى آليات المنصة خلف القناة، لذا يعمل نفس الـ Crew أو الـ Flow دون تغيير عبر كل منصة. تكشف القناة أيضًا معالِجات للترحيبات والمقاطعات والأوامر والتفاعلات والنوافذ (modals) — راجع [مرجع `Channel`](https://docs.copilotkit.ai/reference/channels/classes/Channel) للاطلاع على السطح الكامل.
-
-## دعم المنصات
-
-يغطي مسار Intelligence المُدار **Slack** و**Microsoft Teams** اليوم — يعمل نفس كود القناة على أيٍّ منهما، وتفيد `message.platform` / `thread.platform` بالأصل الأصلي. تُبلَغ المنصات الأخرى (Discord وTelegram وWhatsApp) عبر **محوّلات مباشرة** يشغّلها المطوّر بدلًا من المسار المُدار — تملك عمليتك الخاصة بيانات اعتماد المنصة والنقل. راجع [توثيق CopilotKit Channels](https://docs.copilotkit.ai/slack) للاطلاع على قائمة المنصات الحالية والإعداد الخاص بكل منصة.
-
-## ذات صلة
-
-<CardGroup cols={2}>
-  <Card title="النظرة العامة على الواجهة الأمامية" icon="browser" href="/edge/ar/guides/frontend/overview">
-    قدّم الـ Crew أو الـ Flow الخاص بك عبر AG-UI — الأساس الذي تُبنى عليه كل قناة.
-  </Card>
-  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    أوقف الوكيل مؤقتًا لجمع موافقة المستخدم أو مدخلاته في منتصف التشغيل.
-  </Card>
-</CardGroup>

docs/edge/ar/guides/frontend/overview.mdx

@@ -1,238 +0,0 @@
----
-title: النظرة العامة على الواجهة الأمامية
-description: ابنِ واجهات مستخدم تفاعلية لوكلاء CrewAI الخاصين بك باستخدام CopilotKit وبروتوكول AG-UI.
-icon: browser
-mode: "wide"
----
-
-## امنح وكلاءك واجهة مستخدم
-
-يشغّل CrewAI وكلاءك. ويمنحهم [CopilotKit](https://copilotkit.ai) واجهة أمامية. معًا يتيحان لك بناء تطبيقات يحادث فيها المستخدمون Crew أو Flow، ويشاهدونه يعمل في الوقت الفعلي، ويوافقون على قراراته، ويرون مخرجاته معروضة كواجهة حيّة بدلًا من جدران من النص.
-
-يتصل الاثنان عبر [بروتوكول AG-UI](https://docs.ag-ui.com). تكشف حزمة `ag-ui-crewai` أي Crew أو Flow كنقطة نهاية AG-UI. وتستهلك خطافات (hooks) ومكوّنات React من CopilotKit تلك النقطة. يفتح ذلك تجارب تتجاوز بكثير صندوق المحادثة:
-
-<CardGroup cols={2}>
-  <Card title="واجهة المستخدم التوليدية (Generative UI)" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    اعرض استدعاءات أدوات الوكيل وحالته كمكوّنات React خاصة بك.
-  </Card>
-  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    أوقف الوكيل مؤقتًا لجمع موافقة المستخدم أو مدخلاته في منتصف التشغيل.
-  </Card>
-  <Card title="الحالة المشتركة (Shared State)" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
-    أبقِ حالة الوكيل وواجهة تطبيقك متزامنتين في الاتجاهين.
-  </Card>
-  <Card title="القنوات (Channels)" icon="messages" href="/edge/ar/guides/frontend/channels">
-    شغّل نفس الوكيل كروبوت على Slack أو Discord أو Teams.
-  </Card>
-</CardGroup>
-
-يجعل هذا الدليل Crew أو Flow يتحدث مع واجهة أمامية بـ Next.js من البداية إلى النهاية. تبني بقية القسم على التطبيق الذي تعدّه هنا.
-
-## البنية
-
-هناك ثلاثة أجزاء:
-
-1. **خادم وكيل CrewAI** — عملية Python تقدّم الـ Crew أو الـ Flow الخاص بك عبر AG-UI (FastAPI + `ag-ui-crewai`).
-2. **وقت تشغيل CopilotKit** — مسار Next.js يسجّل وكيلك ويوكّل الطلبات إليه.
-3. **الواجهة الأمامية بـ React** — مزوّد `<CopilotKit>` إلى جانب مكوّنات المحادثة والواجهة التوليدية.
-
-```
-React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-<Note>
-يغطي هذا الدليل المسار **الذاتي الاستضافة**: تشغّل خادم وكيل CrewAI بنفسك باستخدام `ag-ui-crewai`، ويعمل محليًا دون أي خدمة مُدارة. يقدّم CopilotKit أيضًا مسارًا **مُدارًا** (CopilotKit Cloud / Enterprise Intelligence) بخيوط مستضافة وأداة فحص — راجع [دليل البدء السريع لـ CopilotKit مع CrewAI](https://docs.copilotkit.ai/crewai-crews/quickstart) إن أردت ذلك بدلًا منه. كود الواجهة الأمامية في هذا القسم هو نفسه في الحالتين؛ الاختلاف فقط في كيفية استضافة الوكيل وتسجيله.
-</Note>
-
-<Note>
-يعمل CrewAI خلف AG-UI بثلاثة أشكال: الـ **Flows** العادية (المستخدمة في هذه الأدلة)، و**[الـ Flows المحادثية (Conversational Flows)](/edge/en/guides/frontend/conversational-flows)** (أصلية، مدركة للجلسة، قائمة على الأدوار، بتكافؤ كامل في الميزات)، والـ **Crews** (محادثة أساسية). الواجهة الأمامية في هذا القسم متطابقة عبرها جميعًا — الاختلاف فقط في تأليف الخلفية وتسجيلها.
-</Note>
-
-## دليل التكامل
-
-<Steps>
-
-<Step title="قدّم وكيلك عبر AG-UI">
-
-ثبّت حزمة التكامل في مشروع CrewAI الخاص بك:
-
-```bash
-pip install ag-ui-crewai
-```
-
-اكشف وكيلك من تطبيق FastAPI. تستخدم الـ Flows دالة `add_crewai_flow_fastapi_endpoint`؛ وتستخدم الـ Crews دالة `add_crewai_crew_fastapi_endpoint`. يمكنك تسجيل ما تشاء منها، كلٌّ على مساره الخاص.
-
-<CodeGroup>
-
-```python Flow
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
-from my_agents.recipe_flow import RecipeFlow
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_flow_fastapi_endpoint(
-    app=app,
-    flow=RecipeFlow(),
-    path="/recipe",
-)
-```
-
-```python Crew
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
-from my_agents.research_crew import ResearchCrew
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_crew_fastapi_endpoint(
-    app=app,
-    crew=ResearchCrew().crew(),
-    path="/research",
-)
-```
-
-</CodeGroup>
-
-شغّله:
-
-```bash
-uvicorn server:app --port 8000
-```
-
-<Note>
-اضبط متغيّرات البيئة الخاصة بمزوّد الـ LLM الخاص بك (على سبيل المثال `OPENAI_API_KEY`) قبل بدء الخادم.
-</Note>
-
-</Step>
-
-<Step title="أنشئ تطبيق Next.js">
-
-إن لم تكن لديك واجهة أمامية بعد، أنشئ هيكلًا:
-
-```bash
-npx create-next-app@latest my-app
-cd my-app
-```
-
-ثبّت CopilotKit وعميل CrewAI AG-UI:
-
-```bash
-npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="أضف وقت تشغيل CopilotKit">
-
-أنشئ مسارًا يسجّل وكيل (أو وكلاء) CrewAI مع وقت تشغيل CopilotKit. يشير كل وكيل إلى مسار على خادم Python الخاص بك عبر `CrewAIAgent`.
-
-```ts
-// app/api/copilotkit/route.ts
-import {
-  CopilotRuntime,
-  InMemoryAgentRunner,
-  createCopilotEndpoint,
-} from "@copilotkit/runtime/v2";
-import { CrewAIAgent } from "@ag-ui/crewai";
-import { handle } from "hono/vercel";
-
-const runtime = new CopilotRuntime({
-  agents: {
-    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-  },
-  runner: new InMemoryAgentRunner(),
-});
-
-const app = createCopilotEndpoint({
-  runtime,
-  basePath: "/api/copilotkit",
-});
-
-const handler = handle(app);
-export const GET = handler;
-export const POST = handler;
-```
-
-</Step>
-
-<Step title="غلّف تطبيقك بالمزوّد">
-
-وجّه `<CopilotKit>` إلى مسار وقت التشغيل واذكر اسم الوكيل الذي سجّلته.
-
-```tsx
-// app/page.tsx
-"use client";
-import { CopilotKit } from "@copilotkit/react-core";
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-import "@copilotkit/react-core/v2/styles.css";
-
-export default function Page() {
-  return (
-    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
-      <YourApp />
-      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
-    </CopilotKit>
-  );
-}
-```
-
-</Step>
-
-<Step title="شغّله">
-
-ابدأ العمليتين وافتح التطبيق. تشغّل المحادثة في الشريط الجانبي الآن الـ Crew أو الـ Flow الخاص بك.
-
-```bash
-uvicorn server:app --port 8000   # terminal 1
-npm run dev                       # terminal 2
-```
-
-</Step>
-
-</Steps>
-
-## خيارات واجهة المحادثة
-
-يشحن CopilotKit ثلاثة أسطح محادثة قابلة للتبديل. بدّل المكوّن؛ يبقى التوصيل متطابقًا.
-
-<CodeGroup>
-
-```tsx Sidebar
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-
-<CopilotSidebar agentId="recipe" />
-```
-
-```tsx Popup
-import { CopilotPopup } from "@copilotkit/react-core/v2";
-
-<CopilotPopup agentId="recipe" />
-```
-
-```tsx Inline
-import { CopilotChat } from "@copilotkit/react-core/v2";
-
-<CopilotChat agentId="recipe" />
-```
-
-</CodeGroup>
-
-## إلى أين تذهب بعد ذلك
-
-<CardGroup cols={2}>
-  <Card title="واجهة المستخدم التوليدية (Generative UI)" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    اعرض استدعاءات الأدوات وحالة الوكيل كمكوّنات مخصّصة.
-  </Card>
-  <Card title="إجراءات الواجهة الأمامية (Frontend Actions)" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
-    دع الوكيل يستدعي دوالًا تعمل في المتصفح.
-  </Card>
-  <Card title="التدخل البشري (Human-in-the-Loop)" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    قيّد إجراءات الوكيل خلف موافقة المستخدم.
-  </Card>
-  <Card title="الحالة التنبؤية (Predictive State)" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
-    ابثّ الحالة قيد التنفيذ إلى الواجهة أثناء عمل الوكيل.
-  </Card>
-</CardGroup>

docs/edge/ar/learn/litellm-removal-guide.mdx

@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
     # llm = LLM(model="mistral/mistral-large-latest")
 
     # After (Native):
-    llm = LLM(model="gemini/gemini-3.7-flash")
+    llm = LLM(model="gemini/gemini-2.0-flash")
     ```
 
     ```bash
@@ -312,7 +312,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5")    # Fast & affordable
 # Together AI → OpenAI or Gemini
 # llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
 llm = LLM(model="openai/gpt-4o")                 # High quality
-llm = LLM(model="gemini/gemini-3.7-flash")       # Fast & capable
+llm = LLM(model="gemini/gemini-2.0-flash")       # Fast & capable
 
 # Mistral → Anthropic or OpenAI
 # llm = LLM(model="mistral/mistral-large-latest")

docs/edge/ar/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ mode: "wide"
         # Example using Gemini's OpenAI-compatible API.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Add your Gemini model here, under openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Add your Gemini model here, under openai/
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ mode: "wide"
             ```python Google
             # Example using Gemini's OpenAI-compatible API
             llm = LLM(
-                model="openai/gemini-3.7-flash",
+                model="openai/gemini-2.0-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Should start with AIza...
             )

docs/edge/ar/learn/llm-selection-guide.mdx

@@ -144,7 +144,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
+manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -409,7 +409,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
     # Manager or coordination agents
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini/gemini-3.7-flash"),  # Premium for coordination
+        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
         # ... rest of config
     )
 

docs/edge/ar/learn/streaming-runtime-contract.mdx

@@ -151,7 +151,7 @@ result = stream.result
 
 ```python
 from crewai import Flow
-from crewai.flow import ConversationConfig, ConversationState
+from crewai.experimental.conversational import ConversationConfig, ConversationState
 
 
 @ConversationConfig(llm="gpt-4o-mini", defer_trace_finalization=True)

docs/edge/ar/observability/arize-phoenix.mdx

@@ -7,9 +7,9 @@ mode: "wide"
 
 # تكامل Arize Phoenix
 
-يوضح هذا الدليل كيفية دمج **Arize Phoenix** مع **CrewAI** باستخدام OpenTelemetry عبر حزمة [OpenInference](https://github.com/openinference/openinference) SDK. بنهاية هذا الدليل، ستتمكن من تتبع وكلاء CrewAI وتصحيح سلوك الوكلاء.
+يوضح هذا الدليل كيفية دمج **Arize Phoenix** مع **CrewAI** باستخدام OpenTelemetry عبر حزمة [OpenInference](https://github.com/openinference/openinference) SDK. بنهاية هذا الدليل، ستتمكن من تتبع وكلاء CrewAI وتصحيح أخطاء وكلائك بسهولة.
 
-> **ما هو Arize Phoenix؟** [Arize Phoenix](https://arize.com/phoenix/) هو خيار المراقبة والتقييم مفتوح المصدر من [Arize AI](https://arize.com/?utm_source=crewai-docs&utm_medium=partner&utm_campaign=partner-docs&utm_content=observability-arize-phoenix). استخدم Phoenix عندما تريد التشغيل محلياً أو الاستضافة الذاتية. استخدم [Arize AX](https://arize.com/products/ax/) لمنصة سحابية مُدارة أو ذاتية الاستضافة للمؤسسات لأنظمة الذكاء الاصطناعي في الإنتاج.
+> **ما هو Arize Phoenix؟** [Arize Phoenix](https://phoenix.arize.com) هو منصة مراقبة LLM توفر التتبع والتقييم لتطبيقات الذكاء الاصطناعي.
 
 [![شاهد عرض فيديو لتكاملنا مع Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
 
@@ -27,7 +27,7 @@ pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoen
 
 ### الخطوة 2: إعداد متغيرات البيئة
 
-قم بإعداد مفتاح API الخاص بـ Phoenix ونقطة نهاية OpenTelemetry لإرسال التتبعات إلى Phoenix. يعمل الإعداد نفسه مع نقطة نهاية Phoenix محلية أو ذاتية الاستضافة عن طريق تغيير عنوان المجمع.
+قم بإعداد مفاتيح API لـ Phoenix Cloud وإعداد OpenTelemetry لإرسال التتبعات إلى Phoenix. Phoenix Cloud هو إصدار مستضاف من Arize Phoenix، لكنه ليس مطلوباً لاستخدام هذا التكامل.
 
 يمكنك الحصول على مفتاح Serper API المجاني [هنا](https://serper.dev/).
 
@@ -35,8 +35,8 @@ pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoen
 import os
 from getpass import getpass
 
-# Get your Phoenix API key
-PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix API key: ")
+# Get your Phoenix Cloud credentials
+PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix Cloud API Key: ")
 
 # Get API keys for services
 OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
@@ -44,7 +44,7 @@ SERPER_API_KEY = getpass("🔑 Enter your Serper API key: ")
 
 # Set environment variables
 os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
-os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Change this to your own endpoint if you are using a self-hosted instance
+os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, change this to your own endpoint if you are using a self-hosted instance
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 os.environ["SERPER_API_KEY"] = SERPER_API_KEY
 ```
@@ -131,7 +131,7 @@ print(result)
 
 بعد تشغيل الوكيل، يمكنك عرض التتبعات المولدة من تطبيق CrewAI في Phoenix. سترى خطوات مفصلة لتفاعلات الوكلاء واستدعاءات LLM، مما يساعدك في التصحيح والتحسين.
 
-افتح مشروع Phoenix وانتقل إلى المشروع الذي حددته في معامل `project_name`. سترى عرض زمني للتتبع مع جميع تفاعلات الوكلاء واستخدامات الأدوات واستدعاءات LLM.
+سجل الدخول إلى حساب Phoenix Cloud الخاص بك وانتقل إلى المشروع الذي حددته في معامل `project_name`. سترى عرض زمني للتتبع مع جميع تفاعلات الوكلاء واستخدامات الأدوات واستدعاءات LLM.
 
 ![مثال تتبع في Phoenix يوضح تفاعلات الوكلاء](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
 
@@ -145,9 +145,6 @@ print(result)
 
 ### المراجع
 - [وثائق Phoenix](https://docs.arize.com/phoenix/) - نظرة عامة على منصة Phoenix.
-- [Arize AX](https://arize.com/products/ax/) - مراقبة وتقييم مُداران سحابياً أو ذاتيا الاستضافة للمؤسسات.
-- [دليل Arize لتقييم الوكلاء](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) - سير عمل إنتاجي لتقييم سلوك الوكلاء من التتبعات.
-- [دليل Arize لتقييم LLM](https://arize.com/resources/llm-evaluation/) - طرق ومقاييس لتقييم تطبيقات LLM.
 - [وثائق CrewAI](https://docs.crewai.com/) - نظرة عامة على إطار عمل CrewAI.
 - [وثائق OpenTelemetry](https://opentelemetry.io/docs/) - دليل OpenTelemetry
 - [OpenInference GitHub](https://github.com/openinference/openinference) - الكود المصدري لـ OpenInference SDK.

docs/edge/ar/telemetry.mdx

@@ -23,7 +23,7 @@ mode: "wide"
 عند تفعيل ميزة `share_crew`، يتم جمع بيانات تفصيلية تشمل أوصاف المهام وخلفيات وأهداف الوكلاء وسمات محددة أخرى
 لتوفير رؤى أعمق. قد يتضمن جمع البيانات الموسع هذا معلومات شخصية إذا دمجها المستخدمون في طواقمهم أو مهامهم.
 يجب على المستخدمين النظر بعناية في محتوى طواقمهم ومهامهم قبل تفعيل `share_crew`.
-يمكن للمستخدمين تعطيل القياس عن بُعد في CrewAI عبر تعيين `CREWAI_DISABLE_TELEMETRY` إلى `true` أو `1` أو `yes` أو `on` (بغض النظر عن حالة الأحرف). `OTEL_SDK_DISABLED` بنفس القيم يعطّل أيضاً مُصدِّر CrewAI. مجموعة أدوات OpenTelemetry نفسها ما تزال تقبل `true` فقط لتعطيل بقية أدوات القياس في العملية.
+يمكن للمستخدمين تعطيل القياس عن بُعد عبر تعيين متغير البيئة `CREWAI_DISABLE_TELEMETRY` إلى `true` أو تعيين `OTEL_SDK_DISABLED` إلى `true` (لاحظ أن الأخير يعطل جميع أدوات OpenTelemetry عالمياً).
 
 ### أمثلة:
 ```python
@@ -34,8 +34,6 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
-`CREWAI_DISABLE_TELEMETRY=1` (أو `yes` / `on`) يعمل بنفس طريقة `true`. تُتجاهل القيم غير المعروفة ويبقى القياس عن بُعد مفعّلاً.
-
 ### العزل عن إعداد OpenTelemetry الخاص بك
 
 يعمل القياس عن بُعد الخاص بـ CrewAI على `TracerProvider` خاص به ولا يسجل نفسه
@@ -54,17 +52,16 @@ os.environ['OTEL_SDK_DISABLED'] = 'true'
 | افتراضي | البيانات                                      | السبب والتفاصيل                                                                                                       |
 |:----------|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
 | نعم       | إصدار CrewAI وPython                 | تتبع إصدارات البرمجيات. مثال: CrewAI v1.2.3، Python 3.8.10. لا بيانات شخصية. |
-| نعم       | بيانات وصفية للطاقم | تشمل: مفتاح ومعرّف مُولّد عشوائياً، نوع العملية (مثل 'sequential'، 'parallel')، علم منطقي لاستخدام الذاكرة (true/false)، علم منطقي يوضح ما إذا تم تمرير أي مدخلات للتشغيل (true/false — وليس مفاتيح المدخلات أو قيمها، والتي لا تُجمع إلا عند تمكين `share_crew`)، عدد المهام، عدد الوكلاء. كلها غير شخصية. |
+| نعم       | بيانات وصفية للطاقم | تشمل: مفتاح ومعرّف مُولّد عشوائياً، نوع العملية (مثل 'sequential'، 'parallel')، علم منطقي لاستخدام الذاكرة (true/false)، عدد المهام، عدد الوكلاء. كلها غير شخصية. |
 | نعم       | بيانات الوكيل | تشمل: مفتاح ومعرّف مُولّد عشوائياً، اسم الدور (يجب ألا يتضمن معلومات شخصية)، إعدادات منطقية (verbose، التفويض مُفعّل، تنفيذ الكود مسموح)، أقصى عدد تكرارات، أقصى RPM، أقصى حد لإعادة المحاولة، معلومات LLM (انظر سمات LLM)، قائمة أسماء الأدوات (يجب ألا تتضمن معلومات شخصية). لا بيانات شخصية. |
 | نعم       | بيانات وصفية للمهمة | تشمل: مفتاح ومعرّف مُولّد عشوائياً، إعدادات تنفيذ منطقية (async_execution، human_input)، دور ومفتاح الوكيل المرتبط، قائمة أسماء الأدوات. كلها غير شخصية. |
 | نعم       | إحصائيات استخدام الأدوات | تشمل: اسم الأداة (يجب ألا يتضمن معلومات شخصية)، عدد محاولات الاستخدام (عدد صحيح)، سمات LLM المستخدمة. لا بيانات شخصية. |
 | نعم       | بيانات تنفيذ الاختبار | تشمل: مفتاح ومعرّف الطاقم المُولّد عشوائياً، عدد التكرارات، اسم النموذج المستخدم، درجة الجودة (عدد عشري)، وقت التنفيذ (بالثواني). كلها غير شخصية. |
-| نعم       | بيانات دورة حياة المهمة | تشمل: أوقات الإنشاء وبدء/انتهاء التنفيذ، معرّفات الطاقم والمهمة، وما إذا نجحت المهمة أو فشلت. وعند فشل المهمة، يُسجَّل **اسم صنف** الاستثناء (مثل `TimeoutError`) بحيث يمكن عدّ حالات الفشل وتشخيصها — وليس رسالة الخطأ أبدًا، فهي قد تحتوي على مطالبات أو مخرجات نموذج أو مسارات ملفات أو بيانات اعتماد. مخزنة كنطاقات مع طوابع زمنية. لا بيانات شخصية. |
+| نعم       | بيانات دورة حياة المهمة | تشمل: أوقات الإنشاء وبدء/انتهاء التنفيذ، معرّفات الطاقم والمهمة. مخزنة كنطاقات مع طوابع زمنية. لا بيانات شخصية. |
 | نعم       | سمات LLM | تشمل: الاسم، model_name، model، top_k، temperature، واسم فئة LLM. كلها بيانات تقنية غير شخصية. |
-| نعم       | إنشاء مشروع باستخدام CLI الخاص بـ CrewAI | تشمل: أن مشروعًا جديدًا أُنشئ عبر `crewai create`، ونوعه (`crew` أو `json_crew` أو `flow`)، ومعرّف المشروع الذي تم توليده لهذا المشروع الجديد وكُتب في ملف `pyproject.toml` الخاص به. وهو معرّف المشروع الجديد نفسه، ويُسجَّل بشكل منفصل عن `project_id` الخاص بالمجلد الذي شُغّل منه الأمر — وقد يختلفان. لا اسم مشروع، ولا محتويات ملفات، ولا شيفرة. لا بيانات شخصية. |
 | نعم       | محاولة نشر الطاقم باستخدام CLI الخاص بـ CrewAI | تشمل: حقيقة إجراء النشر ومعرّف الطاقم، وما إذا كان يحاول سحب السجلات، وما إذا بدأ النشر من أمر CLI أو من واجهة التشغيل TUI. لا تُسجَّل محتويات المشروع أو الطاقم. لا توجد بيانات شخصية. |
-| نعم       | بيئة التنفيذ | تشمل: مساعد البرمجة بالذكاء الاصطناعي الذي يشغّل العملية إن وُجد (واحد من قائمة ثابتة مثل `claude_code` أو `codex` أو `cursor` أو `unknown`)، ومكان تشغيل العملية (واحد من قائمة ثابتة مثل `ci` أو `container` أو `serverless` أو `interactive`)، و`project_id` من ملف `pyproject.toml` عند ضبطه، ونطاقًا تقريبيًا لحجم الجهاز (واحد من `1-2` أو `3-4` أو `5-8` أو `9-16` أو `17-32` أو `33+` أو `unknown`). النطاق مجال وليس العدد الدقيق للأنوية أبدًا — العدد الدقيق اختياري فقط، ضمن «معلومات البيئة» أدناه. تأتي فئة الحجم من عدد أنوية المضيف؛ ويتحقق اكتشاف المساعد وموقع التشغيل فقط مما إذا كانت متغيرات البيئة المعروفة مضبوطة، ولا يقرأ قيمها أبدًا. لا بيانات شخصية. |
-| نعم       | إشارات دورة حياة التدفق | تشمل: بدء التدفق، وما إذا اكتمل أو فشل، وما إذا فشلت إحدى طرقه، وما إذا توقف مؤقتًا لانتظار إدخال أو ملاحظات بشرية، وما إذا كان البدء تشغيلًا مستأنفًا، وما إذا فشل دور محادثة، ومدة تشغيل التدفق، وما إذا كان التدفق مما تشغّله CrewAI داخليًا أو مما كتبته أنت. ويُسجَّل اسم التدفق، كما هو الحال بالفعل لإنشاء التدفق وتنفيذه. وعند فشل تدفق أو إحدى طرقه، يُسجَّل **اسم فئة** الاستثناء (مثل `TimeoutError`) لتشخيص الأعطال — ولا تُسجَّل أبدًا رسالة الخطأ، التي قد تحتوي على مطالبات أو مخرجات النموذج أو مسارات ملفات أو بيانات اعتماد. ولا تُسجَّل أبدًا أسماء الطرق أو حالة التدفق. لا توجد بيانات شخصية. |
+| نعم       | بيئة التنفيذ | تشمل: مساعد البرمجة بالذكاء الاصطناعي الذي يشغّل العملية إن وُجد (واحد من قائمة ثابتة مثل `claude_code` أو `codex` أو `cursor` أو `unknown`)، ومكان تشغيل العملية (واحد من قائمة ثابتة مثل `ci` أو `container` أو `serverless` أو `interactive`)، و`project_id` من ملف `pyproject.toml` عند ضبطه. يتحقق الاكتشاف فقط مما إذا كانت متغيرات البيئة المعروفة مضبوطة، ولا يقرأ قيمها أبدًا. لا بيانات شخصية. |
+| نعم       | إشارات دورة حياة التدفق | تشمل: بدء التدفق، وما إذا اكتمل أو فشل، وما إذا فشلت إحدى دواله، وما إذا توقّف مؤقتًا لطلب إدخال أو ملاحظات بشرية، وما إذا كان البدء استئنافًا لتشغيل سابق، وما إذا فشلت دورة محادثة، ومدة تشغيل التدفق، وما إذا كان التدفق من التدفقات التي يشغّلها CrewAI داخليًا أم من كتابتك. يُسجَّل اسم التدفق (يجب ألا يتضمن معلومات شخصية)، كما هو الحال بالفعل عند إنشاء التدفق وتنفيذه. لا تُسجَّل أبدًا أسماء الدوال أو رسائل الأخطاء أو حالة التدفق. لا بيانات شخصية. |
 | نعم       | إشارة مشاركة التتبع | تشمل: نجاح مشاركة دفعة من عمليات التتبع مع CrewAI AMP، وما إذا تمت المشاركة بشكل مجهول (قبل إنشاء حساب) أو مرتبطة بحسابك. ومثل كل span، تحمل أيضًا سمات بيئة التنفيذ الموضحة أعلاه (`project_id` عند تكوينه، ومساعد البرمجة، وبيئة التشغيل). يصف هذا الصف بيانات القياس عن بُعد الخاصة بالمشاركة فقط — وليس محتويات التتبع أو الوصول الذي تمنحه روابط التتبع المشتركة. لا تُسجَّل محتويات التتبع أو المدخلات أو المخرجات في هذه الإشارة. قبل مشاركة التتبعات، راجع الأسرار والبيانات الشخصية وإعدادات التنقيح والاحتفاظ في AMP. |
 | لا        | بيانات الوكيل الموسّعة | تشمل: وصف الهدف، نص الخلفية، معرّف ملف موجهات i18n. يجب على المستخدمين التأكد من عدم تضمين معلومات شخصية في حقول النص. |
 | لا        | معلومات المهمة التفصيلية | تشمل: وصف المهمة، وصف المخرجات المتوقعة، مراجع السياق. يجب على المستخدمين التأكد من عدم تضمين معلومات شخصية في هذه الحقول. |

docs/edge/ar/tools/ai-ml/overview.mdx

@@ -50,15 +50,16 @@ mode: "wide"
 - **سلامة الذكاء الاصطناعي**: تنفيذ فحوصات الإشراف على المحتوى والسلامة
 
 ```python
-from crewai_tools import DallETool, VisionTool
+from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
+code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor],
+    tools=[image_generator, vision_processor, code_executor],
     goal="Create and analyze content using AI capabilities"
 )

docs/edge/ar/tools/file-document/filereadtool.mdx

@@ -1,6 +1,6 @@
 ---
 title: قراءة الملفات
-description: أداة `FileReadTool` مصممة لقراءة الملفات من نظام الملفات المحلي.
+description: أداة `FileReadTool` تقرأ الملفات من نظام الملفات المحلي.
 icon: folders
 mode: "wide"
 ---
@@ -8,35 +8,63 @@ mode: "wide"
 ## نظرة عامة
 
 <Note>
-    لا نزال نعمل على تحسين الأدوات، لذا قد يحدث سلوك غير متوقع أو تغييرات في المستقبل.
+    لا نزال نعمل على تحسين الأدوات، لذا قد يتغير السلوك.
 </Note>
 
-تمثل أداة FileReadTool مفهوميًا مجموعة من الوظائف ضمن حزمة crewai_tools تهدف إلى تسهيل قراءة الملفات واسترجاع المحتوى. تتضمن هذه المجموعة أدوات لمعالجة ملفات نصية دفعية، وقراءة ملفات التكوين أثناء التشغيل، واستيراد البيانات للتحليلات. تدعم مجموعة متنوعة من صيغ الملفات النصية مثل `.txt` و `.csv` و `.json` وغيرها. يُعاد المحتوى دائمًا نصًا عاديًا.
+تقرأ أداة `FileReadTool` ملفًا محليًا وتعيد محتواه كنص.
+استخدمها لمعالجة الملفات النصية، أو قراءة ملفات التكوين، أو تحميل البيانات للتحليل.
+تعمل مع أي صيغة نصية، مثل `.txt` و `.csv` و `.json` و `.md`.
+تعيد الأداة دائمًا نصًا عاديًا. إذا احتجت بيانات منظمة (مثل JSON)، فقم بتحليلها في الـ Agent أو في التعليمات البرمجية الخاصة بك.
+
+للملفات الكبيرة، يمكن للـ Agent تمرير `start_line` و `line_count` لقراءة نطاق من الأسطر فقط.
+تتوقف الأداة بمجرد الحصول على تلك الأسطر، لذا لا تمسح بقية الملف.
 
 ## التثبيت
 
-لاستخدام الوظائف المنسوبة سابقاً لأداة FileReadTool، قم بتثبيت حزمة crewai_tools:
-
 ```shell
-pip install 'crewai[tools]'
+uv add 'crewai[tools]'
 ```
 
 ## مثال على الاستخدام
 
-للبدء مع FileReadTool:
-
 ```python Code
 from crewai_tools import FileReadTool
 
-# Initialize the tool to read any files the agents knows or lean the path for
-file_read_tool = FileReadTool()
+# Agent chooses the file path at runtime
+tool = FileReadTool()
 
-# OR
+# OR set a default file the agent can read with no path argument
+tool = FileReadTool(file_path='path/to/your/file.txt')
 
-# Initialize the tool with a specific file path, so the agent can only read the content of the specified file
-file_read_tool = FileReadTool(file_path='path/to/your/file.txt')
+# OR let the agent read any file under a directory
+tool = FileReadTool(base_dir='/data')
 ```
 
+امنح الأداة لـ Agent. في وقت التشغيل يمرر الـ LLM قيمة `file_path`، واختياريًا `start_line` و `line_count`.
+
 ## المعاملات
 
-- `file_path`: مسار الملف المراد قراءته. يقبل كلاً من المسارات المطلقة والنسبية. تأكد من وجود الملف وأن لديك الصلاحيات اللازمة للوصول إليه.
+يمكن للـ Agent تمرير هذه المعاملات في وقت التشغيل:
+
+- `file_path`: (اختياري) مسار الملف المراد قراءته. المسارات المطلقة والنسبية صالحة فقط عندما تُحل داخل بيئة الحماية `base_dir`. يُحل المسار النسبي بالنسبة إلى `base_dir` عند تعيينه، وإلا بالنسبة إلى مجلد العمل الحالي (بيئة الحماية الافتراضية). احذفه لقراءة الملف الافتراضي المحدد عند الإنشاء. إذا لم يكن هناك افتراضي، تعيد الأداة خطأ يفيد بعدم توفير مسار.
+- `start_line`: (اختياري) أول سطر للقراءة. تبدأ أرقام الأسطر من `1`. الافتراضي هو `1`.
+- `line_count`: (اختياري) عدد الأسطر المراد قراءتها. إذا حُذف، تقرأ الأداة من `start_line` حتى نهاية الملف.
+
+يمكنك تعيين هذه المعاملات عند إنشاء الأداة:
+
+- `file_path`: (اختياري) الملف الافتراضي للقراءة عندما يستدعي الـ Agent الأداة بدون مسار. يُحل المسار النسبي بالنسبة إلى `base_dir` عند توفير `base_dir`، وإلا بالنسبة إلى مجلد العمل الحالي.
+- `base_dir`: (اختياري) المجلد الذي يجب أن تبقى داخله مسارات وقت التشغيل. الافتراضي هو مجلد العمل الحالي. تحل الأداة هذا المسار عند إنشائها، لذا لا يؤدي تغيير لاحق لمجلد العمل إلى نقل بيئة الحماية.
+- `encoding`: (اختياري) ترميز النص المستخدم لفك تشفير الملف. الافتراضي هو `utf-8`. إذا فشل فك التشفير، تعيد الأداة خطأ وتقترح تمرير `encoding` مختلف.
+
+تعيد حالات الفشل الشائعة (ملف مفقود، رفض الإذن، ترميز خاطئ، أو مسار خارج بيئة الحماية) سلسلة خطأ. ولا تُثير استثناءً.
+
+## المسارات المسموح بها
+
+عادةً ما يختار الـ LLM مسار الملف في وقت التشغيل، لذا تقتصر عمليات القراءة على بيئة حماية:
+
+- يجب أن تُحل مسارات وقت التشغيل داخل `base_dir` (الافتراضي: مجلد العمل الحالي). تحل الأداة مقاطع `..` والروابط الرمزية قبل فحص المسار، لذا لا يمكنها الخروج من بيئة الحماية.
+- مسار `file_path` الذي تمرره إلى المُنشئ مسموح به دائمًا، حتى لو كان خارج `base_dir`. قد تفشل القراءة نفسها إذا كان الملف مفقودًا أو مجلدًا أو لا يمكن الوصول إليه. يُثبَّت هذا المسار عند إنشاء الأداة، لذا لا يغيّر تغيير لاحق لمجلد العمل الملف الذي يشير إليه. يمكن للـ Agent قراءته بحذف `file_path`، أو باستخدام الاسم الظاهر في وصف الأداة. إعلان ملف واحد لا يتيح الوصول إلى ملفات أخرى في نفس المجلد.
+
+للسماح لـ Agent بقراءة ملفات خارج مجلد العمل، عيّن `base_dir` عند إنشاء الأداة (انظر المثال أعلاه).
+
+كحل أخير، عيّن `CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true` لإيقاف فحوصات المسار. ينطبق هذا الإعداد على كل أداة من crewai-tools في العملية، بما في ذلك حماية SSRF على أدوات جلب عناوين URL. فضّل `base_dir` بدلاً من ذلك.

docs/edge/ar/tools/web-scraping/scrapeelementfromwebsitetool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## الوصف
 
-أداة `ScrapeElementFromWebsiteTool` مصممة لاستخراج عناصر محددة من المواقع باستخدام محددات CSS. تسمح هذه الأداة لوكلاء CrewAI باستخراج محتوى مستهدف من صفحات الويب، مما يجعلها مفيدة لمهام استخراج البيانات حيث تكون أجزاء محددة فقط من صفحة الويب مطلوبة. تمر الطلبات عبر مساعد HTTP الآمن ضد SSRF في CrewAI: يتم فحص عنوان URL المطلوب وكل قفزة إعادة توجيه مقابل النطاقات الخاصة والمحجوزة (بما في ذلك بيانات تعريف السحابة)، ويُثبَّت اتصال TCP على عنوان IP الذي اجتاز هذا الفحص.
+أداة `ScrapeElementFromWebsiteTool` مصممة لاستخراج عناصر محددة من المواقع باستخدام محددات CSS. تسمح هذه الأداة لوكلاء CrewAI باستخراج محتوى مستهدف من صفحات الويب، مما يجعلها مفيدة لمهام استخراج البيانات حيث تكون أجزاء محددة فقط من صفحة الويب مطلوبة.
 
 ## التثبيت
 

docs/edge/ar/tools/web-scraping/scrapewebsitetool.mdx

@@ -16,8 +16,6 @@ mode: "wide"
 أداة مصممة لاستخراج وقراءة محتوى موقع محدد. قادرة على التعامل مع أنواع مختلفة من صفحات الويب عن طريق إجراء طلبات HTTP وتحليل محتوى HTML المستلم.
 يمكن أن تكون هذه الأداة مفيدة بشكل خاص لمهام استخراج البيانات من الويب وجمع البيانات أو استخراج معلومات محددة من المواقع.
 
-تمر الطلبات عبر مساعد HTTP الآمن ضد SSRF في CrewAI: يتم فحص عنوان URL المطلوب وكل قفزة إعادة توجيه مقابل النطاقات الخاصة والمحجوزة (بما في ذلك بيانات تعريف السحابة)، ويُثبَّت اتصال TCP على عنوان IP الذي اجتاز هذا الفحص.
-
 ## التثبيت
 
 ثبّت حزمة crewai_tools

docs/edge/en/changelog.mdx

@@ -4,104 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="Aug 27, 2026">
-  ## v1.15.18
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.18)
-
-  ## What's Changed
-
-  ### Features
-  - Promote conversational flows to stable
-  - Record a created deployment with the given UUID
-  - Enhance conversational flow documentation and APIs
-  - Let a declaration name the router's response format
-  - Let a chat flow declare its own state shape
-  - Accept crew-style LLM config in a conversational declaration
-  - Report project creation with the minted ID
-  - Record whether a run had inputs, without recording the inputs
-  - Backfill project ID from every user-invoked project command
-
-  ### Bug Fixes
-  - Preserve tool results when the final answer is empty
-  - Map default Claude Sonnet 4.6 to its 1M context window
-  - Raise Anthropic default max_tokens for large tool calls
-  - Render message content parts as text, not as a Python repr
-  - Keep message roles when Agent.kickoff gets a conversation
-  - Skip interception hooks on crewai-internal flows
-  - Record task failures as failures, not successes
-  - Emit the flow lifecycle on a suppressed resume
-  - Open the conversational TUI for a declarative chat flow
-  - Record crew_memory as a string, not a bool
-  - Always emit project_id so absent and empty stay distinct
-
-  ### Documentation
-  - Clarify Arize Phoenix observability docs
-
-  ## Contributors
-
-  @Vidit-Ostwal, @arizedatngo, @joaomdmoura, @lorenzejay, @lucasgomide
-
-</Update>
-
-<Update label="Aug 19, 2026">
-  ## v1.15.17
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.17)
-
-  ## What's Changed
-
-  ### Features
-  - Add declarative conversational flows documentation
-  - Synthesize built-in conversational methods for declarations
-  - Enable declarations to drive conversational mode
-  - Make conversational opt-in unmistakable
-  - Carry the AMP slug on tools resolved from a slug reference
-  - Handle oversized single messages during chunking
-
-  ### Bug Fixes
-  - Fix usage of the URL hostname as MCP HTTP and SSE server_name
-  - Close the agent scope on every failed attempt
-  - Attribute tool errors to the tool that failed
-  - Pin SSRF checks to each redirect hop and peer IP
-  - Resolve issues with native tool calls broken over OpenAI Responses API
-
-  ### Documentation
-  - Update documentation with a snapshot and changelog for v1.15.16
-
-  ## Contributors
-
-  @Copilot, @Vidit-Ostwal, @github-code-quality[bot], @joaomdmoura, @lorenzejay, @lucasgomide, @theCyberTech
-
-</Update>
-
-<Update label="Aug 13, 2026">
-  ## v1.15.16
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.16)
-
-  ## What's Changed
-
-  ### Features
-  - Introduce execution context management with UUID support
-  - Record what kind of exception ended a flow
-  - Record when a trace batch is shared with AMP
-  - Count deployments from any origin and record where they started
-
-  ### Bug Fixes
-  - Record the running release on every emitted span
-  - Fix MySQL search table name validation
-  - Stop a failed turn from marking the next one as failed
-
-  ### Documentation
-  - Add Frontend guides for CopilotKit and AG-UI
-
-  ## Contributors
-
-  @joaomdmoura, @lorenzejay, @ranst91, @theCyberTech
-
-</Update>
-
 <Update label="Aug 11, 2026">
   ## v1.15.15
 

docs/edge/en/concepts/agents.mdx

@@ -623,12 +623,6 @@ messages = [
 result = researcher.kickoff(messages)
 ```
 
-The last `user` message is the request the agent answers. Every other message
-keeps its role and its position around that request, so a conversation that
-ends in assistant or tool messages still asks the user's question and still
-delivers those trailing turns after it. With no `user` message at all, the last
-message is treated as the request.
-
 ### Async Support
 
 An asynchronous version is available via `kickoff_async()` with the same parameters:

docs/edge/en/concepts/memory.mdx

@@ -740,7 +740,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Use Google Gemini
-memory = Memory(llm="gemini/gemini-3.7-flash")
+memory = Memory(llm="gemini/gemini-2.0-flash")
 
 # Pass a pre-configured LLM instance with custom settings
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/en/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ Under the hood, CrewAI employs a modular prompt system that you can customize ex
 - **Error handling** – Direct how agents respond to failures, exceptions, or timeouts.
 - **Tool-specific prompts** – Define detailed instructions for how tools are invoked or utilized.
 
-Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
+Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
 
 ## Understanding Default System Instructions
 

docs/edge/en/guides/crews/first-crew.mdx

@@ -77,7 +77,7 @@ Replace the generated `agents/researcher.jsonc` file and add `agents/analyst.jso
 }
 ```
 
-Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-3.7-flash`.
+Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-2.0-flash-001`.
 
 ## Step 3: Define Tasks and Crew Settings
 

docs/edge/en/guides/flows/conversational-flows.mdx

@@ -1,19 +1,19 @@
 ---
 title: Conversational Flows
-description: Build multi-turn chat apps with handle_turn per turn, message history, intent routing, tracing, and structured streaming.
+description: Build multi-turn chat apps with handle_turn per turn, message history, intent routing, tracing, and WebSocket bridges.
 icon: comments
 mode: "wide"
 ---
 
 ## Overview
 
-Conversational apps treat each user line as a **new flow run** with the **same session id**. CrewAI adds helpers for message history, optional intent routing, deferred tracing, structured turn streaming, and a local `flow.chat()` REPL.
+Conversational apps treat each user line as a **new flow run** with the **same session id**. CrewAI adds helpers for message history, optional intent routing, deferred tracing, UI bridges, and a local `flow.chat()` REPL for conversational flows.
 
 | Concept | Implementation |
 |---------|----------------|
 | Session id | `handle_turn(..., session_id=...)` → `kickoff(inputs={"id": ...})` → `state.id` |
 | User line | `handle_turn(message)` appends to `state.messages` before the graph runs |
-| Turn complete | `conversation_turn_completed`; with default trace deferral, `FlowFinished` waits for `finalize_session_traces()` |
+| Turn complete | `FlowFinished` for **this run** only; chat continues on the next `handle_turn` |
 | Full-session trace | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
 
 ## Turn APIs
@@ -30,8 +30,7 @@ Use **`flow.handle_turn(message, session_id=...)`** for every user message from
 | `kickoff(inputs={...})` | Advanced flow execution without conversational turn handling |
 | `ask()` | Blocking prompt **inside** one step (wizard, clarification) |
 | `@human_feedback` | Approve/reject **a step output** — not the next chat line |
-
-`handle_turn()`, `stream_turn()`, and `chat()` raise `ValueError` unless conversational mode is enabled. Applying `@ConversationConfig(...)` enables it automatically; otherwise set `conversational = True`.
+| `ChatSession.handle_turn(...)` | Transport layer over `handle_turn` (SSE / WebSocket) |
 
 ## Quick start
 
@@ -40,7 +39,7 @@ from uuid import uuid4
 
 from crewai import Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
 )
@@ -48,6 +47,8 @@ from crewai.flow import (
 
 @ConversationConfig(defer_trace_finalization=True)
 class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
     def route_turn(self, context):
         message = (self.state.current_user_message or "").lower()
         if "order" in message:
@@ -95,12 +96,12 @@ stream = flow.stream_turn("Where is my order?", session_id=session_id)
 with stream:
     for frame in stream.events:
         if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
+            print(frame.data.get("chunk", ""), end="", flush=True)
 
 result = stream.result
 ```
 
-For the full frame contract and channel list, see [Streaming Runtime Contract](/edge/en/learn/streaming-runtime-contract).
+For the full frame contract, channel list, and async API, see [Streaming Runtime Contract](/edge/en/learn/streaming-runtime-contract).
 
 ## Turn lifecycle
 
@@ -110,18 +111,29 @@ Each `handle_turn` runs this pipeline:
 2. **State restore** — if `inputs["id"]` exists and `@persist` is configured, loads the latest snapshot.
 3. **`FlowStarted`** — emitted on the first deferred session turn only.
 4. **Pending turn hydration** — appends the user message to `state.messages`, sets `current_user_message` / `last_user_message`, and optionally classifies when `intents` / `default_intents` + `intent_llm` are set.
-5. **Graph execution** — user-defined `@start` methods (if any) → `route_conversation` (the built-in start/router) → the selected `@listen` handler. `route_conversation` also calls the overridable `conversation_start()` helper.
+5. **Graph execution** — `conversation_start` → `route_conversation` → the selected `@listen` handler.
 6. **End of run** — per-turn `flow_finished` and trace finalization are **skipped** when deferral is enabled; nested `Agent.kickoff()` / crews do not close the parent batch either.
 
-Handlers should call **`append_assistant_message(reply)`** when the visible reply is not the return value, or when you trim history. A public string return is also recorded as assistant and included in the `@persist` snapshot, so a fresh Flow instance restores it. The user line is already stored by `handle_turn` — do not append it again in handlers.
+Handlers should call **`append_assistant_message(reply)`** so the next turn’s `conversation_messages` includes assistant text. The user line is already stored by `handle_turn` — do not append it again in handlers.
 
-## Configuration overview
+## `ConversationConfig` (class-level defaults)
 
-Decorating a `Flow` subclass with `ConversationConfig` both attaches the chat defaults and enables conversational mode. See the [full field reference](#conversationconfig) below. Override pre-classification per turn with `handle_turn(..., intents=..., intent_llm=...)`.
+Decorate your conversational `Flow` subclass with `ConversationConfig`.
+
+| Field | Default | Purpose |
+|-------|---------|---------|
+| `system_prompt` | Framework default | System message used by the built-in `converse_turn`. |
+| `llm` | `None` | Conversation LLM used by `converse_turn` and as router fallback. |
+| `router` | `None` | `RouterConfig` for LLM-driven routing. |
+| `intent_llm` | `None` | LLM for `intents=` / `default_intents` pre-classification. |
+| `default_intents` | `None` | Outcome labels for pre-classification. |
+| `defer_trace_finalization` | `True` | Keep one trace batch open across `handle_turn()` calls. |
+
+Override pre-classification per turn with `handle_turn(..., intents=..., intent_llm=...)`.
 
 ## Lower-level `ChatState` helpers
 
-`ChatState`, the legacy `ConversationalConfig`, and `crewai.flow.conversation` helpers are still importable for advanced orchestration, tests, or custom wrappers. They are separate from the `ConversationState` / `ConversationConfig` API and do not add `user_message=` or `session_id=` keyword arguments to `Flow.kickoff()`.
+`ChatState`, `ConversationalConfig`, and `crewai.flow.conversation` helpers are still importable for advanced orchestration, tests, or custom wrappers. They do not add `user_message=` or `session_id=` keyword arguments to `Flow.kickoff()`.
 
 ```python
 from crewai.flow import ChatState
@@ -143,8 +155,6 @@ class MyChatState(ChatState):
 
 `ConversationalInputs` is a `TypedDict` for conventional `kickoff(inputs={...})` keys: `id`, `user_message`, `last_intent`.
 
-`ConversationState` stores `messages` as `ConversationMessage` objects and additionally provides `current_user_message`, `ended`, `events`, and `agent_threads`. Use `conversation_messages` when passing its canonical history to an LLM.
-
 ## `Flow` conversational API
 
 ### `handle_turn` parameters
@@ -166,9 +176,9 @@ class MyChatState(ChatState):
 | Attribute | Purpose |
 |-----------|---------|
 | `conversational` | Set to `True` to enable the conversational graph and `handle_turn()` |
-| `defer_trace_finalization` | Optional instance override. Otherwise `_should_defer_trace_finalization()` reads `ConversationConfig.defer_trace_finalization`. |
-| `suppress_flow_events` | Hides console flow panels and suppresses method execution events; flow start/finish events still emit |
-| `stream` | Generic Flow streaming flag. For conversational turns, use `stream_turn()` instead of combining this flag with `handle_turn()`. |
+| `defer_trace_finalization` | Instance flag; set automatically from config on `handle_turn()` |
+| `suppress_flow_events` | Hides console flow panels; **tracing still records** method/flow events |
+| `stream` | Enable streaming; use with `ChatSession.handle_turn(..., stream=True)` |
 
 ### Methods and properties
 
@@ -180,12 +190,12 @@ class MyChatState(ChatState):
 | `classify_intent(text, outcomes, *, llm, context=None)` | Map text to one outcome (same collapse logic as `@human_feedback`) |
 | `receive_user_message(text, *, outcomes=None, llm=None)` | Append user message; optionally set `last_intent` |
 | `finalize_session_traces()` | Emit deferred `flow_finished` and finalize the session trace batch |
-| `_should_defer_trace_finalization()` | Advanced/internal hook that resolves whether per-turn trace finalization is deferred |
+| `_should_defer_trace_finalization()` | Whether this flow defers per-turn trace finalization |
 | `input_history` | Audit trail of `ask()` prompts and responses |
 
 ### Module helpers (`crewai.flow.conversation`)
 
-Importable from `crewai.flow.conversation` for tests or custom orchestration. These helpers use the legacy `ConversationalConfig` shape; `prepare_conversational_turn()` also clears `last_intent`, unlike `handle_turn()`, which preserves it as router context.
+Importable for tests or custom orchestration:
 
 | Function | Description |
 |----------|-------------|
@@ -202,7 +212,7 @@ Importable from `crewai.flow.conversation` for tests or custom orchestration. Th
 
 ### A. Pre-classify via `ConversationConfig` (simplest)
 
-Set `default_intents` and `intent_llm`. Each `handle_turn()` pre-classifies the current message. A non-empty result returned by a custom `route_turn()` takes precedence; otherwise `route_conversation` uses the current turn's classified intent.
+Set `default_intents` and `intent_llm`. Each `handle_turn()` runs classification before routing; read `self.state.last_intent` in `route_turn()`.
 
 ### B. Classify inside `route_turn` (richer prompts)
 
@@ -223,15 +233,24 @@ Use **`@listen("RESEARCH")`** (or similar) for steps that run `Agent.kickoff()`
 
 ## When the flow finishes but the user keeps chatting
 
-Each `handle_turn()` completes one graph run, and the conversation continues with another `handle_turn()` using the same `session_id`. With the default deferred trace lifecycle, that run emits `conversation_turn_completed`, while `FlowFinished` is emitted once when `finalize_session_traces()` closes the session. `@persist` restores `messages`, flags, and context.
+`FlowFinished` means **this graph run** completed. The conversation continues with another `handle_turn()` and the same `session_id`. `@persist` restores `messages`, flags, and context.
 
 **Persist pattern:** prefer `@persist` on a **single terminal step** (for example `finalize`) rather than on the whole `Flow` class. Class-level persist saves after every method; `load_state` uses the latest row, which may be a mid-run snapshot (for example right after `bootstrap`) and miss handler updates from the same turn.
 
 Do **not** use `@human_feedback` for follow-up chat lines unless a human must approve a specific step output before it is shown.
 
-## Conversational `Flow`
+## Conversational `Flow` (experimental)
 
-Opt into the conversational chat graph by setting `conversational = True` on a `Flow` subclass or applying `@ConversationConfig(...)`. The base `Flow` then supplies `route_conversation` as the built-in start/router plus the `converse_turn` and `end_conversation` listeners. The deprecated `answer_from_history_turn` listener remains available for compatibility. The framework manages `state.messages`, can drive a router LLM, and keeps the trace batch open across turns. You write the **custom routes**; the framework owns the rest.
+<Warning>
+  **This is an experimental feature.** The conversational `Flow` surface
+  (`conversational = True`, `handle_turn`, `ConversationConfig`,
+  `RouterConfig`, `ConversationState`, the built-in graph + helpers) lives
+  under `crewai.experimental` and may change shape before it graduates.
+  Pin your CrewAI version if you depend on specific behavior, and watch the
+  changelog for breaking updates. Open issues / feedback welcome.
+</Warning>
+
+Opt into the conversational chat graph by setting `conversational = True` on a `Flow` subclass. The base `Flow` then ships a built-in `@start` / `@router` / `converse_turn` / `end_conversation` graph, manages `state.messages`, can drive a router LLM, and keeps the trace batch open across turns. You write the **custom routes**; the framework owns the rest.
 
 Use this when you want a multi-turn chat with a router and per-route handlers without wiring the lifecycle yourself. Use `Flow[ChatState]` (the lower-level pattern above) when you need full control.
 
@@ -240,7 +259,7 @@ Use this when you want a multi-turn chat with a router and per-route handlers wi
 ```python
 from crewai import Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
 )
@@ -248,6 +267,8 @@ from crewai.flow import (
 
 @ConversationConfig(defer_trace_finalization=True)
 class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
     def route_turn(self, context: dict) -> str | None:
         message = (self.state.current_user_message or "").lower()
         if "search" in message or "news" in message:
@@ -297,26 +318,14 @@ Class decorator that attaches per-class chat defaults.
 |-------|---------|---------|
 | `system_prompt` | `slices.conversational_system_prompt` from i18n | System message used by the built-in `converse_turn`. Pass `""` to opt out entirely. |
 | `llm` | `None` | Conversation LLM (used by `converse_turn` and as router fallback). |
-| `router` | `None` | Optional `RouterConfig` overrides. With custom listeners and a resolvable LLM, routing auto-enables even when this is omitted. |
-| `answer_from_history_prompt` | Framework default | **Deprecated.** Use the `converse` system prompt or override `converse_turn()`. |
-| `answer_from_history_llm` | `None` | **Deprecated.** Use `llm`; `converse` already receives canonical history. |
+| `router` | `None` | `RouterConfig` for LLM-driven routing. Without it, the flow always falls through to `converse`. |
+| `answer_from_history_prompt` | Framework default | System message for the optional `answer_from_history` route. |
+| `answer_from_history_llm` | `None` | Enables the `answer_from_history` short-circuit when set. |
 | `intent_llm` | `None` | LLM for legacy `intents=`/`default_intents` pre-classification. |
 | `default_intents` | `None` | Outcome labels for legacy pre-classification. |
 | `visible_agent_outputs` | `None` | `"all"`, or a list of agent names whose `append_agent_result()` calls should be promoted to public assistant messages. |
 | `defer_trace_finalization` | `True` | Keep one trace batch open across `handle_turn()` calls. |
 
-<Warning>
-  `answer_from_history_prompt`, `answer_from_history_llm`, and the
-  `answer_from_history` route are deprecated and will be removed in a future
-  release. They duplicate `converse`, add an eligibility LLM call, and are
-  bypassed when the normal auto-router returns a route. Existing configurations
-  continue to work and emit `DeprecationWarning`.
-</Warning>
-
-With no custom routes, turns fall through to `converse`. With custom routes and a conversation/router LLM, the framework synthesizes a default `RouterConfig`; provide one explicitly only to customize its prompt, route list, descriptions, or fallback behavior. Setting `default_intents` uses the legacy pre-classification path instead.
-
-If no conversation LLM is configured, the built-in `converse_turn` returns a configuration placeholder rather than generating an answer.
-
 ### `RouterConfig` and the auto-built route catalog
 
 ```python
@@ -325,7 +334,7 @@ from typing import Literal
 from pydantic import BaseModel
 
 from crewai import LLM
-from crewai.flow import RouterConfig
+from crewai.experimental.conversational import RouterConfig
 
 
 class MyRoute(BaseModel):
@@ -351,10 +360,9 @@ router_config = RouterConfig(
 The router prompt that gets sent to the LLM is built automatically. For each route the framework picks a description with this precedence:
 
 1. `RouterConfig.route_descriptions[label]` — explicit override.
-2. `Flow.builtin_route_descriptions[label]` — framework-canned text for `converse`, `end`, and the deprecated `answer_from_history` compatibility route (phrased for the router LLM).
-3. The method's declared `description` (used by declarative flows and DSL projections).
-4. First non-empty line of the `@listen(label)` handler's docstring.
-5. Empty (the route is listed without a description).
+2. `Flow.builtin_route_descriptions[label]` — framework-canned text for `converse`, `end`, `answer_from_history` (phrased for the router LLM).
+3. First non-empty line of the `@listen(label)` handler's docstring.
+4. Empty (the route is listed without a description).
 
 So in practice, **adding a new route is `@listen("X")` + a one-line docstring**:
 
@@ -407,7 +415,7 @@ Routes:
 |-------|---------|---------|
 | `converse` | `converse_turn` | Default chat handler. Calls `ConversationConfig.llm` with the system prompt + canonical message history. |
 | `end` | `end_conversation` | Sets `state.ended = True` and emits a terminator reply. |
-| `answer_from_history` | `answer_from_history_turn` | **Deprecated compatibility route.** Use `converse`, which already receives canonical history. |
+| `answer_from_history` | `answer_from_history_turn` | Optional. Routes here when `ConversationConfig.answer_from_history_llm` is set and the message can be answered from existing history. |
 
 You can override any of these by defining a same-named handler in your subclass.
 
@@ -417,9 +425,9 @@ You can override any of these by defining a same-named handler in your subclass.
 
 1. Resets per-execution tracking (`_completed_methods`, `_method_outputs`) so the graph re-runs — without this, repeated `kickoff` calls on the same flow instance would short-circuit on turn 2+ because `Flow.kickoff_async` treats `inputs={"id": ...}` as a checkpoint restore.
 2. Appends the user message to `state.messages`, sets `current_user_message` / `last_user_message`. `last_intent` is **preserved from the prior turn** so the router LLM can use it as a signal.
-3. Runs user-defined `@start` methods (if any), then `route_conversation` as the built-in start/router, then the chosen `@listen` handler. `route_conversation` invokes the overridable `conversation_start()` helper.
+3. Runs `conversation_start` → `route_conversation` → the chosen `@listen` handler.
 4. The router stores its decision in `state.last_intent` (visible to the next turn's router context).
-5. If your handler returned a string and didn't already call `append_assistant_message`, `handle_turn` appends it for you and persists the updated `state.messages` so `@persist` restore includes the assistant turn.
+5. If your handler returned a string and didn't already call `append_assistant_message`, `handle_turn` appends it for you.
 
 Call `handle_turn()` for chat messages. Calling `kickoff(inputs={"id": ...})` directly runs the flow graph without applying the conversational turn wrapper.
 
@@ -440,8 +448,6 @@ It handles the common local loop:
 4. Prints the assistant result.
 5. Finalizes deferred session traces in a `finally` block.
 
-`chat(defer_trace_finalization=True)` temporarily enables the instance deferral flag for the REPL and restores its prior value on exit.
-
 Customize the terminal behavior with injectable I/O:
 
 ```python
@@ -463,7 +469,7 @@ To run side effects (event bus setup, telemetry) on every routing decision, over
 from typing import Any
 
 from crewai import Flow
-from crewai.flow import ConversationState
+from crewai.experimental.conversational import ConversationState
 
 
 class SupportFlow(Flow[ConversationState]):
@@ -474,7 +480,7 @@ class SupportFlow(Flow[ConversationState]):
         return super().route_turn(context)
 ```
 
-To bypass the LLM router entirely and pick a route programmatically, return a non-empty string from `route_turn`. A falsy return does **not** invoke `_route_with_config()` from your override; routing falls through to this turn's pre-classified intent, then the deprecated `answer_from_history` compatibility path when configured, and finally `converse`. A previous turn's `last_intent` is available in router context but is never replayed as a fallback.
+To bypass the LLM router entirely and pick a route programmatically, return a string from `route_turn`; returning `None` falls back to `_route_with_config(...)`.
 
 ### `append_assistant_message` and `append_agent_result`
 
@@ -485,73 +491,6 @@ Inside a `@listen(label)` handler, choose:
 
 `ConversationConfig.visible_agent_outputs` can promote specific agents' private results to public globally (`"all"`, or a list of agent names).
 
-## Declaring a conversational flow in JSON/YAML
-
-A [declarative Flow](/edge/en/concepts/cli) can be conversational too. Add a top-level `conversational` block and declare your own routes as methods that `listen` to a route label:
-
-```yaml
-schema: crewai.flow/v1
-name: SupportFlow
-
-conversational:
-  system_prompt: You are a terse support assistant.
-  llm: gpt-4o-mini
-  router:
-    llm: gpt-4o-mini
-
-methods:
-  handle_order:
-    description: Order status, shipping and delivery questions.
-    listen: order
-    do:
-      call: agent
-      with:
-        role: Support specialist
-        goal: Answer order questions accurately
-        backstory: Knows the fulfilment pipeline.
-        input: "${state.current_user_message}"
-```
-
-Declaring the block is the opt-in — `enabled` defaults to `true`. Set `enabled: false` to keep the configuration while turning chat off. This also disables built-in method synthesis, so the declaration must provide a normal non-conversational graph.
-
-Three things are supplied for you:
-
-| Supplied | Detail |
-|----------|--------|
-| The built-in graph | `route_conversation`, `converse_turn`, and `end_conversation` are added automatically. Deprecated `answer_from_history_turn` is retained for compatibility. Declare a method under one of those names to override it. |
-| Conversation state | `ConversationState` is used when there is no `state` block. A Pydantic `ref` or `json_schema` state is automatically composed with the conversational fields; it does not need to extend `ConversationState`. |
-| The route catalog | Inferred from non-router methods with `listen` labels, excluding internal routes. Descriptions follow the precedence above, and explicit `router.routes` can limit the choices. |
-
-Declarative `llm`, `router.llm`, and `intent_llm` fields accept either a model id or a configuration mapping such as `{model: openai/gpt-4o-mini, max_tokens: 512}`. The `conversational` block also supports `default_intents`, `visible_agent_outputs`, `defer_trace_finalization`, and the `RouterConfig` fields shown above. Deprecated `answer_from_history_prompt` / `answer_from_history_llm` declarations remain accepted for compatibility.
-
-Run it from Python with the same turn APIs as a class-based conversational Flow:
-
-```python
-from crewai.flow import Flow
-
-flow = Flow.from_declaration(path="flow.yaml")
-
-try:
-    flow.handle_turn("Where is my order?", session_id="session-1")
-finally:
-    flow.finalize_session_traces()
-```
-
-### Naming routes
-
-Route labels and method names share one trigger namespace, so a handler must not be named after the route it listens to — `create_video` listening to `create_video` is rejected when the flow is built. Use a `handle_*` prefix.
-
-### What a declaration cannot express
-
-| Not expressible | Use instead |
-|-----------------|-------------|
-| A live `LLM` instance or a custom `BaseLLM` | A model id string or static configuration mapping |
-| `router.response_format` as a live model class | Name the class with a python ref: `response_format: {python: my_project.schemas.ConversationRoute}`. Omit it and the framework synthesizes one |
-| A `route_turn()` override | Author the Flow in Python, or replace the declarative `route_conversation` method with a `call: code` / expression action |
-| A `can_answer_from_history()` override | Deprecated. Use `converse` or override `converse_turn()` in Python. |
-
-`crewai run` opens the chat TUI for a declarative conversational flow — the same one a Python conversational Flow gets. A chat loop needs a terminal, so a headless run exits non-zero with guidance instead of running a single turn; drive it from Python there with `handle_turn()` or `stream_turn()`. A declarative method with a `human_feedback:` block (Python: `@human_feedback`) runs on a terminal REPL, because the runtime collects feedback with a blocking prompt the TUI cannot service. `--inputs` is not accepted for a conversational flow — each turn's input is the message you type — and resuming a session by id is not wired into the CLI yet; use `flow.handle_turn(message, session_id=...)` from Python for that.
-
 ## Tracing across turns
 
 With `defer_trace_finalization=True` (default in `ConversationConfig`):
@@ -569,28 +508,15 @@ flow.chat(session_id=session_id)
 with `handle_turn()`, call `finalize_session_traces()` when
 the session ends.
 
-`suppress_flow_events=True` hides Rich console panels and suppresses method execution events. Flow start/finish events still emit, so the outer Flow lifecycle remains traceable, but individual method spans are omitted.
+`suppress_flow_events=True` only hides Rich console panels; trace and method events still emit for observability.
 
 ### Conversational `Flow` trace lifecycle
 
-The [conversational `Flow`](#conversational-flow) uses the same tracing lifecycle: `defer_trace_finalization` defaults to `True`, so each `handle_turn()` keeps the session trace open. Deferred turns also suppress per-turn `flow_failed`; on a turn error or session abort, finalize the session explicitly. This closes the batch with the session-level `FlowFinished` event rather than a per-turn `FlowFailed` event. Always wrap your REPL/loop in `try/finally` and call `flow.finalize_session_traces()` on exit. Without it, the trace batch stays open and the final conversation may never export.
+The experimental [conversational `Flow`](#conversational-flow-experimental) uses the same tracing lifecycle: `defer_trace_finalization` defaults to `True`, so each `handle_turn()` keeps the session trace open. Always finalize at the end of the session — wrap your REPL/loop in `try/finally` and call `flow.finalize_session_traces()` on exit. Without it, the trace batch stays open and the final conversation may never export.
 
 ## Streaming
 
-For conversational UIs, use `stream_turn()` and iterate its ordered `StreamFrame` objects:
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-reply = stream.result
-```
-
-For a non-conversational Flow, setting `stream = True` makes `kickoff()` return a `StreamSession`. Do not set `flow.stream = True` when using `handle_turn()`; `stream_turn()` owns the conversational streaming lifecycle.
+Set `stream = True` on the `Flow` class. `kickoff(...)` will then emit `assistant_delta` (and related) events through the standard event bus.
 
 ## Imports
 
@@ -605,15 +531,10 @@ from crewai.flow import (
     router,
     start,
 )
-from crewai.flow.conversation import prepare_conversational_turn
-from crewai.flow import (
-    ConversationConfig,
-    ConversationState,
-    RouterConfig,
-)
 ```
 
 ## See also
 
 - [Mastering Flow State Management](/en/guides/flows/mastering-flow-state) — persistence, Pydantic state, `@persist`
 - [Build Your First Flow](/en/guides/flows/first-flow) — flow basics
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — minimal REPL with `RESEARCH` + Exa agent

docs/edge/en/guides/flows/first-flow.mdx

@@ -136,7 +136,7 @@ Now, let's configure the content writer crew with JSONC. We'll set up two specia
 }
 ```
 
-Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-3.7-flash`, or `anthropic/claude-sonnet-4-6`.
+Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, or `anthropic/claude-sonnet-4-6`.
 
 3. Create `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 
@@ -483,7 +483,7 @@ Flows allow you to make direct calls to language models when you need simple, st
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/en/guides/frontend/a2ui.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## The agent assembles the UI
 
-[Tool-based rendering](/edge/en/guides/frontend/tool-based-generative-ui) maps one tool to one component: the agent picks a component, you draw it. A2UI is the **declarative** tier of the [generative-UI spectrum](/edge/en/guides/frontend/generative-ui#declarative) — instead of picking a single component, the agent **assembles a surface** by combining building blocks from a catalog you define.
+[Tool-based rendering](/en/guides/frontend/tool-based-generative-ui) maps one tool to one component: the agent picks a component, you draw it. A2UI is the **declarative** tier of the [generative-UI spectrum](/en/guides/frontend/generative-ui#declarative) — instead of picking a single component, the agent **assembles a surface** by combining building blocks from a catalog you define.
 
 You still own the components. The agent can only use what is in your catalog, so it can never render something you did not ship. What the agent decides is the **layout and the data** — how those building blocks come together into a panel, and what goes in them.
 
@@ -115,13 +115,13 @@ Both modes share the same frontend: one catalog, registered once on the provider
 ## Related
 
 <CardGroup cols={3}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/en/guides/frontend/generative-ui">
     The full spectrum — A2UI is its declarative tier.
   </Card>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map one tool to one component (controlled).
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state (controlled).
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/agentic-generative-ui.mdx

@@ -17,12 +17,12 @@ The pattern has two halves:
 The Flow's state reaches the frontend over AG-UI without you wiring up any transport. A state snapshot is emitted automatically at each step (method) boundary of the Flow, and you can push intermediate updates during a long-running step by calling `copilotkit_emit_state` explicitly. You subclass the state to add your own fields, update them in the Flow, and read them in React.
 
 <Note>
-State-driven rendering requires a **Flow** with custom state (`Flow[AgentState]`). Crews are chat-oriented and do not expose custom state this way, so with a Crew use [tool rendering](/edge/en/guides/frontend/tool-based-generative-ui) instead.
+State-driven rendering requires a **Flow** with custom state (`Flow[AgentState]`). Crews are chat-oriented and do not expose custom state this way, so with a Crew use [tool rendering](/en/guides/frontend/tool-based-generative-ui) instead.
 </Note>
 
 ## Build a live task planner
 
-This example builds a planner that breaks a request into about ten steps and streams them to the UI as a checklist. It assumes you already have a CrewAI server and a CopilotKit frontend wired up. If you do not, start with the [Frontend Overview](/edge/en/guides/frontend/overview).
+This example builds a planner that breaks a request into about ten steps and streams them to the UI as a checklist. It assumes you already have a CrewAI server and a CopilotKit frontend wired up. If you do not, start with the [Frontend Overview](/en/guides/frontend/overview).
 
 <Steps>
 
@@ -146,7 +146,7 @@ add_crewai_flow_fastapi_endpoint(
 )
 ```
 
-See the [Frontend Overview](/edge/en/guides/frontend/overview) for the full server, runtime, and provider setup, and remember to register the agent (here `task_planner`) in your CopilotKit runtime route.
+See the [Frontend Overview](/en/guides/frontend/overview) for the full server, runtime, and provider setup, and remember to register the agent (here `task_planner`) in your CopilotKit runtime route.
 
 </Step>
 
@@ -190,19 +190,19 @@ function TaskPlan() {
 
 Reading state is the foundation. Two guides build directly on it:
 
-- [Shared State](/edge/en/guides/frontend/shared-state) adds the other direction: editing the agent's state from the UI and having the Flow pick up the change.
-- [Predictive State](/edge/en/guides/frontend/predictive-state-updates) streams a tool's in-progress arguments into state so the UI reflects work before it is committed.
+- [Shared State](/en/guides/frontend/shared-state) adds the other direction: editing the agent's state from the UI and having the Flow pick up the change.
+- [Predictive State](/en/guides/frontend/predictive-state-updates) streams a tool's in-progress arguments into state so the UI reflects work before it is committed.
 
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Sync agent state and app UI in both directions.
   </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+  <Card title="Predictive State" icon="gauge-high" href="/en/guides/frontend/predictive-state-updates">
     Stream in-progress tool arguments into state.
   </Card>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map agent tool calls to components.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/channels.mdx

@@ -1,156 +1,125 @@
 ---
 title: Channels
-description: Run the same CrewAI agent as a Slack or Teams bot with the CopilotKit Channels SDK and managed Intelligence platform.
-icon: messages
+description: Run the same CrewAI agent as a chat bot on Slack and Discord with the CopilotKit Channels SDK.
+icon: slack
 mode: "wide"
 ---
 
 ## Meet your users where they already are
 
-The CrewAI agent you built in the [Overview](/edge/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a **channel** drives it from Slack or Microsoft Teams.
+The CrewAI agent you built in the [Overview](/en/guides/frontend/overview) does not have to live behind a web app. The same Crew or Flow can run as a bot inside a messaging platform. No rebuild, no second copy of your agent logic: the agent stays exposed over the [AG-UI protocol](https://docs.ag-ui.com), and a bot process drives it.
 
-CopilotKit's [Channels SDK](https://docs.copilotkit.ai/slack) provides that channel. You declare a `createChannel` in a small runtime, point it at your CrewAI agent, and CopilotKit's managed **Intelligence** platform brokers the connection to the messaging provider.
-
-<Note>
-Unlike the rest of this section, Channels is **not self-hosted**. It runs through **CopilotKit Intelligence** — a required surface for Channels, by design (a free tier is available). Intelligence holds the platform connection and credentials, receives each platform event, and delivers the turn to your channel process; your process runs the agent and streams the reply back. You configure Slack once in the Intelligence dashboard, and platform credentials never enter your process. Your agent, tools, and state stay yours.
-</Note>
+CopilotKit's [Channels SDK](https://docs.copilotkit.ai/reference/channels) provides that bot process. It ships a platform-agnostic engine plus per-platform adapters.
 
 ## How it fits together
 
-Nothing about your CrewAI agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate long-running Node process built with `@copilotkit/channels`: it registers a channel on the `CopilotRuntime`, connects to Intelligence, and runs your agent whenever a message arrives.
+Nothing about your agent server changes. It keeps serving your Crew or Flow over AG-UI exactly as in the Overview. What you add is a separate **bot process**: it connects to a platform adapter, listens for messages, and runs your agent when it is messaged. The reply streams back into the channel.
 
 ```
-Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
+Slack / Discord  ──►  Channels bot process  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
 ```
 
-The channel process holds a persistent connection to the Intelligence gateway, so it needs a long-running host — a serverless request handler cannot own that connection. Your CrewAI server can keep serving the web frontend from the Overview at the same time: the web app and the channel are just two clients of one AG-UI endpoint.
+Your agent server can keep serving the web frontend from the Overview at the same time. The web app and the bot are just two clients of one AG-UI endpoint.
 
-## Integration guide
+## Slack
 
 <Steps>
 
 <Step title="Install the Channels packages">
 
-The Channels SDK is batteries-included — every platform ships in the one package, with no per-platform adapter to install. Add it alongside the runtime that hosts the channel and the CrewAI AG-UI client:
-
 ```bash
-npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
+npm install @copilotkit/channels @copilotkit/channels-slack @ag-ui/crewai
 ```
 
 </Step>
 
-<Step title="Create a Channel in Intelligence">
+<Step title="Create a Slack app and get tokens">
 
-In the [CopilotKit dashboard](https://docs.copilotkit.ai/slack), create a Channel and connect Slack — Intelligence walks you through creating the Slack app and holds its credentials. That leaves two environment variables for your process, both from the dashboard:
+Create an app in the Slack API dashboard for your workspace, enable Socket Mode, and grant it the message and event scopes it needs to read and post in channels. Then expose its tokens to the bot process:
 
 ```bash
-export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
-export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
+export SLACK_BOT_TOKEN=xoxb-...   # bot user token
+export SLACK_APP_TOKEN=xapp-...   # app-level token (Socket Mode)
 ```
 
 </Step>
 
-<Step title="Define the channel">
+<Step title="Point the bot at your CrewAI agent">
 
-`createChannel` declares the channel and attaches your agent. Build the agent as a per-thread factory so each conversation gets its own session, using the same `CrewAIAgent` the Overview uses in the web runtime, pointed at your AG-UI endpoint. `identifyUser: "platform"` lets Intelligence map each platform user to a stable identity.
+`createBot` wires a Slack adapter to your agent. The `agent` factory returns a `CrewAIAgent` pointed at the AG-UI path your server exposes (the same URL you registered in the runtime in the Overview).
 
 ```ts
-// channel.ts
-import { createChannel } from "@copilotkit/channels";
+// bot.ts
+import { createBot } from "@copilotkit/channels";
+import { slack, defaultSlackTools, defaultSlackContext } from "@copilotkit/channels-slack";
 import { CrewAIAgent } from "@ag-ui/crewai";
 
-const channel = createChannel({
-  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
-  identifyUser: "platform",
-  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
-  agent: (threadId) => {
-    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
-    agent.threadId = threadId;
-    return agent;
-  },
+const bot = createBot({
+  adapters: [
+    slack({
+      botToken: process.env.SLACK_BOT_TOKEN!, // xoxb-…
+      appToken: process.env.SLACK_APP_TOKEN!, // xapp-… (Socket Mode)
+    }),
+  ],
+  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
+  tools: [...defaultSlackTools],
+  context: [...defaultSlackContext],
 });
 
-// A mention subscribes the thread and runs the agent; afterwards every message
-// in a subscribed thread runs it without needing another mention.
-channel.onMention(async ({ thread }) => {
-  await thread.subscribe();
-  await thread.runAgent();
-});
-
-channel.onMessage(async ({ thread }) => {
-  if (await thread.isSubscribed()) await thread.runAgent();
-});
-
-export { channel };
+bot.start();
 ```
 
 </Step>
 
-<Step title="Register the channel on the runtime">
+<Step title="Run the bot">
 
-Create a `CopilotRuntime` with the Intelligence gateway and your channel, then serve it with `createCopilotNodeListener`. The `agents` map stays empty — the channel supplies its own agent. Wait for the channel to be ready so a broken config fails startup loudly.
-
-```ts
-// server.ts
-import { createServer } from "node:http";
-import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
-import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
-import { channel } from "./channel";
-
-const runtime = new CopilotRuntime({
-  agents: {}, // the channel supplies its own agent; no web-facing agents needed
-  intelligence: new CopilotKitIntelligence({
-    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
-  }),
-  channels: [channel],
-});
-
-const listener = createCopilotNodeListener({ runtime });
-await listener.channels?.ready({ timeoutMs: 15_000 });
-
-createServer(listener).listen(3123, () => {
-  console.log("Channels runtime listening on port 3123");
-});
-```
-
-</Step>
-
-<Step title="Run the channel runtime">
-
-Start it alongside your CrewAI agent server:
+Start the bot process alongside your agent server:
 
 ```bash
 uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-npx tsx server.ts                 # terminal 2 — Channels runtime
+node bot.ts                       # terminal 2 — Slack bot
 ```
 
-Mention the bot in Slack or Teams and it runs your Crew or Flow, streaming the reply back into the thread. The thread stays subscribed, so follow-up messages run without another mention.
+Message the bot in Slack and it runs your Crew or Flow, streaming the reply back into the thread.
 
 </Step>
 
 </Steps>
 
-## The event model
+<Note>
+Slack app scopes, Socket Mode setup, and the full adapter options are maintained by CopilotKit. Follow the [Slack channel reference](https://docs.copilotkit.ai/reference/channels/slack) together with Slack's own app setup guide for the authoritative steps.
+</Note>
 
-A channel reacts to platform events with handlers, and each handler receives a `thread` you drive with a few methods:
+## Discord
 
-- **`channel.onMention`** fires when a user @-mentions the bot. Call `thread.subscribe()` to join the thread, then `thread.runAgent()` to run your CrewAI agent on the mention.
-- **`channel.onMessage`** fires on every message in a thread the bot can see. Gate it with `thread.isSubscribed()` so the agent only responds where it has joined, then `thread.runAgent()`.
-- **`thread.runAgent()`** runs the attached CrewAI agent for the current turn and streams its output back into the channel. Pass `{ prompt }` to override the text the agent runs on.
+Discord uses the same `createBot` engine with the Discord adapter from `@copilotkit/channels-discord`:
 
-Your agent receives an ordinary AG-UI `RunAgentInput` and emits ordinary AG-UI events; the platform mechanics stay behind the channel, so the same Crew or Flow runs unchanged across every platform. The channel also exposes handlers for welcomes, interrupts, commands, reactions, and modals — see the [`Channel` reference](https://docs.copilotkit.ai/reference/channels/classes/Channel) for the full surface.
+```ts
+import { createBot } from "@copilotkit/channels";
+import { discord } from "@copilotkit/channels-discord";
+import { CrewAIAgent } from "@ag-ui/crewai";
+
+const bot = createBot({
+  adapters: [discord({ token: process.env.DISCORD_BOT_TOKEN! })],
+  agent: (threadId) => new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
+});
+
+bot.start();
+```
+
+See the [Discord channel reference](https://docs.copilotkit.ai/reference/channels/discord) for the exact adapter options and bot setup.
 
 ## Platform support
 
-The managed Intelligence path covers **Slack** and **Microsoft Teams** today — the same channel code runs on either, and `message.platform` / `thread.platform` report the native origin. Other platforms (Discord, Telegram, WhatsApp) are reached through developer-operated **direct adapters** rather than the managed path — your own process holds the platform credentials and transport. Check the [CopilotKit Channels documentation](https://docs.copilotkit.ai/slack) for the current platform list and per-platform setup.
+Slack and Discord have official Channels adapters (`@copilotkit/channels-slack`, `@copilotkit/channels-discord`). Microsoft Teams is available through CopilotKit's managed offering (currently waitlisted). Check the [Channels reference](https://docs.copilotkit.ai/reference/channels) for the current list before promising a platform.
 
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Frontend Overview" icon="browser" href="/edge/en/guides/frontend/overview">
+  <Card title="Frontend Overview" icon="browser" href="/en/guides/frontend/overview">
     Serve your Crew or Flow over AG-UI — the foundation every channel builds on.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Pause the agent to collect user approval or input mid-run.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/conversational-flows.mdx

@@ -18,7 +18,7 @@ Behind the AG-UI bridge, a CrewAI backend can take one of three shapes. Knowing
 Conversational Flows are a newer CrewAI capability, and an important thing to be clear about up front: **they are Flows, not Crews.** They now run at full regular-Flow feature parity. This page introduces them and shows how they fit the rest of the frontend guides.
 
 <Note>
-Reach for a Conversational Flow when you want native multi-turn conversation with CrewAI managing session state and history for you, rather than wiring turn and state handling into a regular Flow yourself. If you are new here, start with the [Frontend Overview](/edge/en/guides/frontend/overview) for the base server, runtime, and provider setup.
+Reach for a Conversational Flow when you want native multi-turn conversation with CrewAI managing session state and history for you, rather than wiring turn and state handling into a regular Flow yourself. If you are new here, start with the [Frontend Overview](/en/guides/frontend/overview) for the base server, runtime, and provider setup.
 </Note>
 
 ## Register a Conversational Flow
@@ -66,25 +66,25 @@ This is the point to hold onto: **Conversational Flows run through the same even
 There is no Conversational-Flow-specific frontend API. Every feature in these guides works exactly the same way with a Conversational Flow as it does with a regular Flow, using the same hooks and components:
 
 <CardGroup cols={2}>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map agent tool calls to your React components.
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render the Flow's live state as it works.
   </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Keep agent state and app UI in two-way sync.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Pause the agent for user approval or input mid-turn.
   </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+  <Card title="Predictive State" icon="gauge-high" href="/en/guides/frontend/predictive-state-updates">
     Stream in-progress tool arguments into state.
   </Card>
-  <Card title="Reasoning" icon="brain" href="/edge/en/guides/frontend/reasoning">
+  <Card title="Reasoning" icon="brain" href="/en/guides/frontend/reasoning">
     Show the model's thinking in the chat.
   </Card>
-  <Card title="A2UI" icon="table-cells" href="/edge/en/guides/frontend/a2ui">
+  <Card title="A2UI" icon="table-cells" href="/en/guides/frontend/a2ui">
     Render agent-authored UI from a component catalog.
   </Card>
 </CardGroup>
@@ -94,13 +94,13 @@ The only difference is on the backend: how you author the Flow (turn-based `stre
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Frontend Overview" icon="browser" href="/edge/en/guides/frontend/overview">
+  <Card title="Frontend Overview" icon="browser" href="/en/guides/frontend/overview">
     Wire a Crew or Flow to a Next.js frontend end to end.
   </Card>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/en/guides/frontend/generative-ui">
     Render tool calls and agent state as custom components.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Gate agent actions behind user approval.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/frontend-actions.mdx

@@ -89,7 +89,7 @@ class AssistantFlow(Flow[CopilotKitState]):
 
 <Step title="Serve the Flow">
 
-Expose the Flow over AG-UI with `add_crewai_flow_fastapi_endpoint(...)` and register it in the CopilotKit runtime, exactly as in the [Frontend Overview](/edge/en/guides/frontend/overview). Once both are running, asking the assistant to "switch to dark mode" triggers `set_theme`, and the page flips.
+Expose the Flow over AG-UI with `add_crewai_flow_fastapi_endpoint(...)` and register it in the CopilotKit runtime, exactly as in the [Frontend Overview](/en/guides/frontend/overview). Once both are running, asking the assistant to "switch to dark mode" triggers `set_theme`, and the page flips.
 
 </Step>
 
@@ -104,18 +104,18 @@ Expose the Flow over AG-UI with `add_crewai_flow_fastapi_endpoint(...)` and regi
 | **`handler`** | Runs code in the browser (a frontend action) |
 | **`render`** | Draws UI for the tool call (generative UI) |
 
-You can supply either one, or both. A `handler` with a `render` alongside it performs the action and draws UI while it runs. For render-only tools that just display the result of an agent action, see [Tool-Based Generative UI](/edge/en/guides/frontend/tool-based-generative-ui).
+You can supply either one, or both. A `handler` with a `render` alongside it performs the action and draws UI while it runs. For render-only tools that just display the result of an agent action, see [Tool-Based Generative UI](/en/guides/frontend/tool-based-generative-ui).
 
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map agent tool calls to React components.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Gate agent actions behind user approval.
   </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Keep agent state and your app UI in two-way sync.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/generative-ui.mdx

@@ -14,7 +14,7 @@ CopilotKit renders generative UI along a **spectrum**, from fully author-control
 | Tier | Who decides the UI | CrewAI mechanism |
 | --- | --- | --- |
 | **[Controlled](#controlled)** | You — a fixed set of components the agent picks from | `useRenderTool`, `useAgent`, reasoning |
-| **[Declarative](#declarative)** | The agent — assembles a surface from *your* component catalog | [A2UI](/edge/en/guides/frontend/a2ui) |
+| **[Declarative](#declarative)** | The agent — assembles a surface from *your* component catalog | [A2UI](/en/guides/frontend/a2ui) |
 | **[Open-ended](#open-ended)** | An external tool/server invents the surface | MCP tools |
 
 The tiers compose freely; a single app usually mixes them.
@@ -43,10 +43,10 @@ useRenderTool({
 ```
 
 <Note>
-`useRenderTool` renders a tool call. When a tool also needs to *run* code in the browser, use [`useFrontendTool`](/edge/en/guides/frontend/frontend-actions) (a `handler`, with optional `render`).
+`useRenderTool` renders a tool call. When a tool also needs to *run* code in the browser, use [`useFrontendTool`](/en/guides/frontend/frontend-actions) (a `handler`, with optional `render`).
 </Note>
 
-See [Tool-Based Generative UI](/edge/en/guides/frontend/tool-based-generative-ui) for the full walkthrough, including progressive rendering as arguments stream, and [Backend Tool Rendering](/edge/en/guides/frontend/tool-based-generative-ui#backend-tools) for tools your Crew or Flow executes server-side.
+See [Tool-Based Generative UI](/en/guides/frontend/tool-based-generative-ui) for the full walkthrough, including progressive rendering as arguments stream, and [Backend Tool Rendering](/en/guides/frontend/tool-based-generative-ui#backend-tools) for tools your Crew or Flow executes server-side.
 
 ### State rendering
 
@@ -63,17 +63,17 @@ function TaskProgress() {
 }
 ```
 
-See [Agentic Generative UI](/edge/en/guides/frontend/agentic-generative-ui) for streaming state from a Flow, and [Shared State](/edge/en/guides/frontend/shared-state) for editing that state from the UI.
+See [Agentic Generative UI](/en/guides/frontend/agentic-generative-ui) for streaming state from a Flow, and [Shared State](/en/guides/frontend/shared-state) for editing that state from the UI.
 
 ### Reasoning
 
-When the model reasons before answering, that thinking renders in the chat automatically. No component to write. See [Reasoning](/edge/en/guides/frontend/reasoning).
+When the model reasons before answering, that thinking renders in the chat automatically. No component to write. See [Reasoning](/en/guides/frontend/reasoning).
 
 ## Declarative
 
 The agent goes beyond picking a component: it **assembles a surface** by combining building blocks from a catalog *you* define. You still own the components (the agent can only use what is in your catalog), but the layout is the agent's.
 
-This is [A2UI](/edge/en/guides/frontend/a2ui). You register a catalog on the provider:
+This is [A2UI](/en/guides/frontend/a2ui). You register a catalog on the provider:
 
 ```tsx
 <CopilotKit runtimeUrl="/api/copilotkit" agent="assistant" a2ui={{ catalog }}>
@@ -81,7 +81,7 @@ This is [A2UI](/edge/en/guides/frontend/a2ui). You register a catalog on the pro
 </CopilotKit>
 ```
 
-The agent then builds surfaces from that catalog — either dynamically (it designs the layout from the conversation) or from a fixed schema your backend fills with data. See [A2UI](/edge/en/guides/frontend/a2ui) for both modes and error recovery.
+The agent then builds surfaces from that catalog — either dynamically (it designs the layout from the conversation) or from a fixed schema your backend fills with data. See [A2UI](/en/guides/frontend/a2ui) for both modes and error recovery.
 
 ## Open-ended
 
@@ -92,16 +92,16 @@ MCP tool calls surface as standard tool-call UI — render them with `useRenderT
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map agent tool calls to components (controlled).
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state (controlled).
   </Card>
-  <Card title="A2UI" icon="table-cells" href="/edge/en/guides/frontend/a2ui">
+  <Card title="A2UI" icon="table-cells" href="/en/guides/frontend/a2ui">
     Let the agent assemble surfaces from your catalog (declarative).
   </Card>
-  <Card title="Reasoning" icon="brain" href="/edge/en/guides/frontend/reasoning">
+  <Card title="Reasoning" icon="brain" href="/en/guides/frontend/reasoning">
     Render the agent's thinking.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/human-in-the-loop.mdx

@@ -74,7 +74,7 @@ class HumanInTheLoopFlow(Flow[CopilotKitState]):
 
 <Step title="Serve the Flow over AG-UI">
 
-Expose the Flow from your FastAPI server with `add_crewai_flow_fastapi_endpoint`, the same way as every other agent. See [Frontend Overview](/edge/en/guides/frontend/overview) for the full server, runtime, and provider setup.
+Expose the Flow from your FastAPI server with `add_crewai_flow_fastapi_endpoint`, the same way as every other agent. See [Frontend Overview](/en/guides/frontend/overview) for the full server, runtime, and provider setup.
 
 ```python
 # server.py
@@ -179,13 +179,13 @@ Once the user clicks Confirm, `respond()` fires, the run resumes, and the Flow's
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+  <Card title="Frontend Actions" icon="bolt" href="/en/guides/frontend/frontend-actions">
     Let the agent call functions that run in the browser.
   </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Keep agent state and your app UI in two-way sync.
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state as custom components.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/overview.mdx

@@ -12,16 +12,16 @@ CrewAI runs your agents. [CopilotKit](https://copilotkit.ai) gives them a fronte
 The two connect through the [AG-UI protocol](https://docs.ag-ui.com). The `ag-ui-crewai` package exposes any Crew or Flow as an AG-UI endpoint. CopilotKit's React hooks and components consume that endpoint. This unlocks experiences that go well beyond a chat box:
 
 <CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/en/guides/frontend/generative-ui">
     Render agent tool calls and state as your own React components.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Pause the agent to collect user approval or input mid-run.
   </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Keep agent state and your app UI in two-way sync.
   </Card>
-  <Card title="Channels" icon="messages" href="/edge/en/guides/frontend/channels">
+  <Card title="Channels" icon="slack" href="/en/guides/frontend/channels">
     Run the same agent as a Slack, Discord, or Teams bot.
   </Card>
 </CardGroup>
@@ -45,7 +45,7 @@ This guide covers the **self-hosted** path: you run the CrewAI agent server your
 </Note>
 
 <Note>
-CrewAI runs behind AG-UI in three shapes: regular **Flows** (used throughout these guides), **[Conversational Flows](/edge/en/guides/frontend/conversational-flows)** (native, session-aware, turn-based, at full feature parity), and **Crews** (basic chat). The frontend in this section is identical across them — only the backend authoring and registration differ.
+CrewAI runs behind AG-UI in three shapes: regular **Flows** (used throughout these guides), **[Conversational Flows](/en/guides/frontend/conversational-flows)** (native, session-aware, turn-based, at full feature parity), and **Crews** (basic chat). The frontend in this section is identical across them — only the backend authoring and registration differ.
 </Note>
 
 ## Integration guide
@@ -223,16 +223,16 @@ import { CopilotChat } from "@copilotkit/react-core/v2";
 ## Where to go next
 
 <CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/en/guides/frontend/generative-ui">
     Render tool calls and agent state as custom components.
   </Card>
-  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+  <Card title="Frontend Actions" icon="bolt" href="/en/guides/frontend/frontend-actions">
     Let the agent call functions that run in the browser.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Gate agent actions behind user approval.
   </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+  <Card title="Predictive State" icon="gauge-high" href="/en/guides/frontend/predictive-state-updates">
     Stream in-progress state to the UI as the agent works.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/predictive-state-updates.mdx

@@ -22,13 +22,13 @@ Both patterns read the agent's state from the frontend, but they solve different
 | Pattern | What it does |
 | --- | --- |
 | **Predictive state** | One-way. Streams an in-progress tool argument into a state field so the UI updates *during* generation, before the call completes. |
-| **[Shared State](/edge/en/guides/frontend/shared-state)** | Two-way. The UI reads *and writes* the agent's committed state, keeping app and agent in sync across turns. |
+| **[Shared State](/en/guides/frontend/shared-state)** | Two-way. The UI reads *and writes* the agent's committed state, keeping app and agent in sync across turns. |
 
-Reach for predictive state when you want an optimistic, in-flight preview of what the agent is producing. Reach for [Shared State](/edge/en/guides/frontend/shared-state) when the user needs to edit that state back.
+Reach for predictive state when you want an optimistic, in-flight preview of what the agent is producing. Reach for [Shared State](/en/guides/frontend/shared-state) when the user needs to edit that state back.
 
 ## Walkthrough
 
-This assumes you already have a Crew or Flow served over AG-UI and a CopilotKit frontend wired up. If not, start with the [Frontend Overview](/edge/en/guides/frontend/overview).
+This assumes you already have a Crew or Flow served over AG-UI and a CopilotKit frontend wired up. If not, start with the [Frontend Overview](/en/guides/frontend/overview).
 
 <Steps>
 
@@ -99,7 +99,7 @@ Call `copilotkit_predict_state` **before** you start streaming the completion. I
 
 The key is `copilotkit_predict_state({ "<state_field>": {"tool_name": ..., "tool_argument": ...} })`. Without it, the frontend would only see `document` once the tool call completed. With it, the partial argument streams onto the field while the agent is still generating.
 
-Serve the Flow with `add_crewai_flow_fastapi_endpoint(...)` as shown in the [Frontend Overview](/edge/en/guides/frontend/overview).
+Serve the Flow with `add_crewai_flow_fastapi_endpoint(...)` as shown in the [Frontend Overview](/en/guides/frontend/overview).
 
 </Step>
 
@@ -130,13 +130,13 @@ The `document` field fills in progressively as the agent generates the `write_do
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
+  <Card title="Shared State" icon="arrows-rotate" href="/en/guides/frontend/shared-state">
     Read and write the agent's state two-way.
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state as it changes.
   </Card>
-  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/edge/en/guides/frontend/tool-based-generative-ui">
+  <Card title="Tool-Based Generative UI" icon="puzzle-piece" href="/en/guides/frontend/tool-based-generative-ui">
     Map agent tool calls to components.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/reasoning.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## Thinking, rendered for free
 
-When a reasoning-capable model thinks before it answers, CopilotKit renders that thinking right in the chat. This is the simplest generative-UI pattern in the whole section: there is nothing to build. No hook, no component, no props. Use a reasoning-capable model, keep the streaming wrapper your Flows already have, and the chat surface from the [Overview](/edge/en/guides/frontend/overview) does the rest.
+When a reasoning-capable model thinks before it answers, CopilotKit renders that thinking right in the chat. This is the simplest generative-UI pattern in the whole section: there is nothing to build. No hook, no component, no props. Use a reasoning-capable model, keep the streaming wrapper your Flows already have, and the chat surface from the [Overview](/en/guides/frontend/overview) does the rest.
 
 ## Use a reasoning-capable model
 
@@ -56,13 +56,13 @@ There is no `useReasoning` hook and no reasoning component to write. Reasoning i
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
+  <Card title="Generative UI" icon="wand-magic-sparkles" href="/en/guides/frontend/generative-ui">
     The full spectrum, from author-controlled to agent-invented UI.
   </Card>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state as the Flow works.
   </Card>
-  <Card title="Frontend Overview" icon="browser" href="/edge/en/guides/frontend/overview">
+  <Card title="Frontend Overview" icon="browser" href="/en/guides/frontend/overview">
     Set up the chat surface and runtime.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/shared-state.mdx

@@ -115,13 +115,13 @@ class SharedStateFlow(Flow[AgentState]):
         pass
 ```
 
-Two things make this shared rather than one-way: dumping `self.state` into the prompt means the agent always works from the latest recipe (including edits the user made in the UI), and assigning `self.state.recipe` puts the new value into the state snapshot sent to connected clients at the end of the step. For updates during a long step, emit explicitly with `copilotkit_emit_state` (see [Agentic Generative UI](/edge/en/guides/frontend/agentic-generative-ui)).
+Two things make this shared rather than one-way: dumping `self.state` into the prompt means the agent always works from the latest recipe (including edits the user made in the UI), and assigning `self.state.recipe` puts the new value into the state snapshot sent to connected clients at the end of the step. For updates during a long step, emit explicitly with `copilotkit_emit_state` (see [Agentic Generative UI](/en/guides/frontend/agentic-generative-ui)).
 
 </Step>
 
 <Step title="Serve the Flow over AG-UI">
 
-Expose the Flow from your FastAPI app with `add_crewai_flow_fastapi_endpoint`, then register it in the CopilotKit runtime. See the [Frontend Overview](/edge/en/guides/frontend/overview) for the full server and runtime setup.
+Expose the Flow from your FastAPI app with `add_crewai_flow_fastapi_endpoint`, then register it in the CopilotKit runtime. See the [Frontend Overview](/en/guides/frontend/overview) for the full server and runtime setup.
 
 ```python
 # server.py
@@ -198,13 +198,13 @@ Putting the pieces together, a single recipe object is kept in sync in both dire
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state as it changes.
   </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
+  <Card title="Predictive State" icon="gauge-high" href="/en/guides/frontend/predictive-state-updates">
     Stream in-progress state to the UI as the agent works.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Pause the agent to collect user approval or input mid-run.
   </Card>
 </CardGroup>

docs/edge/en/guides/frontend/tool-based-generative-ui.mdx

@@ -11,7 +11,7 @@ When your Crew or Flow calls a tool, you rarely want the raw arguments dumped in
 
 Because CopilotKit streams the tool call to the frontend as the model generates it, the arguments fill in progressively. Your component can paint the moment the first field arrives and update as the rest stream in.
 
-This guide builds a haiku generator: the agent calls a `generate_haiku` tool, and the frontend renders each haiku as a card. It assumes you already have a Crew or Flow talking to a Next.js app. If not, start with the [Frontend Overview](/edge/en/guides/frontend/overview) for the full server, runtime, and provider setup.
+This guide builds a haiku generator: the agent calls a `generate_haiku` tool, and the frontend renders each haiku as a card. It assumes you already have a Crew or Flow talking to a Next.js app. If not, start with the [Frontend Overview](/en/guides/frontend/overview) for the full server, runtime, and provider setup.
 
 <Note>
 Tool rendering works with both Crews and Flows. The example below uses a Flow, but the frontend wiring is identical either way.
@@ -108,7 +108,7 @@ add_crewai_flow_fastapi_endpoint(
 )
 ```
 
-Register the agent with the CopilotKit runtime and point `<CopilotKit>` at it exactly as shown in the [Frontend Overview](/edge/en/guides/frontend/overview). The rest of this guide assumes the agent is registered under the id `haiku`.
+Register the agent with the CopilotKit runtime and point `<CopilotKit>` at it exactly as shown in the [Frontend Overview](/en/guides/frontend/overview). The rest of this guide assumes the agent is registered under the id `haiku`.
 
 </Step>
 
@@ -117,7 +117,7 @@ Register the agent with the CopilotKit runtime and point `<CopilotKit>` at it ex
 On the frontend, call `useRenderTool` with the same `name` the backend declared. `useRenderTool` is the hook for *rendering* a tool call: it takes a `render` function and nothing to execute, because this tool is pure display.
 
 <Note>
-Use `useRenderTool` when the tool only draws UI. If the tool also needs to *run* something in the browser, use [`useFrontendTool`](/edge/en/guides/frontend/frontend-actions) instead, which pairs a `handler` with an optional `render`.
+Use `useRenderTool` when the tool only draws UI. If the tool also needs to *run* something in the browser, use [`useFrontendTool`](/en/guides/frontend/frontend-actions) instead, which pairs a `handler` with an optional `render`.
 </Note>
 
 ```tsx
@@ -223,13 +223,13 @@ A backend tool must return a **JSON string**, not a Python dict. The bridge stri
 ## Related
 
 <CardGroup cols={2}>
-  <Card title="Agentic Generative UI" icon="list-check" href="/edge/en/guides/frontend/agentic-generative-ui">
+  <Card title="Agentic Generative UI" icon="list-check" href="/en/guides/frontend/agentic-generative-ui">
     Render live agent state as it changes across a multi-step run.
   </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
+  <Card title="Human-in-the-Loop" icon="user-check" href="/en/guides/frontend/human-in-the-loop">
     Pause the agent to collect user approval or input mid-run.
   </Card>
-  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
+  <Card title="Frontend Actions" icon="bolt" href="/en/guides/frontend/frontend-actions">
     Let the agent call functions that run in the browser.
   </Card>
 </CardGroup>

docs/edge/en/learn/litellm-removal-guide.mdx

@@ -176,7 +176,7 @@ grep -r "llm:" --include="*.yaml" .
     # llm = LLM(model="mistral/mistral-large-latest")
 
     # After (Native):
-    llm = LLM(model="gemini/gemini-3.7-flash")
+    llm = LLM(model="gemini/gemini-2.0-flash")
     ```
 
     ```bash
@@ -399,7 +399,7 @@ llm = LLM(model="anthropic/claude-haiku-3-5")    # Fast & affordable
 # Together AI → OpenAI or Gemini
 # llm = LLM(model="together_ai/meta-llama/Meta-Llama-3.1-70B")
 llm = LLM(model="openai/gpt-4o")                 # High quality
-llm = LLM(model="gemini/gemini-3.7-flash")       # Fast & capable
+llm = LLM(model="gemini/gemini-2.0-flash")       # Fast & capable
 
 # Mistral → Anthropic or OpenAI
 # llm = LLM(model="mistral/mistral-large-latest")

docs/edge/en/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
         # Example using Gemini's OpenAI-compatible API.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Add your Gemini model here, under openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Add your Gemini model here, under openai/
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
             ```python Google
             # Example using Gemini's OpenAI-compatible API
             llm = LLM(
-                model="openai/gemini-3.7-flash",
+                model="openai/gemini-2.0-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Should start with AIza...
             )

docs/edge/en/learn/llm-selection-guide.mdx

@@ -147,7 +147,7 @@ Planning agents benefit from reasoning models that can handle complex strategic
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
+manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -412,7 +412,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i
     # Manager or coordination agents
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini/gemini-3.7-flash"),  # Premium for coordination
+        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
         # ... rest of config
     )
 

docs/edge/en/learn/streaming-runtime-contract.mdx

@@ -151,7 +151,7 @@ Conversational Flows can stream one user turn with `stream_turn()`:
 
 ```python
 from crewai import Flow
-from crewai.flow import ConversationConfig, ConversationState
+from crewai.experimental.conversational import ConversationConfig, ConversationState
 
 
 @ConversationConfig(llm="gpt-4o-mini", defer_trace_finalization=True)

docs/edge/en/observability/arize-phoenix.mdx

@@ -7,9 +7,9 @@ mode: "wide"
 
 # Arize Phoenix Integration
 
-This guide demonstrates how to integrate **Arize Phoenix** with **CrewAI** using OpenTelemetry via the [OpenInference](https://github.com/openinference/openinference) SDK. By the end of this guide, you will be able to trace your CrewAI agents and debug agent behavior.
+This guide demonstrates how to integrate **Arize Phoenix** with **CrewAI** using OpenTelemetry via the [OpenInference](https://github.com/openinference/openinference) SDK. By the end of this guide, you will be able to trace your CrewAI agents and easily debug your agents.
 
-> **What is Arize Phoenix?** [Arize Phoenix](https://arize.com/phoenix/) is the open-source observability and evaluation option from [Arize AI](https://arize.com/?utm_source=crewai-docs&utm_medium=partner&utm_campaign=partner-docs&utm_content=observability-arize-phoenix). Use Phoenix when you want to run locally or self-host. Use [Arize AX](https://arize.com/products/ax/) for a managed cloud or enterprise self-hosted platform for production AI systems.
+> **What is Arize Phoenix?** [Arize Phoenix](https://phoenix.arize.com) is an LLM observability platform that provides tracing and evaluation for AI applications.
 
 [![Watch a Video Demo of Our Integration with Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
 
@@ -27,7 +27,7 @@ pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoen
 
 ### Step 2: Set Up Environment Variables
 
-Configure your Phoenix API key and OpenTelemetry endpoint to send traces to Phoenix. The same setup works with a local or self-hosted Phoenix endpoint by changing the collector URL.
+Setup Phoenix Cloud API keys and configure OpenTelemetry to send traces to Phoenix. Phoenix Cloud is a hosted version of Arize Phoenix, but it is not required to use this integration.
 
 You can get your free Serper API key [here](https://serper.dev/).
 
@@ -35,8 +35,8 @@ You can get your free Serper API key [here](https://serper.dev/).
 import os
 from getpass import getpass
 
-# Get your Phoenix API key
-PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix API key: ")
+# Get your Phoenix Cloud credentials
+PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix Cloud API Key: ")
 
 # Get API keys for services
 OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
@@ -44,7 +44,7 @@ SERPER_API_KEY = getpass("🔑 Enter your Serper API key: ")
 
 # Set environment variables
 os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
-os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Change this to your own endpoint if you are using a self-hosted instance
+os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, change this to your own endpoint if you are using a self-hosted instance
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 os.environ["SERPER_API_KEY"] = SERPER_API_KEY
 ```
@@ -133,7 +133,7 @@ print(result)
 
 After running the agent, you can view the traces generated by your CrewAI application in Phoenix. You should see detailed steps of the agent interactions and LLM calls, which can help you debug and optimize your AI agents.
 
-Open your Phoenix project and navigate to the project you specified in the `project_name` parameter. You'll see a timeline view of your trace with all the agent interactions, tool usages, and LLM calls.
+Log into your Phoenix Cloud account and navigate to the project you specified in the `project_name` parameter. You'll see a timeline view of your trace with all the agent interactions, tool usages, and LLM calls.
 
 ![Example trace in Phoenix showing agent interactions](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
 
@@ -147,9 +147,6 @@ Open your Phoenix project and navigate to the project you specified in the `proj
 
 ### References
 - [Phoenix Documentation](https://docs.arize.com/phoenix/) - Overview of the Phoenix platform.
-- [Arize AX](https://arize.com/products/ax/) - Managed cloud and enterprise self-hosted observability and evaluation.
-- [Arize agent evaluation guide](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) - Production workflow for evaluating agent behavior from traces.
-- [Arize LLM evaluation guide](https://arize.com/resources/llm-evaluation/) - Methods and metrics for evaluating LLM applications.
 - [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework.
 - [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
 - [OpenInference GitHub](https://github.com/openinference/openinference) - Source code for OpenInference SDK.

docs/edge/en/telemetry.mdx

@@ -23,7 +23,7 @@ usage of tools, API calls, responses, any data processed by the agents, or secre
 When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected 
 to provide deeper insights. This expanded data collection may include personal information if users have incorporated it into their crews or tasks. 
 Users should carefully consider the content of their crews and tasks before enabling `share_crew`. 
-Users can disable CrewAI telemetry by setting `CREWAI_DISABLE_TELEMETRY` to `true`, `1`, `yes`, or `on` (any case). `OTEL_SDK_DISABLED` with the same values also disables CrewAI's exporter. The OpenTelemetry SDK itself still only honors `true` for disabling other instrumentation in the process.
+Users can disable telemetry by setting the environment variable `CREWAI_DISABLE_TELEMETRY` to `true` or by setting `OTEL_SDK_DISABLED` to `true` (note that the latter disables all OpenTelemetry instrumentation globally).
 
 ### Examples:
 ```python
@@ -34,8 +34,6 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
-`CREWAI_DISABLE_TELEMETRY=1` (or `yes` / `on`) works the same as `true`. Unrecognized values are ignored and leave telemetry on.
-
 ### Isolation from your own OpenTelemetry setup
 
 CrewAI's telemetry runs on its own private `TracerProvider` and never registers
@@ -54,17 +52,16 @@ own tracer provider, which is independent of the one described here.
 | Defaulted | Data                                      | Reason and Specifics                                                                                                       |
 |:----------|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
 | Yes       | CrewAI and Python Version                 | Tracks software versions. Example: CrewAI v1.2.3, Python 3.8.10. No personal data. |
-| Yes       | Crew Metadata | Includes: randomly generated key and ID, process type (e.g., 'sequential', 'parallel'), boolean flag for memory usage (true/false), a boolean flag for whether any inputs were passed to the run (true/false — never the input keys or values, which are only collected when `share_crew` is enabled), count of tasks, count of agents. All non-personal. |
+| Yes       | Crew Metadata | Includes: randomly generated key and ID, process type (e.g., 'sequential', 'parallel'), boolean flag for memory usage (true/false), count of tasks, count of agents. All non-personal. |
 | Yes       | Agent Data | Includes: randomly generated key and ID, role name (should not include personal info), boolean settings (verbose, delegation enabled, code execution allowed), max iterations, max RPM, max retry limit, LLM info (see LLM Attributes), list of tool names (should not include personal info). No personal data. |
 | Yes       | Task Metadata | Includes: randomly generated key and ID, boolean execution settings (async_execution, human_input), associated agent's role and key, list of tool names. All non-personal. |
 | Yes       | Tool Usage Statistics | Includes: tool name (should not include personal info), number of usage attempts (integer), LLM attributes used. No personal data. |
 | Yes       | Test Execution Data | Includes: crew's randomly generated key and ID, number of iterations, model name used, quality score (float), execution time (in seconds). All non-personal. |
-| Yes       | Task Lifecycle Data | Includes: creation and execution start/end times, crew and task identifiers, and whether the task succeeded or failed. When a task fails, the **class name** of the exception is recorded (for example `TimeoutError`) so failures can be counted and diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Stored as spans with timestamps. No personal data. |
+| Yes       | Task Lifecycle Data | Includes: creation and execution start/end times, crew and task identifiers. Stored as spans with timestamps. No personal data. |
 | Yes       | LLM Attributes | Includes: name, model_name, model, top_k, temperature, and class name of the LLM. All technical, non-personal data. |
-| Yes       | Project Creation using crewAI CLI | Includes: that a new project was scaffolded by `crewai create`, which kind it was (`crew`, `json_crew` or `flow`), and the project ID minted for that new project and written into its own `pyproject.toml`. That is the new project's own ID, recorded separately from the `project_id` of the directory the command was run from — the two can differ. No project name, no file contents, no code. No personal data. |
 | Yes       | Crew Deployment attempt using crewAI CLI | Includes: The fact a deploy is being made and crew id, whether it's trying to pull logs, and whether the deploy was started from a CLI command or from the run TUI. No project or crew contents. No personal data. |
-| Yes       | Execution Environment | Includes: which AI coding assistant is running the process, if any (one of a fixed list such as `claude_code`, `codex`, `cursor`, or `unknown`), where the process runs (one of a fixed list such as `ci`, `container`, `serverless`, `interactive`), the `project_id` from your `pyproject.toml` when one is configured, and a coarse size band for the machine (one of `1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+`, or `unknown`). The band is a range, never the exact core count — the exact count is opt-in only, under Environment Information below. The size band comes from the host CPU count; assistant and location detection reads only whether known environment variables are set, never their values. No personal data. |
-| Yes       | Flow Lifecycle Signals | Includes: that a flow started, whether it completed or failed, whether one of its methods failed, whether it paused for human input or feedback, whether the start was a resumed run, whether a conversation turn failed, how long the flow ran, and whether the flow is one CrewAI runs internally or one you wrote. The flow name is recorded, as it already is for flow creation and execution. When a flow or one of its methods fails, the **class name** of the exception is recorded (for example `TimeoutError`) so that failures can be diagnosed — never the error message, which can contain prompts, model output, file paths or credentials. Method names and flow state are never recorded. No personal data. |
+| Yes       | Execution Environment | Includes: which AI coding assistant is running the process, if any (one of a fixed list such as `claude_code`, `codex`, `cursor`, or `unknown`), where the process runs (one of a fixed list such as `ci`, `container`, `serverless`, `interactive`), and the `project_id` from your `pyproject.toml` when one is configured. Detection reads only whether known environment variables are set, never their values. No personal data. |
+| Yes       | Flow Lifecycle Signals | Includes: that a flow started, whether it completed or failed, whether one of its methods failed, whether it paused for human input or feedback, whether the start was a resumed run, whether a conversation turn failed, how long the flow ran, and whether the flow is one CrewAI runs internally or one you wrote. The flow name is recorded (should not include personal info), as it already is for flow creation and execution. Method names, error messages and flow state are never recorded. No personal data. |
 | Yes       | Trace Sharing Signal | Includes: that a batch of traces was successfully shared with CrewAI AMP, and whether it was shared anonymously (before you have an account) or linked to your account. Like every span, it also carries the Execution Environment attributes described above (`project_id` when configured, the coding assistant, and the runtime). This row describes sharing telemetry only — not the trace contents or access granted by shared trace links. Trace contents, inputs, and outputs are never recorded on this signal. Before sharing traces, review secrets, personal data, and AMP redaction and retention settings. |
 | No        | Agent's Expanded Data | Includes: goal description, backstory text, i18n prompt file identifier. Users should ensure no personal info is included in text fields. |
 | No        | Detailed Task Information | Includes: task description, expected output description, context references. Users should ensure no personal info is included in these fields. |

docs/edge/en/tools/ai-ml/overview.mdx

@@ -50,15 +50,16 @@ These tools integrate with AI and machine learning services to enhance your agen
 - **AI Safety**: Implement content moderation and safety checks
 
 ```python
-from crewai_tools import DallETool, VisionTool
+from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
+code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor],
+    tools=[image_generator, vision_processor, code_executor],
     goal="Create and analyze content using AI capabilities"
 )

docs/edge/en/tools/file-document/filereadtool.mdx

@@ -1,6 +1,6 @@
 ---
 title: File Read
-description: The `FileReadTool` is designed to read files from the local file system.
+description: The `FileReadTool` reads files from the local file system.
 icon: folders
 mode: "wide"
 ---
@@ -8,73 +8,63 @@ mode: "wide"
 ## Overview
 
 <Note>
-    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
+    We are still improving tools, so behavior may change.
 </Note>
 
-The `FileReadTool` reads the contents of a file from the local file system and returns it as text.
-It is useful for batch text file processing, reading runtime configuration files, and importing data for analytics.
-It supports any text-based file format, such as `.txt`, `.csv`, `.json`, and `.md`.
-Content is always returned as plain text — parsing it (for example, `json.loads` on a `.json` file) is up to the agent or your own code.
+The `FileReadTool` reads a local file and returns its content as text.
+Use it to process text files, read config files, or load data for analysis.
+It works with any text format, such as `.txt`, `.csv`, `.json`, and `.md`.
+The tool always returns plain text. If you need structured data (for example, JSON), parse it in your agent or your own code.
 
-For large files, `start_line` and `line_count` read just a window of lines instead of loading the whole file.
+For large files, the agent can pass `start_line` and `line_count` to read only a range of lines.
+The tool stops once it has those lines, so it does not scan the rest of the file.
 
 ## Installation
 
-To utilize the functionalities previously attributed to the FileReadTool, install the crewai_tools package:
-
 ```shell
-pip install 'crewai[tools]'
+uv add 'crewai[tools]'
 ```
 
 ## Usage Example
 
-To get started with the FileReadTool:
-
 ```python Code
 from crewai_tools import FileReadTool
 
-# Initialize the tool to read any file the agent knows or learns the path for
-file_read_tool = FileReadTool()
+# Agent chooses the file path at runtime
+tool = FileReadTool()
 
-# OR
+# OR set a default file the agent can read with no path argument
+tool = FileReadTool(file_path='path/to/your/file.txt')
 
-# Initialize with a specific file path, so the agent reads that file by default
-file_read_tool = FileReadTool(file_path='path/to/your/file.txt')
-
-# Read a window of lines (lines 100-149) instead of the whole file
-partial_content = file_read_tool.run(
-    file_path='path/to/your/file.txt',
-    start_line=100,
-    line_count=50,
-)
+# OR let the agent read any file under a directory
+tool = FileReadTool(base_dir='/data')
 ```
 
+Give the tool to an agent. At runtime the LLM passes `file_path`, and optionally `start_line` and `line_count`.
+
 ## Arguments
 
-The agent supplies these at runtime:
+The agent can pass these at runtime:
 
-- `file_path`: (Optional) The path to the file you want to read. Accepts absolute and relative paths. Ensure the file exists and you have the necessary permissions to access it. Omit it to read the default file configured at construction; if there is no default, the tool reports that no path was provided.
-- `start_line`: (Optional) The line number to start reading from (1-indexed). Defaults to `1`.
-- `line_count`: (Optional) The number of lines to read. If omitted, reads from `start_line` to the end of the file.
+- `file_path`: (Optional) Path to the file to read. Absolute and relative paths are both valid only when they resolve inside the `base_dir` sandbox. A relative path resolves against `base_dir` when set, otherwise against the current working directory (the default sandbox). Omit it to read the default file set at construction. If there is no default, the tool returns an error saying no path was provided.
+- `start_line`: (Optional) First line to read. Line numbers start at `1`. Default is `1`.
+- `line_count`: (Optional) How many lines to read. If omitted, the tool reads from `start_line` to the end of the file.
 
-You set these when constructing the tool:
+You can set these when you create the tool:
 
-- `file_path`: (Optional) A default file to read when the agent calls the tool with no arguments.
-- `base_dir`: (Optional) The directory that runtime paths must stay inside. Defaults to the current working directory.
-- `encoding`: (Optional) Text encoding used to decode the file. Defaults to `utf-8`.
+- `file_path`: (Optional) Default file to read when the agent calls the tool with no path. A relative path resolves against `base_dir` when `base_dir` is provided, otherwise against the current working directory.
+- `base_dir`: (Optional) Directory that runtime paths must stay inside. Default is the current working directory. The tool resolves this path when the tool is created, so a later change of working directory does not move the sandbox.
+- `encoding`: (Optional) Text encoding used to decode the file. Default is `utf-8`. If decoding fails, the tool returns an error and suggests passing a different `encoding`.
+
+Common failures (missing file, permission denied, wrong encoding, or a path outside the sandbox) return an error string. They do not raise an exception.
 
 ## Allowed paths
 
-Because the file path is usually chosen by an LLM at runtime, reads are confined to a sandbox:
+An LLM usually chooses the file path at runtime, so reads are limited to a sandbox:
 
-- Paths supplied at runtime must resolve inside `base_dir`, which defaults to the current working directory. `..` segments and symlinks are resolved before the check, so they cannot be used to escape.
-- A `file_path` passed to the constructor is developer-declared intent, so it is always allowed past the containment check — even outside `base_dir`. The read itself can still fail if the file is missing, is a directory, or is not permitted. It is pinned when the tool is built, so a later change of working directory cannot repoint it, and the agent can address it either by omitting `file_path` or by using the name shown in the tool's description. Declaring one file does not expose its siblings.
+- Runtime paths must resolve inside `base_dir` (default: the current working directory). The tool resolves `..` segments and symlinks before it checks the path, so they cannot escape the sandbox.
+- A `file_path` you pass to the constructor is always allowed, even if it is outside `base_dir`. The read can still fail if the file is missing, is a directory, or cannot be accessed. That path is fixed when the tool is created, so a later change of working directory does not change which file it points to. The agent can read it by omitting `file_path`, or by using the name shown in the tool description. Declaring one file does not allow access to other files in the same folder.
 
-To let an agent read a directory tree outside the working directory, point `base_dir` at it:
+To let an agent read files outside the working directory, set `base_dir` when you create the tool (see the example above).
 
-```python Code
-# The agent may read anything under /data, and nothing outside it
-file_read_tool = FileReadTool(base_dir='/data')
-```
-
-As a last resort, setting `CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true` disables path validation. This applies process-wide to every crewai-tools tool, including the SSRF protections on URL-fetching tools, so prefer `base_dir`. Managed workers should set `CREWAI_TOOLS_FORCE_SAFE_PATHS=true` so a tenant cannot disable those checks by exporting the escape hatch.
+As a last resort, set `CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true` to turn off path checks. This setting applies to every crewai-tools tool in the process, including SSRF protections on URL-fetching tools. Prefer `base_dir` instead.

docs/edge/en/tools/web-scraping/scrapeelementfromwebsitetool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## Description
 
-The `ScrapeElementFromWebsiteTool` is designed to extract specific elements from websites using CSS selectors. This tool allows CrewAI agents to scrape targeted content from web pages, making it useful for data extraction tasks where only specific parts of a webpage are needed. Fetches go through CrewAI's SSRF-safe HTTP helper: the requested URL and every redirect hop are checked against private and reserved ranges (including cloud metadata), and the TCP connection is pinned to an IP that passed that check.
+The `ScrapeElementFromWebsiteTool` is designed to extract specific elements from websites using CSS selectors. This tool allows CrewAI agents to scrape targeted content from web pages, making it useful for data extraction tasks where only specific parts of a webpage are needed.
 
 ## Installation
 

docs/edge/en/tools/web-scraping/scrapewebsitetool.mdx

@@ -16,8 +16,6 @@ mode: "wide"
 A tool designed to extract and read the content of a specified website. It is capable of handling various types of web pages by making HTTP requests and parsing the received HTML content. 
 This tool can be particularly useful for web scraping tasks, data collection, or extracting specific information from websites.
 
-Fetches go through CrewAI's SSRF-safe HTTP helper: the requested URL and every redirect hop are checked against private and reserved ranges (including cloud metadata), and the TCP connection is pinned to an IP that passed that check.
-
 ## Installation
 
 Install the crewai_tools package

docs/edge/ko/changelog.mdx

@@ -4,104 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="2026년 8월 27일">
-  ## v1.15.18
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.18)
-
-  ## 변경 사항
-
-  ### 기능
-  - 대화 흐름을 안정 상태로 승격
-  - 주어진 UUID로 생성된 배포를 기록
-  - 대화 흐름 문서 및 API 개선
-  - 선언이 라우터의 응답 형식을 지정하도록 허용
-  - 채팅 흐름이 자체 상태 형태를 선언하도록 허용
-  - 대화 선언에서 크루 스타일 LLM 구성 수용
-  - 발급된 ID로 프로젝트 생성 보고
-  - 실행에 입력이 있었는지 여부를 기록하되 입력은 기록하지 않음
-  - 모든 사용자 호출 프로젝트 명령에서 프로젝트 ID를 백필
-
-  ### 버그 수정
-  - 최종 답변이 비어 있을 때 도구 결과 보존
-  - 기본 Claude Sonnet 4.6을 1M 컨텍스트 윈도우에 매핑
-  - 대형 도구 호출을 위한 Anthropic 기본 max_tokens 증가
-  - 메시지 내용 부분을 텍스트로 렌더링, Python repr로 렌더링하지 않음
-  - Agent.kickoff가 대화를 받을 때 메시지 역할 유지
-  - crewai 내부 흐름에서 가로채기 후크 건너뛰기
-  - 작업 실패를 실패로 기록하고 성공으로 기록하지 않음
-  - 억제된 재개에서 흐름 생명 주기 방출
-  - 선언적 채팅 흐름을 위한 대화형 TUI 열기
-  - crew_memory를 문자열로 기록하고 불리언으로 기록하지 않음
-  - 항상 project_id를 방출하여 부재 및 비어 있는 상태를 구분
-
-  ### 문서
-  - Arize Phoenix 가시성 문서 명확화
-
-  ## 기여자
-
-  @Vidit-Ostwal, @arizedatngo, @joaomdmoura, @lorenzejay, @lucasgomide
-
-</Update>
-
-<Update label="2026년 8월 19일">
-  ## v1.15.17
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.17)
-
-  ## 변경 사항
-
-  ### 기능
-  - 선언적 대화 흐름 문서 추가
-  - 선언을 위한 내장 대화 방법 합성
-  - 선언이 대화 모드를 주도할 수 있도록 활성화
-  - 대화 선택 참여를 명확하게 표시
-  - 슬러그 참조에서 해결된 도구에 AMP 슬러그 전달
-  - 청크 처리 중 과도한 단일 메시지 처리
-
-  ### 버그 수정
-  - MCP HTTP 및 SSE server_name으로 URL 호스트 이름 사용 수정
-  - 모든 실패한 시도에서 에이전트 범위 닫기
-  - 도구 오류를 실패한 도구에 귀속
-  - 각 리디렉션 홉 및 피어 IP에 SSRF 검사 고정
-  - OpenAI Responses API를 통해 깨진 네이티브 도구 호출 문제 해결
-
-  ### 문서
-  - v1.15.16에 대한 스냅샷 및 변경 로그로 문서 업데이트
-
-  ## 기여자
-
-  @Copilot, @Vidit-Ostwal, @github-code-quality[bot], @joaomdmoura, @lorenzejay, @lucasgomide, @theCyberTech
-
-</Update>
-
-<Update label="2026년 8월 13일">
-  ## v1.15.16
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.15.16)
-
-  ## 변경 사항
-
-  ### 기능
-  - UUID 지원을 통한 실행 컨텍스트 관리 도입
-  - 흐름을 종료한 예외의 종류 기록
-  - 트레이스 배치가 AMP와 공유된 시점 기록
-  - 모든 출처에서 배포를 카운트하고 시작 위치 기록
-
-  ### 버그 수정
-  - 모든 생성된 스팬에서 실행 중인 릴리스를 기록
-  - MySQL 검색 테이블 이름 유효성 검사 수정
-  - 실패한 턴이 다음 턴을 실패로 표시하지 않도록 중지
-
-  ### 문서
-  - CopilotKit 및 AG-UI에 대한 프론트엔드 가이드 추가
-
-  ## 기여자
-
-  @joaomdmoura, @lorenzejay, @ranst91, @theCyberTech
-
-</Update>
-
 <Update label="2026년 8월 11일">
   ## v1.15.15
 

docs/edge/ko/concepts/agents.mdx

@@ -630,11 +630,6 @@ messages = [
 result = researcher.kickoff(messages)
 ```
 
-마지막 `user` 메시지가 에이전트가 답변할 요청입니다. 나머지 메시지는 각자의 역할과
-그 요청을 기준으로 한 위치를 그대로 유지하므로, assistant 또는 tool 메시지로 끝나는
-대화도 사용자의 질문을 그대로 전달하며 뒤따르는 턴도 요청 뒤에 그대로 전달됩니다.
-`user` 메시지가 전혀 없으면 마지막 메시지를 요청으로 처리합니다.
-
 ### 비동기 지원
 
 동일한 매개변수를 사용하는 비동기 버전은 `kickoff_async()`를 통해 사용할 수 있습니다:

docs/edge/ko/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Google Gemini 사용
-memory = Memory(llm="gemini/gemini-3.7-flash")
+memory = Memory(llm="gemini/gemini-2.0-flash")
 
 # 사용자 정의 설정이 있는 사전 구성된 LLM 인스턴스 전달
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/ko/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ CrewAI의 기본 프롬프트는 많은 시나리오에서 잘 작동하지만,
 - **오류 처리** – agent가 실패, 예외, 또는 타임아웃에 어떻게 반응할지 지정합니다.
 - **도구별 prompt** – 도구가 호출되거나 사용되는 방법에 대한 상세 지침을 정의합니다.
 
-이 요소들이 어떻게 구성되어 있는지 보려면 [CrewAI 저장소의 원본 prompt 템플릿](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json)을 확인하세요. 여기서 필요에 따라 오버라이드하거나 수정하여 고급 동작을 구현할 수 있습니다.
+이 요소들이 어떻게 구성되어 있는지 보려면 [CrewAI 저장소의 원본 prompt 템플릿](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json)을 확인하세요. 여기서 필요에 따라 오버라이드하거나 수정하여 고급 동작을 구현할 수 있습니다.
 
 ## 기본 시스템 지침 이해하기
 

docs/edge/ko/guides/crews/first-crew.mdx

@@ -75,7 +75,7 @@ research_crew/
 }
 ```
 
-`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-3.7-flash` 같은 모델로 바꾸세요.
+`provider/model-id`를 `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, `gemini/gemini-2.0-flash-001` 같은 모델로 바꾸세요.
 
 ## 3단계: 태스크와 Crew 설정
 

docs/edge/ko/guides/flows/conversational-flows.mdx

@@ -1,37 +1,35 @@
 ---
 title: 대화형 Flow
-description: 턴별 handle_turn, 메시지 기록, 의도 라우팅, 트레이싱, 구조화된 스트리밍으로 멀티턴 채팅 앱을 만듭니다.
+description: 턴마다 kickoff, 메시지 기록, 의도 라우팅, 트레이싱, WebSocket 브리지로 멀티턴 채팅 앱을 만듭니다.
 icon: comments
 mode: "wide"
 ---
 
 ## 개요
 
-대화형 앱은 각 사용자 입력을 **동일한 세션 id**로 **새 flow 실행**으로 처리합니다. CrewAI는 메시지 기록, 선택적 의도 라우팅, 지연 트레이싱, 구조화된 턴 스트리밍, 로컬 `flow.chat()` REPL을 위한 헬퍼를 제공합니다.
+대화형 앱은 각 사용자 입력을 **동일한 세션 id**로 **새 flow 실행**으로 처리합니다. CrewAI는 메시지 기록, 선택적 의도 분류, 지연 트레이싱, UI 브리지, 그리고 대화형 flow용 로컬 `flow.chat()` REPL을 제공합니다.
 
 | 개념 | 구현 |
 |------|------|
 | 세션 id | `handle_turn(..., session_id=...)` → `kickoff(inputs={"id": ...})` → `state.id` |
 | 사용자 입력 | `handle_turn(message)`가 그래프 실행 전 `state.messages`에 추가 |
-| 턴 완료 | `conversation_turn_completed`; 기본 trace 지연을 사용하면 `FlowFinished`는 `finalize_session_traces()`까지 대기 |
+| 턴 완료 | `FlowFinished`는 **이번 실행**만 의미; 다음 `handle_turn`로 대화 계속 |
 | 세션 전체 트레이스 | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
 
 ## 턴 API
 
 REST, WebSocket, 테스트, 커스텀 UI에서 오는 모든 사용자 메시지에는 **`flow.handle_turn(message, session_id=...)`**를 사용하세요. 대화형 `Flow`를 로컬 터미널 채팅 루프로 실행하고 싶을 때는 **`flow.chat()`**을 사용하세요.
 
-`Flow.kickoff()`는 `user_message=` 또는 `session_id=` 키워드 인자를 받지 않습니다. 대화형 flow에서는 `handle_turn()`이 보류 중인 메시지를 저장하고 턴별 실행 상태를 초기화한 뒤 내부적으로 `kickoff(inputs={"id": session_id})`를 호출합니다.
+`Flow.kickoff()`는 `user_message=` 또는 `session_id=` 키워드 인자를 받지 않습니다. 대화형 flow에서는 `handle_turn()`이 보류 중인 메시지를 저장하고 내부적으로 `kickoff(inputs={"id": session_id})`를 호출합니다.
 
 | API | 용도 |
 |-----|------|
 | `handle_turn(message, session_id=...)` | 대화형 `Flow`용 한 턴 편의 래퍼 |
-| `stream_turn(message, session_id=...)` | 대화형 한 턴을 순서가 보장된 런타임 frame으로 스트리밍 |
 | `chat()` | 대화형 `Flow`용 로컬 터미널 REPL |
-| `kickoff(inputs={...})` | 대화형 턴 처리 없이 flow를 직접 실행하는 고급 용도 |
+| `kickoff(inputs={...})` | 대화형 턴 처리 없이 flow를 직접 실행 |
 | `ask()` | 한 스텝 **내부** 블로킹 프롬프트 (마법사, 확인) |
 | `@human_feedback` | **스텝 출력** 승인/거부 — 다음 채팅 줄이 아님 |
-
-대화형 모드가 활성화되지 않으면 `handle_turn()`, `stream_turn()`, `chat()`은 `ValueError`를 발생시킵니다. `@ConversationConfig(...)`를 적용하면 자동으로 활성화되며, 그렇지 않으면 `conversational = True`로 설정하세요.
+| `ChatSession.handle_turn(...)` | `handle_turn` 위의 전송 계층 (SSE / WebSocket) |
 
 ## 빠른 시작
 
@@ -40,7 +38,7 @@ from uuid import uuid4
 
 from crewai import Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
 )
@@ -48,6 +46,8 @@ from crewai.flow import (
 
 @ConversationConfig(defer_trace_finalization=True)
 class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
     def route_turn(self, context):
         message = self.state.current_user_message or ""
         if "주문" in message or "order" in message.lower():
@@ -85,43 +85,35 @@ finally:
     flow.finalize_session_traces()  # 전체 대화에 대한 단일 trace 링크
 ```
 
-## 턴 스트리밍
-
-UI나 런타임에서 한 채팅 턴의 구조화된 이벤트가 필요하면 `stream_turn()`을 사용하세요. Flow 라우팅, LLM chunk, tool 활동, 대화 메시지를 순서가 보장된 frame으로 제공하는 stream session을 반환합니다.
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-result = stream.result
-```
-
-전체 frame 계약과 channel 목록은 [스트리밍 런타임 계약](/edge/ko/learn/streaming-runtime-contract)을 참고하세요.
-
 ## 턴 생명주기
 
 각 `handle_turn`은 다음 파이프라인을 실행합니다:
 
-1. **턴 설정** — 보류 중인 사용자 메시지를 저장하고 세션 id를 결정하며 턴별 실행 추적을 초기화한 뒤 `kickoff(inputs={"id": session_id})`를 호출.
+1. **`_configure_conversational_kickoff`** — `session_id` / `user_message`를 `inputs`에 병합, `ConversationalConfig` 적용, 설정 시 지연 트레이싱 활성화.
 2. **상태 복원** — `inputs["id"]`가 있고 `@persist`가 설정되면 최신 스냅샷 로드.
 3. **`FlowStarted`** — 지연 세션의 첫 턴에서만 발생.
-4. **보류 중인 턴 수화** — 사용자 메시지를 `state.messages`에 추가하고 `current_user_message` / `last_user_message`를 설정하며, `intents` / `default_intents` + `intent_llm` 설정 시 선택적으로 분류.
-5. **그래프 실행** — 사용자 정의 `@start` 메서드(있는 경우) → `route_conversation`(내장 start/router) → 선택된 `@listen` 핸들러. `route_conversation`은 재정의 가능한 `conversation_start()` 헬퍼도 호출합니다.
+4. **`prepare_conversational_turn`** — 사용자 메시지를 `state.messages`에 추가, `last_user_message` 설정, `last_intent` 초기화, `intents` / `default_intents` + `intent_llm` 설정 시 분류.
+5. **그래프 실행** — `@start` → `@router` → `@listen` 핸들러.
 6. **실행 종료** — 지연 활성화 시 턴별 `flow_finished` 및 trace 종료 **건너뜀**; 중첩 `Agent.kickoff()` / crew도 부모 batch를 닫지 않음.
 
-핸들러는 보이는 응답이 반환값과 다를 때, 또는 히스토리를 자를 때 **`append_assistant_message(reply)`**를 호출하세요. public 문자열 반환값도 assistant로 기록되며 `@persist` 스냅샷에 포함되므로, 새 Flow 인스턴스에서도 복원됩니다. 사용자 입력은 `handle_turn`이 이미 저장합니다 — 핸들러에서 다시 추가하지 마세요.
+핸들러는 **`append_assistant_message(reply)`**를 호출해 다음 턴의 `conversation_messages`에 어시스턴트 응답이 포함되게 하세요. 사용자 입력은 `handle_turn`이 이미 저장합니다 — 핸들러에서 다시 추가하지 마세요.
 
-## 설정 개요
+## `ConversationalConfig` (클래스 수준 기본값)
 
-`Flow` 서브클래스에 `ConversationConfig`를 데코레이터로 적용하면 채팅 기본값이 부착되고 대화형 모드도 활성화됩니다. 아래의 [전체 필드 레퍼런스](#conversationconfig)를 참고하세요. 턴마다 `handle_turn(..., intents=..., intent_llm=...)`로 사전 분류 설정을 재정의할 수 있습니다.
+`Flow` 서브클래스에 `conversational_config: ClassVar[ConversationalConfig | None]`로 설정합니다.
 
-## 하위 수준 `ChatState` 헬퍼
+| 필드 | 기본값 | 목적 |
+|------|--------|------|
+| `default_intents` | `None` | kickoff 전 자동 분류용 outcome 라벨 |
+| `intent_llm` | `None` | 분류용 모델 (intent 사용 시 필수) |
+| `interactive_prompt` | `"You: "` | `kickoff(interactive=True)` 프롬프트 |
+| `interactive_timeout` | `None` | 대화형 모드 줄 단위 타임아웃 |
+| `exit_commands` | `exit`, `quit` | 대화형 모드 종료 단어 |
+| `defer_trace_finalization` | `True` | 턴 간 하나의 trace batch 유지 |
 
-`ChatState`, 레거시 `ConversationalConfig`, `crewai.flow.conversation` 헬퍼는 고급 오케스트레이션, 테스트, 커스텀 래퍼에서 계속 import할 수 있습니다. 이들은 `ConversationState` / `ConversationConfig` API와 별개이며 `Flow.kickoff()`에 `user_message=` 또는 `session_id=` 키워드 인자를 추가하지 않습니다.
+`intents=` 및 `intent_llm=` 키워드로 kickoff마다 재정의할 수 있습니다.
+
+## `ChatState` (권장 persist 형태)
 
 ```python
 from crewai.flow import ChatState
@@ -135,7 +127,7 @@ class MyChatState(ChatState):
 
 | 필드 | 역할 |
 |------|------|
-| `id` | 세션 UUID (`inputs["id"]`와 동일) |
+| `id` | 세션 UUID (`session_id` / `inputs["id"]`와 동일) |
 | `messages` | LLM 기록용 `{role, content}` 리스트 |
 | `last_user_message` | 이번 턴의 최신 사용자 입력 |
 | `last_intent` | 분류 후 라우트 라벨 (사용 시) |
@@ -143,77 +135,76 @@ class MyChatState(ChatState):
 
 `ConversationalInputs`는 `kickoff(inputs={...})`용 `TypedDict`: `id`, `user_message`, `last_intent`.
 
-`ConversationState`는 `messages`를 `ConversationMessage` 객체로 저장하며 `current_user_message`, `ended`, `events`, `agent_threads`도 제공합니다. 정식 기록을 LLM에 전달할 때는 `conversation_messages`를 사용하세요.
-
 ## `Flow` 대화 API
 
-### `handle_turn` 파라미터
+### `kickoff` / `kickoff_async` 파라미터
 
 | 파라미터 | 목적 |
 |----------|------|
-| `message` | 이번 턴의 텍스트 |
+| `user_message` | 이번 턴 텍스트 (또는 `{"role": "user", "content": "..."}`) |
 | `session_id` | 대화 UUID → `inputs["id"]` / `state.id` |
-| `intents` | kickoff 전 `classify_intent`용 결과 라벨 |
+| `intents` | kickoff 전 `classify_intent`용 outcome 라벨 |
 | `intent_llm` | 분류 LLM (`intents`와 함께 필수) |
-| `**kickoff_kwargs` | `input_files`, `from_checkpoint`, `restore_from_state_id` 같은 옵션을 `kickoff()`로 전달 |
-
-### `kickoff` 파라미터
-
-`Flow.kickoff()`는 `inputs`, `input_files`, `from_checkpoint`, `restore_from_state_id`를 받습니다. 원시 flow 실행이 필요하면 `inputs={"id": session_id}`를 전달할 수 있지만, 채팅 메시지를 나타내는 호출에는 `handle_turn()`을 사용하세요.
+| `interactive` | `ask()` CLI 루프 (로컬 데모 전용) |
+| `interactive_prompt` | 대화형 모드 프롬프트 |
+| `interactive_timeout` | 줄 단위 `ask()` 타임아웃 |
+| `exit_commands` | 대화형 모드 종료 단어 |
+| `inputs` | 추가 상태 필드 |
+| `restore_from_state_id` | 다른 persist flow에서 fork 복원 |
 
 ### 인스턴스 속성
 
 | 속성 | 목적 |
 |------|------|
-| `conversational` | 대화형 그래프와 `handle_turn()`을 활성화하려면 `True`로 설정 |
-| `defer_trace_finalization` | 선택적 인스턴스 재정의. 없으면 `_should_defer_trace_finalization()`이 `ConversationConfig.defer_trace_finalization`을 읽음 |
-| `suppress_flow_events` | 콘솔 flow 패널과 메서드 실행 이벤트를 숨김. flow start/finish 이벤트는 계속 발생 |
-| `stream` | 일반 Flow 스트리밍 플래그. 대화형 턴에서는 이 플래그와 `handle_turn()`을 함께 쓰지 말고 `stream_turn()` 사용 |
+| `conversational_config` | 클래스 수준 `ConversationalConfig` |
+| `defer_trace_finalization` | 인스턴스 플래그; kickoff 시 config에서 자동 설정 |
+| `suppress_flow_events` | 콘솔 flow 패널 숨김; **트레이싱은 계속 기록** |
+| `stream` | 스트리밍; `ChatSession.handle_turn(..., stream=True)`와 함께 |
 
 ### 메서드 및 프로퍼티
 
 | 이름 | 설명 |
 |------|------|
-| `append_assistant_message(content)` | 사용자에게 보이는 어시스턴트 응답을 `state.messages`에 추가 |
 | `append_message(role, content, **extra)` | `state.messages`에 추가 |
 | `conversation_messages` | LLM 호출용 읽기 전용 기록 |
 | `classify_intent(text, outcomes, *, llm, context=None)` | outcome 매핑 (`@human_feedback`와 동일 collapse) |
 | `receive_user_message(text, *, outcomes=None, llm=None)` | 사용자 메시지 추가; 선택적 `last_intent` |
 | `finalize_session_traces()` | 지연 `flow_finished` 발생 및 세션 trace batch 종료 |
-| `_should_defer_trace_finalization()` | 턴별 trace 종료 지연 여부를 결정하는 고급/내부 hook |
+| `_should_defer_trace_finalization()` | 턴별 trace 종료 지연 여부 |
 | `input_history` | `ask()` 프롬프트/응답 감사 기록 |
 
 ### 모듈 헬퍼 (`crewai.flow.conversation`)
 
-테스트 또는 커스텀 오케스트레이션을 위해 `crewai.flow.conversation`에서 import할 수 있습니다. 이 헬퍼들은 레거시 `ConversationalConfig` 형태를 사용합니다. 또한 `prepare_conversational_turn()`은 `last_intent`를 지우지만, `handle_turn()`은 router 컨텍스트로 보존합니다.
+테스트 또는 커스텀 오케스트레이션용:
 
 | 함수 | 설명 |
 |------|------|
-| `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | 대화 kwargs를 `inputs`에 병합 |
+| `normalize_kickoff_inputs(...)` | 대화 kwargs를 `inputs`에 병합 |
 | `get_conversation_messages(flow)` | 상태 또는 내부 버퍼에서 메시지 읽기 |
-| `append_message(flow, role, content, **extra)` | 인스턴스 메서드와 동일 |
-| `prepare_conversational_turn(flow, user_message=..., intents=..., intent_llm=..., config=...)` | 커스텀 래퍼용 하위 수준 턴 수화 |
-| `receive_user_message(flow, text, ...)` | 인스턴스 메서드와 동일 |
+| `append_message(flow, ...)` | 인스턴스 메서드와 동일 |
+| `prepare_conversational_turn(flow, ...)` | 턴 수화 (보통 kickoff가 호출) |
+| `receive_user_message(flow, ...)` | 인스턴스 메서드와 동일 |
 | `set_state_field(flow, name, value)` | dict 또는 Pydantic 상태 필드 설정 |
 | `get_conversational_config(flow)` | 클래스 `conversational_config` 읽기 |
 | `input_history_to_messages(entries)` | `input_history`를 LLM 메시지 형식으로 |
 
 ## 의도 라우팅 패턴
 
-### A. `ConversationConfig`로 사전 분류 (가장 단순)
+### A. `ConversationalConfig`로 사전 분류 (가장 단순)
 
-`default_intents`와 `intent_llm`을 설정하세요. 각 `handle_turn()`이 현재 메시지를 사전 분류합니다. 커스텀 `route_turn()`이 반환한 비어 있지 않은 결과가 우선하며, 그렇지 않으면 `route_conversation`이 현재 턴의 분류된 intent를 사용합니다.
+`default_intents`와 `intent_llm` 설정. 각 kickoff가 `@router` 전에 분류; `route()`에서 `self.state.last_intent` 읽기.
 
-### B. `route_turn` 내부에서 분류 (풍부한 프롬프트)
+### B. `@router` 내부에서 분류 (풍부한 프롬프트)
 
-`default_intents=None`으로 설정하면 `handle_turn()`은 사용자 메시지만 추가합니다. `route_turn()`에서 커스텀 프롬프트나 설명과 함께 `classify_intent`를 호출하세요:
+`default_intents=None`으로 kickoff는 메시지만 추가. `route()`에서 커스텀 프롬프트로 `classify_intent` 호출:
 
 ```python
-def route_turn(self, context):
+@router(bootstrap)
+def route(self):
     intent = self.classify_intent(
-        self._routing_prompt(self.state.current_user_message),
+        self._routing_prompt(self.state.last_user_message),
         ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
-        llm="gpt-4o-mini",
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
     )
     self.state.last_intent = intent
     return intent
@@ -223,59 +214,69 @@ def route_turn(self, context):
 
 ## flow가 끝났지만 사용자는 계속 대화할 때
 
-각 `handle_turn()`은 하나의 그래프 실행을 완료하며, 같은 `session_id`로 다음 `handle_turn()`을 호출해 대화를 이어갑니다. 기본 지연 trace 수명 주기에서는 해당 실행이 `conversation_turn_completed`를 발생시키고, `finalize_session_traces()`가 세션을 닫을 때 `FlowFinished`가 한 번 발생합니다. `@persist`는 `messages`, 플래그, 컨텍스트를 복원합니다.
+`FlowFinished`는 **이번 그래프 실행**이 완료됨을 의미합니다. 같은 `session_id`로 또 다른 `kickoff`로 대화가 이어집니다. `@persist`가 `messages`, 플래그, 컨텍스트를 복원합니다.
 
 **Persist 패턴:** 전체 `Flow` 클래스보다 **단일 종료 스텝**(예: `finalize`)에 `@persist`를 두는 것이 좋습니다. 클래스 수준 persist는 매 메서드 후 저장하며, `load_state`는 최신 행을 사용해 같은 턴의 핸들러 업데이트를 놓칠 수 있습니다.
 
 후속 채팅 줄에 `@human_feedback`를 쓰지 마세요. 특정 스텝 출력을 사람이 승인해야 할 때만 사용하세요.
 
-## 대화형 `Flow`
+## 대화형 `Flow` (실험적)
 
-`Flow` 서브클래스에 `conversational = True`를 지정하거나 `@ConversationConfig(...)`를 적용하면 대화형 채팅 그래프가 활성화됩니다. 베이스 `Flow`는 내장 start/router인 `route_conversation`과 `converse_turn`, `end_conversation` 리스너를 제공합니다. 사용 중단된 `answer_from_history_turn` 리스너는 호환성을 위해 계속 제공됩니다. 또한 `state.messages`를 관리하고 router LLM을 구동할 수 있으며 턴 간 trace batch를 열린 상태로 유지합니다. 여러분은 **커스텀 라우트**를 작성하고 나머지는 프레임워크에 맡기면 됩니다.
+<Warning>
+  **실험적 기능입니다.** 대화형 `Flow`의 API 표면(`conversational = True`,
+  `handle_turn`, `ConversationConfig`, `RouterConfig`, `ConversationState`,
+  내장 그래프와 헬퍼)은 `crewai.experimental` 하위에 있으며 정식 출시
+  전까지 변경될 수 있습니다. 특정 동작에 의존한다면 CrewAI 버전을 고정하고
+  변경 사항이 있는지 changelog를 확인하세요. 피드백과 이슈 환영합니다.
+</Warning>
+
+`Flow` 서브클래스에 `conversational = True`를 지정하면 대화형 챗 그래프가 활성화됩니다. 베이스 `Flow`가 `@start` / `@router` / `converse_turn` / `end_conversation` 그래프를 노출하고, `state.messages`를 관리하며, router LLM을 구동하고, 턴 간 trace 배치를 열린 상태로 유지합니다. 여러분은 **커스텀 라우트**만 작성하면 되고, 나머지는 프레임워크가 담당합니다.
 
 LLM 기반 라우터와 라우트별 핸들러로 멀티턴 챗을 만들고 싶지만 라이프사이클을 직접 배선하고 싶지 않을 때 사용하세요. 완전한 제어가 필요하면 위의 `Flow[ChatState]`로 내려가세요.
 
 ### 빠른 예제
 
 ```python
-from crewai import Flow
+from crewai import LLM, Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
+    RouterConfig,
 )
 
 
-@ConversationConfig(defer_trace_finalization=True)
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # 라우트 + 설명은 @listen 핸들러에서 자동 발견
+)
 class SupportFlow(Flow[ConversationState]):
-    def route_turn(self, context: dict) -> str | None:
-        message = (self.state.current_user_message or "").lower()
-        if "search" in message or "news" in message:
-            return "INTERNET_SEARCH"
-        if "docs" in message or "crewai" in message:
-            return "CREWAI_DOCS"
-        return "converse"
+    conversational = True
 
     @listen("INTERNET_SEARCH")
     def handle_internet_search(self) -> str:
         """Fresh web research, current news, real-time lookups."""
-        reply = "I would run the web research route here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
     @listen("CREWAI_DOCS")
     def handle_crewai_docs(self) -> str:
         """Look up the CrewAI documentation for framework/API questions."""
-        reply = "I would look up the CrewAI docs here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
 
 flow = SupportFlow()
 try:
-    flow.handle_turn("What can you do?")              # routes to converse
-    flow.handle_turn("Search the web for AI news.")   # routes to INTERNET_SEARCH
-    flow.handle_turn("Check the CrewAI docs.")         # routes to CREWAI_DOCS
+    flow.handle_turn("뭘 할 수 있어?")                # converse(빌트인)로 라우팅
+    flow.handle_turn("AI 뉴스를 웹에서 찾아줘.")        # INTERNET_SEARCH로 라우팅
+    flow.handle_turn("첫 번째 결과를 요약해줘.")        # 다시 converse로 라우팅
 finally:
     flow.finalize_session_traces()
 ```
@@ -297,53 +298,27 @@ def kickoff() -> None:
 |------|--------|------|
 | `system_prompt` | i18n `slices.conversational_system_prompt` | 빌트인 `converse_turn`이 사용하는 system 메시지. 빈 문자열(`""`)을 전달하면 system 메시지를 끕니다. |
 | `llm` | `None` | 대화용 LLM (빌트인 `converse_turn`이 사용하고 router 폴백도 됨). |
-| `router` | `None` | 선택적 `RouterConfig` 재정의. 커스텀 listener와 결정 가능한 LLM이 있으면 생략해도 라우팅이 자동 활성화됩니다. |
-| `answer_from_history_prompt` | 프레임워크 기본값 | **사용 중단됨.** `converse` system prompt를 사용하거나 `converse_turn()`을 재정의하세요. |
-| `answer_from_history_llm` | `None` | **사용 중단됨.** `llm`을 사용하세요. `converse`는 이미 정식 기록을 전달받습니다. |
+| `router` | `None` | LLM 기반 라우팅을 위한 `RouterConfig`. 없으면 항상 `converse`로 떨어집니다. |
+| `answer_from_history_prompt` | 프레임워크 기본값 | 선택적인 `answer_from_history` 라우트용 system 메시지. |
+| `answer_from_history_llm` | `None` | 설정되면 `answer_from_history` 단축 경로가 활성화됩니다. |
 | `intent_llm` | `None` | 레거시 `intents=`/`default_intents` 사전 분류용 LLM. |
 | `default_intents` | `None` | 레거시 사전 분류용 outcome 레이블. |
 | `visible_agent_outputs` | `None` | `"all"` 또는 `append_agent_result()` 결과를 사용자에게 공개로 승격할 에이전트 이름 목록. |
 | `defer_trace_finalization` | `True` | `handle_turn()` 호출들 사이에서 하나의 trace 배치를 열어 둡니다. |
 
-<Warning>
-  `answer_from_history_prompt`, `answer_from_history_llm`, `answer_from_history`
-  라우트는 사용 중단되었으며 향후 릴리스에서 제거될 예정입니다. 이들은 이미
-  정식 기록을 처리하는 `converse`와 기능이 중복되고, 답변 가능 여부를 판단하는
-  LLM 호출을 추가하며, 일반 auto-router가 라우트를 반환하면 우회됩니다. 기존
-  설정은 계속 작동하며 `DeprecationWarning`을 발생시킵니다.
-</Warning>
-
-커스텀 라우트가 없으면 턴은 `converse`로 이어집니다. 커스텀 라우트와 대화/router LLM이 있으면 프레임워크가 기본 `RouterConfig`를 합성합니다. prompt, 라우트 목록, 설명, fallback 동작을 바꿔야 할 때만 명시적으로 제공하세요. `default_intents`를 설정하면 레거시 사전 분류 경로를 사용합니다.
-
-대화 LLM을 설정하지 않으면 내장 `converse_turn`은 답변을 생성하는 대신 설정 안내 placeholder를 반환합니다.
-
 ### `RouterConfig`와 자동 생성되는 라우트 카탈로그
 
 ```python
-from typing import Literal
-
-from pydantic import BaseModel
-
-from crewai import LLM
-from crewai.flow import RouterConfig
-
-
-class MyRoute(BaseModel):
-    intent: Literal["INTERNET_SEARCH", "CREWAI_DOCS", "converse"]
-
-
-ROUTER_LLM = LLM(model="gpt-4o-mini")
-
-router_config = RouterConfig(
-    prompt="Optional domain framing (policy, voice, persona).",
-    response_format=MyRoute,        # optional; auto-generated otherwise
-    llm=ROUTER_LLM,                  # falls back to ConversationConfig.llm
-    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # optional; inferred from listeners
+RouterConfig(
+    prompt="선택적인 도메인 프레이밍 (정책, 톤, 페르소나).",
+    response_format=MyRoute,        # 선택; 없으면 자동 생성
+    llm=ROUTER_LLM,                  # ConversationConfig.llm으로 폴백
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # 선택; 리스너에서 추론
     route_descriptions={
-        "INTERNET_SEARCH": "Override the docstring for this one route.",
+        "INTERNET_SEARCH": "이 라우트만 docstring 대신 사용할 설명.",
     },
-    default_intent="converse",       # used when LLM call fails or no LLM available
-    fallback_intent="converse",      # used when LLM returns an invalid route
+    default_intent="converse",       # LLM 호출 실패 또는 LLM 없음일 때 사용
+    fallback_intent="converse",      # LLM이 잘못된 라우트를 반환할 때 사용
     intent_field="intent",
 )
 ```
@@ -351,10 +326,9 @@ router_config = RouterConfig(
 router에 전달되는 프롬프트는 자동으로 만들어집니다. 각 라우트의 설명은 다음 우선순위로 결정됩니다:
 
 1. `RouterConfig.route_descriptions[label]` — 명시적 오버라이드.
-2. `Flow.builtin_route_descriptions[label]` — `converse`, `end`, 사용 중단된 `answer_from_history` 호환 라우트용 프레임워크 기본 텍스트 (router LLM용으로 다듬어진 문구).
-3. 메서드에 선언된 `description` — 선언적 flow와 DSL projection에서 사용.
-4. `@listen(label)` 핸들러 docstring의 첫 번째 비어 있지 않은 줄.
-5. 빈 문자열 — 설명 없이 라우트만 표시.
+2. `Flow.builtin_route_descriptions[label]` — `converse`, `end`, `answer_from_history`용 프레임워크 캐닝 텍스트 (router LLM용으로 다듬어진 문구).
+3. `@listen(label)` 핸들러 docstring의 첫 줄(비어있지 않은 줄).
+4. 빈 문자열 (라우트만 카탈로그에 등장하고 설명은 없음).
 
 실제 사용에서 **새 라우트를 추가하는 방법은 `@listen("X")` + 한 줄짜리 docstring**입니다:
 
@@ -365,27 +339,6 @@ def handle_internet_search(self) -> str:
     ...
 ```
 
-### 핸들러 이름 짓기
-
-`@listen("…")`의 문자열은 Python 메서드 이름이 아니라 **router 라우트 레이블**(이벤트 이름)입니다. 라우트 레이블과 메서드 완료 이벤트는 하나의 트리거 namespace를 공유하므로, 핸들러 이름을 라우트와 같게 지정하면 핸들러가 자기 자신을 반복해서 다시 실행합니다.
-
-서로 다른 메서드 이름을 사용하세요. 문서 예제에서는 `handle_*` 접두사를 사용합니다:
-
-```python
-@listen("create_video")
-def handle_create_video(self) -> str:
-    """User wants a new video."""
-    ...
-```
-
-메서드 이름을 라우트 레이블과 같게 만들지 마세요:
-
-```python
-@listen("create_video")
-def create_video(self) -> str:  # rejected at flow instantiation
-    ...
-```
-
 …그러면 router LLM은 다음을 봅니다:
 
 ```
@@ -404,7 +357,7 @@ Routes:
 |--------|--------|------|
 | `converse` | `converse_turn` | 기본 챗 핸들러. system prompt + 정식 메시지 히스토리와 함께 `ConversationConfig.llm`을 호출합니다. |
 | `end` | `end_conversation` | `state.ended = True`로 설정하고 종료 응답을 보냅니다. |
-| `answer_from_history` | `answer_from_history_turn` | **사용 중단된 호환 라우트.** 이미 정식 기록을 전달받는 `converse`를 사용하세요. |
+| `answer_from_history` | `answer_from_history_turn` | 선택적. `ConversationConfig.answer_from_history_llm`이 설정되어 있고 메시지를 히스토리만으로 답할 수 있을 때 라우팅됩니다. |
 
 서브클래스에 같은 이름의 핸들러를 정의하면 어떤 것이든 오버라이드할 수 있습니다.
 
@@ -414,9 +367,9 @@ Routes:
 
 1. 그래프가 다시 실행되도록 턴 단위 실행 추적(`_completed_methods`, `_method_outputs`)을 초기화합니다 — 이게 없으면 동일 인스턴스에서 반복 `kickoff` 호출 시 `Flow.kickoff_async`가 `inputs={"id": ...}`를 체크포인트 복원으로 간주해 2번째 턴부터 단락 회로가 발생합니다.
 2. 사용자 메시지를 `state.messages`에 추가하고 `current_user_message` / `last_user_message`를 설정합니다. `last_intent`는 **이전 턴 값이 유지**되어 router LLM이 신호로 활용할 수 있습니다.
-3. 사용자 정의 `@start` 메서드(있는 경우)를 실행한 다음 내장 start/router인 `route_conversation`을 거쳐 선택된 `@listen` 핸들러를 실행합니다. `route_conversation`은 재정의 가능한 `conversation_start()` 헬퍼를 호출합니다.
+3. `conversation_start` → `route_conversation` → 선택된 `@listen` 핸들러 순으로 실행됩니다.
 4. router는 결정을 `state.last_intent`에 저장합니다 (다음 턴의 router 컨텍스트에서 보입니다).
-5. 핸들러가 문자열을 반환했지만 `append_assistant_message`를 직접 호출하지 않았다면, `handle_turn`이 대신 추가한 뒤 갱신된 `state.messages`를 persist합니다. `@persist` 복원 시 assistant 턴이 포함됩니다.
+5. 핸들러가 문자열을 반환했지만 `append_assistant_message`를 직접 호출하지 않았다면, `handle_turn`이 대신 추가해 줍니다.
 
 채팅 메시지에는 `handle_turn()`을 호출하세요. `kickoff(inputs={"id": ...})`를 직접 호출하면 대화형 턴 래퍼 없이 flow 그래프가 실행됩니다.
 
@@ -437,8 +390,6 @@ flow.chat()
 4. 어시스턴트 결과를 출력합니다.
 5. `finally` 블록에서 지연된 세션 trace를 finalize합니다.
 
-`chat(defer_trace_finalization=True)`는 REPL 동안 인스턴스의 지연 플래그를 임시로 활성화하고 종료할 때 이전 값으로 복원합니다.
-
 주입 가능한 I/O로 터미널 동작을 커스터마이즈할 수 있습니다:
 
 ```python
@@ -457,12 +408,6 @@ flow.chat(
 매 라우팅 결정마다 사이드 이펙트(이벤트 버스 셋업, 텔레메트리)를 실행하려면 `route_turn`을 오버라이드하세요:
 
 ```python
-from typing import Any
-
-from crewai import Flow
-from crewai.flow import ConversationState
-
-
 class SupportFlow(Flow[ConversationState]):
     conversational = True
 
@@ -471,7 +416,7 @@ class SupportFlow(Flow[ConversationState]):
         return super().route_turn(context)
 ```
 
-LLM router를 완전히 우회하고 프로그램 방식으로 라우트를 선택하려면 `route_turn`에서 비어 있지 않은 문자열을 반환하세요. falsy 값을 반환해도 오버라이드에서 `_route_with_config()`가 호출되지는 않습니다. 대신 현재 턴의 사전 분류된 intent, 설정된 경우 사용 중단된 `answer_from_history` 호환 경로, 마지막으로 `converse` 순으로 fallback합니다. 이전 턴의 `last_intent`는 router 컨텍스트에서 사용할 수 있지만 fallback으로 다시 실행되지는 않습니다.
+LLM router를 우회해 프로그램적으로 라우트를 선택하려면 `route_turn`에서 문자열을 반환하세요. `None`을 반환하면 `_route_with_config(...)`로 떨어집니다.
 
 ### `append_assistant_message`와 `append_agent_result`
 
@@ -482,76 +427,9 @@ LLM router를 완전히 우회하고 프로그램 방식으로 라우트를 선
 
 `ConversationConfig.visible_agent_outputs`로 특정 에이전트의 private 결과를 전역적으로 public으로 승격할 수 있습니다 (`"all"` 또는 이름 리스트).
 
-## JSON/YAML로 대화형 플로우 선언하기
-
-[선언적 Flow](/edge/ko/concepts/cli)도 대화형으로 만들 수 있습니다. 최상위 `conversational` 블록을 추가하고 라우트 레이블을 `listen`하는 메서드로 자체 라우트를 선언하세요:
-
-```yaml
-schema: crewai.flow/v1
-name: SupportFlow
-
-conversational:
-  system_prompt: You are a terse support assistant.
-  llm: gpt-4o-mini
-  router:
-    llm: gpt-4o-mini
-
-methods:
-  handle_order:
-    description: Order status, shipping and delivery questions.
-    listen: order
-    do:
-      call: agent
-      with:
-        role: Support specialist
-        goal: Answer order questions accurately
-        backstory: Knows the fulfilment pipeline.
-        input: "${state.current_user_message}"
-```
-
-블록 선언 자체가 opt-in이며 `enabled`의 기본값은 `true`입니다. 설정은 유지하면서 채팅을 끄려면 `enabled: false`로 지정하세요. 이 경우 내장 메서드 합성도 비활성화되므로 선언에 일반 비대화형 그래프를 제공해야 합니다.
-
-세 가지가 자동으로 제공됩니다:
-
-| 제공 항목 | 설명 |
-|----------|--------|
-| 내장 그래프 | `route_conversation`, `converse_turn`, `end_conversation`이 자동으로 추가됩니다. 사용 중단된 `answer_from_history_turn`은 호환성을 위해 유지됩니다. 같은 이름 중 하나로 메서드를 선언하면 재정의됩니다. |
-| 대화 상태 | `state` 블록이 없으면 `ConversationState`가 사용됩니다. Pydantic `ref` 또는 `json_schema` state는 대화형 필드와 자동으로 합성되며 `ConversationState`를 상속할 필요가 없습니다. |
-| 라우트 카탈로그 | 내부 라우트를 제외하고 `listen` 레이블이 있는 비-router 메서드에서 추론됩니다. 설명에는 위 우선순위가 적용되며 명시적인 `router.routes`로 선택지를 제한할 수 있습니다. |
-
-선언적 `llm`, `router.llm`, `intent_llm` 필드는 모델 id 또는 `{model: openai/gpt-4o-mini, max_tokens: 512}` 같은 설정 mapping을 받습니다. `conversational` 블록은 `default_intents`, `visible_agent_outputs`, `defer_trace_finalization`과 위에 나온 `RouterConfig` 필드도 지원합니다. 사용 중단된 `answer_from_history_prompt` / `answer_from_history_llm` 선언은 호환성을 위해 계속 허용됩니다.
-
-클래스 기반 대화형 플로우와 동일한 턴 API로 Python에서 실행합니다:
-
-```python
-from crewai.flow import Flow
-
-flow = Flow.from_declaration(path="flow.yaml")
-
-try:
-    flow.handle_turn("Where is my order?", session_id="session-1")
-finally:
-    flow.finalize_session_traces()
-```
-
-### 라우트 이름 짓기
-
-라우트 레이블과 메서드 이름은 하나의 트리거 네임스페이스를 공유하므로, 핸들러 이름이 자신이 listen하는 라우트와 같으면 안 됩니다 — `create_video`가 `create_video`를 listen하면 플로우 생성 시 거부됩니다. `handle_*` 접두사를 사용하세요.
-
-### 선언으로 표현할 수 없는 것
-
-| 표현 불가 | 대신 사용 |
-|-----------------|-------------|
-| 살아 있는 `LLM` 인스턴스나 커스텀 `BaseLLM` | 모델 id 문자열 또는 정적 설정 mapping |
-| 살아 있는 모델 클래스로서의 `router.response_format` | python ref로 클래스를 지정하세요: `response_format: {python: my_project.schemas.ConversationRoute}`. 생략하면 프레임워크가 생성합니다 |
-| `route_turn()` 재정의 | Flow를 Python으로 작성하거나 선언적 `route_conversation` 메서드를 `call: code` / expression action으로 교체 |
-| `can_answer_from_history()` 재정의 | 사용 중단됨. `converse`를 사용하거나 Python에서 `converse_turn()`을 재정의하세요. |
-
-`crewai run`은 선언적 대화형 Flow에 대해 Python 대화형 Flow와 같은 채팅 TUI를 엽니다. 채팅 루프에는 터미널이 필요하므로 headless 실행은 단일 턴을 실행하는 대신 안내와 함께 0이 아닌 코드로 종료됩니다. 이런 환경에서는 Python의 `handle_turn()` 또는 `stream_turn()`으로 실행하세요. `human_feedback:` 블록이 있는 선언적 메서드(Python: `@human_feedback`)는 터미널 REPL에서 실행됩니다. 런타임이 TUI가 처리할 수 없는 블로킹 prompt로 feedback을 수집하기 때문입니다. 대화형 Flow에서는 `--inputs`를 받지 않습니다. 각 턴의 입력은 사용자가 입력하는 메시지이며 id로 세션을 재개하는 기능은 아직 CLI에 연결되지 않았습니다. 필요하면 Python에서 `flow.handle_turn(message, session_id=...)`을 사용하세요.
-
 ## 턴 간 트레이싱
 
-`defer_trace_finalization=True` (`ConversationConfig` 기본값):
+`defer_trace_finalization=True` (`ConversationalConfig` 기본값):
 
 - 채팅 세션 전체에 **하나의 trace batch**.
 - 첫 턴에만 **`flow_started`**; `finalize_session_traces()`에서 **`flow_finished`** 한 번.
@@ -562,30 +440,17 @@ finally:
 flow.chat(session_id=session_id)
 ```
 
-`flow.chat()`이 `finalize_session_traces()`를 대신 호출합니다. `handle_turn()`으로 직접 루프를 소유하는 경우 세션이 끝날 때 `finalize_session_traces()`를 호출하세요.
+`flow.chat()`이 `finalize_session_traces()`를 대신 호출합니다. `handle_turn()`이나 `kickoff(...)`로 직접 루프를 소유하는 경우, 세션이 끝날 때 `finalize_session_traces()`를 호출하세요.
 
-`suppress_flow_events=True`는 Rich 콘솔 패널을 숨기고 메서드 실행 이벤트를 억제합니다. Flow start/finish 이벤트는 계속 발생하므로 바깥쪽 Flow 수명 주기는 추적할 수 있지만 개별 메서드 span은 생략됩니다.
+`suppress_flow_events=True`는 Rich 콘솔 패널만 숨깁니다. trace 및 method 이벤트는 계속 발생합니다.
 
 ### 대화형 `Flow` trace 수명 주기
 
-[대화형 `Flow`](#대화형-flow)는 동일한 tracing 수명 주기를 따릅니다. `defer_trace_finalization` 기본값이 `True`이므로 각 `handle_turn()`은 세션 trace를 열린 상태로 유지합니다. 지연된 턴은 턴별 `flow_failed`도 억제합니다. 턴 오류나 세션 중단이 발생하면 세션을 명시적으로 finalize하세요. 그러면 턴별 `FlowFailed` 이벤트 대신 세션 수준 `FlowFinished` 이벤트로 batch가 닫힙니다. REPL/루프는 항상 `try/finally`로 감싸고 종료 시 `flow.finalize_session_traces()`를 호출하세요. 호출하지 않으면 trace batch가 열린 채 남아 최종 대화가 export되지 않을 수 있습니다.
+실험적 [대화형 `Flow`](#대화형-flow-실험적)는 동일한 tracing 수명 주기를 따릅니다. `defer_trace_finalization` 기본값이 `True`이므로 각 `handle_turn()`이 세션 trace를 열어 둡니다. 세션 끝에서 항상 finalize하세요 — REPL/루프를 `try/finally`로 감싸고 종료 시 `flow.finalize_session_traces()`를 호출하세요. 호출하지 않으면 batch가 열린 채 남아 마지막 대화가 export되지 않을 수 있습니다.
 
 ## 스트리밍
 
-대화형 UI에서는 `stream_turn()`을 사용하고 순서가 보장된 `StreamFrame` 객체를 순회하세요:
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-reply = stream.result
-```
-
-비대화형 Flow에서는 `stream = True`로 설정하면 `kickoff()`가 `StreamSession`을 반환합니다. `handle_turn()`을 사용할 때 `flow.stream = True`로 설정하지 마세요. 대화형 스트리밍 수명 주기는 `stream_turn()`이 관리합니다.
+`Flow` 클래스에 `stream = True`. `kickoff(...)`가 표준 이벤트 버스를 통해 `assistant_delta` 등 이벤트를 발생시킵니다.
 
 ## import
 
@@ -600,15 +465,10 @@ from crewai.flow import (
     router,
     start,
 )
-from crewai.flow.conversation import prepare_conversational_turn
-from crewai.flow import (
-    ConversationConfig,
-    ConversationState,
-    RouterConfig,
-)
 ```
 
 ## 참고
 
 - [Flow 상태 관리 마스터하기](/ko/guides/flows/mastering-flow-state)
 - [첫 Flow 만들기](/ko/guides/flows/first-flow)
+- 데모: `lib/crewai/runner_conversational_flow_simple.py`

docs/edge/ko/guides/flows/first-flow.mdx

@@ -135,7 +135,7 @@ crewai flow add-crew content-crew
 }
 ```
 
-`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-3.7-flash`, `anthropic/claude-sonnet-4-6`.
+`provider/model-id`를 사용하는 모델로 바꾸세요. 예: `openai/gpt-4o`, `gemini/gemini-2.0-flash-001`, `anthropic/claude-sonnet-4-6`.
 
 3. `src/guide_creator_flow/crews/content_crew/crew.jsonc`를 만듭니다:
 
@@ -481,7 +481,7 @@ Flow를 사용하면 간단하고 구조화된 응답이 필요할 때 언어
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/ko/guides/frontend/channels.mdx

@@ -1,156 +0,0 @@
----
-title: Channels
-description: CopilotKit Channels SDK와 관리형 Intelligence 플랫폼으로 동일한 CrewAI 에이전트를 Slack 또는 Teams 봇으로 실행하세요.
-icon: messages
-mode: "wide"
----
-
-## 사용자가 이미 있는 곳에서 만나세요
-
-[Overview](/edge/ko/guides/frontend/overview)에서 만든 CrewAI 에이전트는 반드시 웹 앱 뒤에서만 동작할 필요가 없습니다. 동일한 Crew 또는 Flow를 메시징 플랫폼 안에서 봇으로 실행할 수 있습니다. 다시 빌드할 필요도, 에이전트 로직을 두 번 복사할 필요도 없습니다. 에이전트는 [AG-UI 프로토콜](https://docs.ag-ui.com)을 통해 그대로 노출되고, **channel**이 Slack 또는 Microsoft Teams에서 이를 구동합니다.
-
-CopilotKit의 [Channels SDK](https://docs.copilotkit.ai/slack)가 그 channel을 제공합니다. 작은 런타임에 `createChannel`을 선언하고 이를 CrewAI 에이전트에 연결하면, CopilotKit의 관리형 **Intelligence** 플랫폼이 메시징 제공자와의 연결을 중개합니다.
-
-<Note>
-이 섹션의 나머지 내용과 달리 Channels는 **셀프 호스팅되지 않습니다**. Channels는 **CopilotKit Intelligence**를 통해 실행되며, 이는 설계상 Channels에 필수적인 서비스입니다(무료 티어 제공). Intelligence는 플랫폼 연결과 자격 증명을 보관하고, 각 플랫폼 이벤트를 수신하며, 해당 턴을 여러분의 channel 프로세스로 전달합니다. 여러분의 프로세스는 에이전트를 실행하고 응답을 다시 스트리밍합니다. Slack은 Intelligence 대시보드에서 한 번만 구성하면 되며, 플랫폼 자격 증명은 결코 여러분의 프로세스로 들어오지 않습니다. 에이전트, 도구, 상태는 온전히 여러분의 것으로 유지됩니다.
-</Note>
-
-## 어떻게 맞물리는가
-
-CrewAI 에이전트 서버에 관한 것은 아무것도 바뀌지 않습니다. Overview에서와 똑같이 AG-UI를 통해 Crew 또는 Flow를 계속 제공합니다. 여러분이 추가하는 것은 `@copilotkit/channels`로 빌드된 별도의 장시간 실행 Node 프로세스입니다. 이 프로세스는 `CopilotRuntime`에 channel을 등록하고, Intelligence에 연결하며, 메시지가 도착할 때마다 에이전트를 실행합니다.
-
-```
-Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-channel 프로세스는 Intelligence 게이트웨이에 대한 지속적인 연결을 유지하므로, 장시간 실행되는 호스트가 필요합니다. 서버리스 요청 핸들러는 그 연결을 소유할 수 없습니다. CrewAI 서버는 동시에 Overview의 웹 프론트엔드를 계속 제공할 수 있습니다. 웹 앱과 channel은 하나의 AG-UI 엔드포인트에 연결된 두 개의 클라이언트일 뿐입니다.
-
-## 통합 가이드
-
-<Steps>
-
-<Step title="Channels 패키지 설치">
-
-Channels SDK는 모든 것이 포함되어 있습니다. 모든 플랫폼이 하나의 패키지로 제공되며, 플랫폼별로 설치할 어댑터가 없습니다. channel을 호스팅하는 런타임 및 CrewAI AG-UI 클라이언트와 함께 다음을 추가하세요:
-
-```bash
-npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="Intelligence에서 Channel 생성">
-
-[CopilotKit 대시보드](https://docs.copilotkit.ai/slack)에서 Channel을 생성하고 Slack을 연결하세요. Intelligence가 Slack 앱 생성 과정을 안내하고 그 자격 증명을 보관합니다. 그러면 여러분의 프로세스를 위한 두 개의 환경 변수가 남으며, 둘 다 대시보드에서 얻습니다:
-
-```bash
-export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
-export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
-```
-
-</Step>
-
-<Step title="channel 정의">
-
-`createChannel`은 channel을 선언하고 에이전트를 연결합니다. 각 대화가 자신만의 세션을 갖도록 에이전트를 스레드별 팩토리로 빌드하되, Overview가 웹 런타임에서 사용하는 것과 동일한 `CrewAIAgent`를 여러분의 AG-UI 엔드포인트를 가리키도록 설정하세요. `identifyUser: "platform"`은 Intelligence가 각 플랫폼 사용자를 안정적인 신원에 매핑하도록 합니다.
-
-```ts
-// channel.ts
-import { createChannel } from "@copilotkit/channels";
-import { CrewAIAgent } from "@ag-ui/crewai";
-
-const channel = createChannel({
-  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
-  identifyUser: "platform",
-  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
-  agent: (threadId) => {
-    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
-    agent.threadId = threadId;
-    return agent;
-  },
-});
-
-// A mention subscribes the thread and runs the agent; afterwards every message
-// in a subscribed thread runs it without needing another mention.
-channel.onMention(async ({ thread }) => {
-  await thread.subscribe();
-  await thread.runAgent();
-});
-
-channel.onMessage(async ({ thread }) => {
-  if (await thread.isSubscribed()) await thread.runAgent();
-});
-
-export { channel };
-```
-
-</Step>
-
-<Step title="런타임에 channel 등록">
-
-Intelligence 게이트웨이와 여러분의 channel로 `CopilotRuntime`을 생성한 다음, `createCopilotNodeListener`로 이를 제공하세요. `agents` 맵은 비어 있는 상태로 둡니다. channel이 자신의 에이전트를 제공하기 때문입니다. 잘못된 구성이 시작 시 명확하게 실패하도록 channel이 준비될 때까지 기다리세요.
-
-```ts
-// server.ts
-import { createServer } from "node:http";
-import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
-import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
-import { channel } from "./channel";
-
-const runtime = new CopilotRuntime({
-  agents: {}, // the channel supplies its own agent; no web-facing agents needed
-  intelligence: new CopilotKitIntelligence({
-    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
-  }),
-  channels: [channel],
-});
-
-const listener = createCopilotNodeListener({ runtime });
-await listener.channels?.ready({ timeoutMs: 15_000 });
-
-createServer(listener).listen(3123, () => {
-  console.log("Channels runtime listening on port 3123");
-});
-```
-
-</Step>
-
-<Step title="channel 런타임 실행">
-
-CrewAI 에이전트 서버와 함께 시작하세요:
-
-```bash
-uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-npx tsx server.ts                 # terminal 2 — Channels runtime
-```
-
-Slack 또는 Teams에서 봇을 멘션하면 Crew 또는 Flow를 실행하고 응답을 스레드로 다시 스트리밍합니다. 스레드는 구독된 상태로 유지되므로 후속 메시지는 또다시 멘션할 필요 없이 실행됩니다.
-
-</Step>
-
-</Steps>
-
-## 이벤트 모델
-
-channel은 핸들러로 플랫폼 이벤트에 반응하며, 각 핸들러는 몇 가지 메서드로 구동하는 `thread`를 받습니다:
-
-- **`channel.onMention`**은 사용자가 봇을 @-멘션할 때 발생합니다. `thread.subscribe()`를 호출해 스레드에 참여한 다음, `thread.runAgent()`로 멘션에 대해 CrewAI 에이전트를 실행하세요.
-- **`channel.onMessage`**는 봇이 볼 수 있는 스레드의 모든 메시지에서 발생합니다. `thread.isSubscribed()`로 게이트를 걸어 에이전트가 참여한 곳에서만 응답하도록 한 다음, `thread.runAgent()`를 호출하세요.
-- **`thread.runAgent()`**는 현재 턴에 대해 연결된 CrewAI 에이전트를 실행하고 그 출력을 channel로 다시 스트리밍합니다. 에이전트가 실행할 텍스트를 재정의하려면 `{ prompt }`를 전달하세요.
-
-여러분의 에이전트는 일반적인 AG-UI `RunAgentInput`을 받고 일반적인 AG-UI 이벤트를 방출합니다. 플랫폼 메커니즘은 channel 뒤에 머무르므로, 동일한 Crew 또는 Flow가 모든 플랫폼에서 변경 없이 실행됩니다. channel은 환영 인사, 인터럽트, 명령, 반응, 모달을 위한 핸들러도 노출합니다. 전체 표면은 [`Channel` 레퍼런스](https://docs.copilotkit.ai/reference/channels/classes/Channel)를 참조하세요.
-
-## 플랫폼 지원
-
-관리형 Intelligence 경로는 현재 **Slack**과 **Microsoft Teams**를 지원합니다. 동일한 channel 코드가 양쪽에서 실행되며, `message.platform` / `thread.platform`이 원래의 출처를 보고합니다. 다른 플랫폼(Discord, Telegram, WhatsApp)은 관리형 경로가 아니라 개발자가 운영하는 **direct adapters**를 통해 연결됩니다. 여러분 자신의 프로세스가 플랫폼 자격 증명과 전송을 보유합니다. 현재 지원 플랫폼 목록과 플랫폼별 설정은 [CopilotKit Channels 문서](https://docs.copilotkit.ai/slack)를 확인하세요.
-
-## 관련 항목
-
-<CardGroup cols={2}>
-  <Card title="Frontend Overview" icon="browser" href="/edge/ko/guides/frontend/overview">
-    Crew 또는 Flow를 AG-UI를 통해 제공하세요. 모든 channel이 그 위에 세워지는 토대입니다.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    실행 도중 사용자 승인이나 입력을 수집하기 위해 에이전트를 일시 중지하세요.
-  </Card>
-</CardGroup>

docs/edge/ko/guides/frontend/overview.mdx

@@ -1,238 +0,0 @@
----
-title: Frontend Overview
-description: CopilotKit과 AG-UI 프로토콜로 CrewAI 에이전트를 위한 인터랙티브 사용자 인터페이스를 구축하세요.
-icon: browser
-mode: "wide"
----
-
-## 에이전트에 사용자 인터페이스를 부여하세요
-
-CrewAI는 여러분의 에이전트를 실행합니다. [CopilotKit](https://copilotkit.ai)은 그 에이전트에 프론트엔드를 제공합니다. 이 둘을 함께 사용하면 사용자가 Crew 또는 Flow와 대화하고, 실시간으로 작동하는 모습을 지켜보고, 그 결정을 승인하며, 출력을 장황한 텍스트 대신 살아 있는 UI로 렌더링하여 볼 수 있는 애플리케이션을 구축할 수 있습니다.
-
-이 둘은 [AG-UI 프로토콜](https://docs.ag-ui.com)을 통해 연결됩니다. `ag-ui-crewai` 패키지는 어떤 Crew나 Flow든 AG-UI 엔드포인트로 노출합니다. CopilotKit의 React 훅과 컴포넌트가 그 엔드포인트를 소비합니다. 이를 통해 채팅 상자를 훨씬 뛰어넘는 경험이 열립니다:
-
-<CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    에이전트 도구 호출과 상태를 여러분만의 React 컴포넌트로 렌더링하세요.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    실행 도중 사용자 승인이나 입력을 수집하기 위해 에이전트를 일시 중지하세요.
-  </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
-    에이전트 상태와 앱 UI를 양방향으로 동기화하세요.
-  </Card>
-  <Card title="Channels" icon="messages" href="/edge/ko/guides/frontend/channels">
-    동일한 에이전트를 Slack, Discord 또는 Teams 봇으로 실행하세요.
-  </Card>
-</CardGroup>
-
-이 가이드는 Crew 또는 Flow를 Next.js 프론트엔드와 처음부터 끝까지 연동시킵니다. 이 섹션의 나머지 내용은 여기서 설정한 앱을 기반으로 합니다.
-
-## 아키텍처
-
-세 가지 구성 요소가 있습니다:
-
-1. **CrewAI 에이전트 서버** — AG-UI를 통해 Crew 또는 Flow를 제공하는 Python 프로세스(FastAPI + `ag-ui-crewai`).
-2. **CopilotKit 런타임** — 에이전트를 등록하고 요청을 프록시하는 Next.js 라우트.
-3. **React 프론트엔드** — `<CopilotKit>` 프로바이더와 채팅 및 generative-UI 컴포넌트.
-
-```
-React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-<Note>
-이 가이드는 **셀프 호스팅** 경로를 다룹니다. `ag-ui-crewai`로 CrewAI 에이전트 서버를 직접 실행하며, 관리형 서비스 없이 로컬에서 동작합니다. CopilotKit은 호스팅된 스레드와 인스펙터를 갖춘 **관리형** 경로(CopilotKit Cloud / Enterprise Intelligence)도 제공합니다. 그 방식을 원한다면 [CopilotKit CrewAI 퀵스타트](https://docs.copilotkit.ai/crewai-crews/quickstart)를 참조하세요. 이 섹션의 프론트엔드 코드는 어느 쪽이든 동일합니다. 에이전트를 호스팅하고 등록하는 방식만 다릅니다.
-</Note>
-
-<Note>
-CrewAI는 AG-UI 뒤에서 세 가지 형태로 실행됩니다: 일반 **Flows**(이 가이드 전반에서 사용), **[Conversational Flows](/edge/en/guides/frontend/conversational-flows)**(네이티브, 세션 인식, 턴 기반, 완전한 기능 동등성), 그리고 **Crews**(기본 채팅). 이 섹션의 프론트엔드는 이들 전반에서 동일합니다. 백엔드 작성과 등록만 다릅니다.
-</Note>
-
-## 통합 가이드
-
-<Steps>
-
-<Step title="AG-UI를 통해 에이전트 제공">
-
-통합 패키지를 CrewAI 프로젝트에 설치하세요:
-
-```bash
-pip install ag-ui-crewai
-```
-
-FastAPI 앱에서 에이전트를 노출하세요. Flows는 `add_crewai_flow_fastapi_endpoint`를, Crews는 `add_crewai_crew_fastapi_endpoint`를 사용합니다. 원하는 만큼 등록할 수 있으며, 각각 자신의 경로에 배치됩니다.
-
-<CodeGroup>
-
-```python Flow
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
-from my_agents.recipe_flow import RecipeFlow
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_flow_fastapi_endpoint(
-    app=app,
-    flow=RecipeFlow(),
-    path="/recipe",
-)
-```
-
-```python Crew
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
-from my_agents.research_crew import ResearchCrew
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_crew_fastapi_endpoint(
-    app=app,
-    crew=ResearchCrew().crew(),
-    path="/research",
-)
-```
-
-</CodeGroup>
-
-실행하세요:
-
-```bash
-uvicorn server:app --port 8000
-```
-
-<Note>
-서버를 시작하기 전에 LLM 제공자를 위한 환경 변수(예: `OPENAI_API_KEY`)를 설정하세요.
-</Note>
-
-</Step>
-
-<Step title="Next.js 앱 생성">
-
-아직 프론트엔드가 없다면 하나를 스캐폴딩하세요:
-
-```bash
-npx create-next-app@latest my-app
-cd my-app
-```
-
-CopilotKit과 CrewAI AG-UI 클라이언트를 설치하세요:
-
-```bash
-npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="CopilotKit 런타임 추가">
-
-CrewAI 에이전트를 CopilotKit 런타임에 등록하는 라우트를 생성하세요. 각 에이전트는 `CrewAIAgent`를 통해 Python 서버의 경로를 가리킵니다.
-
-```ts
-// app/api/copilotkit/route.ts
-import {
-  CopilotRuntime,
-  InMemoryAgentRunner,
-  createCopilotEndpoint,
-} from "@copilotkit/runtime/v2";
-import { CrewAIAgent } from "@ag-ui/crewai";
-import { handle } from "hono/vercel";
-
-const runtime = new CopilotRuntime({
-  agents: {
-    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-  },
-  runner: new InMemoryAgentRunner(),
-});
-
-const app = createCopilotEndpoint({
-  runtime,
-  basePath: "/api/copilotkit",
-});
-
-const handler = handle(app);
-export const GET = handler;
-export const POST = handler;
-```
-
-</Step>
-
-<Step title="프로바이더로 앱 감싸기">
-
-`<CopilotKit>`을 런타임 라우트로 가리키고 등록한 에이전트의 이름을 지정하세요.
-
-```tsx
-// app/page.tsx
-"use client";
-import { CopilotKit } from "@copilotkit/react-core";
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-import "@copilotkit/react-core/v2/styles.css";
-
-export default function Page() {
-  return (
-    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
-      <YourApp />
-      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
-    </CopilotKit>
-  );
-}
-```
-
-</Step>
-
-<Step title="실행">
-
-두 프로세스를 모두 시작하고 앱을 여세요. 이제 사이드바에서 채팅하면 Crew 또는 Flow가 실행됩니다.
-
-```bash
-uvicorn server:app --port 8000   # terminal 1
-npm run dev                       # terminal 2
-```
-
-</Step>
-
-</Steps>
-
-## 채팅 UI 옵션
-
-CopilotKit은 서로 교체 가능한 세 가지 채팅 표면을 제공합니다. 컴포넌트만 바꾸면 되며, 연결 방식은 동일합니다.
-
-<CodeGroup>
-
-```tsx Sidebar
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-
-<CopilotSidebar agentId="recipe" />
-```
-
-```tsx Popup
-import { CopilotPopup } from "@copilotkit/react-core/v2";
-
-<CopilotPopup agentId="recipe" />
-```
-
-```tsx Inline
-import { CopilotChat } from "@copilotkit/react-core/v2";
-
-<CopilotChat agentId="recipe" />
-```
-
-</CodeGroup>
-
-## 다음으로 갈 곳
-
-<CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    도구 호출과 에이전트 상태를 커스텀 컴포넌트로 렌더링하세요.
-  </Card>
-  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
-    에이전트가 브라우저에서 실행되는 함수를 호출하도록 하세요.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    에이전트 동작을 사용자 승인 뒤에 두세요.
-  </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
-    에이전트가 작동하는 동안 진행 중인 상태를 UI로 스트리밍하세요.
-  </Card>
-</CardGroup>

docs/edge/ko/learn/llm-connections.mdx

@@ -141,7 +141,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
         # Gemini의 OpenAI 호환 API 예시입니다.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # AIza...로 시작해야 합니다.
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Gemini 모델을 여기에 추가하세요. openai/ 하위에 위치.
         ```
         </CodeGroup>
     </Tab>
@@ -159,7 +159,7 @@ OpenAI 호환 LLM에 연결하려면 환경 변수를 사용하거나 LLM 클래
             ```python Google
             # Gemini의 OpenAI 호환 API 예시
             llm = LLM(
-                model="openai/gemini-3.7-flash",
+                model="openai/gemini-2.0-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # AIza...로 시작해야 합니다.
             )

docs/edge/ko/learn/llm-selection-guide.mdx

@@ -145,7 +145,7 @@ planning agent는 복잡한 전략적 사고와 다단계 분석을 처리할
 from crewai import Agent, Task, Crew, LLM
 
 # High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
+manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
 
 # Creative model for content generation
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -411,7 +411,7 @@ tech_writer = Agent(
     # Manager 또는 coordination agent
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini/gemini-3.7-flash"),  # 조율을 위한 프리미엄
+        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # 조율을 위한 프리미엄
         # ... 나머지 설정
     )
 

docs/edge/ko/learn/streaming-runtime-contract.mdx

@@ -151,7 +151,7 @@ result = stream.result
 
 ```python
 from crewai import Flow
-from crewai.flow import ConversationConfig, ConversationState
+from crewai.experimental.conversational import ConversationConfig, ConversationState
 
 
 @ConversationConfig(llm="gpt-4o-mini", defer_trace_finalization=True)

docs/edge/ko/observability/arize-phoenix.mdx

@@ -7,9 +7,9 @@ mode: "wide"
 
 # Arize Phoenix 통합
 
-이 가이드는 [OpenInference](https://github.com/openinference/openinference) SDK를 통해 OpenTelemetry를 사용하여 **Arize Phoenix**를 **CrewAI**와 통합하는 방법을 보여줍니다. 이 가이드를 완료하면 CrewAI agent를 추적하고 agent 동작을 디버그할 수 있습니다.
+이 가이드는 [OpenInference](https://github.com/openinference/openinference) SDK를 통해 OpenTelemetry를 사용하여 **Arize Phoenix**를 **CrewAI**와 통합하는 방법을 보여줍니다. 이 가이드를 완료하면 CrewAI agent를 추적하고 agent를 쉽게 디버그할 수 있습니다.
 
-> **Arize Phoenix란?** [Arize Phoenix](https://arize.com/phoenix/)는 [Arize AI](https://arize.com/?utm_source=crewai-docs&utm_medium=partner&utm_campaign=partner-docs&utm_content=observability-arize-phoenix)의 오픈소스 observability 및 evaluation 옵션입니다. 로컬에서 실행하거나 self-host하려는 경우 Phoenix를 사용하세요. 프로덕션 AI 시스템을 위한 managed cloud 또는 enterprise self-hosted 플랫폼이 필요하면 [Arize AX](https://arize.com/products/ax/)를 사용하세요.
+> **Arize Phoenix란?** [Arize Phoenix](https://phoenix.arize.com)는 AI 애플리케이션을 위한 추적 및 평가 기능을 제공하는 LLM 가시성(observability) 플랫폼입니다.
 
 [![Phoenix와의 통합 영상 데모 보기](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
 
@@ -27,7 +27,7 @@ pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoen
 
 ### 2단계: 환경 변수 설정
 
-Phoenix API 키와 OpenTelemetry endpoint를 구성하여 추적 정보를 Phoenix로 전송합니다. collector URL을 변경하면 동일한 설정을 로컬 또는 self-hosted Phoenix endpoint와 함께 사용할 수 있습니다.
+Phoenix Cloud API 키를 설정하고 OpenTelemetry를 구성하여 추적 정보를 Phoenix로 전송합니다. Phoenix Cloud는 Arize Phoenix의 호스팅 버전이지만, 이 통합을 사용하는 데 필수는 아닙니다.
 
 무료 Serper API 키는 [여기](https://serper.dev/)에서 받을 수 있습니다.
 
@@ -35,8 +35,8 @@ Phoenix API 키와 OpenTelemetry endpoint를 구성하여 추적 정보를 Phoen
 import os
 from getpass import getpass
 
-# Get your Phoenix API key
-PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix API key: ")
+# Get your Phoenix Cloud credentials
+PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix Cloud API Key: ")
 
 # Get API keys for services
 OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
@@ -44,7 +44,7 @@ SERPER_API_KEY = getpass("🔑 Enter your Serper API key: ")
 
 # Set environment variables
 os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
-os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Change this to your own endpoint if you are using a self-hosted instance
+os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, change this to your own endpoint if you are using a self-hosted instance
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 os.environ["SERPER_API_KEY"] = SERPER_API_KEY
 ```
@@ -133,7 +133,7 @@ print(result)
 
 에이전트를 실행한 후, Phoenix에서 CrewAI 애플리케이션에 의해 생성된 트레이스를 볼 수 있습니다. 에이전트 상호작용과 LLM 호출의 상세한 단계가 표시되어 AI 에이전트를 디버깅하고 최적화하는 데 도움이 됩니다.
 
-Phoenix 프로젝트를 열고 `project_name` 파라미터에서 지정한 프로젝트로 이동하세요. 모든 에이전트 상호작용, 도구 사용 및 LLM 호출이 포함된 트레이스의 타임라인 보기를 확인할 수 있습니다.
+Phoenix Cloud 계정에 로그인한 다음 `project_name` 파라미터에서 지정한 프로젝트로 이동하세요. 모든 에이전트 상호작용, 도구 사용 및 LLM 호출이 포함된 트레이스의 타임라인 보기를 확인할 수 있습니다.
 
 ![Phoenix에서 에이전트 상호작용을 보여주는 예시 트레이스](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
 
@@ -145,9 +145,6 @@ Phoenix 프로젝트를 열고 `project_name` 파라미터에서 지정한 프
 
 ### 참고 자료
 - [Phoenix 문서](https://docs.arize.com/phoenix/) - Phoenix 플랫폼 개요.
-- [Arize AX](https://arize.com/products/ax/) - Managed cloud 및 enterprise self-hosted observability와 evaluation.
-- [Arize agent evaluation guide](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) - 트레이스에서 agent 동작을 평가하는 프로덕션 워크플로.
-- [Arize LLM evaluation guide](https://arize.com/resources/llm-evaluation/) - LLM 애플리케이션 평가를 위한 방법과 메트릭.
 - [CrewAI 문서](https://docs.crewai.com/) - CrewAI 프레임워크 개요.
 - [OpenTelemetry 문서](https://opentelemetry.io/docs/) - OpenTelemetry 가이드
-- [OpenInference GitHub](https://github.com/openinference/openinference) - OpenInference SDK 소스 코드.
+- [OpenInference GitHub](https://github.com/openinference/openinference) - OpenInference SDK 소스 코드.

docs/edge/ko/telemetry.mdx

@@ -22,7 +22,7 @@ CrewAI는 익명 텔레메트리를 활용하여 사용 통계를 수집하며,
 `share_crew` 기능이 활성화되면, 보다 심층적인 통찰을 제공하기 위해 작업 설명, 에이전트의 배경 이야기나 목표, 기타 특정 속성 등 상세한 데이터가 수집됩니다.
 이 확대된 데이터 수집에는 사용자가 crew나 작업에 개인정보를 포함한 경우, 개인정보가 포함될 수 있습니다.
 사용자는 `share_crew`를 활성화하기 전에 crew와 작업의 내용을 신중하게 검토해야 합니다.
-사용자는 `CREWAI_DISABLE_TELEMETRY`를 `true`, `1`, `yes`, `on` 중 하나로 설정하여 CrewAI 텔레메트리를 비활성화할 수 있습니다(대소문자 무관). 같은 값의 `OTEL_SDK_DISABLED`도 CrewAI exporter를 끕니다. 프로세스 내 다른 OpenTelemetry 계측을 끄려면 OpenTelemetry SDK는 여전히 `true`만 인식합니다.
+사용자는 환경 변수 `CREWAI_DISABLE_TELEMETRY`를 `true`로 설정하거나, `OTEL_SDK_DISABLED`를 `true`로 설정하여 텔레메트리를 비활성화할 수 있습니다(후자의 경우 전체 OpenTelemetry 계측이 전역에서 비활성화된다는 점에 유의하십시오).
 
 ### 예시:
 ```python
@@ -33,8 +33,6 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
-`CREWAI_DISABLE_TELEMETRY=1`(`yes` / `on`도 동일)은 `true`와 같습니다. 인식되지 않는 값은 무시되며 텔레메트리는 켜진 채로 남습니다.
-
 ### 사용자 OpenTelemetry 설정과의 격리
 
 CrewAI의 telemetry는 자체 전용 `TracerProvider`에서 실행되며 자신을 전역
@@ -52,17 +50,16 @@ provider로 등록하지 않습니다. 이를 통해 양방향이 분리됩니
 | 기본값 | 데이터                                      | 사유 및 세부 사항                                                                                                       |
 |:--------|:-------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|
 | 예      | CrewAI 및 Python 버전                      | 소프트웨어 버전을 추적합니다. 예: CrewAI v1.2.3, Python 3.8.10. 개인 정보 없음. |
-| 예      | Crew 메타데이터                            | 랜덤으로 생성된 키 및 ID, 프로세스 유형(예: 'sequential', 'parallel'), 메모리 사용 플래그(boolean, true/false), 실행에 입력이 전달되었는지를 나타내는 플래그(boolean, true/false — 입력 키나 값 자체는 포함되지 않으며, 이는 `share_crew`가 활성화된 경우에만 수집됩니다), 작업 수, 에이전트 수가 포함됩니다. 모두 비개인 정보입니다. |
+| 예      | Crew 메타데이터                            | 랜덤으로 생성된 키 및 ID, 프로세스 유형(예: 'sequential', 'parallel'), 메모리 사용 플래그(boolean, true/false), 작업 수, 에이전트 수가 포함됩니다. 모두 비개인 정보입니다. |
 | 예      | 에이전트 데이터                            | 랜덤으로 생성된 키 및 ID, 역할 이름(개인 정보 포함 불가), boolean 설정(상세 출력, 위임 가능, 코드 실행 허용), 최대 반복 횟수, 최대 RPM, 최대 재시도 제한, LLM 정보(LLM 속성 참조), 도구 이름 목록(개인 정보 포함 불가) 포함. 개인 정보 없음. |
 | 예      | 작업 메타데이터                            | 랜덤으로 생성된 키 및 ID, boolean 실행 설정(async_execution, human_input), 관련 에이전트 역할 및 키, 도구 이름 목록이 포함됩니다. 모두 비개인 정보입니다. |
 | 예      | 도구 사용 통계                             | 도구 이름(개인 정보 포함 불가), 사용 시도 횟수(정수), 사용된 LLM 속성이 포함됩니다. 개인 정보 없음. |
 | 예      | 테스트 실행 데이터                         | crew의 랜덤 생성 키와 ID, 반복 횟수, 사용된 모델명, 품질 점수(실수), 실행 시간(초 단위)이 포함됩니다. 모두 비개인 정보입니다. |
-| 예      | 작업 라이프사이클 데이터                   | 생성 및 실행 시작/종료 시각, crew 및 작업 식별자, 그리고 작업의 성공 또는 실패 여부가 포함됩니다. 작업이 실패하면 실패를 집계하고 진단할 수 있도록 예외의 **클래스 이름**(예: `TimeoutError`)이 기록되며, 프롬프트·모델 출력·파일 경로·자격 증명이 포함될 수 있는 오류 메시지는 결코 기록되지 않습니다. 타임스탬프를 포함한 span으로 저장됩니다. 개인 정보 없음. |
+| 예      | 작업 라이프사이클 데이터                   | 생성 및 실행 시작/종료 시각, crew 및 작업 식별자가 포함됩니다. 타임스탬프를 포함한 span으로 저장됩니다. 개인 정보 없음. |
 | 예      | LLM 속성                                  | LLM의 이름, model_name, 모델, top_k, temperature 및 클래스명이 포함됩니다. 모두 기술적이고 비개인 정보입니다. |
-| 예      | crewAI CLI를 통한 프로젝트 생성 | 포함 항목: `crewai create`로 새 프로젝트가 생성되었다는 사실, 그 종류(`crew`, `json_crew` 또는 `flow`), 그리고 그 새 프로젝트에 발급되어 해당 프로젝트의 `pyproject.toml`에 기록된 프로젝트 ID. 이는 새 프로젝트 자체의 ID이며, 명령을 실행한 디렉터리의 `project_id`와는 별개로 기록됩니다 — 두 값은 다를 수 있습니다. 프로젝트 이름, 파일 내용, 코드는 기록되지 않습니다. 개인 정보 없음. |
 | 예      | crewAI CLI를 통한 Crew 배포 시도 | 포함 항목: 배포가 시도되고 있다는 사실과 crew id, 로그를 가져오려고 하는지 여부, 그리고 배포가 CLI 명령에서 시작되었는지 실행 TUI에서 시작되었는지 여부. 프로젝트나 crew의 내용은 기록되지 않습니다. 개인 정보 없음. |
-| 예      | 실행 환경                                  | 포함: 프로세스를 실행 중인 AI 코딩 어시스턴트(있는 경우, `claude_code`, `codex`, `cursor`, `unknown` 등 고정 목록 중 하나), 프로세스가 실행되는 위치(`ci`, `container`, `serverless`, `interactive` 등 고정 목록 중 하나), `pyproject.toml`에 설정된 경우 `project_id`, 그리고 머신 크기의 대략적인 구간(`1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+`, `unknown` 중 하나). 구간은 범위이며 정확한 코어 수는 절대 포함하지 않습니다 — 정확한 코어 수는 아래 환경 정보에서 옵트인한 경우에만 수집됩니다. 크기 구간은 호스트 CPU 수에서 가져오며, 어시스턴트와 실행 위치 감지는 알려진 환경 변수의 설정 여부만 확인하고 값은 읽지 않음. 개인 데이터 없음. |
-| 예      | Flow 라이프사이클 신호 | 포함 항목: flow의 시작, 완료 또는 실패 여부, 해당 메서드의 실패 여부, 사람의 입력이나 피드백을 위해 일시 중지되었는지 여부, 해당 시작이 재개된 실행이었는지 여부, 대화 턴의 실패 여부, flow 실행 시간, 그리고 해당 flow가 CrewAI가 내부적으로 실행하는 것인지 사용자가 작성한 것인지 여부. flow 이름은 flow 생성 및 실행에서와 마찬가지로 기록됩니다. flow 또는 해당 메서드가 실패하면 장애 진단을 위해 예외의 **클래스 이름**(예: `TimeoutError`)이 기록되며, 프롬프트·모델 출력·파일 경로·자격 증명이 포함될 수 있는 오류 메시지는 절대 기록되지 않습니다. 메서드 이름과 flow 상태는 절대 기록되지 않습니다. 개인 정보 없음. |
+| 예      | 실행 환경                                  | 포함: 프로세스를 실행 중인 AI 코딩 어시스턴트(있는 경우, `claude_code`, `codex`, `cursor`, `unknown` 등 고정 목록 중 하나), 프로세스가 실행되는 위치(`ci`, `container`, `serverless`, `interactive` 등 고정 목록 중 하나), 그리고 `pyproject.toml`에 설정된 경우 `project_id`. 감지는 알려진 환경 변수의 설정 여부만 확인하며 값은 읽지 않음. 개인 데이터 없음. |
+| 예      | Flow 라이프사이클 신호 | 포함 항목: flow의 시작, 완료 또는 실패 여부, 해당 메서드의 실패 여부, 사람의 입력이나 피드백을 위해 일시 중지되었는지 여부, 해당 시작이 재개된 실행인지 여부, 대화 턴의 실패 여부, flow 실행 시간, 그리고 해당 flow가 CrewAI가 내부적으로 실행하는 것인지 사용자가 작성한 것인지 여부. flow 이름은 기록되며(개인 정보를 포함해서는 안 됨), 이는 flow 생성 및 실행에서 이미 그러합니다. 메서드 이름, 오류 메시지, flow 상태는 절대 기록하지 않습니다. 개인 정보 없음. |
 | 예      | 트레이스 공유 신호 | 포함 항목: 트레이스 배치가 CrewAI AMP에 성공적으로 공유되었는지 여부와, 익명으로(계정 생성 전) 공유되었는지 또는 계정에 연결되어 공유되었는지 여부. 모든 span과 마찬가지로 위에서 설명한 실행 환경 속성(구성된 경우 `project_id`, 코딩 어시스턴트, 런타임)도 함께 기록됩니다. 이 행은 공유 텔레메트리만 설명하며 — 트레이스 내용이나 공유된 트레이스 링크로 부여되는 접근 권한은 설명하지 않습니다. 트레이스 내용, 입력, 출력은 이 신호에는 기록되지 않습니다. 트레이스를 공유하기 전에 비밀 정보, 개인 데이터, AMP 편집 및 보존 설정을 검토하세요. |
 | 아니오  | 에이전트 확장 데이터                      | 목표 설명, 배경 이야기 텍스트, i18n 프롬프트 파일 식별자가 포함됩니다. 사용자들은 텍스트 필드에 개인 정보가 포함되지 않도록 해야 합니다. |
 | 아니오  | 상세 작업 정보                            | 작업 설명, 예상 출력 설명, 컨텍스트 참조가 포함됩니다. 사용자들은 이러한 필드에 개인 정보가 포함되지 않도록 해야 합니다. |

docs/edge/ko/tools/ai-ml/overview.mdx

@@ -48,16 +48,17 @@ mode: "wide"
 - **AI 안전성**: 콘텐츠 모더레이션 및 안전성 점검 구현
 
 ```python
-from crewai_tools import DallETool, VisionTool
+from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
+code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor],
+    tools=[image_generator, vision_processor, code_executor],
     goal="Create and analyze content using AI capabilities"
 )
 ```

docs/edge/ko/tools/file-document/filereadtool.mdx

@@ -1,6 +1,6 @@
 ---
 title: 파일 읽기
-description: FileReadTool은 로컬 파일 시스템에서 파일을 읽도록 설계되었습니다.
+description: FileReadTool은 로컬 파일 시스템에서 파일을 읽습니다.
 icon: folders
 mode: "wide"
 ---
@@ -8,37 +8,63 @@ mode: "wide"
 ## 개요
 
 <Note>
-    우리는 도구를 계속 개선하고 있으므로, 향후 예기치 않은 동작이나 변경 사항이 발생할 수 있습니다.
+    도구를 계속 개선하고 있으므로 동작이 변경될 수 있습니다.
 </Note>
 
-FileReadTool은 crewai_tools 패키지 내에서 파일 읽기와 콘텐츠 검색을 용이하게 하는 기능 모음입니다.
-이 모음에는 배치 텍스트 파일 처리, 런타임 구성 파일 읽기, 분석을 위한 데이터 가져오기 등 다양한 도구가 포함되어 있습니다.
-`.txt`, `.csv`, `.json` 등 다양한 텍스트 기반 파일 형식을 지원합니다. 콘텐츠는 항상 일반 텍스트로 반환됩니다.
+`FileReadTool`은 로컬 파일을 읽고 내용을 텍스트로 반환합니다.
+텍스트 파일 처리, 구성 파일 읽기, 분석용 데이터 로드에 사용하세요.
+`.txt`, `.csv`, `.json`, `.md`와 같은 모든 텍스트 형식에서 동작합니다.
+도구는 항상 일반 텍스트를 반환합니다. 구조화된 데이터(예: JSON)가 필요하면 Agent 또는 사용자 코드에서 파싱하세요.
+
+큰 파일의 경우 Agent가 `start_line`과 `line_count`를 전달해 일부 줄만 읽을 수 있습니다.
+요청한 줄을 모으면 읽기를 멈추므로 파일의 나머지를 스캔하지 않습니다.
 
 ## 설치
 
-이전에 FileReadTool에 할당된 기능을 사용하려면 crewai_tools 패키지를 설치하세요:
-
 ```shell
-pip install 'crewai[tools]'
+uv add 'crewai[tools]'
 ```
 
 ## 사용 예시
 
-FileReadTool을 시작하려면:
-
 ```python Code
 from crewai_tools import FileReadTool
 
-# 에이전트가 알고 있거나 경로를 학습한 파일을 읽기 위해 도구를 초기화합니다.
-file_read_tool = FileReadTool()
+# Agent chooses the file path at runtime
+tool = FileReadTool()
 
-# 또는
+# OR set a default file the agent can read with no path argument
+tool = FileReadTool(file_path='path/to/your/file.txt')
 
-# 특정 파일 경로로 도구를 초기화하여 에이전트가 지정된 파일의 내용만 읽을 수 있도록 합니다.
-file_read_tool = FileReadTool(file_path='path/to/your/file.txt')
+# OR let the agent read any file under a directory
+tool = FileReadTool(base_dir='/data')
 ```
 
+도구를 Agent에 전달하세요. 런타임에 LLM이 `file_path`를 전달하며, 선택적으로 `start_line`과 `line_count`도 전달합니다.
+
 ## 인수
 
-- `file_path`: 읽고자 하는 파일의 경로입니다. 절대 경로와 상대 경로 모두 허용됩니다. 파일이 존재하는지와 필요한 접근 권한이 있는지 반드시 확인하세요.
+Agent가 런타임에 전달할 수 있는 인수:
+
+- `file_path`: (선택) 읽을 파일 경로입니다. 절대 경로와 상대 경로는 `base_dir` 샌드박스 안에서 해석될 때만 유효합니다. 상대 경로는 `base_dir`이 설정된 경우 그 기준이고, 그렇지 않으면 현재 작업 디렉터리(기본 샌드박스)를 기준으로 해석됩니다. 생략하면 생성 시 설정한 기본 파일을 읽습니다. 기본값이 없으면 경로가 제공되지 않았다는 오류를 반환합니다.
+- `start_line`: (선택) 읽기를 시작할 첫 줄입니다. 줄 번호는 `1`부터 시작합니다. 기본값은 `1`입니다.
+- `line_count`: (선택) 읽을 줄 수입니다. 생략하면 `start_line`부터 파일 끝까지 읽습니다.
+
+도구를 만들 때 설정할 수 있는 인수:
+
+- `file_path`: (선택) Agent가 경로 없이 도구를 호출할 때 읽을 기본 파일입니다. 상대 경로는 `base_dir`이 제공되면 `base_dir`을 기준으로, 그렇지 않으면 현재 작업 디렉터리를 기준으로 해석됩니다.
+- `base_dir`: (선택) 런타임 경로가 머물러야 하는 디렉터리입니다. 기본값은 현재 작업 디렉터리입니다. 도구 생성 시 이 경로를 해석하므로, 이후 작업 디렉터리가 바뀌어도 샌드박스는 이동하지 않습니다.
+- `encoding`: (선택) 파일을 디코딩할 때 사용하는 텍스트 인코딩입니다. 기본값은 `utf-8`입니다. 디코딩에 실패하면 오류를 반환하고 다른 `encoding`을 전달하도록 안내합니다.
+
+일반적인 실패(파일 없음, 권한 거부, 잘못된 인코딩, 샌드박스 밖 경로)는 예외를 발생시키지 않고 오류 문자열을 반환합니다.
+
+## 허용 경로
+
+LLM이 보통 런타임에 파일 경로를 선택하므로, 읽기는 샌드박스로 제한됩니다.
+
+- 런타임 경로는 `base_dir`(기본값: 현재 작업 디렉터리) 안에서 해석되어야 합니다. 도구는 경로를 검사하기 전에 `..` 세그먼트와 심볼릭 링크를 해석하므로 샌드박스를 벗어날 수 없습니다.
+- 생성자에 전달한 `file_path`는 `base_dir` 밖이어도 항상 허용됩니다. 파일이 없거나, 디렉터리이거나, 접근할 수 없으면 읽기 자체는 실패할 수 있습니다. 이 경로는 도구 생성 시 고정되므로, 이후 작업 디렉터리가 바뀌어도 가리키는 파일이 바뀌지 않습니다. Agent는 `file_path`를 생략하거나 도구 설명에 표시된 이름으로 읽을 수 있습니다. 파일 하나를 선언해도 같은 폴더의 다른 파일에는 접근할 수 없습니다.
+
+Agent가 작업 디렉터리 밖의 파일을 읽게 하려면 도구를 만들 때 `base_dir`을 설정하세요(위 예시 참고).
+
+최후의 수단으로 `CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true`를 설정하면 경로 검사가 꺼집니다. 이 설정은 프로세스의 모든 crewai-tools 도구에 적용되며, URL을 가져오는 도구의 SSRF 보호도 포함됩니다. 가능하면 `base_dir`을 사용하세요.

docs/edge/ko/tools/web-scraping/scrapeelementfromwebsitetool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## 설명
 
-`ScrapeElementFromWebsiteTool`은 CSS 선택자를 사용하여 웹사이트에서 특정 요소를 추출하도록 설계되었습니다. 이 도구는 CrewAI 에이전트가 웹 페이지에서 타겟이 되는 콘텐츠를 스크래핑할 수 있게 하여, 웹페이지의 특정 부분만이 필요한 데이터 추출 작업에 유용합니다. 가져오기는 CrewAI의 SSRF 안전 HTTP 헬퍼를 거칩니다. 요청된 URL과 모든 리다이렉트 홉이 사설 및 예약 대역(클라우드 메타데이터 포함)에 대해 검사되며, TCP 연결은 그 검사를 통과한 IP에 고정됩니다.
+`ScrapeElementFromWebsiteTool`은 CSS 선택자를 사용하여 웹사이트에서 특정 요소를 추출하도록 설계되었습니다. 이 도구는 CrewAI 에이전트가 웹 페이지에서 타겟이 되는 콘텐츠를 스크래핑할 수 있게 하여, 웹페이지의 특정 부분만이 필요한 데이터 추출 작업에 유용합니다.
 
 ## 설치
 

docs/edge/ko/tools/web-scraping/scrapewebsitetool.mdx

@@ -16,8 +16,6 @@ mode: "wide"
 지정된 웹사이트의 내용을 추출하고 읽을 수 있도록 설계된 도구입니다. 이 도구는 HTTP 요청을 보내고 수신된 HTML 콘텐츠를 파싱함으로써 다양한 유형의 웹 페이지를 처리할 수 있습니다.
 이 도구는 웹 스크래핑 작업, 데이터 수집 또는 웹사이트에서 특정 정보를 추출하는 데 특히 유용할 수 있습니다.
 
-가져오기는 CrewAI의 SSRF 안전 HTTP 헬퍼를 거칩니다. 요청된 URL과 모든 리다이렉트 홉이 사설 및 예약 대역(클라우드 메타데이터 포함)에 대해 검사되며, TCP 연결은 그 검사를 통과한 IP에 고정됩니다.
-
 ## 설치
 
 crewai_tools 패키지를 설치하세요

docs/edge/pt-BR/changelog.mdx

@@ -4,104 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="27 ago 2026">
-  ## v1.15.18
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.18)
-
-  ## O que Mudou
-
-  ### Funcionalidades
-  - Promover fluxos de conversa para estáveis
-  - Registrar uma implantação criada com o UUID fornecido
-  - Melhorar a documentação e as APIs dos fluxos de conversa
-  - Permitir que uma declaração nomeie o formato de resposta do roteador
-  - Permitir que um fluxo de chat declare sua própria forma de estado
-  - Aceitar configuração de LLM estilo crew em uma declaração de conversa
-  - Relatar a criação do projeto com o ID gerado
-  - Registrar se uma execução teve entradas, sem registrar as entradas
-  - Preencher o ID do projeto a partir de cada comando de projeto invocado pelo usuário
-
-  ### Correções de Bugs
-  - Preservar resultados de ferramentas quando a resposta final estiver vazia
-  - Mapear o Claude Sonnet 4.6 padrão para sua janela de contexto de 1M
-  - Aumentar o max_tokens padrão da Anthropic para chamadas de ferramentas grandes
-  - Renderizar partes do conteúdo da mensagem como texto, não como uma representação Python
-  - Manter os papéis das mensagens quando Agent.kickoff recebe uma conversa
-  - Ignorar ganchos de interceptação em fluxos internos do crewai
-  - Registrar falhas de tarefas como falhas, não como sucessos
-  - Emitir o ciclo de vida do fluxo em uma retomada suprimida
-  - Abrir o TUI de conversa para um fluxo de chat declarativo
-  - Registrar crew_memory como uma string, não como um bool
-  - Sempre emitir project_id para que ausente e vazio permaneçam distintos
-
-  ### Documentação
-  - Esclarecer a documentação de observabilidade do Arize Phoenix
-
-  ## Contribuidores
-
-  @Vidit-Ostwal, @arizedatngo, @joaomdmoura, @lorenzejay, @lucasgomide
-
-</Update>
-
-<Update label="19 ago 2026">
-  ## v1.15.17
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.17)
-
-  ## O que Mudou
-
-  ### Recursos
-  - Adicionar documentação de fluxos de conversa declarativos
-  - Sintetizar métodos de conversa embutidos para declarações
-  - Permitir que declarações conduzam o modo de conversa
-  - Tornar a opção de conversa inconfundível
-  - Carregar o slug AMP em ferramentas resolvidas a partir de uma referência de slug
-  - Lidar com mensagens únicas excessivamente grandes durante a fragmentação
-
-  ### Correções de Bugs
-  - Corrigir o uso do nome do host da URL como server_name do MCP HTTP e SSE
-  - Fechar o escopo do agente em cada tentativa falhada
-  - Atribuir erros de ferramenta à ferramenta que falhou
-  - Fixar verificações de SSRF em cada redirecionamento e IP de par
-  - Resolver problemas com chamadas de ferramentas nativas quebradas na API de Respostas do OpenAI
-
-  ### Documentação
-  - Atualizar a documentação com um instantâneo e registro de alterações para v1.15.16
-
-  ## Contribuidores
-
-  @Copilot, @Vidit-Ostwal, @github-code-quality[bot], @joaomdmoura, @lorenzejay, @lucasgomide, @theCyberTech
-
-</Update>
-
-<Update label="13 ago 2026">
-  ## v1.15.16
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.15.16)
-
-  ## O que Mudou
-
-  ### Recursos
-  - Introduzir gerenciamento de contexto de execução com suporte a UUID
-  - Registrar que tipo de exceção finalizou um fluxo
-  - Registrar quando um lote de rastreamento é compartilhado com AMP
-  - Contar implantações de qualquer origem e registrar onde elas começaram
-
-  ### Correções de Bugs
-  - Registrar a versão em execução em cada span emitido
-  - Corrigir a validação do nome da tabela de busca do MySQL
-  - Impedir que uma tentativa falhada marque a próxima como falhada
-
-  ### Documentação
-  - Adicionar guias de Frontend para CopilotKit e AG-UI
-
-  ## Contribuidores
-
-  @joaomdmoura, @lorenzejay, @ranst91, @theCyberTech
-
-</Update>
-
 <Update label="11 ago 2026">
   ## v1.15.15
 

docs/edge/pt-BR/concepts/memory.mdx

@@ -736,7 +736,7 @@ memory = Memory(llm="anthropic/claude-3-haiku-20240307")
 memory = Memory(llm="ollama/llama3.2")
 
 # Usar Google Gemini
-memory = Memory(llm="gemini/gemini-3.7-flash")
+memory = Memory(llm="gemini/gemini-2.0-flash")
 
 # Passar uma instância LLM pré-configurada com configurações customizadas
 llm = LLM(model="gpt-4o", temperature=0)

docs/edge/pt-BR/guides/advanced/customizing-prompts.mdx

@@ -26,7 +26,7 @@ Nos bastidores, o CrewAI adota um sistema de prompt modular que pode ser amplame
 - **Tratamento de erros** – Definem como os agentes respondem a falhas, exceções ou timeouts.
 - **Prompts específicos de ferramentas** – Definem instruções detalhadas para como as ferramentas são invocadas ou utilizadas.
 
-Confira os [templates de prompt originais no repositório do CrewAI](https://github.com/crewAIInc/crewAI/blob/main/lib/crewai/src/crewai/translations/en.json) para ver como esses elementos são organizados. A partir daí, você pode sobrescrever ou adaptar conforme necessário para desbloquear comportamentos avançados.
+Confira os [templates de prompt originais no repositório do CrewAI](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) para ver como esses elementos são organizados. A partir daí, você pode sobrescrever ou adaptar conforme necessário para desbloquear comportamentos avançados.
 
 ## Entendendo as Instruções de Sistema Padrão
 

docs/edge/pt-BR/guides/crews/first-crew.mdx

@@ -77,7 +77,7 @@ Substitua o arquivo gerado `agents/researcher.jsonc` e adicione `agents/analyst.
 }
 ```
 
-Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-3.7-flash`.
+Substitua `provider/model-id` pelo modelo usado, como `openai/gpt-4o`, `anthropic/claude-sonnet-4-6` ou `gemini/gemini-2.0-flash-001`.
 
 ## Etapa 3: Definir tarefas e configurações
 

docs/edge/pt-BR/guides/flows/conversational-flows.mdx

@@ -1,37 +1,35 @@
 ---
 title: Flows Conversacionais
-description: Crie apps de chat multi-turno com handle_turn por turno, histórico de mensagens, roteamento de intenção, tracing e streaming estruturado.
+description: Crie apps de chat multi-turno com kickoff por turno, histórico de mensagens, roteamento de intenção, tracing e pontes WebSocket.
 icon: comments
 mode: "wide"
 ---
 
 ## Visão geral
 
-Apps conversacionais tratam cada linha do usuário como uma **nova execução do flow** com o **mesmo id de sessão**. A CrewAI oferece helpers para histórico de mensagens, roteamento opcional de intenção, tracing adiado, streaming estruturado de turnos e um REPL local `flow.chat()`.
+Apps conversacionais tratam cada linha do usuário como uma **nova execução do flow** com o **mesmo id de sessão**. A CrewAI oferece helpers para histórico de mensagens, classificação opcional de intenção, tracing adiado, pontes para UI e um REPL local `flow.chat()` para flows conversacionais.
 
 | Conceito | Implementação |
 |---------|----------------|
 | Id de sessão | `handle_turn(..., session_id=...)` → `kickoff(inputs={"id": ...})` → `state.id` |
 | Linha do usuário | `handle_turn(message)` acrescenta em `state.messages` antes do grafo rodar |
-| Turno concluído | `conversation_turn_completed`; com o adiamento padrão de traces, `FlowFinished` aguarda `finalize_session_traces()` |
-| Trace da sessão inteira | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
+| Fim do turno | `FlowFinished` só para **esta execução**; o chat segue no próximo `handle_turn` |
+| Trace da sessão | `ConversationConfig(defer_trace_finalization=True)` + `finalize_session_traces()` |
 
 ## APIs de turno
 
 Use **`flow.handle_turn(message, session_id=...)`** para cada mensagem de usuário em REST, WebSocket, testes e UIs customizadas. Use **`flow.chat()`** quando quiser um loop de chat local no terminal para um `Flow` conversacional.
 
-`Flow.kickoff()` não aceita os argumentos nomeados `user_message=` ou `session_id=`. Para flows conversacionais, `handle_turn()` guarda a mensagem pendente e chama `kickoff(inputs={"id": session_id})` internamente depois de redefinir o estado de execução do turno.
+`Flow.kickoff()` não aceita os argumentos nomeados `user_message=` ou `session_id=`. Para flows conversacionais, `handle_turn()` guarda a mensagem pendente e chama `kickoff(inputs={"id": session_id})` internamente.
 
 | API | Uso |
 |-----|-----|
 | `handle_turn(message, session_id=...)` | Wrapper ergonômico de um turno para `Flow` conversacional |
-| `stream_turn(message, session_id=...)` | Transmite um turno conversacional como frames ordenados do runtime |
 | `chat()` | REPL local no terminal para `Flow` conversacional |
 | `kickoff(inputs={...})` | Execução avançada do flow sem tratamento de turno conversacional |
 | `ask()` | Prompt bloqueante **dentro** de um passo (wizard, esclarecimento) |
 | `@human_feedback` | Aprovar/rejeitar **saída de um passo** — não a próxima linha do chat |
-
-`handle_turn()`, `stream_turn()` e `chat()` geram `ValueError` se o modo conversacional não estiver habilitado. Aplicar `@ConversationConfig(...)` o habilita automaticamente; caso contrário, defina `conversational = True`.
+| `ChatSession.handle_turn(...)` | Camada de transporte sobre `handle_turn` (SSE / WebSocket) |
 
 ## Início rápido
 
@@ -40,7 +38,7 @@ from uuid import uuid4
 
 from crewai import Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
 )
@@ -48,29 +46,31 @@ from crewai.flow import (
 
 @ConversationConfig(defer_trace_finalization=True)
 class SupportFlow(Flow[ConversationState]):
+    conversational = True
+
     def route_turn(self, context):
         message = (self.state.current_user_message or "").lower()
-        if "order" in message:
+        if "pedido" in message or "order" in message:
             return "order"
-        if "bye" in message or "goodbye" in message:
+        if "tchau" in message or "goodbye" in message:
             return "goodbye"
         return "help"
 
     @listen("order")
     def handle_order(self):
-        reply = "Your order is on the way."
+        reply = "Seu pedido está a caminho."
         self.append_assistant_message(reply)
         return reply
 
     @listen("help")
     def handle_help(self):
-        reply = "How can I help?"
+        reply = "Como posso ajudar?"
         self.append_assistant_message(reply)
         return reply
 
     @listen("goodbye")
     def handle_goodbye(self):
-        reply = "Goodbye!"
+        reply = "Até logo!"
         self.append_assistant_message(reply)
         return reply
 
@@ -79,49 +79,41 @@ session_id = str(uuid4())
 flow = SupportFlow()
 
 try:
-    flow.handle_turn("Where is my order?", session_id=session_id)
-    flow.handle_turn("What about returns?", session_id=session_id)
+    flow.handle_turn("Onde está meu pedido?", session_id=session_id)
+    flow.handle_turn("E as devoluções?", session_id=session_id)
 finally:
-    flow.finalize_session_traces()  # one trace link for the whole chat
+    flow.finalize_session_traces()  # um link de trace para o chat inteiro
 ```
 
-## Streaming de um turno
-
-Use `stream_turn()` quando uma UI ou um runtime precisar de eventos estruturados para um turno de chat. Ele retorna uma sessão de stream com frames ordenados para roteamento do Flow, chunks do LLM, atividade de tools e mensagens da conversa.
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-result = stream.result
-```
-
-Para o contrato completo dos frames e a lista de canais, consulte [Contrato do Runtime de Streaming](/edge/pt-BR/learn/streaming-runtime-contract).
-
 ## Ciclo de vida do turno
 
 Cada `handle_turn` executa este pipeline:
 
-1. **Preparação do turno** — armazena a mensagem pendente do usuário, resolve o id da sessão, redefine o acompanhamento de execução por turno e chama `kickoff(inputs={"id": session_id})`.
+1. **`_configure_conversational_kickoff`** — mescla `session_id` / `user_message` em `inputs`, aplica `ConversationalConfig`, habilita tracing adiado quando configurado.
 2. **Restauração de estado** — se `inputs["id"]` existe e `@persist` está configurado, carrega o snapshot mais recente.
 3. **`FlowStarted`** — emitido apenas no primeiro turno da sessão adiada.
-4. **Hidratação do turno pendente** — acrescenta a mensagem do usuário em `state.messages`, define `current_user_message` / `last_user_message` e classifica opcionalmente quando `intents` / `default_intents` + `intent_llm` estão definidos.
-5. **Execução do grafo** — métodos `@start` definidos pelo usuário (se houver) → `route_conversation` (o start/router embutido) → o handler `@listen` selecionado. `route_conversation` também chama o helper sobrescrevível `conversation_start()`.
+4. **`prepare_conversational_turn`** — acrescenta a mensagem do usuário em `state.messages`, define `last_user_message`, limpa `last_intent`, classifica opcionalmente quando `intents` / `default_intents` + `intent_llm` estão definidos.
+5. **Execução do grafo** — `@start` → `@router` → handlers `@listen`.
 6. **Fim da execução** — `flow_finished` por turno e finalização de trace são **ignorados** com adiamento; `Agent.kickoff()` / crews aninhados também não fecham o batch pai.
 
-Os handlers devem chamar **`append_assistant_message(reply)`** quando a resposta visível não for o valor de retorno, ou ao recortar o histórico. Um retorno de string pública também é gravado como assistente e entra no snapshot `@persist`, então uma nova instância de Flow o restaura. A linha do usuário já é salva por `handle_turn` — não acrescente de novo nos handlers.
+Os handlers devem chamar **`append_assistant_message(reply)`** para que o próximo turno inclua a resposta do assistente. A linha do usuário já é salva por `handle_turn` — não acrescente de novo nos handlers.
 
-## Visão geral da configuração
+## `ConversationalConfig` (padrões em nível de classe)
 
-Decorar uma subclasse de `Flow` com `ConversationConfig` anexa os padrões de chat e habilita o modo conversacional. Consulte a [referência completa de campos](#conversationconfig) abaixo. Sobrescreva a pré-classificação por turno com `handle_turn(..., intents=..., intent_llm=...)`.
+Defina na subclasse de `Flow` como `conversational_config: ClassVar[ConversationalConfig | None]`.
 
-## Helpers `ChatState` de mais baixo nível
+| Campo | Padrão | Propósito |
+|-------|---------|-----------|
+| `default_intents` | `None` | Rótulos de outcome para classificação automática antes do kickoff |
+| `intent_llm` | `None` | Modelo para classificação (obrigatório quando há intents) |
+| `interactive_prompt` | `"You: "` | Prompt para `kickoff(interactive=True)` |
+| `interactive_timeout` | `None` | Timeout por linha no modo interativo |
+| `exit_commands` | `exit`, `quit` | Palavras que encerram o modo interativo |
+| `defer_trace_finalization` | `True` | Manter um batch de trace aberto entre turnos |
 
-`ChatState`, o `ConversationalConfig` legado e os helpers de `crewai.flow.conversation` continuam disponíveis para importação em orquestração avançada, testes ou wrappers customizados. Eles são separados da API `ConversationState` / `ConversationConfig` e não adicionam os argumentos nomeados `user_message=` ou `session_id=` a `Flow.kickoff()`.
+Sobrescreva por kickoff com `intents=` e `intent_llm=`.
+
+## `ChatState` (formato persistido recomendado)
 
 ```python
 from crewai.flow import ChatState
@@ -135,64 +127,62 @@ class MyChatState(ChatState):
 
 | Campo | Função |
 |-------|--------|
-| `id` | UUID da sessão (igual a `inputs["id"]`) |
+| `id` | UUID da sessão (igual a `session_id` / `inputs["id"]`) |
 | `messages` | `list` de `{role, content}` para histórico de LLM |
 | `last_user_message` | Última linha do usuário neste turno |
 | `last_intent` | Rótulo de rota após classificação (se usado) |
 | `session_ready` | Flag de bootstrap único (permissões, caches, etc.) |
 
-`ConversationalInputs` é um `TypedDict` para as chaves convencionais de `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
-
-O `ConversationState` armazena `messages` como objetos `ConversationMessage` e também fornece `current_user_message`, `ended`, `events` e `agent_threads`. Use `conversation_messages` ao passar seu histórico canônico para um LLM.
+`ConversationalInputs` é um `TypedDict` para `kickoff(inputs={...})`: `id`, `user_message`, `last_intent`.
 
 ## API conversacional em `Flow`
 
-### Parâmetros de `handle_turn`
+### Parâmetros de `kickoff` / `kickoff_async`
 
 | Parâmetro | Propósito |
 |-----------|-----------|
-| `message` | Texto deste turno |
+| `user_message` | Texto deste turno (ou `{"role": "user", "content": "..."}`) |
 | `session_id` | UUID da conversa → `inputs["id"]` / `state.id` |
 | `intents` | Rótulos de outcome para `classify_intent` antes do kickoff |
 | `intent_llm` | LLM para classificação (obrigatório com `intents`) |
-| `**kickoff_kwargs` | Encaminhados para `kickoff()` para opções como `input_files`, `from_checkpoint` e `restore_from_state_id` |
-
-### Parâmetros de `kickoff`
-
-`Flow.kickoff()` aceita `inputs`, `input_files`, `from_checkpoint` e `restore_from_state_id`. Passe `inputs={"id": session_id}` quando precisar executar o flow diretamente, mas use `handle_turn()` quando a chamada representar uma mensagem de chat.
+| `interactive` | Loop CLI via `ask()` (só demos locais) |
+| `interactive_prompt` | Prompt no modo interativo |
+| `interactive_timeout` | Timeout de `ask()` por linha |
+| `exit_commands` | Palavras que encerram o modo interativo |
+| `inputs` | Campos extras de estado (mesclados com chaves conversacionais) |
+| `restore_from_state_id` | Hidratação fork de outro flow persistido |
 
 ### Atributos de instância
 
 | Atributo | Propósito |
 |-----------|-----------|
-| `conversational` | Defina como `True` para habilitar o grafo conversacional e `handle_turn()` |
-| `defer_trace_finalization` | Sobrescrita opcional na instância. Caso contrário, `_should_defer_trace_finalization()` lê `ConversationConfig.defer_trace_finalization`. |
-| `suppress_flow_events` | Oculta painéis do flow no console e suprime eventos de execução de métodos; os eventos de início/fim do flow continuam sendo emitidos |
-| `stream` | Flag genérica de streaming do Flow. Para turnos conversacionais, use `stream_turn()` em vez de combinar esta flag com `handle_turn()`. |
+| `conversational_config` | Padrões `ConversationalConfig` em nível de classe |
+| `defer_trace_finalization` | Flag de instância; definida automaticamente a partir do config no kickoff |
+| `suppress_flow_events` | Oculta painéis Rich no console; **tracing ainda registra** eventos |
+| `stream` | Habilita streaming; use com `ChatSession.handle_turn(..., stream=True)` |
 
 ### Métodos e propriedades
 
 | Nome | Descrição |
 |------|-------------|
-| `append_assistant_message(content)` | Acrescenta uma resposta visível ao usuário em `state.messages` |
-| `append_message(role, content, **extra)` | Acréscimo de mais baixo nível em `state.messages` |
+| `append_message(role, content, **extra)` | Acrescenta em `state.messages` (roles: `user`, `assistant`, `system`, `tool`) |
 | `conversation_messages` | Histórico somente leitura para chamadas LLM |
 | `classify_intent(text, outcomes, *, llm, context=None)` | Mapeia texto a um outcome (mesma lógica de `@human_feedback`) |
 | `receive_user_message(text, *, outcomes=None, llm=None)` | Acrescenta mensagem do usuário; opcionalmente define `last_intent` |
 | `finalize_session_traces()` | Emite `flow_finished` adiado e finaliza o batch de trace da sessão |
-| `_should_defer_trace_finalization()` | Hook avançado/interno que resolve se a finalização de trace por turno é adiada |
+| `_should_defer_trace_finalization()` | Se este flow adia finalização de trace por turno |
 | `input_history` | Trilha de auditoria de prompts e respostas de `ask()` |
 
 ### Helpers do módulo (`crewai.flow.conversation`)
 
-Importáveis de `crewai.flow.conversation` para testes ou orquestração customizada. Esses helpers usam o formato legado de `ConversationalConfig`; `prepare_conversational_turn()` também limpa `last_intent`, ao contrário do `handle_turn()`, que o preserva como contexto do router.
+Importáveis para testes ou orquestração customizada:
 
 | Função | Descrição |
 |----------|-------------|
 | `normalize_kickoff_inputs(inputs, user_message=..., session_id=...)` | Mescla kwargs conversacionais em `inputs` |
 | `get_conversation_messages(flow)` | Lê mensagens do estado ou buffer interno |
 | `append_message(flow, role, content, **extra)` | Igual ao método de instância |
-| `prepare_conversational_turn(flow, user_message=..., intents=..., intent_llm=..., config=...)` | Hidratação de turno de mais baixo nível para wrappers customizados |
+| `prepare_conversational_turn(flow, ...)` | Hidratação do turno (geralmente chamado pelo kickoff) |
 | `receive_user_message(flow, text, ...)` | Igual ao método de instância |
 | `set_state_field(flow, name, value)` | Define campo em estado dict ou Pydantic |
 | `get_conversational_config(flow)` | Lê `conversational_config` da classe |
@@ -202,18 +192,19 @@ Importáveis de `crewai.flow.conversation` para testes ou orquestração customi
 
 ### A. Pré-classificar via `ConversationalConfig` (mais simples)
 
-Defina `default_intents` e `intent_llm`. Cada `handle_turn()` pré-classifica a mensagem atual. Um resultado não vazio retornado por um `route_turn()` customizado tem precedência; caso contrário, `route_conversation` usa a intenção classificada do turno atual.
+Defina `default_intents` e `intent_llm`. Cada kickoff classifica antes do `@router`; leia `self.state.last_intent` em `route()`.
 
-### B. Classificar dentro de `route_turn` (prompts mais ricos)
+### B. Classificar dentro do `@router` (prompts mais ricos)
 
-Defina `default_intents=None` para `handle_turn()` apenas acrescentar a mensagem do usuário. Em `route_turn()`, chame `classify_intent` com um prompt ou descrições customizadas:
+Defina `default_intents=None` para o kickoff só acrescentar a mensagem. Em `route()`, chame `classify_intent` com prompt ou descrições customizadas:
 
 ```python
-def route_turn(self, context):
+@router(bootstrap)
+def route(self):
     intent = self.classify_intent(
-        self._routing_prompt(self.state.current_user_message),
+        self._routing_prompt(self.state.last_user_message),
         ("GREETING", "ORDER", "RESEARCH", "GOODBYE"),
-        llm="gpt-4o-mini",
+        llm=self.conversational_config.intent_llm or "gpt-4o-mini",
     )
     self.state.last_intent = intent
     return intent
@@ -223,59 +214,70 @@ Use **`@listen("RESEARCH")`** (ou similar) para passos com `Agent.kickoff()` e f
 
 ## Quando o flow termina mas o usuário continua conversando
 
-Cada `handle_turn()` conclui uma execução do grafo, e a conversa continua com outro `handle_turn()` usando o mesmo `session_id`. Com o ciclo de vida de trace adiado padrão, essa execução emite `conversation_turn_completed`, enquanto `FlowFinished` é emitido uma vez quando `finalize_session_traces()` encerra a sessão. `@persist` restaura `messages`, flags e contexto.
+`FlowFinished` significa que **esta execução do grafo** terminou. A conversa segue com outro `kickoff` e o mesmo `session_id`. `@persist` restaura `messages`, flags e contexto.
 
 **Padrão de persistência:** prefira `@persist` em um **único passo terminal** (por exemplo `finalize`) em vez de na classe `Flow` inteira. Persist em nível de classe salva após cada método; `load_state` usa a linha mais recente, que pode ser snapshot no meio da execução e perder atualizações dos handlers no mesmo turno.
 
 Não use `@human_feedback` para linhas de chat de follow-up, a menos que um humano precise aprovar uma saída específica antes de exibi-la.
 
-## `Flow` conversacional
+## `Flow` conversacional (experimental)
 
-Habilite o grafo de chat conversacional definindo `conversational = True` em uma subclasse de `Flow` ou aplicando `@ConversationConfig(...)`. O `Flow` base passa a fornecer `route_conversation` como start/router embutido, além dos listeners `converse_turn` e `end_conversation`. O listener descontinuado `answer_from_history_turn` permanece disponível para compatibilidade. O framework gerencia `state.messages`, pode acionar um LLM de roteamento e mantém o batch de trace aberto entre turnos. Você escreve as **rotas customizadas**; o framework cuida do resto.
+<Warning>
+  **Funcionalidade experimental.** A superfície do `Flow` conversacional
+  (`conversational = True`, `handle_turn`, `ConversationConfig`,
+  `RouterConfig`, `ConversationState`, o grafo embutido + helpers) vive em
+  `crewai.experimental` e pode mudar de formato antes de graduar. Fixe a
+  versão do CrewAI se depende de comportamento específico e acompanhe o
+  changelog para mudanças quebradoras. Feedback / issues bem-vindos.
+</Warning>
 
-Use isto quando quiser um chat multi-turno com router e handlers por rota sem cablar o ciclo de vida na mão. Use `Flow[ChatState]` (o padrão de mais baixo nível acima) quando precisar de controle total.
+Habilite o grafo conversacional definindo `conversational = True` em uma subclasse de `Flow`. O `Flow` base passa a expor um grafo embutido `@start` / `@router` / `converse_turn` / `end_conversation`, gerencia `state.messages`, dirige o LLM de roteamento e mantém o batch de trace aberto entre os turnos. Você escreve as **rotas customizadas**; o framework cuida do resto.
+
+Use isto quando quiser um chat multi-turno com router LLM e handlers por rota sem cablar o ciclo de vida na mão. Use `Flow[ChatState]` (o padrão de mais baixo nível acima) quando precisar de controle total.
 
 ### Exemplo rápido
 
 ```python
-from crewai import Flow
+from crewai import LLM, Flow
 from crewai.flow import listen
-from crewai.flow import (
+from crewai.experimental.conversational import (
     ConversationConfig,
     ConversationState,
+    RouterConfig,
 )
 
 
-@ConversationConfig(defer_trace_finalization=True)
+ROUTER_LLM = LLM(model="gpt-4o-mini")
+
+
+@ConversationConfig(
+    system_prompt="A multi-agent assistant for ordinary chat and tool-backed tasks.",
+    llm=ROUTER_LLM,
+    router=RouterConfig(),  # rotas + descrições auto-descobertas pelos handlers @listen
+)
 class SupportFlow(Flow[ConversationState]):
-    def route_turn(self, context: dict) -> str | None:
-        message = (self.state.current_user_message or "").lower()
-        if "search" in message or "news" in message:
-            return "INTERNET_SEARCH"
-        if "docs" in message or "crewai" in message:
-            return "CREWAI_DOCS"
-        return "converse"
+    conversational = True
 
     @listen("INTERNET_SEARCH")
     def handle_internet_search(self) -> str:
         """Fresh web research, current news, real-time lookups."""
-        reply = "I would run the web research route here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
     @listen("CREWAI_DOCS")
     def handle_crewai_docs(self) -> str:
         """Look up the CrewAI documentation for framework/API questions."""
-        reply = "I would look up the CrewAI docs here."
+        ...
         self.append_assistant_message(reply)
         return reply
 
 
 flow = SupportFlow()
 try:
-    flow.handle_turn("What can you do?")              # routes to converse
-    flow.handle_turn("Search the web for AI news.")   # routes to INTERNET_SEARCH
-    flow.handle_turn("Check the CrewAI docs.")         # routes to CREWAI_DOCS
+    flow.handle_turn("O que você pode fazer?")               # roteia para converse (built-in)
+    flow.handle_turn("Pesquise na web por notícias de IA.")  # roteia para INTERNET_SEARCH
+    flow.handle_turn("Resuma o primeiro resultado.")         # volta para converse
 finally:
     flow.finalize_session_traces()
 ```
@@ -297,55 +299,27 @@ Decorador de classe que anexa os defaults de chat por classe.
 |-------|--------|-----------|
 | `system_prompt` | `slices.conversational_system_prompt` (i18n) | System message usado pelo `converse_turn` embutido. Passe `""` para desativar totalmente. |
 | `llm` | `None` | LLM de conversa (usado pelo `converse_turn` e como fallback do router). |
-| `router` | `None` | Sobrescritas opcionais de `RouterConfig`. Com listeners customizados e um LLM que possa ser resolvido, o roteamento é habilitado automaticamente mesmo quando este campo é omitido. |
-| `answer_from_history_prompt` | padrão do framework | **Descontinuado.** Use o system prompt de `converse` ou sobrescreva `converse_turn()`. |
-| `answer_from_history_llm` | `None` | **Descontinuado.** Use `llm`; `converse` já recebe o histórico canônico. |
+| `router` | `None` | `RouterConfig` para roteamento por LLM. Sem ele, o flow sempre cai em `converse`. |
+| `answer_from_history_prompt` | padrão do framework | System message para a rota opcional `answer_from_history`. |
+| `answer_from_history_llm` | `None` | Habilita o atalho `answer_from_history` quando definido. |
 | `intent_llm` | `None` | LLM para o caminho legado `intents=`/`default_intents`. |
 | `default_intents` | `None` | Labels de outcome para pré-classificação legada. |
 | `visible_agent_outputs` | `None` | `"all"` ou lista de nomes de agentes cujos `append_agent_result()` devem virar mensagens públicas. |
 | `defer_trace_finalization` | `True` | Mantém um único batch de trace aberto entre chamadas de `handle_turn()`. |
 
-<Warning>
-  `answer_from_history_prompt`, `answer_from_history_llm` e a rota
-  `answer_from_history` estão descontinuados e serão removidos em uma versão
-  futura. Eles duplicam `converse`, que já recebe o histórico canônico,
-  adicionam uma chamada de LLM para verificar elegibilidade e são ignorados
-  quando o auto-router normal retorna uma rota. As configurações existentes
-  continuam funcionando e emitem `DeprecationWarning`.
-</Warning>
-
-Sem rotas customizadas, os turnos caem em `converse`. Com rotas customizadas e um LLM de conversa/router, o framework sintetiza um `RouterConfig` padrão; forneça um explicitamente apenas para customizar seu prompt, lista de rotas, descrições ou comportamento de fallback. Definir `default_intents` usa o caminho legado de pré-classificação.
-
-Se nenhum LLM de conversa estiver configurado, o `converse_turn` embutido retorna um placeholder de configuração em vez de gerar uma resposta.
-
 ### `RouterConfig` e o catálogo de rotas auto-gerado
 
 ```python
-from typing import Literal
-
-from pydantic import BaseModel
-
-from crewai import LLM
-from crewai.flow import RouterConfig
-
-
-class MyRoute(BaseModel):
-    intent: Literal["INTERNET_SEARCH", "CREWAI_DOCS", "converse"]
-
-
-ROUTER_LLM = LLM(model="gpt-4o-mini")
-
-
-router_config = RouterConfig(
-    prompt="Optional domain framing (policy, voice, persona).",
-    response_format=MyRoute,        # optional; auto-generated otherwise
-    llm=ROUTER_LLM,                  # falls back to ConversationConfig.llm
-    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # optional; inferred from listeners
+RouterConfig(
+    prompt="Enquadramento de domínio opcional (política, voz, persona).",
+    response_format=MyRoute,        # opcional; auto-gerado caso contrário
+    llm=ROUTER_LLM,                  # usa ConversationConfig.llm como fallback
+    routes=["INTERNET_SEARCH", "CREWAI_DOCS"],   # opcional; inferido dos listeners
     route_descriptions={
-        "INTERNET_SEARCH": "Override the docstring for this one route.",
+        "INTERNET_SEARCH": "Sobrescreve a docstring só desta rota.",
     },
-    default_intent="converse",       # used when LLM call fails or no LLM available
-    fallback_intent="converse",      # used when LLM returns an invalid route
+    default_intent="converse",       # usado quando a chamada ao LLM falha ou não há LLM
+    fallback_intent="converse",      # usado quando o LLM retorna rota inválida
     intent_field="intent",
 )
 ```
@@ -353,17 +327,13 @@ router_config = RouterConfig(
 O prompt do router é montado automaticamente. Para cada rota o framework escolhe a descrição nesta precedência:
 
 1. `RouterConfig.route_descriptions[label]` — override explícito.
-2. `Flow.builtin_route_descriptions[label]` — texto canônico do framework para `converse`, `end` e a rota de compatibilidade descontinuada `answer_from_history` (otimizado para o LLM de routing).
-3. O `description` declarado do método (usado por flows declarativos e projeções da DSL).
-4. Primeira linha não vazia da docstring do handler `@listen(label)`.
-5. Vazio (a rota aparece no catálogo sem descrição).
+2. `Flow.builtin_route_descriptions[label]` — texto canônico do framework para `converse`, `end`, `answer_from_history` (otimizado para o LLM de routing).
+3. Primeira linha não vazia da docstring do handler `@listen(label)`.
+4. Vazio (a rota aparece no catálogo sem descrição).
 
 Na prática, **adicionar uma rota é `@listen("X")` + uma docstring de uma linha**:
 
 ```python
-from crewai.flow import listen
-
-
 @listen("INTERNET_SEARCH")
 def handle_internet_search(self) -> str:
     """Fresh web research, current news, real-time lookups."""
@@ -382,34 +352,13 @@ Routes:
 
 `RouterConfig.prompt` é para **enquadramento de domínio** (persona do assistente, regras de negócio, voz). O catálogo de rotas é auto-gerado — não liste rotas em `prompt`; elas vão sair de sincronia assim que você adicionar um handler.
 
-### Nomeando handlers
-
-A string em `@listen("…")` é um **rótulo de rota do router** (um nome de evento), e não o nome do método Python. Rótulos de rota e eventos de conclusão de métodos compartilham o mesmo namespace de gatilhos; portanto, dar ao handler o mesmo nome de sua rota faria o handler acionar a si próprio em loop.
-
-Use um nome de método diferente — os exemplos da documentação usam o prefixo `handle_*`:
-
-```python
-@listen("create_video")
-def handle_create_video(self) -> str:
-    """User wants a new video."""
-    ...
-```
-
-**Não** replique o rótulo da rota no método:
-
-```python
-@listen("create_video")
-def create_video(self) -> str:  # rejected at flow instantiation
-    ...
-```
-
 ### Rotas embutidas
 
 | Rota | Handler | Propósito |
 |------|---------|-----------|
 | `converse` | `converse_turn` | Handler de chat padrão. Chama `ConversationConfig.llm` com o system prompt + histórico canônico. |
 | `end` | `end_conversation` | Define `state.ended = True` e emite uma resposta de encerramento. |
-| `answer_from_history` | `answer_from_history_turn` | **Rota de compatibilidade descontinuada.** Use `converse`, que já recebe o histórico canônico. |
+| `answer_from_history` | `answer_from_history_turn` | Opcional. Cai aqui quando `ConversationConfig.answer_from_history_llm` está definido e a mensagem pode ser respondida só pelo histórico. |
 
 Você pode sobrescrever qualquer uma definindo um handler com o mesmo nome na subclasse.
 
@@ -419,9 +368,9 @@ Você pode sobrescrever qualquer uma definindo um handler com o mesmo nome na su
 
 1. Reseta o tracking por execução (`_completed_methods`, `_method_outputs`) para o grafo re-rodar — sem isso, chamadas repetidas de `kickoff` na mesma instância dariam curto-circuito no turno 2+ porque `Flow.kickoff_async` trata `inputs={"id": ...}` como restauração de checkpoint.
 2. Anexa a mensagem do usuário em `state.messages`, define `current_user_message` / `last_user_message`. `last_intent` é **preservado do turno anterior** para que o LLM de routing possa usá-lo como sinal.
-3. Executa métodos `@start` definidos pelo usuário (se houver), depois `route_conversation` como start/router embutido e, por fim, o handler `@listen` escolhido. `route_conversation` invoca o helper sobrescrevível `conversation_start()`.
+3. Roda `conversation_start` → `route_conversation` → o handler `@listen` escolhido.
 4. O router grava sua decisão em `state.last_intent` (visível para o contexto de routing do próximo turno).
-5. Se seu handler retornou uma string e ainda não chamou `append_assistant_message`, `handle_turn` anexa para você e persiste o `state.messages` atualizado para que a restauração `@persist` inclua o turno do assistente.
+5. Se seu handler retornou uma string e ainda não chamou `append_assistant_message`, `handle_turn` anexa para você.
 
 Chame `handle_turn()` para mensagens de chat. Chamar `kickoff(inputs={"id": ...})` diretamente executa o grafo sem aplicar o wrapper de turno conversacional.
 
@@ -442,8 +391,6 @@ Ele cobre o loop local comum:
 4. Imprime o resultado do assistente.
 5. Finaliza traces de sessão adiados em um bloco `finally`.
 
-`chat(defer_trace_finalization=True)` habilita temporariamente a flag de adiamento na instância durante o REPL e restaura o valor anterior ao sair.
-
 Customize o comportamento do terminal com I/O injetável:
 
 ```python
@@ -462,12 +409,6 @@ Para apps web, workers em background, testes e transportes customizados, continu
 Para rodar efeitos colaterais (setup de event bus, telemetria) em toda decisão de routing, sobrescreva `route_turn`:
 
 ```python
-from typing import Any
-
-from crewai import Flow
-from crewai.flow import ConversationState
-
-
 class SupportFlow(Flow[ConversationState]):
     conversational = True
 
@@ -476,7 +417,7 @@ class SupportFlow(Flow[ConversationState]):
         return super().route_turn(context)
 ```
 
-Para ignorar completamente o router LLM e escolher uma rota programaticamente, retorne uma string não vazia de `route_turn`. Um retorno falsy **não** invoca `_route_with_config()` a partir da sua sobrescrita; o roteamento segue para a intenção pré-classificada deste turno, depois para o caminho de compatibilidade descontinuado `answer_from_history` quando configurado e, por fim, para `converse`. O `last_intent` do turno anterior fica disponível no contexto do router, mas nunca é repetido como fallback.
+Para ignorar o router LLM e escolher uma rota programaticamente, retorne uma string de `route_turn`; retornar `None` cai no `_route_with_config(...)`.
 
 ### `append_assistant_message` e `append_agent_result`
 
@@ -487,76 +428,9 @@ Dentro de um handler `@listen(label)`, escolha:
 
 `ConversationConfig.visible_agent_outputs` pode promover globalmente os resultados privados de agentes específicos para públicos (`"all"` ou lista de nomes).
 
-## Declarando um flow conversacional em JSON/YAML
-
-Um [Flow declarativo](/edge/pt-BR/concepts/cli) também pode ser conversacional. Adicione um bloco `conversational` no nível raiz e declare suas próprias rotas como métodos que fazem `listen` em um rótulo de rota:
-
-```yaml
-schema: crewai.flow/v1
-name: SupportFlow
-
-conversational:
-  system_prompt: You are a terse support assistant.
-  llm: gpt-4o-mini
-  router:
-    llm: gpt-4o-mini
-
-methods:
-  handle_order:
-    description: Order status, shipping and delivery questions.
-    listen: order
-    do:
-      call: agent
-      with:
-        role: Support specialist
-        goal: Answer order questions accurately
-        backstory: Knows the fulfilment pipeline.
-        input: "${state.current_user_message}"
-```
-
-Declarar o bloco já é o opt-in — `enabled` tem valor padrão `true`. Use `enabled: false` para manter a configuração e desligar o chat. Isso também desabilita a síntese de métodos embutidos, portanto a declaração deve fornecer um grafo não conversacional normal.
-
-Três coisas são fornecidas para você:
-
-| Fornecido | Detalhe |
-|----------|--------|
-| O grafo interno | `route_conversation`, `converse_turn` e `end_conversation` são adicionados automaticamente. O `answer_from_history_turn` descontinuado é mantido para compatibilidade. Declare um método com um desses nomes para sobrescrevê-lo. |
-| Estado da conversa | `ConversationState` é usado quando não há bloco `state`. Um estado Pydantic definido por `ref` ou `json_schema` é composto automaticamente com os campos conversacionais; ele não precisa estender `ConversationState`. |
-| O catálogo de rotas | Inferido de métodos que não são routers e têm rótulos `listen`, excluindo rotas internas. As descrições seguem a precedência acima, e `router.routes` explícito pode limitar as opções. |
-
-Os campos declarativos `llm`, `router.llm` e `intent_llm` aceitam um id de modelo ou um mapping de configuração, como `{model: openai/gpt-4o-mini, max_tokens: 512}`. O bloco `conversational` também aceita `default_intents`, `visible_agent_outputs`, `defer_trace_finalization` e os campos de `RouterConfig` mostrados acima. As declarações descontinuadas `answer_from_history_prompt` / `answer_from_history_llm` continuam sendo aceitas para compatibilidade.
-
-Execute a partir do Python com as mesmas APIs de turno de um Flow conversacional baseado em classe:
-
-```python
-from crewai.flow import Flow
-
-flow = Flow.from_declaration(path="flow.yaml")
-
-try:
-    flow.handle_turn("Where is my order?", session_id="session-1")
-finally:
-    flow.finalize_session_traces()
-```
-
-### Nomeando rotas
-
-Rótulos de rota e nomes de métodos compartilham um único namespace de gatilhos, então um handler não pode ter o nome da rota que escuta — `create_video` escutando `create_video` é rejeitado na construção do flow. Use o prefixo `handle_*`.
-
-### O que uma declaração não consegue expressar
-
-| Não expressável | Use no lugar |
-|-----------------|-------------|
-| Uma instância `LLM` viva ou um `BaseLLM` customizado | Um id de modelo em string ou mapping estático de configuração |
-| `router.response_format` como classe de modelo viva | Nomeie a classe com um ref python: `response_format: {python: my_project.schemas.ConversationRoute}`. Omita e o framework sintetiza uma |
-| Uma sobrescrita de `route_turn()` | Escreva o Flow em Python ou substitua o método declarativo `route_conversation` por uma ação `call: code` / expressão |
-| Uma sobrescrita de `can_answer_from_history()` | Descontinuado. Use `converse` ou sobrescreva `converse_turn()` no Python. |
-
-O `crewai run` abre a TUI de chat para um flow conversacional declarativo — a mesma que um Flow conversacional em Python recebe. Um loop de chat precisa de um terminal, então uma execução headless encerra com código diferente de zero e orientações, em vez de rodar um único turno; ali, conduza pelo Python com `handle_turn()` ou `stream_turn()`. Um método declarativo com um bloco `human_feedback:` (Python: `@human_feedback`) roda em um REPL de terminal, porque o runtime coleta feedback com um prompt bloqueante que a TUI não consegue atender. O `--inputs` não é aceito em um flow conversacional — a entrada de cada turno é a mensagem que você digita — e retomar uma sessão por id ainda não está ligado à CLI; use `flow.handle_turn(message, session_id=...)` no Python para isso.
-
 ## Tracing entre turnos
 
-Com `defer_trace_finalization=True` (padrão em `ConversationConfig`):
+Com `defer_trace_finalization=True` (padrão em `ConversationalConfig`):
 
 - **Um batch de trace** para toda a sessão de chat.
 - **`flow_started`** só no primeiro turno; **`flow_finished`** uma vez em `finalize_session_traces()`.
@@ -567,30 +441,17 @@ Com `defer_trace_finalization=True` (padrão em `ConversationConfig`):
 flow.chat(session_id=session_id)
 ```
 
-`flow.chat()` chama `finalize_session_traces()` para você. Quando você controla o loop com `handle_turn()`, chame `finalize_session_traces()` quando a sessão terminar.
+`flow.chat()` chama `finalize_session_traces()` para você. Quando você controla o loop com `handle_turn()` ou `kickoff(...)`, chame `finalize_session_traces()` quando a sessão terminar.
 
-`suppress_flow_events=True` oculta painéis Rich no console e suprime eventos de execução de métodos. Os eventos de início/fim do Flow continuam sendo emitidos, portanto o ciclo de vida externo do Flow permanece rastreável, mas os spans de métodos individuais são omitidos.
+`suppress_flow_events=True` só oculta painéis do console; eventos de trace e método ainda são emitidos.
 
 ### Ciclo de vida de trace do `Flow` conversacional
 
-O [`Flow` conversacional](#flow-conversacional) usa o mesmo ciclo de vida de tracing: `defer_trace_finalization` é `True` por padrão, então cada `handle_turn()` mantém o trace da sessão aberto. Turnos adiados também suprimem `flow_failed` por turno; em caso de erro em um turno ou encerramento antecipado da sessão, finalize a sessão explicitamente. Isso fecha o batch com o evento `FlowFinished` no nível da sessão, em vez de um evento `FlowFailed` por turno. Sempre envolva seu REPL/loop em `try/finally` e chame `flow.finalize_session_traces()` na saída. Sem isso, o batch fica aberto e a conversa final pode nunca ser exportada.
+O [`Flow` conversacional](#flow-conversacional-experimental) experimental usa o mesmo ciclo de vida de tracing: `defer_trace_finalization` é `True` por padrão, então cada `handle_turn()` mantém o trace da sessão aberto. Sempre finalize ao fim da sessão — envolva seu loop em `try/finally` e chame `flow.finalize_session_traces()` na saída. Sem isso, o batch fica aberto e a última conversa pode nunca ser exportada.
 
 ## Streaming
 
-Para UIs conversacionais, use `stream_turn()` e itere sobre seus objetos `StreamFrame` ordenados:
-
-```python
-stream = flow.stream_turn("Where is my order?", session_id=session_id)
-
-with stream:
-    for frame in stream.events:
-        if frame.channel == "llm" and frame.type == "llm_stream_chunk":
-            print(frame.content, end="", flush=True)
-
-reply = stream.result
-```
-
-Para um Flow não conversacional, definir `stream = True` faz `kickoff()` retornar uma `StreamSession`. Não defina `flow.stream = True` ao usar `handle_turn()`; `stream_turn()` controla o ciclo de vida do streaming conversacional.
+Defina `stream = True` na classe `Flow`. `kickoff(...)` então emitirá `assistant_delta` (e eventos relacionados) pelo event bus padrão.
 
 ## Imports
 
@@ -605,15 +466,10 @@ from crewai.flow import (
     router,
     start,
 )
-from crewai.flow.conversation import prepare_conversational_turn
-from crewai.flow import (
-    ConversationConfig,
-    ConversationState,
-    RouterConfig,
-)
 ```
 
 ## Veja também
 
 - [Dominando o Gerenciamento de Estado em Flows](/pt-BR/guides/flows/mastering-flow-state) — persistência, estado Pydantic, `@persist`
 - [Construa Seu Primeiro Flow](/pt-BR/guides/flows/first-flow) — fundamentos de flow
+- Demo: `lib/crewai/runner_conversational_flow_simple.py` — REPL mínimo com `RESEARCH` + agente Exa

docs/edge/pt-BR/guides/flows/first-flow.mdx

@@ -135,7 +135,7 @@ Agora, vamos configurar o crew de redatores com JSONC. Vamos definir dois agente
 }
 ```
 
-Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-3.7-flash` ou `anthropic/claude-sonnet-4-6`.
+Substitua `provider/model-id` pelo modelo que você usa, como `openai/gpt-4o`, `gemini/gemini-2.0-flash-001` ou `anthropic/claude-sonnet-4-6`.
 
 3. Crie `src/guide_creator_flow/crews/content_crew/crew.jsonc`:
 
@@ -481,7 +481,7 @@ Flows permitem que você faça chamadas diretas a modelos de linguagem quando pr
 
 ```python
 llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini/gemini-3.7-flash, anthropic/claude...
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
     response_format=GuideOutline
 )
 response = llm.call(messages=messages)

docs/edge/pt-BR/guides/frontend/channels.mdx

@@ -1,156 +0,0 @@
----
-title: Channels
-description: Execute o mesmo agente CrewAI como um bot do Slack ou Teams com o Channels SDK do CopilotKit e a plataforma gerenciada Intelligence.
-icon: messages
-mode: "wide"
----
-
-## Encontre seus usuários onde eles já estão
-
-O agente CrewAI que você construiu na [Visão geral](/edge/pt-BR/guides/frontend/overview) não precisa viver por trás de um web app. O mesmo Crew ou Flow pode rodar como um bot dentro de uma plataforma de mensagens. Sem reconstruir, sem uma segunda cópia da lógica do seu agente: o agente permanece exposto pelo [protocolo AG-UI](https://docs.ag-ui.com), e um **channel** o aciona a partir do Slack ou do Microsoft Teams.
-
-O [Channels SDK](https://docs.copilotkit.ai/slack) do CopilotKit fornece esse channel. Você declara um `createChannel` em um pequeno runtime, aponta-o para o seu agente CrewAI, e a plataforma gerenciada **Intelligence** do CopilotKit intermedia a conexão com o provedor de mensagens.
-
-<Note>
-Diferentemente do restante desta seção, Channels **não é self-hosted**. Ele roda através do **CopilotKit Intelligence** — uma superfície obrigatória para Channels, por design (há um plano gratuito disponível). O Intelligence detém a conexão com a plataforma e as credenciais, recebe cada evento da plataforma e entrega o turno ao processo do seu channel; seu processo executa o agente e transmite a resposta de volta. Você configura o Slack uma vez no painel do Intelligence, e as credenciais da plataforma nunca entram no seu processo. Seu agente, suas tools e seu estado continuam sendo seus.
-</Note>
-
-## Como tudo se encaixa
-
-Nada muda no servidor do seu agente CrewAI. Ele continua servindo o seu Crew ou Flow por AG-UI exatamente como na Visão geral. O que você adiciona é um processo Node separado, de longa duração, construído com `@copilotkit/channels`: ele registra um channel no `CopilotRuntime`, conecta-se ao Intelligence e executa o seu agente sempre que chega uma mensagem.
-
-```
-Slack / Teams  ──►  CopilotKit Intelligence  ──►  channel process (Node)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-O processo do channel mantém uma conexão persistente com o gateway do Intelligence, então ele precisa de um host de longa duração — um handler de requisições serverless não consegue ser dono dessa conexão. Seu servidor CrewAI pode continuar servindo o frontend web da Visão geral ao mesmo tempo: o web app e o channel são apenas dois clientes de um único endpoint AG-UI.
-
-## Guia de integração
-
-<Steps>
-
-<Step title="Instale os pacotes do Channels">
-
-O Channels SDK vem com tudo incluído — cada plataforma é entregue no mesmo pacote, sem nenhum adaptador por plataforma para instalar. Adicione-o junto ao runtime que hospeda o channel e ao cliente AG-UI do CrewAI:
-
-```bash
-npm install @copilotkit/channels @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="Crie um Channel no Intelligence">
-
-No [painel do CopilotKit](https://docs.copilotkit.ai/slack), crie um Channel e conecte o Slack — o Intelligence guia você na criação do app do Slack e detém suas credenciais. Isso deixa duas variáveis de ambiente para o seu processo, ambas vindas do painel:
-
-```bash
-export INTELLIGENCE_API_KEY=...      # authenticates the runtime with Intelligence (free tier available)
-export INTELLIGENCE_CHANNEL_ID=...   # the Channel ID, matched by createChannel({ name })
-```
-
-</Step>
-
-<Step title="Defina o channel">
-
-`createChannel` declara o channel e anexa o seu agente. Construa o agente como uma factory por thread, para que cada conversa ganhe sua própria sessão, usando o mesmo `CrewAIAgent` que a Visão geral usa no runtime web, apontado para o seu endpoint AG-UI. `identifyUser: "platform"` permite que o Intelligence mapeie cada usuário da plataforma para uma identidade estável.
-
-```ts
-// channel.ts
-import { createChannel } from "@copilotkit/channels";
-import { CrewAIAgent } from "@ag-ui/crewai";
-
-const channel = createChannel({
-  name: process.env.INTELLIGENCE_CHANNEL_ID!, // must match the Channel ID in Intelligence
-  identifyUser: "platform",
-  // A fresh agent per conversation, pointed at your CrewAI AG-UI endpoint.
-  agent: (threadId) => {
-    const agent = new CrewAIAgent({ url: "http://localhost:8000/recipe" });
-    agent.threadId = threadId;
-    return agent;
-  },
-});
-
-// A mention subscribes the thread and runs the agent; afterwards every message
-// in a subscribed thread runs it without needing another mention.
-channel.onMention(async ({ thread }) => {
-  await thread.subscribe();
-  await thread.runAgent();
-});
-
-channel.onMessage(async ({ thread }) => {
-  if (await thread.isSubscribed()) await thread.runAgent();
-});
-
-export { channel };
-```
-
-</Step>
-
-<Step title="Registre o channel no runtime">
-
-Crie um `CopilotRuntime` com o gateway do Intelligence e o seu channel, e então sirva-o com `createCopilotNodeListener`. O mapa `agents` permanece vazio — o channel fornece seu próprio agente. Aguarde o channel ficar pronto, para que uma configuração quebrada faça a inicialização falhar de forma visível.
-
-```ts
-// server.ts
-import { createServer } from "node:http";
-import { CopilotRuntime, CopilotKitIntelligence } from "@copilotkit/runtime/v2";
-import { createCopilotNodeListener } from "@copilotkit/runtime/v2/node";
-import { channel } from "./channel";
-
-const runtime = new CopilotRuntime({
-  agents: {}, // the channel supplies its own agent; no web-facing agents needed
-  intelligence: new CopilotKitIntelligence({
-    apiKey: process.env.INTELLIGENCE_API_KEY!, // free tier available
-  }),
-  channels: [channel],
-});
-
-const listener = createCopilotNodeListener({ runtime });
-await listener.channels?.ready({ timeoutMs: 15_000 });
-
-createServer(listener).listen(3123, () => {
-  console.log("Channels runtime listening on port 3123");
-});
-```
-
-</Step>
-
-<Step title="Execute o runtime do channel">
-
-Inicie-o junto ao servidor do seu agente CrewAI:
-
-```bash
-uvicorn server:app --port 8000   # terminal 1 — CrewAI agent server
-npx tsx server.ts                 # terminal 2 — Channels runtime
-```
-
-Mencione o bot no Slack ou no Teams e ele executa o seu Crew ou Flow, transmitindo a resposta de volta para a thread. A thread permanece inscrita, então mensagens de acompanhamento rodam sem outra menção.
-
-</Step>
-
-</Steps>
-
-## O modelo de eventos
-
-Um channel reage a eventos da plataforma com handlers, e cada handler recebe uma `thread` que você aciona com alguns métodos:
-
-- **`channel.onMention`** dispara quando um usuário @-menciona o bot. Chame `thread.subscribe()` para entrar na thread, e então `thread.runAgent()` para executar o seu agente CrewAI na menção.
-- **`channel.onMessage`** dispara em cada mensagem de uma thread que o bot consegue ver. Restrinja com `thread.isSubscribed()` para que o agente só responda onde tiver entrado, e então `thread.runAgent()`.
-- **`thread.runAgent()`** executa o agente CrewAI anexado para o turno atual e transmite a saída dele de volta para o channel. Passe `{ prompt }` para sobrescrever o texto sobre o qual o agente roda.
-
-Seu agente recebe um `RunAgentInput` comum do AG-UI e emite eventos comuns do AG-UI; as mecânicas da plataforma ficam por trás do channel, então o mesmo Crew ou Flow roda sem alterações em todas as plataformas. O channel também expõe handlers para boas-vindas, interrupções, comandos, reações e modais — consulte a [referência de `Channel`](https://docs.copilotkit.ai/reference/channels/classes/Channel) para conhecer toda a superfície.
-
-## Suporte a plataformas
-
-O caminho gerenciado do Intelligence cobre **Slack** e **Microsoft Teams** hoje — o mesmo código de channel roda em qualquer um dos dois, e `message.platform` / `thread.platform` reportam a origem nativa. Outras plataformas (Discord, Telegram, WhatsApp) são alcançadas através de **adaptadores diretos** operados pelo desenvolvedor, em vez do caminho gerenciado — o seu próprio processo detém as credenciais da plataforma e o transporte. Consulte a [documentação de Channels do CopilotKit](https://docs.copilotkit.ai/slack) para a lista atual de plataformas e a configuração por plataforma.
-
-## Relacionados
-
-<CardGroup cols={2}>
-  <Card title="Visão geral do Frontend" icon="browser" href="/edge/pt-BR/guides/frontend/overview">
-    Sirva o seu Crew ou Flow por AG-UI — a base sobre a qual todo channel é construído.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    Pause o agente para coletar aprovação ou input do usuário no meio da execução.
-  </Card>
-</CardGroup>

docs/edge/pt-BR/guides/frontend/overview.mdx

@@ -1,238 +0,0 @@
----
-title: Frontend Overview
-description: Construa interfaces de usuário interativas para seus agentes CrewAI com o CopilotKit e o protocolo AG-UI.
-icon: browser
-mode: "wide"
----
-
-## Dê uma interface de usuário aos seus agentes
-
-O CrewAI executa seus agentes. O [CopilotKit](https://copilotkit.ai) dá a eles um frontend. Juntos, eles permitem que você construa aplicações em que os usuários conversam com um Crew ou Flow, o observam trabalhar em tempo real, aprovam suas decisões e veem sua saída renderizada como UI ao vivo, em vez de paredes de texto.
-
-Os dois se conectam através do [protocolo AG-UI](https://docs.ag-ui.com). O pacote `ag-ui-crewai` expõe qualquer Crew ou Flow como um endpoint AG-UI. Os hooks e componentes React do CopilotKit consomem esse endpoint. Isso desbloqueia experiências que vão muito além de uma caixa de chat:
-
-<CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    Renderize as chamadas de tool e o estado do agente como seus próprios componentes React.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    Pause o agente para coletar aprovação ou input do usuário no meio da execução.
-  </Card>
-  <Card title="Shared State" icon="arrows-rotate" href="/edge/en/guides/frontend/shared-state">
-    Mantenha o estado do agente e a UI do seu app em sincronia bidirecional.
-  </Card>
-  <Card title="Channels" icon="messages" href="/edge/pt-BR/guides/frontend/channels">
-    Execute o mesmo agente como um bot do Slack, Discord ou Teams.
-  </Card>
-</CardGroup>
-
-Este guia coloca um Crew ou Flow conversando com um frontend Next.js de ponta a ponta. O restante da seção se apoia no app que você configura aqui.
-
-## Arquitetura
-
-Há três peças:
-
-1. **CrewAI agent server** — um processo Python que serve o seu Crew ou Flow por AG-UI (FastAPI + `ag-ui-crewai`).
-2. **CopilotKit runtime** — uma rota Next.js que registra o seu agente e faz o proxy das requisições para ele.
-3. **React frontend** — o provider `<CopilotKit>` mais os componentes de chat e de generative UI.
-
-```
-React app  ──►  CopilotKit runtime (/api/copilotkit)  ──►  CrewAI server (AG-UI)  ──►  Crew / Flow
-```
-
-<Note>
-Este guia cobre o caminho **self-hosted**: você mesmo executa o servidor do agente CrewAI com `ag-ui-crewai`, e ele funciona localmente sem nenhum serviço gerenciado. O CopilotKit também oferece um caminho **gerenciado** (CopilotKit Cloud / Enterprise Intelligence) com threads hospedadas e um inspetor — consulte o [quickstart de CrewAI do CopilotKit](https://docs.copilotkit.ai/crewai-crews/quickstart) se preferir isso. O código do frontend nesta seção é o mesmo de qualquer forma; apenas como o agente é hospedado e registrado é que muda.
-</Note>
-
-<Note>
-O CrewAI roda por trás do AG-UI em três formatos: **Flows** comuns (usados ao longo destes guias), **[Conversational Flows](/edge/en/guides/frontend/conversational-flows)** (nativos, cientes de sessão, baseados em turnos, com paridade total de recursos) e **Crews** (chat básico). O frontend nesta seção é idêntico entre eles — apenas a autoria e o registro no backend é que diferem.
-</Note>
-
-## Guia de integração
-
-<Steps>
-
-<Step title="Sirva seu agente por AG-UI">
-
-Instale o pacote de integração no seu projeto CrewAI:
-
-```bash
-pip install ag-ui-crewai
-```
-
-Exponha o seu agente a partir de um app FastAPI. Flows usam `add_crewai_flow_fastapi_endpoint`; Crews usam `add_crewai_crew_fastapi_endpoint`. Você pode registrar quantos quiser, cada um em seu próprio path.
-
-<CodeGroup>
-
-```python Flow
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
-from my_agents.recipe_flow import RecipeFlow
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_flow_fastapi_endpoint(
-    app=app,
-    flow=RecipeFlow(),
-    path="/recipe",
-)
-```
-
-```python Crew
-# server.py
-from fastapi import FastAPI
-from ag_ui_crewai.endpoint import add_crewai_crew_fastapi_endpoint
-from my_agents.research_crew import ResearchCrew
-
-app = FastAPI(title="CrewAI Agent Server")
-
-add_crewai_crew_fastapi_endpoint(
-    app=app,
-    crew=ResearchCrew().crew(),
-    path="/research",
-)
-```
-
-</CodeGroup>
-
-Execute:
-
-```bash
-uvicorn server:app --port 8000
-```
-
-<Note>
-Defina as variáveis de ambiente do seu provedor de LLM (por exemplo `OPENAI_API_KEY`) antes de iniciar o servidor.
-</Note>
-
-</Step>
-
-<Step title="Crie um app Next.js">
-
-Se você ainda não tem um frontend, gere um:
-
-```bash
-npx create-next-app@latest my-app
-cd my-app
-```
-
-Instale o CopilotKit e o cliente AG-UI do CrewAI:
-
-```bash
-npm install @copilotkit/react-core @copilotkit/react-ui @copilotkit/runtime @ag-ui/crewai
-```
-
-</Step>
-
-<Step title="Adicione o runtime do CopilotKit">
-
-Crie uma rota que registre o(s) seu(s) agente(s) CrewAI no runtime do CopilotKit. Cada agente aponta para um path no seu servidor Python via `CrewAIAgent`.
-
-```ts
-// app/api/copilotkit/route.ts
-import {
-  CopilotRuntime,
-  InMemoryAgentRunner,
-  createCopilotEndpoint,
-} from "@copilotkit/runtime/v2";
-import { CrewAIAgent } from "@ag-ui/crewai";
-import { handle } from "hono/vercel";
-
-const runtime = new CopilotRuntime({
-  agents: {
-    recipe: new CrewAIAgent({ url: "http://localhost:8000/recipe" }),
-  },
-  runner: new InMemoryAgentRunner(),
-});
-
-const app = createCopilotEndpoint({
-  runtime,
-  basePath: "/api/copilotkit",
-});
-
-const handler = handle(app);
-export const GET = handler;
-export const POST = handler;
-```
-
-</Step>
-
-<Step title="Envolva seu app com o provider">
-
-Aponte `<CopilotKit>` para a rota do runtime e nomeie o agente que você registrou.
-
-```tsx
-// app/page.tsx
-"use client";
-import { CopilotKit } from "@copilotkit/react-core";
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-import "@copilotkit/react-core/v2/styles.css";
-
-export default function Page() {
-  return (
-    <CopilotKit runtimeUrl="/api/copilotkit" agent="recipe">
-      <YourApp />
-      <CopilotSidebar agentId="recipe" labels={{ modalHeaderTitle: "Assistant" }} />
-    </CopilotKit>
-  );
-}
-```
-
-</Step>
-
-<Step title="Execute">
-
-Inicie os dois processos e abra o app. Conversar na sidebar agora executa o seu Crew ou Flow.
-
-```bash
-uvicorn server:app --port 8000   # terminal 1
-npm run dev                       # terminal 2
-```
-
-</Step>
-
-</Steps>
-
-## Opções de UI de chat
-
-O CopilotKit entrega três superfícies de chat intercambiáveis. Troque o componente; a fiação é idêntica.
-
-<CodeGroup>
-
-```tsx Sidebar
-import { CopilotSidebar } from "@copilotkit/react-core/v2";
-
-<CopilotSidebar agentId="recipe" />
-```
-
-```tsx Popup
-import { CopilotPopup } from "@copilotkit/react-core/v2";
-
-<CopilotPopup agentId="recipe" />
-```
-
-```tsx Inline
-import { CopilotChat } from "@copilotkit/react-core/v2";
-
-<CopilotChat agentId="recipe" />
-```
-
-</CodeGroup>
-
-## Para onde ir em seguida
-
-<CardGroup cols={2}>
-  <Card title="Generative UI" icon="wand-magic-sparkles" href="/edge/en/guides/frontend/generative-ui">
-    Renderize chamadas de tool e o estado do agente como componentes personalizados.
-  </Card>
-  <Card title="Frontend Actions" icon="bolt" href="/edge/en/guides/frontend/frontend-actions">
-    Permita que o agente chame funções que rodam no navegador.
-  </Card>
-  <Card title="Human-in-the-Loop" icon="user-check" href="/edge/en/guides/frontend/human-in-the-loop">
-    Restrinja ações do agente por trás da aprovação do usuário.
-  </Card>
-  <Card title="Predictive State" icon="gauge-high" href="/edge/en/guides/frontend/predictive-state-updates">
-    Transmita o estado em andamento para a UI enquanto o agente trabalha.
-  </Card>
-</CardGroup>

docs/edge/pt-BR/learn/llm-connections.mdx

@@ -140,7 +140,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
         # Exemplo usando a API compatível com OpenAI do Gemini.
         os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Deve começar com AIza...
         os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
-        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Adicione aqui seu modelo do Gemini, sob openai/
+        os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-2.0-flash"  # Adicione aqui seu modelo do Gemini, sob openai/
         ```
         </CodeGroup>
     </Tab>
@@ -158,7 +158,7 @@ Você pode se conectar a LLMs compatíveis com a OpenAI usando variáveis de amb
             ```python Google
             # Exemplo usando a API compatível com OpenAI do Gemini
             llm = LLM(
-                model="openai/gemini-3.7-flash",
+                model="openai/gemini-2.0-flash",
                 base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
                 api_key="your-gemini-key",  # Deve começar com AIza...
             )

docs/edge/pt-BR/learn/llm-selection-guide.mdx

@@ -148,7 +148,7 @@ Agentes de planejamento se beneficiam de modelos de raciocínio para pensamento
 from crewai import Agent, Task, Crew, LLM
 
 # Modelo de raciocínio para planejamento estratégico
-manager_llm = LLM(model="gemini/gemini-3.7-flash", temperature=0.1)
+manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
 
 # Modelo criativo para gerar conteúdo
 content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
@@ -413,7 +413,7 @@ Em vez de repetir o framework estratégico, segue um checklist tático para impl
     # Agentes gerenciadores ou de coordenação
     manager_agent = Agent(
         role="Project Manager",
-        llm=LLM(model="gemini/gemini-3.7-flash"),
+        llm=LLM(model="gemini-2.5-flash-preview-05-20"),
         # ... demais configs
     )
 

docs/edge/pt-BR/learn/streaming-runtime-contract.mdx

@@ -151,7 +151,7 @@ Flows conversacionais podem transmitir um turno de usuário com `stream_turn()`:
 
 ```python
 from crewai import Flow
-from crewai.flow import ConversationConfig, ConversationState
+from crewai.experimental.conversational import ConversationConfig, ConversationState
 
 
 @ConversationConfig(llm="gpt-4o-mini", defer_trace_finalization=True)

docs/edge/pt-BR/observability/arize-phoenix.mdx

@@ -7,9 +7,9 @@ mode: "wide"
 
 # Integração com Arize Phoenix
 
-Este guia demonstra como integrar o **Arize Phoenix** ao **CrewAI** usando o OpenTelemetry através do [OpenInference](https://github.com/openinference/openinference) SDK. Ao final deste guia, você será capaz de rastrear seus agentes CrewAI e depurar o comportamento dos agentes.
+Este guia demonstra como integrar o **Arize Phoenix** ao **CrewAI** usando o OpenTelemetry através do [OpenInference](https://github.com/openinference/openinference) SDK. Ao final deste guia, você será capaz de rastrear seus agentes CrewAI e depurá-los com facilidade.
 
-> **O que é o Arize Phoenix?** O [Arize Phoenix](https://arize.com/phoenix/) é a opção open-source de observabilidade e avaliação da [Arize AI](https://arize.com/?utm_source=crewai-docs&utm_medium=partner&utm_campaign=partner-docs&utm_content=observability-arize-phoenix). Use o Phoenix quando quiser executar localmente ou fazer self-host. Use o [Arize AX](https://arize.com/products/ax/) para uma plataforma gerenciada em cloud ou enterprise self-hosted para sistemas de IA em produção.
+> **O que é o Arize Phoenix?** O [Arize Phoenix](https://phoenix.arize.com) é uma plataforma de observabilidade de LLM que oferece rastreamento e avaliação para aplicações de IA.
 
 [![Assista a um vídeo demonstrando a nossa integração com o Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
 
@@ -27,7 +27,7 @@ pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoen
 
 ### Passo 2: Configure as Variáveis de Ambiente
 
-Configure sua chave de API do Phoenix e o endpoint do OpenTelemetry para enviar rastros ao Phoenix. A mesma configuração funciona com um endpoint local ou self-hosted do Phoenix alterando a URL do coletor.
+Configure as chaves de API do Phoenix Cloud e ajuste o OpenTelemetry para enviar rastros ao Phoenix. O Phoenix Cloud é uma versão hospedada do Arize Phoenix, mas não é obrigatório para utilizar esta integração.
 
 Você pode obter uma chave de API gratuita do Serper [aqui](https://serper.dev/).
 
@@ -35,8 +35,8 @@ Você pode obter uma chave de API gratuita do Serper [aqui](https://serper.dev/)
 import os
 from getpass import getpass
 
-# Obtenha sua chave de API do Phoenix
-PHOENIX_API_KEY = getpass("🔑 Digite sua Phoenix API key: ")
+# Obtenha suas credenciais do Phoenix Cloud
+PHOENIX_API_KEY = getpass("🔑 Digite sua Phoenix Cloud API Key: ")
 
 # Obtenha as chaves de API para os serviços
 OPENAI_API_KEY = getpass("🔑 Digite sua OpenAI API key: ")
@@ -44,7 +44,7 @@ SERPER_API_KEY = getpass("🔑 Digite sua Serper API key: ")
 
 # Defina as variáveis de ambiente
 os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
-os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Altere para seu próprio endpoint se estiver utilizando uma instância self-hosted
+os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, altere para seu endpoint se estiver utilizando uma instância self-hosted
 os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
 os.environ["SERPER_API_KEY"] = SERPER_API_KEY
 ```
@@ -126,7 +126,7 @@ print(result)
 
 Após executar o agente, você poderá visualizar os rastros gerados pela sua aplicação CrewAI no Phoenix. Você verá etapas detalhadas das interações dos agentes e chamadas de LLM, o que pode ajudar na depuração e otimização dos seus agentes de IA.
 
-Abra seu projeto no Phoenix e navegue até o projeto que você especificou no parâmetro `project_name`. Você verá uma visualização de linha do tempo do seu rastro, incluindo todas as interações dos agentes, uso de ferramentas e chamadas LLM.
+Acesse sua conta Phoenix Cloud e navegue até o projeto que você especificou no parâmetro `project_name`. Você verá uma visualização de linha do tempo do seu rastro, incluindo todas as interações dos agentes, uso de ferramentas e chamadas LLM.
 
 ![Exemplo de rastro no Phoenix mostrando interações de agentes](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
 
@@ -140,9 +140,6 @@ Abra seu projeto no Phoenix e navegue até o projeto que você especificou no pa
 
 ### Referências
 - [Documentação do Phoenix](https://docs.arize.com/phoenix/) - Visão geral da plataforma Phoenix.
-- [Arize AX](https://arize.com/products/ax/) - Observabilidade e avaliação gerenciadas em cloud ou enterprise self-hosted.
-- [Guia de avaliação de agentes da Arize](https://arize.com/guides/ai-agent-handbook/agent-evaluation/) - Workflow de produção para avaliar o comportamento de agentes a partir de rastros.
-- [Guia de avaliação de LLM da Arize](https://arize.com/resources/llm-evaluation/) - Métodos e métricas para avaliar aplicações de LLM.
 - [Documentação do CrewAI](https://docs.crewai.com/) - Visão geral do framework CrewAI.
 - [Documentação do OpenTelemetry](https://opentelemetry.io/docs/) - Guia do OpenTelemetry
-- [OpenInference GitHub](https://github.com/openinference/openinference) - Código-fonte do SDK OpenInference.
+- [OpenInference GitHub](https://github.com/openinference/openinference) - Código-fonte do SDK OpenInference.

docs/edge/pt-BR/telemetry.mdx

@@ -23,7 +23,7 @@ uso de ferramentas, chamadas de API, respostas, quaisquer dados processados pelo
 Quando o recurso `share_crew` está ativado, dados detalhados, incluindo descrições das tarefas, histórias ou objetivos dos agentes e outros atributos específicos são coletados
 para fornecer insights mais detalhados. Essa coleta expandida pode incluir informações pessoais caso o usuário as tenha inserido em seus crews ou tarefas.
 Usuários devem considerar cuidadosamente o conteúdo de seus crews e tarefas antes de habilitar o `share_crew`.
-A telemetria do CrewAI pode ser desabilitada ao definir `CREWAI_DISABLE_TELEMETRY` como `true`, `1`, `yes` ou `on` (qualquer capitalização). `OTEL_SDK_DISABLED` com os mesmos valores também desabilita o exportador do CrewAI. O SDK do OpenTelemetry em si ainda só reconhece `true` para desabilitar as demais instrumentações do processo.
+A telemetria pode ser desabilitada ao definir a variável de ambiente `CREWAI_DISABLE_TELEMETRY` como `true` ou ao definir `OTEL_SDK_DISABLED` como `true` (observe que esta última desabilita toda instrumentação OpenTelemetry globalmente).
 
 ### Exemplos:
 ```python
@@ -34,8 +34,6 @@ os.environ['CREWAI_DISABLE_TELEMETRY'] = 'true'
 os.environ['OTEL_SDK_DISABLED'] = 'true'
 ```
 
-`CREWAI_DISABLE_TELEMETRY=1` (ou `yes` / `on`) funciona como `true`. Valores não reconhecidos são ignorados e a telemetria permanece ligada.
-
 ### Isolamento da sua própria configuração do OpenTelemetry
 
 A telemetria do CrewAI roda em seu próprio `TracerProvider` privado e nunca se
@@ -54,17 +52,16 @@ por meio do próprio tracer provider, que é independente do descrito aqui.
 | Padrão | Dados                                      | Razão e Especificidades                                                                                                                         |
 |--------|--------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|
 | Sim    | Versão do CrewAI e Python                  | Rastreia versões dos softwares. Exemplo: CrewAI v1.2.3, Python 3.8.10. Sem dados pessoais.                 |
-| Sim    | Metadados do Crew                         | Inclui: chave e ID gerados aleatoriamente, tipo de processo (ex: 'sequential', 'parallel'), flag booleana para uso de memória (true/false), uma flag booleana indicando se alguma entrada foi passada para a execução (true/false — nunca as chaves ou os valores das entradas, que só são coletados quando `share_crew` está habilitado), quantidade de tarefas, quantidade de agentes. Tudo não pessoal.  |
+| Sim    | Metadados do Crew                         | Inclui: chave e ID gerados aleatoriamente, tipo de processo (ex: 'sequential', 'parallel'), flag booleana para uso de memória (true/false), quantidade de tarefas, quantidade de agentes. Tudo não pessoal.  |
 | Sim    | Dados do Agente                           | Inclui: chave e ID gerados aleatoriamente, nome da função (não deve incluir info pessoal), configurações booleanas (verbose, delegação habilitada, execução de código permitida), máximo de iterações, máximo de RPM, limite de tentativas, info do LLM (ver Atributos LLM), lista de nomes de ferramentas (não deve conter info pessoal). Sem dados pessoais.   |
 | Sim    | Metadados da Tarefa                       | Inclui: chave e ID gerados aleatoriamente, configurações de execução booleanas (async_execution, human_input), função e chave do agente associado, lista de nomes de ferramentas. Tudo não pessoal.      |
 | Sim    | Estatísticas de Uso de Ferramentas         | Inclui: nome da ferramenta (não deve incluir info pessoal), número de tentativas de uso (inteiro), atributos LLM utilizados. Sem dados pessoais.                        |
 | Sim    | Dados de Execução de Testes                | Inclui: chave e ID aleatórias do crew, número de iterações, nome do modelo usado, score de qualidade (float), tempo de execução (em segundos). Tudo não pessoal.         |
-| Sim    | Dados do Ciclo de Vida da Tarefa           | Inclui: horários de criação, início/fim de execução, identificadores de crew e tarefa, e se a tarefa foi bem-sucedida ou falhou. Quando uma tarefa falha, o **nome da classe** da exceção é registrado (por exemplo `TimeoutError`) para que as falhas possam ser contadas e diagnosticadas — nunca a mensagem de erro, que pode conter prompts, saída do modelo, caminhos de arquivos ou credenciais. Armazenado como spans com timestamps. Sem dados pessoais.            |
+| Sim    | Dados do Ciclo de Vida da Tarefa           | Inclui: horários de criação, início/fim de execução, identificadores de crew e tarefa. Armazenado como spans com timestamps. Sem dados pessoais.            |
 | Sim    | Atributos do LLM                           | Inclui: nome, model_name, model, top_k, temperatura e nome da classe do LLM. Todos técnicos, sem dados pessoais.                  |
-| Sim    | Criação de Projeto pelo CLI do crewAI | Inclui: o fato de um novo projeto ter sido criado por `crewai create`, de qual tipo ele é (`crew`, `json_crew` ou `flow`) e o ID de projeto gerado para esse novo projeto e gravado no `pyproject.toml` dele. É o ID do próprio projeto novo, registrado separadamente do `project_id` do diretório de onde o comando foi executado — os dois podem diferir. Sem nome de projeto, sem conteúdo de arquivos, sem código. Sem dados pessoais. |
 | Sim    | Tentativa de Deploy do Crew pelo CLI do crewAI | Inclui: O fato de um deploy estar sendo realizado e o crew id, se está tentando buscar logs, e se o deploy foi iniciado por um comando do CLI ou pela TUI de execução. Não inclui conteúdo do projeto ou do crew nem dados pessoais. |
-| Sim    | Ambiente de Execução                       | Inclui: qual assistente de código com IA está executando o processo, se houver (um valor de uma lista fixa como `claude_code`, `codex`, `cursor` ou `unknown`), onde o processo é executado (um valor de uma lista fixa como `ci`, `container`, `serverless`, `interactive`), o `project_id` do seu `pyproject.toml` quando houver um configurado e uma faixa aproximada de tamanho da máquina (uma de `1-2`, `3-4`, `5-8`, `9-16`, `17-32`, `33+` ou `unknown`). A faixa é um intervalo, nunca a contagem exata de núcleos — a contagem exata é opcional, em Informações de Ambiente abaixo. A faixa de tamanho vem da contagem de núcleos do host; a detecção do assistente e do local de execução lê apenas se variáveis de ambiente conhecidas estão definidas, nunca seus valores. Sem dados pessoais. |
-| Sim    | Sinais de Ciclo de Vida do Flow | Inclui: que um flow iniciou, se foi concluído ou falhou, se um de seus métodos falhou, se pausou para entrada ou feedback humano, se o início foi uma execução retomada, se um turno de conversa falhou, quanto tempo o flow executou, e se o flow é um que a CrewAI executa internamente ou um que você escreveu. O nome do flow é registrado, como já é para criação e execução de flow. Quando um flow ou um de seus métodos falha, o **nome da classe** da exceção é registrado (por exemplo `TimeoutError`) para permitir o diagnóstico de falhas — nunca a mensagem de erro, que pode conter prompts, saída do modelo, caminhos de arquivo ou credenciais. Nomes de métodos e estado do flow nunca são registrados. Nenhum dado pessoal. |
+| Sim    | Ambiente de Execução                       | Inclui: qual assistente de código com IA está executando o processo, se houver (um de uma lista fixa como `claude_code`, `codex`, `cursor` ou `unknown`), onde o processo é executado (um de uma lista fixa como `ci`, `container`, `serverless`, `interactive`) e o `project_id` do seu `pyproject.toml` quando houver um configurado. A detecção lê apenas se variáveis de ambiente conhecidas estão definidas, nunca seus valores. Sem dados pessoais. |
+| Sim    | Sinais de Ciclo de Vida do Flow | Inclui: que um flow iniciou, se foi concluído ou falhou, se um de seus métodos falhou, se pausou para entrada ou feedback humano, se o início foi uma execução retomada, se um turno de conversa falhou, por quanto tempo o flow executou e se o flow é um que o CrewAI executa internamente ou um que você escreveu. O nome do flow é registrado (não deve incluir informações pessoais), como já ocorre na criação e execução do flow. Nomes de métodos, mensagens de erro e estado do flow nunca são registrados. Sem dados pessoais. |
 | Sim    | Sinal de Compartilhamento de Trace | Inclui: que um lote de traces foi compartilhado com sucesso com o CrewAI AMP, e se foi compartilhado anonimamente (antes de você ter uma conta) ou vinculado à sua conta. Como todo span, também carrega os atributos de Ambiente de Execução descritos acima (`project_id` quando configurado, o assistente de programação e o runtime). Esta linha descreve apenas a telemetria do compartilhamento — não o conteúdo dos traces nem o acesso concedido por links de traces compartilhados. O conteúdo dos traces, entradas e saídas nunca são registrados neste sinal. Antes de compartilhar traces, revise segredos, dados pessoais e as configurações de redação e retenção do AMP. |
 | Não    | Dados Expandidos do Agente                 | Inclui: descrição do objetivo, texto da história, identificador de arquivo i18n prompt. Usuários devem garantir que não haja info pessoal nesses campos de texto.            |
 | Não    | Informações Detalhadas da Tarefa           | Inclui: descrição da tarefa, descrição do resultado esperado, referências de contexto. Usuários devem garantir que não haja info pessoal nessas áreas.           |

docs/edge/pt-BR/tools/ai-ml/overview.mdx

@@ -50,16 +50,17 @@ Essas ferramentas se integram com serviços de IA e machine learning para aprimo
 - **Segurança em IA**: Implemente moderação de conteúdo e checagens de segurança
 
 ```python
-from crewai_tools import DallETool, VisionTool
+from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
 
 # Create AI tools
 image_generator = DallETool()
 vision_processor = VisionTool()
+code_executor = CodeInterpreterTool()
 
 # Add to your agent
 agent = Agent(
     role="AI Specialist",
-    tools=[image_generator, vision_processor],
+    tools=[image_generator, vision_processor, code_executor],
     goal="Create and analyze content using AI capabilities"
 )
 ```

docs/edge/pt-BR/tools/file-document/filereadtool.mdx

@@ -1,6 +1,6 @@
 ---
 title: Leitura de Arquivo
-description: O `FileReadTool` foi desenvolvido para ler arquivos do sistema de arquivos local.
+description: O `FileReadTool` lê arquivos do sistema de arquivos local.
 icon: folders
 mode: "wide"
 ---
@@ -8,37 +8,63 @@ mode: "wide"
 ## Visão Geral
 
 <Note>
-    Ainda estamos trabalhando para melhorar as ferramentas, portanto pode haver comportamentos inesperados ou alterações no futuro.
+    Ainda estamos melhorando as ferramentas, então o comportamento pode mudar.
 </Note>
 
-O FileReadTool representa conceitualmente um conjunto de funcionalidades dentro do pacote crewai_tools voltadas para facilitar a leitura e a recuperação de conteúdo de arquivos.
-Esse conjunto inclui ferramentas para processar arquivos de texto em lote, ler arquivos de configuração em tempo de execução e importar dados para análise.
-Ele suporta uma variedade de formatos de arquivo baseados em texto, como `.txt`, `.csv`, `.json` e outros. O conteúdo é sempre retornado como texto simples.
+O `FileReadTool` lê um arquivo local e retorna o conteúdo como texto.
+Use-o para processar arquivos de texto, ler arquivos de configuração ou carregar dados para análise.
+Ele funciona com qualquer formato de texto, como `.txt`, `.csv`, `.json` e `.md`.
+A ferramenta sempre retorna texto simples. Se você precisar de dados estruturados (por exemplo, JSON), faça o parse no Agent ou no seu próprio código.
+
+Para arquivos grandes, o Agent pode passar `start_line` e `line_count` para ler apenas um intervalo de linhas.
+A ferramenta para assim que obtém essas linhas, então não percorre o restante do arquivo.
 
 ## Instalação
 
-Para utilizar as funcionalidades anteriormente atribuídas ao FileReadTool, instale o pacote crewai_tools:
-
 ```shell
-pip install 'crewai[tools]'
+uv add 'crewai[tools]'
 ```
 
 ## Exemplo de Uso
 
-Para começar a usar o FileReadTool:
-
 ```python Code
 from crewai_tools import FileReadTool
 
-# Inicialize a ferramenta para ler quaisquer arquivos que os agentes conhecem ou informe o caminho para
-file_read_tool = FileReadTool()
+# Agent chooses the file path at runtime
+tool = FileReadTool()
 
-# OU
+# OR set a default file the agent can read with no path argument
+tool = FileReadTool(file_path='path/to/your/file.txt')
 
-# Inicialize a ferramenta com um caminho de arquivo específico, assim o agente poderá ler apenas o conteúdo do arquivo especificado
-file_read_tool = FileReadTool(file_path='path/to/your/file.txt')
+# OR let the agent read any file under a directory
+tool = FileReadTool(base_dir='/data')
 ```
 
+Passe a ferramenta para um Agent. Em tempo de execução, o LLM envia `file_path` e, opcionalmente, `start_line` e `line_count`.
+
 ## Argumentos
 
-- `file_path`: O caminho para o arquivo que você deseja ler. Aceita caminhos absolutos e relativos. Certifique-se de que o arquivo exista e de que você tenha as permissões necessárias para acessá-lo.
+O Agent pode passar estes argumentos em tempo de execução:
+
+- `file_path`: (Opcional) Caminho do arquivo a ler. Caminhos absolutos e relativos só são válidos quando resolvem dentro do sandbox de `base_dir`. Um caminho relativo resolve em relação a `base_dir` quando definido; caso contrário, em relação ao diretório de trabalho atual (o sandbox padrão). Omita-o para ler o arquivo padrão definido na construção. Se não houver padrão, a ferramenta retorna um erro dizendo que nenhum caminho foi fornecido.
+- `start_line`: (Opcional) Primeira linha a ler. A numeração começa em `1`. O padrão é `1`.
+- `line_count`: (Opcional) Quantidade de linhas a ler. Se omitido, a ferramenta lê de `start_line` até o fim do arquivo.
+
+Você pode definir estes argumentos ao criar a ferramenta:
+
+- `file_path`: (Opcional) Arquivo padrão a ler quando o Agent chama a ferramenta sem caminho. Um caminho relativo resolve em relação a `base_dir` quando `base_dir` é fornecido; caso contrário, em relação ao diretório de trabalho atual.
+- `base_dir`: (Opcional) Diretório dentro do qual os caminhos em tempo de execução devem permanecer. O padrão é o diretório de trabalho atual. A ferramenta resolve este caminho na criação, então uma mudança posterior do diretório de trabalho não move o sandbox.
+- `encoding`: (Opcional) Codificação de texto usada para decodificar o arquivo. O padrão é `utf-8`. Se a decodificação falhar, a ferramenta retorna um erro e sugere passar um `encoding` diferente.
+
+Falhas comuns (arquivo ausente, permissão negada, encoding incorreto ou caminho fora do sandbox) retornam uma string de erro. Elas não levantam uma exceção.
+
+## Caminhos permitidos
+
+Um LLM geralmente escolhe o caminho do arquivo em tempo de execução, então as leituras ficam limitadas a um sandbox:
+
+- Os caminhos em tempo de execução devem resolver dentro de `base_dir` (padrão: o diretório de trabalho atual). A ferramenta resolve segmentos `..` e links simbólicos antes de verificar o caminho, então eles não podem escapar do sandbox.
+- Um `file_path` passado ao construtor é sempre permitido, mesmo fora de `base_dir`. A leitura ainda pode falhar se o arquivo estiver ausente, for um diretório ou não puder ser acessado. Esse caminho fica fixo quando a ferramenta é criada, então uma mudança posterior do diretório de trabalho não altera o arquivo apontado. O Agent pode lê-lo omitindo `file_path` ou usando o nome mostrado na descrição da ferramenta. Declarar um arquivo não permite acesso a outros arquivos na mesma pasta.
+
+Para permitir que um Agent leia arquivos fora do diretório de trabalho, defina `base_dir` ao criar a ferramenta (veja o exemplo acima).
+
+Como último recurso, defina `CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true` para desativar as verificações de caminho. Essa configuração se aplica a todas as ferramentas crewai-tools no processo, incluindo proteções SSRF em ferramentas que buscam URLs. Prefira `base_dir`.

docs/edge/pt-BR/tools/web-scraping/scrapeelementfromwebsitetool.mdx

@@ -9,7 +9,7 @@ mode: "wide"
 
 ## Descrição
 
-A `ScrapeElementFromWebsiteTool` foi projetada para extrair elementos específicos de websites utilizando seletores CSS. Esta ferramenta permite que agentes CrewAI capturem conteúdos direcionados de páginas web, tornando-se útil para tarefas de extração de dados em que apenas partes específicas de uma página são necessárias. As buscas passam pelo helper HTTP seguro contra SSRF do CrewAI: a URL solicitada e cada hop de redirecionamento são verificados contra faixas privadas e reservadas (incluindo metadados de nuvem), e a conexão TCP é fixada no IP que passou nessa verificação.
+A `ScrapeElementFromWebsiteTool` foi projetada para extrair elementos específicos de websites utilizando seletores CSS. Esta ferramenta permite que agentes CrewAI capturem conteúdos direcionados de páginas web, tornando-se útil para tarefas de extração de dados em que apenas partes específicas de uma página são necessárias.
 
 ## Instalação
 

docs/edge/pt-BR/tools/web-scraping/scrapewebsitetool.mdx

@@ -16,8 +16,6 @@ mode: "wide"
 Uma ferramenta desenvolvida para extrair e ler o conteúdo de um site especificado. Ela é capaz de lidar com diversos tipos de páginas web fazendo requisições HTTP e analisando o conteúdo HTML recebido.
 Esta ferramenta pode ser especialmente útil para tarefas de raspagem de dados, coleta de dados ou extração de informações específicas de sites.
 
-As buscas passam pelo helper HTTP seguro contra SSRF do CrewAI: a URL solicitada e cada hop de redirecionamento são verificados contra faixas privadas e reservadas (incluindo metadados de nuvem), e a conexão TCP é fixada no IP que passou nessa verificação.
-
 ## Instalação
 
 Instale o pacote crewai_tools

docs/v1.15.16/ar/api-reference/inputs.mdx

@@ -1,8 +0,0 @@
----
-title: "GET /inputs"
-description: "الحصول على المدخلات المطلوبة لطاقمك"
-openapi: "/v1.15.16/enterprise-api.en.yaml GET /inputs"
-mode: "wide"
----
-
-

docs/v1.15.16/ar/api-reference/introduction.mdx

@@ -1,135 +0,0 @@
----
-title: "مقدمة"
-description: "المرجع الكامل لواجهة برمجة تطبيقات CrewAI AMP REST"
-icon: "code"
-mode: "wide"
----
-
-# واجهة برمجة تطبيقات CrewAI AMP
-
-مرحبًا بك في مرجع واجهة برمجة تطبيقات CrewAI AMP. تتيح لك هذه الواجهة التفاعل برمجيًا مع الأطقم المنشورة، مما يمكّنك من دمجها مع تطبيقاتك وسير عملك وخدماتك.
-
-## البدء السريع
-
-<Steps>
-  <Step title="الحصول على بيانات اعتماد API">
-    انتقل إلى صفحة تفاصيل طاقمك في لوحة تحكم CrewAI AMP وانسخ رمز Bearer من علامة تبويب الحالة.
-  </Step>
-
-<Step title="اكتشاف المدخلات المطلوبة">
-  استخدم نقطة النهاية `GET /inputs` لمعرفة المعاملات التي يتوقعها طاقمك.
-</Step>
-
-<Step title="بدء تنفيذ الطاقم">
-  استدعِ `POST /kickoff` مع مدخلاتك لبدء تنفيذ الطاقم واستلام
-  `kickoff_id`.
-</Step>
-
-  <Step title="مراقبة التقدم">
-    استخدم `GET /status/{kickoff_id}` للتحقق من حالة التنفيذ واسترجاع النتائج.
-  </Step>
-</Steps>
-
-## المصادقة
-
-تتطلب جميع طلبات API المصادقة باستخدام رمز Bearer. أدرج رمزك في ترويسة `Authorization`:
-
-```bash
-curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
-  https://your-crew-url.crewai.com/inputs
-```
-
-### أنواع الرموز
-
-| نوع الرمز            | النطاق                     | حالة الاستخدام                                                     |
-| :-------------------- | :------------------------ | :----------------------------------------------------------- |
-| **Bearer Token**      | وصول على مستوى المؤسسة | عمليات الطاقم الكاملة، مثالي للتكامل بين الخوادم |
-| **User Bearer Token** | وصول محدد بالمستخدم        | صلاحيات محدودة، مناسب للعمليات الخاصة بالمستخدم   |
-
-<Tip>
-  يمكنك العثور على كلا نوعي الرموز في علامة تبويب الحالة من صفحة تفاصيل طاقمك في
-  لوحة تحكم CrewAI AMP.
-</Tip>
-
-## عنوان URL الأساسي
-
-لكل طاقم منشور نقطة نهاية API فريدة خاصة به:
-
-```
-https://your-crew-name.crewai.com
-```
-
-استبدل `your-crew-name` بعنوان URL الفعلي لطاقمك من لوحة التحكم.
-
-## سير العمل النموذجي
-
-1. **الاكتشاف**: استدعِ `GET /inputs` لفهم ما يحتاجه طاقمك
-2. **التنفيذ**: أرسل المدخلات عبر `POST /kickoff` لبدء المعالجة
-3. **المراقبة**: استعلم عن `GET /status/{kickoff_id}` حتى الاكتمال
-4. **النتائج**: استخرج المخرجات النهائية من الاستجابة المكتملة
-
-## معالجة الأخطاء
-
-تستخدم الواجهة أكواد حالة HTTP القياسية:
-
-| الكود  | المعنى                                    |
-| ----- | :----------------------------------------- |
-| `200` | نجاح                                    |
-| `400` | طلب غير صالح - تنسيق مدخلات غير صحيح         |
-| `401` | غير مصرّح - رمز bearer غير صالح        |
-| `404` | غير موجود - المورد غير موجود         |
-| `422` | خطأ في التحقق - مدخلات مطلوبة مفقودة |
-| `500` | خطأ في الخادم - تواصل مع الدعم             |
-
-## الاختبار التفاعلي
-
-<Info>
-  **لماذا لا يوجد زر "إرسال"؟** نظرًا لأن كل مستخدم CrewAI AMP لديه عنوان URL
-  فريد للطاقم، نستخدم **وضع المرجع** بدلاً من بيئة تفاعلية لتجنب
-  الالتباس. يوضح لك هذا بالضبط كيف يجب أن تبدو الطلبات بدون
-  أزرار إرسال غير فعالة.
-</Info>
-
-تعرض لك كل صفحة نقطة نهاية:
-
-- **تنسيق الطلب الدقيق** مع جميع المعاملات
-- **أمثلة الاستجابة** لحالات النجاح والخطأ
-- **عينات الكود** بلغات متعددة (cURL، Python، JavaScript، إلخ)
-- **أمثلة المصادقة** بتنسيق رمز Bearer الصحيح
-
-### **لاختبار واجهتك الفعلية:**
-
-<CardGroup cols={2}>
-  <Card title="نسخ أمثلة cURL" icon="terminal">
-    انسخ أمثلة cURL واستبدل العنوان URL + الرمز بقيمك الحقيقية
-  </Card>
-  <Card title="استخدام Postman/Insomnia" icon="play">
-    استورد الأمثلة في أداة اختبار API المفضلة لديك
-  </Card>
-</CardGroup>
-
-**مثال على سير العمل:**
-
-1. **انسخ مثال cURL هذا** من أي صفحة نقطة نهاية
-2. **استبدل `your-actual-crew-name.crewai.com`** بعنوان URL الحقيقي لطاقمك
-3. **استبدل رمز Bearer** برمزك الحقيقي من لوحة التحكم
-4. **نفّذ الطلب** في طرفيتك أو عميل API
-
-## هل تحتاج مساعدة؟
-
-<CardGroup cols={2}>
-  <Card
-    title="دعم المؤسسات"
-    icon="headset"
-    href="mailto:support@crewai.com"
-  >
-    احصل على مساعدة في تكامل API واستكشاف الأخطاء وإصلاحها
-  </Card>
-  <Card
-    title="لوحة تحكم المؤسسات"
-    icon="chart-line"
-    href="https://app.crewai.com"
-  >
-    إدارة أطقمك وعرض سجلات التنفيذ
-  </Card>
-</CardGroup>

docs/v1.15.16/ar/api-reference/kickoff.mdx

@@ -1,8 +0,0 @@
----
-title: "POST /kickoff"
-description: "بدء تنفيذ الطاقم"
-openapi: "/v1.15.16/enterprise-api.en.yaml POST /kickoff"
-mode: "wide"
----
-
-

docs/v1.15.16/ar/api-reference/resume.mdx

@@ -1,6 +0,0 @@
----
-title: "POST /resume"
-description: "استئناف تنفيذ الطاقم مع التغذية الراجعة البشرية"
-openapi: "/v1.15.16/enterprise-api.en.yaml POST /resume"
-mode: "wide"
----

docs/v1.15.16/ar/api-reference/status.mdx

@@ -1,6 +0,0 @@
----
-title: "GET /status/{kickoff_id}"
-description: "الحصول على حالة التنفيذ"
-openapi: "/v1.15.16/enterprise-api.en.yaml GET /status/{kickoff_id}"
-mode: "wide"
----

docs/v1.15.16/ar/concepts/agent-capabilities.mdx

@@ -1,147 +0,0 @@
----
-title: "قدرات الوكيل"
-description: "فهم الطرق الخمس لتوسيع وكلاء CrewAI: الأدوات، MCP، التطبيقات، المهارات، والمعرفة."
-icon: puzzle-piece
-mode: "wide"
----
-
-## نظرة عامة
-
-يمكن توسيع وكلاء CrewAI بـ **خمسة أنواع مميزة من القدرات**، كل منها يخدم غرضًا مختلفًا. فهم متى تستخدم كل نوع — وكيف يعملون معًا — هو المفتاح لبناء وكلاء فعّالين.
-
-<CardGroup cols={2}>
-  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools" color="#3B82F6">
-    **دوال قابلة للاستدعاء** — تمنح الوكلاء القدرة على اتخاذ إجراءات. البحث على الويب، عمليات الملفات، استدعاءات API، تنفيذ الكود.
-  </Card>
-  <Card title="خوادم MCP" icon="plug" href="/ar/mcp/overview" color="#8B5CF6">
-    **خوادم أدوات عن بُعد** — تربط الوكلاء بخوادم أدوات خارجية عبر Model Context Protocol. نفس تأثير الأدوات، لكن مستضافة خارجيًا.
-  </Card>
-  <Card title="التطبيقات" icon="grid-2" color="#EC4899">
-    **تكاملات المنصة** — تربط الوكلاء بتطبيقات SaaS (Gmail، Slack، Jira، Salesforce) عبر منصة CrewAI. تعمل محليًا مع رمز تكامل المنصة.
-  </Card>
-  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills" color="#F59E0B">
-    **خبرة المجال** — تحقن التعليمات والإرشادات والمواد المرجعية في إرشادات الوكلاء. المهارات تخبر الوكلاء *كيف يفكرون*.
-  </Card>
-  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge" color="#10B981">
-    **حقائق مُسترجعة** — توفر للوكلاء بيانات من المستندات والملفات وعناوين URL عبر البحث الدلالي (RAG). المعرفة تعطي الوكلاء *ما يحتاجون معرفته*.
-  </Card>
-</CardGroup>
-
----
-
-## التمييز الأساسي
-
-أهم شيء يجب فهمه: **هذه القدرات تنقسم إلى فئتين**.
-
-### قدرات الإجراء (الأدوات، MCP، التطبيقات)
-
-تمنح الوكلاء القدرة على **فعل أشياء** — استدعاء APIs، قراءة الملفات، البحث على الويب، إرسال رسائل البريد الإلكتروني. عند التنفيذ، تتحول الأنواع الثلاثة إلى نفس التنسيق الداخلي (مثيلات `BaseTool`) وتظهر في قائمة أدوات موحدة يمكن للوكيل استدعاؤها.
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool
-
-agent = Agent(
-    role="Researcher",
-    goal="Find and compile market data",
-    backstory="Expert market analyst",
-    tools=[SerperDevTool(), FileReadTool()],  # أدوات محلية
-    mcps=["https://mcp.example.com/sse"],     # أدوات خادم MCP عن بُعد
-    apps=["gmail", "google_sheets"],           # تكاملات المنصة
-)
-```
-
-### قدرات السياق (المهارات، المعرفة)
-
-تُعدّل **إرشادات** الوكيل — بحقن الخبرة أو التعليمات أو البيانات المُسترجعة قبل أن يبدأ الوكيل في التفكير. لا تمنح الوكلاء إجراءات جديدة؛ بل تُشكّل كيف يفكر الوكلاء وما هي المعلومات التي يمكنهم الوصول إليها.
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Security Auditor",
-    goal="Audit cloud infrastructure for vulnerabilities",
-    backstory="Expert in cloud security with 10 years of experience",
-    skills=["./skills/security-audit"],        # تعليمات المجال
-    knowledge_sources=[pdf_source, url_source], # حقائق مُسترجعة
-)
-```
-
----
-
-## متى تستخدم ماذا
-
-| تحتاج إلى...                                            | استخدم            | مثال                                     |
-| :------------------------------------------------------- | :---------------- | :--------------------------------------- |
-| الوكيل يبحث على الويب                                    | **الأدوات**       | `tools=[SerperDevTool()]`                |
-| الوكيل يستدعي API عن بُعد عبر MCP                        | **MCP**           | `mcps=["https://api.example.com/sse"]`   |
-| الوكيل يرسل بريد إلكتروني عبر Gmail                      | **التطبيقات**     | `apps=["gmail"]`                         |
-| الوكيل يتبع إجراءات محددة                                | **المهارات**      | `skills=["./skills/code-review"]`        |
-| الوكيل يرجع لمستندات الشركة                              | **المعرفة**       | `knowledge_sources=[pdf_source]`         |
-| الوكيل يبحث على الويب ويتبع إرشادات المراجعة              | **الأدوات + المهارات** | استخدم كليهما معًا                  |
-
----
-
-## دمج القدرات
-
-في الممارسة العملية، غالبًا ما يستخدم الوكلاء **أنواعًا متعددة من القدرات معًا**. إليك مثال واقعي:
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-# وكيل بحث مجهز بالكامل
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Produce comprehensive market analysis reports",
-    backstory="Expert analyst with deep industry knowledge",
-
-    # الإجراء: ما يمكن للوكيل فعله
-    tools=[
-        SerperDevTool(),         # البحث على الويب
-        FileReadTool(),          # قراءة الملفات المحلية
-        CodeInterpreterTool(),   # تشغيل كود Python للتحليل
-    ],
-    mcps=["https://data-api.example.com/sse"],  # الوصول لـ API بيانات عن بُعد
-    apps=["google_sheets"],                      # الكتابة في Google Sheets
-
-    # السياق: ما يعرفه الوكيل
-    skills=["./skills/research-methodology"],    # كيفية إجراء البحث
-    knowledge_sources=[company_docs],            # بيانات خاصة بالشركة
-)
-```
-
----
-
-## جدول المقارنة
-
-| الميزة | الأدوات | MCP | التطبيقات | المهارات | المعرفة |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **يمنح الوكيل إجراءات** | ✅ | ✅ | ✅ | ❌ | ❌ |
-| **يُعدّل الإرشادات** | ❌ | ❌ | ❌ | ✅ | ✅ |
-| **يتطلب كود** | نعم | إعداد فقط | إعداد فقط | Markdown فقط | إعداد فقط |
-| **يعمل محليًا** | نعم | يعتمد | نعم (مع متغير بيئة) | غير متاح | نعم |
-| **يحتاج مفاتيح API** | لكل أداة | لكل خادم | رمز التكامل | لا | المُضمّن فقط |
-| **يُعيَّن على Agent** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
-| **يُعيَّن على Crew** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
-
----
-
-## تعمّق أكثر
-
-هل أنت مستعد لمعرفة المزيد عن كل نوع من أنواع القدرات؟
-
-<CardGroup cols={2}>
-  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools">
-    إنشاء أدوات مخصصة، استخدام كتالوج OSS مع أكثر من 75 خيارًا، تكوين التخزين المؤقت والتنفيذ غير المتزامن.
-  </Card>
-  <Card title="تكامل MCP" icon="plug" href="/ar/mcp/overview">
-    الاتصال بخوادم MCP عبر stdio أو SSE أو HTTP. تصفية الأدوات، تكوين المصادقة.
-  </Card>
-  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills">
-    بناء حزم المهارات مع SKILL.md، حقن خبرة المجال، استخدام الكشف التدريجي.
-  </Card>
-  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge">
-    إضافة المعرفة من ملفات PDF وCSV وعناوين URL والمزيد. تكوين المُضمّنات والاسترجاع.
-  </Card>
-</CardGroup>

docs/v1.15.16/ar/concepts/agents.mdx

@@ -1,383 +0,0 @@
----
-title: الوكلاء
-description: دليل تفصيلي حول إنشاء وإدارة الوكلاء ضمن إطار عمل CrewAI.
-icon: robot
-mode: "wide"
----
-
-## نظرة عامة على الوكيل
-
-في إطار عمل CrewAI، الـ `Agent` هو وحدة مستقلة يمكنها:
-
-- أداء مهام محددة
-- اتخاذ قرارات بناءً على دوره وهدفه
-- استخدام الأدوات لتحقيق الأهداف
-- التواصل والتعاون مع وكلاء آخرين
-- الاحتفاظ بذاكرة التفاعلات
-- تفويض المهام عند السماح بذلك
-
-<Tip>
-  فكّر في الوكيل كعضو فريق متخصص بمهارات وخبرات ومسؤوليات محددة.
-  على سبيل المثال، قد يتفوق وكيل `Researcher` في جمع وتحليل المعلومات،
-  بينما قد يكون وكيل `Writer` أفضل في إنشاء المحتوى.
-</Tip>
-
-<Note type="info" title="تحسين المؤسسات: منشئ الوكلاء المرئي">
-يتضمن CrewAI AMP منشئ وكلاء مرئي يبسّط إنشاء وتهيئة الوكلاء بدون كتابة كود. صمم وكلاءك بصريًا واختبرهم في الوقت الفعلي.
-
-![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
-
-يُمكّن منشئ الوكلاء المرئي من:
-
-- تهيئة وكلاء بديهية بواجهات نماذج
-- اختبار والتحقق في الوقت الفعلي
-- مكتبة قوالب مع أنواع وكلاء مهيأة مسبقًا
-- تخصيص سهل لخصائص وسلوكيات الوكيل
-</Note>
-
-## خصائص الوكيل
-
-| الخاصية                               | المعامل                | النوع                                  | الوصف                                                                                              |
-| :-------------------------------------- | :----------------------- | :------------------------------------ | :------------------------------------------------------------------------------------------------------- |
-| **الدور**                                | `role`                   | `str`                                 | يحدد وظيفة الوكيل وخبرته ضمن الطاقم.                                              |
-| **الهدف**                                | `goal`                   | `str`                                 | الهدف الفردي الذي يوجه عملية اتخاذ القرار لدى الوكيل.                                        |
-| **الخلفية**                           | `backstory`              | `str`                                 | يوفر سياقًا وشخصية للوكيل، مما يثري التفاعلات.                                   |
-| **LLM** _(اختياري)_                    | `llm`                    | `Union[str, LLM, Any]`                | نموذج اللغة الذي يشغّل الوكيل. افتراضيًا النموذج المحدد في `OPENAI_MODEL_NAME` أو "gpt-4". |
-| **الأدوات** _(اختياري)_                  | `tools`                  | `List[BaseTool]`                      | القدرات أو الوظائف المتاحة للوكيل. افتراضيًا قائمة فارغة.                             |
-| **LLM استدعاء الدوال** _(اختياري)_   | `function_calling_llm`   | `Optional[Any]`                       | نموذج لغة لاستدعاء الأدوات، يتجاوز LLM الطاقم إذا حُدد.                                      |
-| **الحد الأقصى للتكرارات** _(اختياري)_         | `max_iter`               | `int`                                 | الحد الأقصى للتكرارات قبل أن يقدم الوكيل أفضل إجابته. الافتراضي 20.                         |
-| **الحد الأقصى لـ RPM** _(اختياري)_                | `max_rpm`                | `Optional[int]`                       | الحد الأقصى للطلبات في الدقيقة لتجنب حدود المعدل.                                                        |
-| **الحد الأقصى لوقت التنفيذ** _(اختياري)_     | `max_execution_time`     | `Optional[int]`                       | الحد الأقصى للوقت (بالثواني) لتنفيذ المهمة.                                                            |
-| **الوضع المفصل** _(اختياري)_                | `verbose`                | `bool`                                | تفعيل سجلات التنفيذ المفصلة للتصحيح. الافتراضي False.                                          |
-| **السماح بالتفويض** _(اختياري)_       | `allow_delegation`       | `bool`                                | السماح للوكيل بتفويض المهام لوكلاء آخرين. الافتراضي False.                                     |
-| **دالة الخطوة** _(اختياري)_          | `step_callback`          | `Optional[Any]`                       | دالة تُستدعى بعد كل خطوة للوكيل، تتجاوز دالة الطاقم.                                          |
-| **التخزين المؤقت** _(اختياري)_                  | `cache`                  | `bool`                                | تفعيل التخزين المؤقت لاستخدام الأدوات. الافتراضي True.                                                          |
-| **قالب النظام** _(اختياري)_        | `system_template`        | `Optional[str]`                       | قالب أمر نظام مخصص للوكيل.                                                             |
-| **قالب الأمر** _(اختياري)_        | `prompt_template`        | `Optional[str]`                       | قالب أمر مخصص للوكيل.                                                                    |
-| **قالب الاستجابة** _(اختياري)_      | `response_template`      | `Optional[str]`                       | قالب استجابة مخصص للوكيل.                                                                  |
-| **السماح بتنفيذ الكود** _(اختياري)_   | `allow_code_execution`   | `Optional[bool]`                      | تفعيل تنفيذ الكود للوكيل. الافتراضي False.                                                   |
-| **الحد الأقصى لإعادة المحاولة** _(اختياري)_        | `max_retry_limit`        | `int`                                 | الحد الأقصى لإعادات المحاولة عند حدوث خطأ. الافتراضي 2.                                            |
-| **احترام نافذة السياق** _(اختياري)_ | `respect_context_window` | `bool`                                | إبقاء الرسائل تحت حجم نافذة السياق عبر التلخيص. الافتراضي True.                                 |
-| **وضع تنفيذ الكود** _(اختياري)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`           | وضع تنفيذ الكود: 'safe' (باستخدام Docker) أو 'unsafe' (مباشر). الافتراضي 'safe'.                  |
-| **متعدد الوسائط** _(اختياري)_             | `multimodal`             | `bool`                                | ما إذا كان الوكيل يدعم القدرات متعددة الوسائط. الافتراضي False.                                    |
-| **حقن التاريخ** _(اختياري)_            | `inject_date`            | `bool`                                | ما إذا كان يتم حقن التاريخ الحالي تلقائيًا في أمر الوكيل. الافتراضي False.                        |
-| **تنسيق التاريخ** _(اختياري)_            | `date_format`            | `str`                                 | سلسلة تنسيق التاريخ عند تفعيل inject_date. الافتراضي "%Y-%m-%d" (تنسيق ISO).                  |
-| **الاستدلال** _(اختياري)_              | `reasoning`              | `bool`                                | ما إذا كان يجب على الوكيل التأمل وإنشاء خطة قبل تنفيذ المهمة. الافتراضي False.            |
-| **الحد الأقصى لمحاولات الاستدلال** _(اختياري)_ | `max_reasoning_attempts` | `Optional[int]`                       | الحد الأقصى لمحاولات الاستدلال قبل تنفيذ المهمة. إذا None، سيحاول حتى الاستعداد.           |
-| **المُضمّن** _(اختياري)_               | `embedder`               | `Optional[Dict[str, Any]]`            | تهيئة المُضمّن المستخدم من قبل الوكيل.                                                        |
-| **مصادر المعرفة** _(اختياري)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | مصادر المعرفة المتاحة للوكيل.                                                                |
-| **استخدام أمر النظام** _(اختياري)_      | `use_system_prompt`      | `Optional[bool]`                      | ما إذا كان يُستخدم أمر النظام (لدعم نموذج o1). الافتراضي True.                                    |
-
-## إنشاء الوكلاء
-
-هناك طريقتان شائعتان لإنشاء الوكلاء في CrewAI: باستخدام **تهيئة JSONC (الموصى بها للـ crews الجديدة)** أو تعريفهم **مباشرة في الكود**.
-
-### تهيئة JSONC (موصى بها)
-
-المشاريع الجديدة التي تُنشأ عبر `crewai create crew <name>` تستخدم تهيئة JSON-first. يُعرّف كل Agent في `agents/<agent_name>.jsonc`، ويحدد `crew.jsonc` أي Agents تدخل في الـ crew.
-
-```jsonc agents/researcher.jsonc
-{
-  "role": "{topic} Senior Data Researcher",
-  "goal": "Uncover cutting-edge developments in {topic}",
-  "backstory": "You find the most relevant information and present it clearly.",
-  "llm": "openai/gpt-4o",
-  "tools": ["SerperDevTool"],
-  "settings": {
-    "verbose": true,
-    "allow_delegation": false
-  }
-}
-```
-
-استخدم `{placeholder}` داخل `role` أو `goal` أو `backstory`. ضع القيم الافتراضية في `inputs` داخل `crew.jsonc`؛ وسيطلب `crewai run` أي قيم ناقصة. يمكن وضع حقول السلوك مثل `verbose` و `allow_delegation` و `max_iter` و `memory` و `cache` و `planning_config` في المستوى الأعلى أو داخل `settings`.
-
-<Note>
-يدعم JSONC التعليقات والفواصل النهائية. إذا وُجد `agents/<name>.jsonc` و `agents/<name>.json` معًا، يستخدم CrewAI ملف JSONC.
-</Note>
-
-### تهيئة YAML الكلاسيكية
-
-المشاريع الكلاسيكية التي تُنشأ عبر `crewai create crew <name> --classic` تستخدم `config/agents.yaml` وفئة `@CrewBase` في `crew.py`.
-
-تظل تهيئة YAML مدعومة للمشاريع الحالية المبنية بـ Python/YAML وللفِرق التي تفضل تعريف الوكلاء من خلال فئة `@CrewBase`.
-
-بعد إنشاء مشروع كلاسيكي، انتقل إلى ملف `src/<project_name>/config/agents.yaml` وعدّل القالب ليتوافق مع متطلباتك.
-
-<Note>
-ستُستبدل المتغيرات في ملفات YAML (مثل `{topic}`) بقيم من مدخلاتك عند تشغيل الطاقم:
-```python Code
-crew.kickoff(inputs={'topic': 'AI Agents'})
-```
-</Note>
-
-إليك مثالًا على كيفية تهيئة الوكلاء باستخدام YAML:
-
-```yaml agents.yaml
-# src/<project_name>/config/agents.yaml
-researcher:
-  role: >
-    {topic} Senior Data Researcher
-  goal: >
-    Uncover cutting-edge developments in {topic}
-  backstory: >
-    You're a seasoned researcher with a knack for uncovering the latest
-    developments in {topic}. Known for your ability to find the most relevant
-    information and present it in a clear and concise manner.
-
-reporting_analyst:
-  role: >
-    {topic} Reporting Analyst
-  goal: >
-    Create detailed reports based on {topic} data analysis and research findings
-  backstory: >
-    You're a meticulous analyst with a keen eye for detail. You're known for
-    your ability to turn complex data into clear and concise reports, making
-    it easy for others to understand and act on the information you provide.
-```
-
-لاستخدام تهيئة YAML في الكود، أنشئ فئة طاقم ترث من `CrewBase`:
-
-```python Code
-# src/<project_name>/crew.py
-from crewai import Agent, Crew, Process
-from crewai.project import CrewBase, agent, crew
-from crewai_tools import SerperDevTool
-
-@CrewBase
-class LatestAiDevelopmentCrew():
-  """LatestAiDevelopment crew"""
-
-  agents_config = "config/agents.yaml"
-
-  @agent
-  def researcher(self) -> Agent:
-    return Agent(
-      config=self.agents_config['researcher'], # type: ignore[index]
-      verbose=True,
-      tools=[SerperDevTool()]
-    )
-
-  @agent
-  def reporting_analyst(self) -> Agent:
-    return Agent(
-      config=self.agents_config['reporting_analyst'], # type: ignore[index]
-      verbose=True
-    )
-```
-
-<Note>
-  يجب أن تتطابق الأسماء المستخدمة في ملفات YAML (`agents.yaml`) مع أسماء
-  الطرق في كود Python.
-</Note>
-
-### تعريف مباشر في الكود
-
-يمكنك إنشاء الوكلاء مباشرة في الكود بإنشاء فئة `Agent`. إليك مثالًا شاملًا يوضح جميع المعاملات المتاحة:
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool
-
-# إنشاء وكيل بجميع المعاملات المتاحة
-agent = Agent(
-    role="Senior Data Scientist",
-    goal="Analyze and interpret complex datasets to provide actionable insights",
-    backstory="With over 10 years of experience in data science and machine learning, "
-              "you excel at finding patterns in complex datasets.",
-    llm="gpt-4",
-    function_calling_llm=None,
-    verbose=False,
-    allow_delegation=False,
-    max_iter=20,
-    max_rpm=None,
-    max_execution_time=None,
-    max_retry_limit=2,
-    allow_code_execution=False,
-    code_execution_mode="safe",
-    respect_context_window=True,
-    use_system_prompt=True,
-    multimodal=False,
-    inject_date=False,
-    date_format="%Y-%m-%d",
-    reasoning=False,
-    max_reasoning_attempts=None,
-    tools=[SerperDevTool()],
-    knowledge_sources=None,
-    embedder=None,
-    system_template=None,
-    prompt_template=None,
-    response_template=None,
-    step_callback=None,
-)
-```
-
-دعنا نستعرض بعض تركيبات المعاملات الرئيسية لحالات الاستخدام الشائعة:
-
-#### وكيل بحث أساسي
-
-```python Code
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Find and summarize information about specific topics",
-    backstory="You are an experienced researcher with attention to detail",
-    tools=[SerperDevTool()],
-    verbose=True
-)
-```
-
-#### وكيل تطوير الكود
-
-```python Code
-dev_agent = Agent(
-    role="Senior Python Developer",
-    goal="Write and debug Python code",
-    backstory="Expert Python developer with 10 years of experience",
-    allow_code_execution=True,
-    code_execution_mode="safe",
-    max_execution_time=300,
-    max_retry_limit=3
-)
-```
-
-#### وكيل تحليل طويل المدى
-
-```python Code
-analysis_agent = Agent(
-    role="Data Analyst",
-    goal="Perform deep analysis of large datasets",
-    backstory="Specialized in big data analysis and pattern recognition",
-    memory=True,
-    respect_context_window=True,
-    max_rpm=10,
-    function_calling_llm="gpt-4o-mini"
-)
-```
-
-### تفاصيل المعاملات
-
-#### المعاملات الحرجة
-
-- `role` و `goal` و `backstory` مطلوبة وتشكّل سلوك الوكيل
-- `llm` يحدد نموذج اللغة المستخدم (افتراضي: GPT-4 من OpenAI)
-
-#### الذاكرة والسياق
-
-- `memory`: تفعيل للحفاظ على سجل المحادثة
-- `respect_context_window`: يمنع مشاكل حد الرموز
-- `knowledge_sources`: إضافة قواعد معرفة خاصة بالمجال
-
-#### التحكم في التنفيذ
-
-- `max_iter`: الحد الأقصى للمحاولات قبل تقديم أفضل إجابة
-- `max_execution_time`: المهلة بالثواني
-- `max_rpm`: تحديد معدل استدعاءات API
-- `max_retry_limit`: إعادات المحاولة عند الخطأ
-
-#### تنفيذ الكود
-
-<Warning>
-  `allow_code_execution` و`code_execution_mode` مهجوران. تمت إزالة `CodeInterpreterTool` من `crewai-tools`. استخدم خدمة بيئة معزولة مخصصة مثل [E2B](https://e2b.dev) أو [Modal](https://modal.com) لتنفيذ الكود بأمان.
-</Warning>
-
-- `allow_code_execution` _(مهجور)_: كان يُمكّن تنفيذ الكود المدمج عبر `CodeInterpreterTool`.
-- `code_execution_mode` _(مهجور)_: كان يتحكم في وضع التنفيذ (`"safe"` لـ Docker، `"unsafe"` للتنفيذ المباشر).
-
-#### الميزات المتقدمة
-
-- `multimodal`: تفعيل القدرات متعددة الوسائط لمعالجة النص والمحتوى المرئي
-- `reasoning`: تمكين الوكيل من التأمل وإنشاء خطط قبل تنفيذ المهام
-- `inject_date`: حقن التاريخ الحالي تلقائيًا في أمر الوكيل
-
-#### القوالب
-
-- `system_template`: يحدد السلوك الأساسي للوكيل
-- `prompt_template`: ينظم تنسيق الإدخال
-- `response_template`: ينسّق استجابات الوكيل
-
-<Note>
-  عند استخدام القوالب المخصصة، تأكد من تعريف كل من `system_template` و
-  `prompt_template`. `response_template` اختياري لكن يُوصى به
-  لتنسيق مخرجات متسق.
-</Note>
-
-## أدوات الوكيل
-
-يمكن تجهيز الوكلاء بأدوات متنوعة لتعزيز قدراتهم. يدعم CrewAI أدوات من:
-
-- [مجموعة أدوات CrewAI](https://github.com/joaomdmoura/crewai-tools)
-- [أدوات LangChain](https://python.langchain.com/docs/integrations/tools)
-
-إليك كيفية إضافة أدوات لوكيل:
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool, WikipediaTools
-
-# إنشاء الأدوات
-search_tool = SerperDevTool()
-wiki_tool = WikipediaTools()
-
-# إضافة أدوات للوكيل
-researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
-    tools=[search_tool, wiki_tool],
-    verbose=True
-)
-```
-
-## التفاعل المباشر مع الوكيل عبر `kickoff()`
-
-يمكن استخدام الوكلاء مباشرة بدون المرور بمهمة أو سير عمل طاقم باستخدام طريقة `kickoff()`. يوفر هذا طريقة أبسط للتفاعل مع وكيل عندما لا تحتاج إلى إمكانيات تنسيق الطاقم الكاملة.
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool
-
-# إنشاء وكيل
-researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
-    tools=[SerperDevTool()],
-    verbose=True
-)
-
-# استخدام kickoff() للتفاعل مباشرة مع الوكيل
-result = researcher.kickoff("What are the latest developments in language models?")
-
-# الوصول إلى الاستجابة الخام
-print(result.raw)
-```
-
-## اعتبارات مهمة وأفضل الممارسات
-
-### الأمان وتنفيذ الكود
-
-<Warning>
-  `allow_code_execution` و`code_execution_mode` مهجوران وتمت إزالة `CodeInterpreterTool`. استخدم خدمة بيئة معزولة مخصصة مثل [E2B](https://e2b.dev) أو [Modal](https://modal.com) لتنفيذ الكود بأمان.
-</Warning>
-
-### تحسين الأداء
-
-- استخدم `respect_context_window: true` لمنع مشاكل حد الرموز
-- عيّن `max_rpm` مناسبًا لتجنب تحديد المعدل
-- فعّل `cache: true` لتحسين الأداء للمهام المتكررة
-- اضبط `max_iter` و `max_retry_limit` بناءً على تعقيد المهمة
-
-### إدارة الذاكرة والسياق
-
-- استفد من `knowledge_sources` للمعلومات الخاصة بالمجال
-- هيّئ `embedder` عند استخدام نماذج تضمين مخصصة
-- استخدم القوالب المخصصة للتحكم الدقيق في سلوك الوكيل
-
-### التعاون بين الوكلاء
-
-- فعّل `allow_delegation: true` عندما يحتاج الوكلاء للعمل معًا
-- استخدم `step_callback` لمراقبة وتسجيل تفاعلات الوكلاء
-- فكّر في استخدام نماذج LLM مختلفة لأغراض مختلفة
-
-### توافق النموذج
-
-- عيّن `use_system_prompt: false` للنماذج القديمة التي لا تدعم رسائل النظام
-- تأكد من أن `llm` المختار يدعم الميزات التي تحتاجها

docs/v1.15.16/ar/concepts/checkpointing.mdx

@@ -1,423 +0,0 @@
----
-title: Checkpointing
-description: حفظ حالة التنفيذ تلقائيا حتى تتمكن الطواقم والتدفقات والوكلاء من الاستئناف بعد الفشل.
-icon: floppy-disk
-mode: "wide"
----
-
-الـ Checkpointing يحفظ لقطة من حالة التنفيذ أثناء التشغيل بحيث يمكن لطاقم أو تدفق أو وكيل الاستئناف بعد الفشل أو التفرع إلى فرع بديل.
-
-<CardGroup cols={2}>
-  <Card title="الشرح" icon="lightbulb" href="#الشرح">
-    كيف يعمل الـ Checkpointing: الأحداث والتخزين والوراثة.
-  </Card>
-  <Card title="درس تطبيقي" icon="graduation-cap" href="#درس-تطبيقي-استئناف-طاقم-فاشل">
-    دليل 5 دقائق: تشغيل، إيقاف، استئناف.
-  </Card>
-  <Card title="ادلة عملية" icon="screwdriver-wrench" href="#ادلة-عملية">
-    وصفات مركزة على المهام لسير العمل الشائع.
-  </Card>
-  <Card title="المرجع" icon="book" href="#المرجع">
-    `CheckpointConfig` والأحداث والمزودات وسطر الأوامر.
-  </Card>
-</CardGroup>
-
-## الشرح
-
-### ما هي نقطة الحفظ
-
-تلتقط نقطة الحفظ كل ما يحتاجه CrewAI لإعادة إنشاء تشغيل أثناء سيره: الحالة الكاملة للطاقم أو التدفق أو الوكيل — التكوين، وذاكرة الوكلاء ومصادر المعرفة، وتقدم المهام، والمخرجات الوسيطة، والحالة الداخلية والسمات — إلى جانب مدخلات الـ kickoff، وسجل الأحداث حتى تلك النقطة، ومعرف نسب يربط نقطة الحفظ بالتشغيل الذي جاءت منه.
-
-الاستعادة تعيد بناء تلك الحالة وتستمر. تتخطى المهام المكتملة، وتعاد ترطيب الذاكرة والمعرفة، ويعمل العمل التابع على نفس المخرجات التي أنتجها التشغيل الأصلي. التفرع يجري نفس الاستعادة تحت نسب جديد، بحيث يكتب الفرع الجديد والتشغيل الأصلي نقاط الحفظ جنبا إلى جنب دون أن يطمس أحدهما الآخر.
-
-### متى تكتب نقاط الحفظ
-
-الـ Checkpointing مدفوع بالأحداث. يشترك وقت التشغيل في الأحداث التي تحددها عبر `on_events` ويكتب نقطة حفظ عند إطلاق أحدها. الافتراضي `task_completed` ينتج نقطة حفظ لكل مهمة منتهية — توازن معقول بين الدقة واستخدام القرص. الأحداث عالية التردد مثل `llm_call_completed` متاحة للاستعادة الدقيقة لكنها تكتب ملفات أكثر بكثير.
-
-### التخزين
-
-يتضمن CrewAI مزودين:
-
-- `JsonProvider` يكتب ملفا لكل نقطة حفظ. قابل للقراءة وسهل التفقد.
-- `SqliteProvider` يكتب إلى قاعدة بيانات SQLite واحدة. أفضل لنقاط الحفظ عالية التردد.
-
-كلاهما يحذف أقدم نقاط الحفظ عند تحديد `max_checkpoints`.
-
-<Note>
-كتابة نقاط الحفظ بأفضل جهد. فشل نقطة حفظ يسجل لكنه لا يقاطع التشغيل.
-</Note>
-
-### نموذج الوراثة
-
-`Crew` و`Flow` و`Agent` كلها تقبل وسيط `checkpoint`. يرث الأبناء من الأب ما لم يحددوا قيمتهم الخاصة أو يمرروا `False` للانسحاب. فعل الـ Checkpointing مرة واحدة على الطاقم وتشارك كل الوكلاء، أو استبعد وكيلا واحدا بشكل انتقائي.
-
-## درس تطبيقي: استئناف طاقم فاشل
-
-هذا الدليل يستغرق حوالي 5 دقائق. ستشغل طاقما بمهمتين، توقفه في المنتصف، ثم تستأنف من نقطة الحفظ المحفوظة.
-
-<Steps>
-  <Step title="أنشئ الطاقم مع تفعيل الـ Checkpointing">
-    ```python
-    from crewai import Agent, Crew, Task
-
-    researcher = Agent(role="Researcher", goal="Research", backstory="Expert")
-    writer = Agent(role="Writer", goal="Write", backstory="Expert")
-
-    crew = Crew(
-        agents=[researcher, writer],
-        tasks=[
-            Task(description="Research AI trends", agent=researcher, expected_output="bullets"),
-            Task(description="Write a summary", agent=writer, expected_output="paragraph"),
-        ],
-        checkpoint=True,
-    )
-    ```
-  </Step>
-  <Step title="شغله وأوقفه بعد المهمة الأولى">
-    ```python
-    result = crew.kickoff()
-    ```
-
-    اضغط `Ctrl+C` بعد انتهاء المهمة الأولى. في `./.checkpoints/`، الملف بصيغة `<timestamp>_<uuid>.json` هو نقطة الحفظ.
-  </Step>
-  <Step title="استأنف من نقطة الحفظ">
-    ```python
-    from crewai import CheckpointConfig
-
-    result = crew.kickoff(
-        from_checkpoint=CheckpointConfig(
-            restore_from="./.checkpoints/<timestamp>_<uuid>.json",
-        ),
-    )
-    ```
-
-    يتم تخطي مهمة البحث، ويعمل الكاتب على مخرجات البحث المحفوظة، وينتهي الطاقم.
-  </Step>
-</Steps>
-
-## ادلة عملية
-
-<AccordionGroup>
-  <Accordion title="تفعيل الـ Checkpointing بالإعدادات الافتراضية" icon="play">
-    ```python
-    crew = Crew(agents=[...], tasks=[...], checkpoint=True)
-    ```
-
-    يكتب إلى `./.checkpoints/` عند كل `task_completed`.
-  </Accordion>
-
-  <Accordion title="تخصيص التخزين والتردد" icon="sliders">
-    ```python
-    from crewai import Crew, CheckpointConfig
-
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        checkpoint=CheckpointConfig(
-            location="./my_checkpoints",
-            on_events=["task_completed", "crew_kickoff_completed"],
-            max_checkpoints=5,
-        ),
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="اختيار مزود التخزين" icon="database">
-    <CodeGroup>
-    ```python JsonProvider
-    from crewai import Crew, CheckpointConfig
-    from crewai.state import JsonProvider
-
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        checkpoint=CheckpointConfig(
-            location="./my_checkpoints",
-            provider=JsonProvider(),
-            max_checkpoints=5,
-        ),
-    )
-    ```
-    ```python SqliteProvider
-    from crewai import Crew, CheckpointConfig
-    from crewai.state import SqliteProvider
-
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        checkpoint=CheckpointConfig(
-            location="./.checkpoints.db",
-            provider=SqliteProvider(),
-            max_checkpoints=50,
-        ),
-    )
-    ```
-    </CodeGroup>
-
-    <Tip>
-    SQLite يفعل وضع journal WAL للقراءات المتزامنة. يفضل لنقاط الحفظ عالية التردد.
-    </Tip>
-  </Accordion>
-
-  <Accordion title="استبعاد وكيل واحد" icon="user-slash">
-    ```python
-    crew = Crew(
-        agents=[
-            Agent(role="Researcher", ...),
-            Agent(role="Writer", ..., checkpoint=False),
-        ],
-        tasks=[...],
-        checkpoint=True,
-    )
-    ```
-  </Accordion>
-
-  <Accordion title="التفرع إلى فرع جديد" icon="code-branch">
-    `fork()` يستعيد نقطة حفظ تحت نسب جديد بحيث لا يتصادم التشغيل الجديد مع الأصلي.
-
-    ```python
-    config = CheckpointConfig(restore_from="./my_checkpoints/<file>.json")
-    crew = Crew.fork(config, branch="experiment-a")
-    result = crew.kickoff(inputs={"strategy": "aggressive"})
-    ```
-
-    تسمية `branch` اختيارية؛ يتم إنشاء واحدة إذا أغفلت.
-  </Accordion>
-
-  <Accordion title="Checkpointing لـ Crew أو Flow أو Agent" icon="cubes">
-    <Tabs>
-      <Tab title="Crew">
-        ```python
-        crew = Crew(
-            agents=[researcher, writer],
-            tasks=[research_task, write_task, review_task],
-            checkpoint=CheckpointConfig(location="./crew_cp"),
-        )
-        ```
-
-        المشغل الافتراضي: `task_completed`.
-      </Tab>
-      <Tab title="Flow">
-        ```python
-        from crewai.flow.flow import Flow, start, listen
-        from crewai import CheckpointConfig
-
-        class MyFlow(Flow):
-            @start()
-            def step_one(self):
-                return "data"
-
-            @listen(step_one)
-            def step_two(self, data):
-                return process(data)
-
-        flow = MyFlow(
-            checkpoint=CheckpointConfig(
-                location="./flow_cp",
-                on_events=["method_execution_finished"],
-            ),
-        )
-        result = flow.kickoff()
-        ```
-      </Tab>
-      <Tab title="Agent">
-        ```python
-        agent = Agent(
-            role="Researcher",
-            goal="Research topics",
-            backstory="Expert researcher",
-            checkpoint=CheckpointConfig(
-                location="./agent_cp",
-                on_events=["lite_agent_execution_completed"],
-            ),
-        )
-        result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
-        ```
-      </Tab>
-    </Tabs>
-  </Accordion>
-
-  <Accordion title="كتابة نقطة حفظ يدويا" icon="code">
-    سجل معالجا على أي حدث واستدع `state.checkpoint()`.
-
-    <CodeGroup>
-    ```python Sync
-    from __future__ import annotations
-
-    from typing import TYPE_CHECKING, Any
-
-    from crewai.events.event_bus import crewai_event_bus
-    from crewai.events.types.llm_events import LLMCallCompletedEvent
-
-    if TYPE_CHECKING:
-        from crewai.state.runtime import RuntimeState
-
-
-    @crewai_event_bus.on(LLMCallCompletedEvent)
-    def on_llm_done(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
-        path = state.checkpoint("./my_checkpoints")
-        print(f"تم حفظ نقطة الحفظ: {path}")
-    ```
-    ```python Async
-    from __future__ import annotations
-
-    from typing import TYPE_CHECKING, Any
-
-    from crewai.events.event_bus import crewai_event_bus
-    from crewai.events.types.llm_events import LLMCallCompletedEvent
-
-    if TYPE_CHECKING:
-        from crewai.state.runtime import RuntimeState
-
-
-    @crewai_event_bus.on(LLMCallCompletedEvent)
-    async def on_llm_done_async(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
-        path = await state.acheckpoint("./my_checkpoints")
-        print(f"تم حفظ نقطة الحفظ: {path}")
-    ```
-    </CodeGroup>
-
-    يتم تمرير وسيط `state` تلقائيا عندما يقبل المعالج ثلاثة معاملات. راجع [Event Listeners](/ar/concepts/event-listener) لقائمة الأحداث الكاملة.
-  </Accordion>
-
-  <Accordion title="التصفح والاستئناف والتفرع من سطر الأوامر" icon="terminal">
-    ```bash
-    crewai checkpoint
-    crewai checkpoint --location ./my_checkpoints
-    crewai checkpoint --location ./.checkpoints.db
-    ```
-
-    <Frame caption="شجرة نقاط الحفظ — الفروع والتفرعات تتداخل تحت أبيها.">
-      <img src="/images/checkpoint-tui-tree.png" alt="Checkpoint TUI tree view" />
-    </Frame>
-
-    اللوحة اليسرى تجمع نقاط الحفظ حسب الفرع؛ التفرعات تتداخل تحت أبيها. اختيار نقطة حفظ يفتح لوحة التفاصيل مع بياناتها الوصفية وحالة الكيان وتقدم المهام. **Resume** يكمل التشغيل؛ **Fork** يبدأ فرعا جديدا.
-
-    <Frame caption="تبويب النظرة العامة — البيانات الوصفية وحالة الكيان وملخص التشغيل.">
-      <img src="/images/checkpoint-tui-detail-overview.png" alt="Checkpoint detail overview tab" />
-    </Frame>
-
-    لوحة التفاصيل تعرض منطقتين قابلتين للتحرير:
-
-    - **Inputs** — مدخلات الـ kickoff الأصلية، معبأة مسبقا وقابلة للتحرير.
-
-      <Frame>
-        <img src="/images/checkpoint-tui-detail-inputs.png" alt="Editable kickoff inputs" />
-      </Frame>
-
-    - **مخرجات المهام** — مخرجات المهام المكتملة. تحرير مخرج والضغط على **Fork** يبطل المهام التابعة لتعاد بالسياق المعدل.
-
-      <Frame>
-        <img src="/images/checkpoint-tui-detail-tasks.png" alt="Editable task outputs" />
-      </Frame>
-
-    <Frame caption="عرض التفرع — تأكيد فرع جديد من نقطة الحفظ المختارة.">
-      <img src="/images/checkpoint-tui-details-fork.png" alt="Fork confirmation panel" />
-    </Frame>
-
-    <Tip>
-    مفيد لاستكشاف "ماذا لو": تفرع، عدل، راقب.
-    </Tip>
-  </Accordion>
-
-  <Accordion title="تفقد نقاط الحفظ بدون TUI" icon="magnifying-glass">
-    ```bash
-    crewai checkpoint list ./my_checkpoints
-    crewai checkpoint info ./my_checkpoints/<file>.json
-    crewai checkpoint info ./.checkpoints.db
-    ```
-  </Accordion>
-</AccordionGroup>
-
-## المرجع
-
-### `CheckpointConfig`
-
-<ParamField path="location" type="str" default='"./.checkpoints"'>
-  وجهة التخزين. مجلد لـ `JsonProvider`، مسار ملف قاعدة بيانات لـ `SqliteProvider`.
-</ParamField>
-
-<ParamField path="on_events" type='list[CheckpointEventType | Literal["*"]]' default='["task_completed"]'>
-  أنواع الأحداث التي تطلق نقطة حفظ. `CheckpointEventType` هو `Literal` — مدقق الأنواع يكمل تلقائيا ويرفض القيم غير المدعومة. راجع [أنواع الأحداث](#أنواع-الأحداث) للقائمة الكاملة.
-</ParamField>
-
-<ParamField path="provider" type="BaseProvider" default="JsonProvider()">
-  واجهة التخزين. `JsonProvider` أو `SqliteProvider`.
-</ParamField>
-
-<ParamField path="max_checkpoints" type="int | None" default="None">
-  الحد الاقصى لنقاط الحفظ المحتفظ بها. الأقدم تحذف بعد كل كتابة.
-</ParamField>
-
-<ParamField path="restore_from" type="Path | str | None" default="None">
-  نقطة الحفظ المراد استعادتها عند تمريرها عبر `from_checkpoint`.
-</ParamField>
-
-### قيم حقل `checkpoint`
-
-مقبولة في `Crew` و`Flow` و`Agent`.
-
-<ParamField path="None" type="افتراضي">
-  يرث من الأب.
-</ParamField>
-
-<ParamField path="True" type="bool">
-  تفعيل بالإعدادات الافتراضية.
-</ParamField>
-
-<ParamField path="False" type="bool">
-  انسحاب صريح. يوقف الوراثة.
-</ParamField>
-
-<ParamField path="CheckpointConfig(...)" type="CheckpointConfig">
-  إعدادات مخصصة.
-</ParamField>
-
-### أنواع الأحداث
-
-يقبل `on_events` أي مجموعة من قيم `CheckpointEventType`. الافتراضي `["task_completed"]` يكتب نقطة حفظ لكل مهمة منتهية، و`["*"]` يطابق جميع الأحداث.
-
-<Warning>
-`["*"]` والأحداث عالية التردد مثل `llm_call_completed` تكتب نقاط حفظ كثيرة وقد تضر بالاداء. استخدمها مع `max_checkpoints`.
-</Warning>
-
-<Expandable title="جميع الأحداث المدعومة">
-
-- **Task** — `task_started`, `task_completed`, `task_failed`, `task_evaluation`
-- **Crew** — `crew_kickoff_started`, `crew_kickoff_completed`, `crew_kickoff_failed`, `crew_train_started`, `crew_train_completed`, `crew_train_failed`, `crew_test_started`, `crew_test_completed`, `crew_test_failed`, `crew_test_result`
-- **Agent** — `agent_execution_started`, `agent_execution_completed`, `agent_execution_error`, `lite_agent_execution_started`, `lite_agent_execution_completed`, `lite_agent_execution_error`, `agent_evaluation_started`, `agent_evaluation_completed`, `agent_evaluation_failed`
-- **Flow** — `flow_created`, `flow_started`, `flow_finished`, `flow_paused`, `method_execution_started`, `method_execution_finished`, `method_execution_failed`, `method_execution_paused`, `human_feedback_requested`, `human_feedback_received`, `flow_input_requested`, `flow_input_received`
-- **LLM** — `llm_call_started`, `llm_call_completed`, `llm_call_failed`, `llm_stream_chunk`, `llm_thinking_chunk`
-- **LLM Guardrail** — `llm_guardrail_started`, `llm_guardrail_completed`, `llm_guardrail_failed`
-- **Tool** — `tool_usage_started`, `tool_usage_finished`, `tool_usage_error`, `tool_validate_input_error`, `tool_selection_error`, `tool_execution_error`
-- **Memory** — `memory_save_started`, `memory_save_completed`, `memory_save_failed`, `memory_query_started`, `memory_query_completed`, `memory_query_failed`, `memory_retrieval_started`, `memory_retrieval_completed`, `memory_retrieval_failed`
-- **Knowledge** — `knowledge_search_query_started`, `knowledge_search_query_completed`, `knowledge_query_started`, `knowledge_query_completed`, `knowledge_query_failed`, `knowledge_search_query_failed`
-- **Reasoning** — `agent_reasoning_started`, `agent_reasoning_completed`, `agent_reasoning_failed`
-- **MCP** — `mcp_connection_started`, `mcp_connection_completed`, `mcp_connection_failed`, `mcp_tool_execution_started`, `mcp_tool_execution_completed`, `mcp_tool_execution_failed`, `mcp_config_fetch_failed`
-- **Observation** — `step_observation_started`, `step_observation_completed`, `step_observation_failed`, `plan_refinement`, `plan_replan_triggered`, `goal_achieved_early`
-- **Skill** — `skill_discovery_started`, `skill_discovery_completed`, `skill_loaded`, `skill_activated`, `skill_load_failed`
-- **Logging** — `agent_logs_started`, `agent_logs_execution`
-- **A2A** — `a2a_delegation_started`, `a2a_delegation_completed`, `a2a_conversation_started`, `a2a_conversation_completed`, `a2a_message_sent`, `a2a_response_received`, `a2a_polling_started`, `a2a_polling_status`, `a2a_push_notification_registered`, `a2a_push_notification_received`, `a2a_push_notification_sent`, `a2a_push_notification_timeout`, `a2a_streaming_started`, `a2a_streaming_chunk`, `a2a_agent_card_fetched`, `a2a_authentication_failed`, `a2a_artifact_received`, `a2a_connection_error`, `a2a_server_task_started`, `a2a_server_task_completed`, `a2a_server_task_canceled`, `a2a_server_task_failed`, `a2a_parallel_delegation_started`, `a2a_parallel_delegation_completed`, `a2a_transport_negotiated`, `a2a_content_type_negotiated`, `a2a_context_created`, `a2a_context_expired`, `a2a_context_idle`, `a2a_context_completed`, `a2a_context_pruned`
-- **إشارات النظام** — `SIGTERM`, `SIGINT`, `SIGHUP`, `SIGTSTP`, `SIGCONT`
-- **حرف بدل** — `"*"` يطابق جميع الأحداث.
-
-</Expandable>
-
-### مزودات التخزين
-
-<ParamField path="JsonProvider" type="provider">
-  ملف واحد لكل نقطة حفظ بصيغة `<timestamp>_<uuid>.json` داخل `location`.
-</ParamField>
-
-<ParamField path="SqliteProvider" type="provider">
-  ملف قاعدة بيانات واحد في `location` مع journaling WAL.
-</ParamField>
-
-### سطر الأوامر
-
-| الامر | الغرض |
-|:------|:------|
-| `crewai checkpoint` | تشغيل TUI؛ كشف التخزين تلقائيا. |
-| `crewai checkpoint --location <path>` | تشغيل TUI على موقع محدد. |
-| `crewai checkpoint list <path>` | سرد نقاط الحفظ. |
-| `crewai checkpoint info <path>` | تفقد ملف نقطة حفظ أو آخر مدخل في قاعدة بيانات SQLite. |

docs/v1.15.16/ar/concepts/cli.mdx

@@ -1,312 +0,0 @@
----
-title: واجهة سطر الأوامر
-description: تعرّف على كيفية استخدام واجهة سطر أوامر CrewAI للتفاعل مع CrewAI.
-icon: terminal
-mode: "wide"
----
-
-<Warning>
-  منذ الإصدار 0.140.0، بدأ CrewAI AMP عملية نقل مزود تسجيل الدخول.
-  لذلك، تم تحديث تدفق المصادقة عبر CLI. المستخدمون الذين يسجلون الدخول
-  باستخدام Google، أو الذين أنشأوا حساباتهم بعد 3 يوليو 2025 لن يتمكنوا
-  من تسجيل الدخول مع الإصدارات القديمة من مكتبة `crewai`.
-</Warning>
-
-## نظرة عامة
-
-توفر واجهة سطر أوامر CrewAI مجموعة من الأوامر للتفاعل مع CrewAI، مما يتيح لك إنشاء وتدريب وتشغيل وإدارة الأطقم والتدفقات.
-
-## التثبيت
-
-لاستخدام واجهة سطر أوامر CrewAI، تأكد من تثبيت CrewAI:
-
-```shell Terminal
-pip install crewai
-```
-
-## الاستخدام الأساسي
-
-الهيكل الأساسي لأمر CrewAI CLI هو:
-
-```shell Terminal
-crewai [COMMAND] [OPTIONS] [ARGUMENTS]
-```
-
-## الأوامر المتاحة
-
-### 1. إنشاء
-
-إنشاء طاقم أو تدفق جديد.
-
-```shell Terminal
-crewai create [OPTIONS] TYPE NAME
-```
-
-- `TYPE`: اختر بين "crew" أو "flow"
-- `NAME`: اسم الطاقم أو التدفق
-
-مثال:
-
-```shell Terminal
-crewai create crew my_new_crew
-crewai create flow my_new_flow
-```
-
-افتراضيًا، ينشئ `crewai create crew` مشروعًا JSON-first يحتوي على `crew.jsonc` و `agents/*.jsonc`. استخدم `crewai create crew my_new_crew --classic` فقط إذا أردت البنية القديمة Python/YAML مع `crew.py` و `config/agents.yaml` و `config/tasks.yaml`.
-
-#### أسماء مستعار قديمة للأعلام (مهملة)
-
-لا تزال أعلام snake_case القديمة تعمل، لكنها مخفية من `--help`. يُفضّل استخدام صيغ kebab-case الموثّقة في أقسام الأوامر أدناه.
-
-| مهمل | استخدم بدلاً منه |
-| :--- | :--- |
-| `--skip_provider` (في `crewai create crew`) | `--skip-provider` |
-| `--n_iterations` (في `crewai train`، `crewai test`) | `--n-iterations` |
-| `--task_id` (في `crewai replay`) | `--task-id` |
-
-### 2. الإصدار
-
-عرض الإصدار المثبت من CrewAI.
-
-```shell Terminal
-crewai version [OPTIONS]
-```
-
-- `--tools`: (اختياري) عرض الإصدار المثبت من أدوات CrewAI
-
-### 3. التدريب
-
-تدريب الطاقم لعدد محدد من التكرارات.
-
-```shell Terminal
-crewai train [OPTIONS]
-```
-
-- `-n, --n-iterations INTEGER`: عدد تكرارات التدريب (افتراضي: 5)
-- `-f, --filename TEXT`: مسار ملف مخصص للتدريب (افتراضي: "trained_agents_data.pkl")
-
-### 4. الإعادة
-
-إعادة تنفيذ الطاقم من مهمة محددة.
-
-```shell Terminal
-crewai replay [OPTIONS]
-```
-
-- `-t, --task-id TEXT`: إعادة تنفيذ الطاقم من معرّف المهمة هذا، بما في ذلك جميع المهام اللاحقة
-
-### 5. سجل مخرجات المهام
-
-استرجاع أحدث مخرجات مهام crew.kickoff().
-
-```shell Terminal
-crewai log-tasks-outputs
-```
-
-### 6. إعادة تعيين الذاكرة
-
-إعادة تعيين ذاكرة الطاقم (طويلة، قصيرة، الكيانات، أحدث مخرجات التشغيل).
-
-```shell Terminal
-crewai reset-memories [OPTIONS]
-```
-
-- `-l, --long`: إعادة تعيين الذاكرة طويلة المدى
-- `-s, --short`: إعادة تعيين الذاكرة قصيرة المدى
-- `-e, --entities`: إعادة تعيين ذاكرة الكيانات
-- `-k, --kickoff-outputs`: إعادة تعيين أحدث مخرجات التشغيل
-- `-kn, --knowledge`: إعادة تعيين تخزين المعرفة
-- `-akn, --agent-knowledge`: إعادة تعيين تخزين معرفة الوكيل
-- `-a, --all`: إعادة تعيين جميع الذاكرات
-
-### 7. الاختبار
-
-اختبار الطاقم وتقييم النتائج.
-
-```shell Terminal
-crewai test [OPTIONS]
-```
-
-- `-n, --n-iterations INTEGER`: عدد تكرارات الاختبار (افتراضي: 3)
-- `-m, --model TEXT`: نموذج LLM لتشغيل الاختبارات (افتراضي: "gpt-4o-mini")
-
-### 8. التشغيل
-
-تشغيل الطاقم أو التدفق.
-
-```shell Terminal
-crewai run
-```
-
-<Note>
-  بدءًا من الإصدار 0.103.0، يمكن استخدام أمر `crewai run` لتشغيل
-  كل من الأطقم القياسية والتدفقات. للتدفقات، يكتشف تلقائيًا النوع
-  من pyproject.toml ويشغّل الأمر المناسب. هذه هي الطريقة الموصى بها
-  لتشغيل كل من الأطقم والتدفقات.
-</Note>
-
-### 9. الدردشة
-
-بدءًا من الإصدار `0.98.0`، عند تشغيل أمر `crewai chat`، تبدأ جلسة تفاعلية مع طاقمك. سيرشدك المساعد الذكي بطلب المدخلات اللازمة لتنفيذ الطاقم. بمجرد توفير جميع المدخلات، سينفذ الطاقم مهامه.
-
-```shell Terminal
-crewai chat
-```
-
-<Note>
-مهم: عيّن خاصية `chat_llm` في تعريف الـ crew لتفعيل هذا الأمر.
-
-للـ crews بنمط JSON-first، أضفها إلى `crew.jsonc`:
-
-```jsonc
-{
-  "name": "My Crew",
-  "agents": ["researcher"],
-  "tasks": [],
-  "chat_llm": "openai/gpt-4o"
-}
-```
-
-للـ crews الكلاسيكية Python/YAML، عيّنها في `crew.py`:
-
-```python
-@crew
-def crew(self) -> Crew:
-    return Crew(
-        agents=self.agents,
-        tasks=self.tasks,
-        process=Process.sequential,
-        verbose=True,
-        chat_llm="gpt-4o",
-    )
-```
-</Note>
-
-### 10. النشر
-
-نشر الطاقم أو التدفق إلى [CrewAI AMP](https://app.crewai.com).
-
-- **المصادقة**: تحتاج لتكون مصادقًا للنشر إلى CrewAI AMP.
-
-  ```shell Terminal
-  crewai login
-  ```
-
-- **إنشاء نشر**:
-  ```shell Terminal
-  crewai deploy create
-  ```
-
-- **نشر الطاقم**:
-  ```shell Terminal
-  crewai deploy push
-  ```
-
-- **حالة النشر**:
-  ```shell Terminal
-  crewai deploy status
-  ```
-
-- **سجلات النشر**:
-  ```shell Terminal
-  crewai deploy logs
-  ```
-
-- **عرض النشرات**:
-  ```shell Terminal
-  crewai deploy list
-  ```
-
-- **حذف النشر**:
-  ```shell Terminal
-  crewai deploy remove
-  ```
-
-### 11. إدارة المؤسسة
-
-إدارة مؤسسات CrewAI AMP.
-
-```shell Terminal
-crewai org [COMMAND] [OPTIONS]
-```
-
-- `list`: عرض جميع المؤسسات
-- `current`: عرض المؤسسة النشطة حاليًا
-- `switch`: التبديل إلى مؤسسة محددة
-
-### 12. تسجيل الدخول
-
-المصادقة مع CrewAI AMP باستخدام تدفق رمز الجهاز الآمن.
-
-```shell Terminal
-crewai login
-```
-
-### 13. إدارة التهيئة
-
-إدارة إعدادات تهيئة CLI لـ CrewAI.
-
-```shell Terminal
-crewai config [COMMAND] [OPTIONS]
-```
-
-- `list`: عرض جميع معاملات التهيئة
-- `set`: تعيين معامل تهيئة
-- `reset`: إعادة تعيين جميع المعاملات إلى القيم الافتراضية
-
-### 14. إدارة التتبع
-
-إدارة تفضيلات جمع التتبع لعمليات الطاقم والتدفق.
-
-```shell Terminal
-crewai traces [COMMAND]
-```
-
-- `enable`: تفعيل جمع التتبع
-- `disable`: تعطيل جمع التتبع
-- `status`: عرض حالة جمع التتبع الحالية
-
-#### كيف يعمل التتبع
-
-يتم التحكم في جمع التتبع بفحص ثلاثة إعدادات بترتيب الأولوية:
-
-1. **علامة صريحة في الكود** (الأولوية الأعلى):
-   ```python
-   crew = Crew(agents=[...], tasks=[...], tracing=True)   # تفعيل دائمًا
-   crew = Crew(agents=[...], tasks=[...], tracing=False)  # تعطيل دائمًا
-   crew = Crew(agents=[...], tasks=[...])                 # فحص الأولويات الأدنى
-   ```
-
-2. **متغير البيئة** (الأولوية الثانية):
-   ```env
-   CREWAI_TRACING_ENABLED=true
-   ```
-
-3. **تفضيل المستخدم** (الأولوية الأدنى):
-   ```shell Terminal
-   crewai traces enable
-   ```
-
-<Note>
-**لتفعيل التتبع**، استخدم أيًا من هذه الطرق:
-- عيّن `tracing=True` في كود الطاقم/التدفق، أو
-- أضف `CREWAI_TRACING_ENABLED=true` إلى ملف `.env`، أو
-- شغّل `crewai traces enable`
-
-**لتعطيل التتبع**، استخدم أيًا من هذه الطرق:
-- عيّن `tracing=False` في كود الطاقم/التدفق، أو
-- أزل أو عيّن `false` لمتغير `CREWAI_TRACING_ENABLED`، أو
-- شغّل `crewai traces disable`
-</Note>
-
-<Tip>
-  يتعامل CrewAI CLI مع المصادقة لمستودع الأدوات تلقائيًا عند
-  إضافة حزم إلى مشروعك. فقط أضف `crewai` قبل أي أمر `uv`
-  لاستخدامه. مثلًا `crewai uv add requests`.
-</Tip>
-
-<Note>
-  تُخزن إعدادات التهيئة في `~/.config/crewai/settings.json`. بعض
-  الإعدادات مثل اسم المؤسسة ومعرّفها للقراءة فقط وتُدار من خلال
-  أوامر المصادقة والمؤسسة.
-</Note>