Merge branch 'devin/1771500257-fix-code-interpreter-security' of https://git-manager.devin.ai/proxy/github.com/crewAIInc/crewAI into devin/1771500257-fix-code-interpreter-security

Co-Authored-By: João <joao@crewai.com>

.env.test

@@ -21,6 +21,7 @@ OPENROUTER_API_KEY=fake-openrouter-key
 AWS_ACCESS_KEY_ID=fake-aws-access-key
 AWS_SECRET_ACCESS_KEY=fake-aws-secret-key
 AWS_DEFAULT_REGION=us-east-1
+AWS_REGION_NAME=us-east-1
 
 # -----------------------------------------------------------------------------
 # Azure OpenAI Configuration

.github/workflows/linter.yml

@@ -55,7 +55,6 @@ jobs:
          echo "${{ steps.changed-files.outputs.files }}" \
            | tr ' ' '\n' \
            | grep -v 'src/crewai/cli/templates/' \
-           | grep -v 'src/crewai_cli/templates/' \
            | grep -v '/tests/' \
            | xargs -I{} uv run ruff check "{}"
 

.github/workflows/nightly.yml

@@ -1,127 +0,0 @@
-name: Nightly Canary Release
-
-on:
-  schedule:
-    - cron: '0 6 * * *' # daily at 6am UTC
-  workflow_dispatch:
-
-jobs:
-  check:
-    name: Check for new commits
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    outputs:
-      has_changes: ${{ steps.check.outputs.has_changes }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Check for commits in last 24h
-        id: check
-        run: |
-          RECENT=$(git log --since="24 hours ago" --oneline | head -1)
-          if [ -n "$RECENT" ]; then
-            echo "has_changes=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "has_changes=false" >> "$GITHUB_OUTPUT"
-          fi
-
-  build:
-    name: Build nightly packages
-    needs: check
-    if: needs.check.outputs.has_changes == 'true' || github.event_name == 'workflow_dispatch'
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Stamp nightly versions
-        run: |
-          DATE=$(date +%Y%m%d)
-          for init_file in \
-            lib/crewai/src/crewai/__init__.py \
-            lib/crewai-tools/src/crewai_tools/__init__.py \
-            lib/crewai-files/src/crewai_files/__init__.py; do
-            CURRENT=$(python -c "
-          import re
-          text = open('$init_file').read()
-          print(re.search(r'__version__\s*=\s*\"(.*?)\"\s*$', text, re.MULTILINE).group(1))
-          ")
-            NIGHTLY="${CURRENT}.dev${DATE}"
-            sed -i "s/__version__ = .*/__version__ = \"${NIGHTLY}\"/" "$init_file"
-            echo "$init_file: $CURRENT -> $NIGHTLY"
-          done
-
-          # Update cross-package dependency pins to nightly versions
-          sed -i "s/\"crewai-tools==[^\"]*\"/\"crewai-tools==${NIGHTLY}\"/" lib/crewai/pyproject.toml
-          sed -i "s/\"crewai==[^\"]*\"/\"crewai==${NIGHTLY}\"/" lib/crewai-tools/pyproject.toml
-          echo "Updated cross-package dependency pins to ${NIGHTLY}"
-
-      - name: Build packages
-        run: |
-          uv build --all-packages
-          rm dist/.gitignore
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: dist/
-
-  publish:
-    name: Publish nightly to PyPI
-    needs: build
-    runs-on: ubuntu-latest
-    environment:
-      name: pypi
-      url: https://pypi.org/p/crewai
-    permissions:
-      id-token: write
-      contents: read
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-        with:
-          version: "0.8.4"
-          python-version: "3.12"
-          enable-cache: false
-
-      - name: Download artifacts
-        uses: actions/download-artifact@v4
-        with:
-          name: dist
-          path: dist
-
-      - name: Publish to PyPI
-        env:
-          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          failed=0
-          for package in dist/*; do
-            if [[ "$package" == *"crewai_devtools"* ]]; then
-              echo "Skipping private package: $package"
-              continue
-            fi
-            echo "Publishing $package"
-            if ! uv publish "$package"; then
-              echo "Failed to publish $package"
-              failed=1
-            fi
-          done
-          if [ $failed -eq 1 ]; then
-            echo "Some packages failed to publish"
-            exit 1
-          fi

.github/workflows/publish.yml

@@ -1,6 +1,8 @@
 name: Publish to PyPI
 
 on:
+  repository_dispatch:
+    types: [deployment-tests-passed]
   workflow_dispatch:
     inputs:
       release_tag:
@@ -18,8 +20,11 @@ jobs:
       - name: Determine release tag
         id: release
         run: |
+          # Priority: workflow_dispatch input > repository_dispatch payload > default branch
           if [ -n "${{ inputs.release_tag }}" ]; then
             echo "tag=${{ inputs.release_tag }}" >> $GITHUB_OUTPUT
+          elif [ -n "${{ github.event.client_payload.release_tag }}" ]; then
+            echo "tag=${{ github.event.client_payload.release_tag }}" >> $GITHUB_OUTPUT
           else
             echo "tag=" >> $GITHUB_OUTPUT
           fi
@@ -59,8 +64,6 @@ jobs:
       contents: read
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.release_tag || github.ref }}
 
       - name: Install uv
         uses: astral-sh/setup-uv@v6
@@ -95,72 +98,3 @@ jobs:
             echo "Some packages failed to publish"
             exit 1
           fi
-
-      - name: Build Slack payload
-        if: success()
-        id: slack
-        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          RELEASE_TAG: ${{ inputs.release_tag }}
-        run: |
-          payload=$(uv run python -c "
-          import json, re, subprocess, sys
-
-          with open('lib/crewai/src/crewai/__init__.py') as f:
-              m = re.search(r\"__version__\s*=\s*[\\\"']([^\\\"']+)\", f.read())
-          version = m.group(1) if m else 'unknown'
-
-          import os
-          tag = os.environ.get('RELEASE_TAG') or version
-
-          try:
-              r = subprocess.run(['gh','release','view',tag,'--json','body','-q','.body'],
-                                 capture_output=True, text=True, check=True)
-              body = r.stdout.strip()
-          except Exception:
-              body = ''
-
-          blocks = [
-              {'type':'section','text':{'type':'mrkdwn',
-                  'text':f':rocket: \`crewai v{version}\` published to PyPI'}},
-              {'type':'section','text':{'type':'mrkdwn',
-                  'text':f'<https://pypi.org/project/crewai/{version}/|View on PyPI> · <https://github.com/crewAIInc/crewAI/releases/tag/{tag}|Release notes>'}},
-              {'type':'divider'},
-          ]
-
-          if body:
-              heading, items = '', []
-              for line in body.split('\n'):
-                  line = line.strip()
-                  if not line: continue
-                  hm = re.match(r'^#{2,3}\s+(.*)', line)
-                  if hm:
-                      if heading and items:
-                          skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
-                          if not skip:
-                              txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
-                              blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
-                      heading, items = hm.group(1), []
-                  elif line.startswith('- ') or line.startswith('* '):
-                      items.append(re.sub(r'\*\*([^*]*)\*\*', r'*\1*', line[2:]))
-              if heading and items:
-                  skip = heading in ('What\\'s Changed','') or 'Contributors' in heading
-                  if not skip:
-                      txt = f'*{heading}*\n' + '\n'.join(f'• {i}' for i in items)
-                      blocks.append({'type':'section','text':{'type':'mrkdwn','text':txt}})
-
-          blocks.append({'type':'divider'})
-          blocks.append({'type':'section','text':{'type':'mrkdwn',
-              'text':f'\`\`\`uv add \"crewai[tools]=={version}\"\`\`\`'}})
-
-          print(json.dumps({'blocks':blocks}))
-          ")
-          echo "payload=$payload" >> $GITHUB_OUTPUT
-
-      - name: Notify Slack
-        if: success()
-        uses: slackapi/slack-github-action@v2.1.0
-        with:
-          webhook: ${{ secrets.SLACK_WEBHOOK_URL }}
-          webhook-type: incoming-webhook
-          payload: ${{ steps.slack.outputs.payload }}

.github/workflows/trigger-deployment-tests.yml

@@ -0,0 +1,18 @@
+name: Trigger Deployment Tests
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  trigger:
+    name: Trigger deployment tests
+    runs-on: ubuntu-latest
+    steps:
+      - name: Trigger deployment tests
+        uses: peter-evans/repository-dispatch@v3
+        with:
+          token: ${{ secrets.CREWAI_DEPLOYMENTS_PAT }}
+          repository: ${{ secrets.CREWAI_DEPLOYMENTS_REPOSITORY }}
+          event-type: crewai-release
+          client-payload: '{"release_tag": "${{ github.event.release.tag_name }}", "release_name": "${{ github.event.release.name }}"}'

.pre-commit-config.yaml

@@ -19,7 +19,7 @@ repos:
         language: system
         pass_filenames: true
         types: [python]
-        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/cli/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/)
+        exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/)
   - repo: https://github.com/astral-sh/uv-pre-commit
     rev: 0.9.3
     hooks:

conftest.py

@@ -12,7 +12,6 @@ from dotenv import load_dotenv
 import pytest
 from vcr.request import Request  # type: ignore[import-untyped]
 
-
 try:
     import vcr.stubs.httpx_stubs as httpx_stubs  # type: ignore[import-untyped]
 except ModuleNotFoundError:
@@ -226,7 +225,7 @@ def vcr_cassette_dir(request: Any) -> str:
 
     for parent in test_file.parents:
         if (
-            parent.name in ("crewai", "crewai-tools", "crewai-files", "cli")
+            parent.name in ("crewai", "crewai-tools", "crewai-files")
             and parent.parent.name == "lib"
         ):
             package_root = parent

docs/en/changelog.mdx

@@ -4,214 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="Mar 14, 2026">
-  ## v1.10.2rc2
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc2)
-
-  ## What's Changed
-
-  ### Bug Fixes
-  - Remove exclusive locks from read-only storage operations
-
-  ### Documentation
-  - Update changelog and version for v1.10.2rc1
-
-  ## Contributors
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="Mar 13, 2026">
-  ## v1.10.2rc1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc1)
-
-  ## What's Changed
-
-  ### Features
-  - Add release command and trigger PyPI publish
-
-  ### Bug Fixes
-  - Fix cross-process and thread-safe locking to unprotected I/O
-  - Propagate contextvars across all thread and executor boundaries
-  - Propagate ContextVars into async task threads
-
-  ### Documentation
-  - Update changelog and version for v1.10.2a1
-
-  ## Contributors
-
-  @danglies007, @greysonlalonde
-
-</Update>
-
-<Update label="Mar 11, 2026">
-  ## v1.10.2a1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## What's Changed
-
-  ### Features
-  - Add support for tool search, saving tokens, and dynamically injecting appropriate tools during execution for Anthropics.
-  - Introduce more Brave Search tools.
-  - Create action for nightly releases.
-
-  ### Bug Fixes
-  - Fix LockException under concurrent multi-process execution.
-  - Resolve issues with grouping parallel tool results in a single user message.
-  - Address MCP tools resolutions and eliminate all shared mutable connections.
-  - Update LLM parameter handling in the human_feedback function.
-  - Add missing list/dict methods to LockedListProxy and LockedDictProxy.
-  - Propagate contextvars context to parallel tool call threads.
-  - Bump gitpython dependency to >=3.1.41 to resolve CVE path traversal vulnerability.
-
-  ### Refactoring
-  - Refactor memory classes to be serializable.
-
-  ### Documentation
-  - Update changelog and version for v1.10.1.
-
-  ## Contributors
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
-<Update label="Mar 04, 2026">
-  ## v1.10.1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## What's Changed
-
-  ### Features
-  - Upgrade Gemini GenAI
-
-  ### Bug Fixes
-  - Adjust executor listener value to avoid recursion
-  - Group parallel function response parts in a single Content object in Gemini
-  - Surface thought output from thinking models in Gemini
-  - Load MCP and platform tools when agent tools are None
-  - Support Jupyter environments with running event loops in A2A
-  - Use anonymous ID for ephemeral traces
-  - Conditionally pass plus header
-  - Skip signal handler registration in non-main threads for telemetry
-  - Inject tool errors as observations and resolve name collisions
-  - Upgrade pypdf from 4.x to 6.7.4 to resolve Dependabot alerts
-  - Resolve critical and high Dependabot security alerts
-
-  ### Documentation
-  - Sync Composio tool documentation across locales
-
-  ## Contributors
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
-<Update label="Feb 27, 2026">
-  ## v1.10.1a1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## What's Changed
-
-  ### Features
-  - Implement asynchronous invocation support in step callback methods
-  - Implement lazy loading for heavy dependencies in Memory module
-
-  ### Documentation
-  - Update changelog and version for v1.10.0
-
-  ### Refactoring
-  - Refactor step callback methods to support asynchronous invocation
-  - Refactor to implement lazy loading for heavy dependencies in Memory module
-
-  ### Bug Fixes
-  - Fix branch for release notes
-
-  ## Contributors
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="Feb 27, 2026">
-  ## v1.10.1a1
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## What's Changed
-
-  ### Refactoring
-  - Refactor step callback methods to support asynchronous invocation
-  - Implement lazy loading for heavy dependencies in Memory module
-
-  ### Documentation
-  - Update changelog and version for v1.10.0
-
-  ### Bug Fixes
-  - Make branch for release notes
-
-  ## Contributors
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="Feb 26, 2026">
-  ## v1.10.0
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0)
-
-  ## What's Changed
-
-  ### Features
-  - Enhance MCP tool resolution and related events
-  - Update lancedb version and add lance-namespace packages
-  - Enhance JSON argument parsing and validation in CrewAgentExecutor and BaseTool
-  - Migrate CLI HTTP client from requests to httpx
-  - Add versioned documentation
-  - Add yanked detection for version notes
-  - Implement user input handling in Flows
-  - Enhance HITL self-loop functionality in human feedback integration tests
-  - Add started_event_id and set in eventbus
-  - Auto update tools.specs
-
-  ### Bug Fixes
-  - Validate tool kwargs even when empty to prevent cryptic TypeError
-  - Preserve null types in tool parameter schemas for LLM
-  - Map output_pydantic/output_json to native structured output
-  - Ensure callbacks are ran/awaited if promise
-  - Capture method name in exception context
-  - Preserve enum type in router result; improve types
-  - Fix cyclic flows silently breaking when persistence ID is passed in inputs
-  - Correct CLI flag format from --skip-provider to --skip_provider
-  - Ensure OpenAI tool call stream is finalized
-  - Resolve complex schema $ref pointers in MCP tools
-  - Enforce additionalProperties=false in schemas
-  - Reject reserved script names for crew folders
-  - Resolve race condition in guardrail event emission test
-
-  ### Documentation
-  - Add litellm dependency note for non-native LLM providers
-  - Clarify NL2SQL security model and hardening guidance
-  - Add 96 missing actions across 9 integrations
-
-  ### Refactoring
-  - Refactor crew to provider
-  - Extract HITL to provider pattern
-  - Improve hook typing and registration
-
-  ## Contributors
-
-  @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl
-
-</Update>
-
 <Update label="Jan 26, 2026">
   ## v1.9.0
 

docs/en/concepts/llms.mdx

@@ -106,15 +106,6 @@ There are different places in CrewAI code where you can specify the model to use
   </Tab>
 </Tabs>
 
-<Info>
-  CrewAI provides native SDK integrations for OpenAI, Anthropic, Google (Gemini API), Azure, and AWS Bedrock — no extra install needed beyond the provider-specific extras (e.g. `uv add "crewai[openai]"`).
-
-  All other providers are powered by **LiteLLM**. If you plan to use any of them, add it as a dependency to your project:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## Provider Configuration Examples
 
 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
@@ -284,11 +275,6 @@ In this section, you'll find detailed examples that help you select, configure,
     | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
     | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
     | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Anthropic">
@@ -484,7 +470,7 @@ In this section, you'll find detailed examples that help you select, configure,
       To get an Express mode API key:
       - New Google Cloud users: Get an [express mode API key](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey)
       - Existing Google Cloud users: Get a [Google Cloud API key bound to a service account](https://cloud.google.com/docs/authentication/api-keys)
-
+      
       For more details, see the [Vertex AI Express mode documentation](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/quickstart?usertype=apikey).
     </Info>
 
@@ -585,11 +571,6 @@ In this section, you'll find detailed examples that help you select, configure,
     | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
     | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
     | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Azure">
@@ -671,7 +652,6 @@ In this section, you'll find detailed examples that help you select, configure,
     # Optional
     AWS_SESSION_TOKEN=<your-session-token>  # For temporary credentials
     AWS_DEFAULT_REGION=<your-region>  # Defaults to us-east-1
-    AWS_REGION_NAME=<your-region>  # Alternative configuration for backwards compatibility with LiteLLM. Defaults to us-east-1
     ```
 
     **Basic Usage:**
@@ -715,7 +695,6 @@ In this section, you'll find detailed examples that help you select, configure,
     - `AWS_SECRET_ACCESS_KEY`: AWS secret key (required)
     - `AWS_SESSION_TOKEN`: AWS session token for temporary credentials (optional)
     - `AWS_DEFAULT_REGION`: AWS region (defaults to `us-east-1`)
-    - `AWS_REGION_NAME`: AWS region (defaults to `us-east-1`). Alternative configuration for backwards compatibility with LiteLLM
 
     **Features:**
     - Native tool calling support via Converse API
@@ -785,11 +764,6 @@ In this section, you'll find detailed examples that help you select, configure,
         model="sagemaker/<my-endpoint>"
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Mistral">
@@ -805,11 +779,6 @@ In this section, you'll find detailed examples that help you select, configure,
         temperature=0.7
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Nvidia NIM">
@@ -896,11 +865,6 @@ In this section, you'll find detailed examples that help you select, configure,
     | rakuten/rakutenai-7b-instruct                                     | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
     | rakuten/rakutenai-7b-chat                                         | 1,024 tokens   | Advanced state-of-the-art LLM with language understanding, superior reasoning, and text generation. |
     | baichuan-inc/baichuan2-13b-chat                                  | 4,096 tokens   | Support Chinese and English chat, coding, math, instruction following, solving quizzes |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -941,11 +905,6 @@ In this section, you'll find detailed examples that help you select, configure,
 
         # ...
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Groq">
@@ -967,11 +926,6 @@ In this section, you'll find detailed examples that help you select, configure,
     | Llama 3.1 70B/8B  | 131,072 tokens   | High-performance, large context tasks      |
     | Llama 3.2 Series  | 8,192 tokens     | General-purpose tasks                      |
     | Mixtral 8x7B      | 32,768 tokens    | Balanced performance and context           |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="IBM watsonx.ai">
@@ -994,11 +948,6 @@ In this section, you'll find detailed examples that help you select, configure,
         base_url="https://api.watsonx.ai/v1"
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Ollama (Local LLMs)">
@@ -1012,11 +961,6 @@ In this section, you'll find detailed examples that help you select, configure,
         base_url="http://localhost:11434"
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Fireworks AI">
@@ -1032,11 +976,6 @@ In this section, you'll find detailed examples that help you select, configure,
         temperature=0.7
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Perplexity AI">
@@ -1052,11 +991,6 @@ In this section, you'll find detailed examples that help you select, configure,
         base_url="https://api.perplexity.ai/"
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Hugging Face">
@@ -1071,11 +1005,6 @@ In this section, you'll find detailed examples that help you select, configure,
         model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
     )
     ```
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="SambaNova">
@@ -1099,11 +1028,6 @@ In this section, you'll find detailed examples that help you select, configure,
     | Llama 3.2 Series   | 8,192 tokens           | General-purpose, multimodal tasks            |
     | Llama 3.3 70B      | Up to 131,072 tokens   | High-performance and output quality          |
     | Qwen2 familly      | 8,192 tokens           | High-performance and output quality          |
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Cerebras">
@@ -1129,11 +1053,6 @@ In this section, you'll find detailed examples that help you select, configure,
       - Good balance of speed and quality
       - Support for long context windows
     </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Open Router">
@@ -1156,11 +1075,6 @@ In this section, you'll find detailed examples that help you select, configure,
       - openrouter/deepseek/deepseek-r1
       - openrouter/deepseek/deepseek-chat
     </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Nebius AI Studio">
@@ -1183,11 +1097,6 @@ In this section, you'll find detailed examples that help you select, configure,
       - Competitive pricing
       - Good balance of speed and quality
     </Info>
-
-    **Note:** This provider uses LiteLLM. Add it as a dependency to your project:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 </AccordionGroup>
 

docs/en/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ CrewAI Enterprise provides a comprehensive Human-in-the-Loop (HITL) management s
 Configure human review checkpoints within your Flows using the `@human_feedback` decorator. When execution reaches a review point, the system pauses, notifies the assignee via email, and waits for a response.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # AI generates content
         return "Generated marketing copy for Q1 campaign..."
 
+    @listen(generate_content)
     @human_feedback(
         message="Please review this content for brand compliance:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Marketing copy for review..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"Content rejected. Reason: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"Revision requested: {result.feedback}")
 ```
 
 For complete implementation details, see the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide.

docs/en/enterprise/guides/deploy-to-amp.mdx

@@ -177,11 +177,6 @@ You need to push your crew to a GitHub repository. If you haven't created a crew
       ![Set Environment Variables](/images/enterprise/set-env-variables.png)
     </Frame>
 
-    <Info>
-      Using private Python packages? You'll need to add your registry credentials here too.
-      See [Private Package Registries](/en/enterprise/guides/private-package-registry) for the required variables.
-    </Info>
-
   </Step>
 
   <Step title="Deploy Your Crew">

docs/en/enterprise/guides/prepare-for-deployment.mdx

@@ -256,12 +256,6 @@ Before deployment, ensure you have:
 1. **LLM API keys** ready (OpenAI, Anthropic, Google, etc.)
 2. **Tool API keys** if using external tools (Serper, etc.)
 
-<Info>
-  If your project depends on packages from a **private PyPI registry**, you'll also need to configure
-  registry authentication credentials as environment variables. See the
-  [Private Package Registries](/en/enterprise/guides/private-package-registry) guide for details.
-</Info>
-
 <Tip>
   Test your project locally with the same environment variables before deploying
   to catch configuration issues early.

docs/en/enterprise/guides/private-package-registry.mdx

@@ -1,263 +0,0 @@
----
-title: "Private Package Registries"
-description: "Install private Python packages from authenticated PyPI registries in CrewAI AMP"
-icon: "lock"
-mode: "wide"
----
-
-<Note>
-  This guide covers how to configure your CrewAI project to install Python packages
-  from private PyPI registries (Azure DevOps Artifacts, GitHub Packages, GitLab, AWS CodeArtifact, etc.)
-  when deploying to CrewAI AMP.
-</Note>
-
-## When You Need This
-
-If your project depends on internal or proprietary Python packages hosted on a private registry
-rather than the public PyPI, you'll need to:
-
-1. Tell UV **where** to find the package (an index URL)
-2. Tell UV **which** packages come from that index (a source mapping)
-3. Provide **credentials** so UV can authenticate during install
-
-CrewAI AMP uses [UV](https://docs.astral.sh/uv/) for dependency resolution and installation.
-UV supports authenticated private registries through `pyproject.toml` configuration combined
-with environment variables for credentials.
-
-## Step 1: Configure pyproject.toml
-
-Three pieces work together in your `pyproject.toml`:
-
-### 1a. Declare the dependency
-
-Add the private package to your `[project.dependencies]` like any other dependency:
-
-```toml
-[project]
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-```
-
-### 1b. Define the index
-
-Register your private registry as a named index under `[[tool.uv.index]]`:
-
-```toml
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-```
-
-<Info>
-  The `name` field is important — UV uses it to construct the environment variable names
-  for authentication (see [Step 2](#step-2-set-authentication-credentials) below).
-
-  Setting `explicit = true` means UV won't search this index for every package — only the
-  ones you explicitly map to it in `[tool.uv.sources]`. This avoids unnecessary queries
-  against your private registry and protects against dependency confusion attacks.
-</Info>
-
-### 1c. Map the package to the index
-
-Tell UV which packages should be resolved from your private index using `[tool.uv.sources]`:
-
-```toml
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-### Complete example
-
-```toml
-[project]
-name = "my-crew-project"
-version = "0.1.0"
-requires-python = ">=3.10,<=3.13"
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-
-[tool.crewai]
-type = "crew"
-
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-After updating `pyproject.toml`, regenerate your lock file:
-
-```bash
-uv lock
-```
-
-<Warning>
-  Always commit the updated `uv.lock` along with your `pyproject.toml` changes.
-  The lock file is required for deployment — see [Prepare for Deployment](/en/enterprise/guides/prepare-for-deployment).
-</Warning>
-
-## Step 2: Set Authentication Credentials
-
-UV authenticates against private indexes using environment variables that follow a naming convention
-based on the index name you defined in `pyproject.toml`:
-
-```
-UV_INDEX_{UPPER_NAME}_USERNAME
-UV_INDEX_{UPPER_NAME}_PASSWORD
-```
-
-Where `{UPPER_NAME}` is your index name converted to **uppercase** with **hyphens replaced by underscores**.
-
-For example, an index named `my-private-registry` uses:
-
-| Variable | Value |
-|----------|-------|
-| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | Your registry username or token name |
-| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | Your registry password or token/PAT |
-
-<Warning>
-  These environment variables **must** be added via the CrewAI AMP **Environment Variables** settings —
-  either globally or at the deployment level. They cannot be set in `.env` files or hardcoded in your project.
-
-  See [Setting Environment Variables in AMP](#setting-environment-variables-in-amp) below.
-</Warning>
-
-## Registry Provider Reference
-
-The table below shows the index URL format and credential values for common registry providers.
-Replace placeholder values with your actual organization and feed details.
-
-| Provider | Index URL | Username | Password |
-|----------|-----------|----------|----------|
-| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | Any non-empty string (e.g. `token`) | Personal Access Token (PAT) with Packaging Read scope |
-| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | GitHub username | Personal Access Token (classic) with `read:packages` scope |
-| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | Project or Personal Access Token with `read_api` scope |
-| **AWS CodeArtifact** | Use the URL from `aws codeartifact get-repository-endpoint` | `aws` | Token from `aws codeartifact get-authorization-token` |
-| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Base64-encoded service account key |
-| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | Username or email | API key or identity token |
-| **Self-hosted (devpi, Nexus, etc.)** | Your registry's simple API URL | Registry username | Registry password |
-
-<Tip>
-  For **AWS CodeArtifact**, the authorization token expires periodically.
-  You'll need to refresh the `UV_INDEX_*_PASSWORD` value when it expires.
-  Consider automating this in your CI/CD pipeline.
-</Tip>
-
-## Setting Environment Variables in AMP
-
-Private registry credentials must be configured as environment variables in CrewAI AMP.
-You have two options:
-
-<Tabs>
-  <Tab title="Web Interface">
-    1. Log in to [CrewAI AMP](https://app.crewai.com)
-    2. Navigate to your automation
-    3. Open the **Environment Variables** tab
-    4. Add each variable (`UV_INDEX_*_USERNAME` and `UV_INDEX_*_PASSWORD`) with its value
-
-    See the [Deploy to AMP — Set Environment Variables](/en/enterprise/guides/deploy-to-amp#set-environment-variables) step for details.
-  </Tab>
-  <Tab title="CLI Deployment">
-    Add the variables to your local `.env` file before running `crewai deploy create`.
-    The CLI will securely transfer them to the platform:
-
-    ```bash
-    # .env
-    OPENAI_API_KEY=sk-...
-    UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-    UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here
-    ```
-
-    ```bash
-    crewai deploy create
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  **Never** commit credentials to your repository. Use AMP environment variables for all secrets.
-  The `.env` file should be listed in `.gitignore`.
-</Warning>
-
-To update credentials on an existing deployment, see [Update Your Crew — Environment Variables](/en/enterprise/guides/update-crew).
-
-## How It All Fits Together
-
-When CrewAI AMP builds your automation, the resolution flow works like this:
-
-<Steps>
-  <Step title="Build starts">
-    AMP pulls your repository and reads `pyproject.toml` and `uv.lock`.
-  </Step>
-  <Step title="UV resolves dependencies">
-    UV reads `[tool.uv.sources]` to determine which index each package should come from.
-  </Step>
-  <Step title="UV authenticates">
-    For each private index, UV looks up `UV_INDEX_{NAME}_USERNAME` and `UV_INDEX_{NAME}_PASSWORD`
-    from the environment variables you configured in AMP.
-  </Step>
-  <Step title="Packages install">
-    UV downloads and installs all packages — both public (from PyPI) and private (from your registry).
-  </Step>
-  <Step title="Automation runs">
-    Your crew or flow starts with all dependencies available.
-  </Step>
-</Steps>
-
-## Troubleshooting
-
-### Authentication Errors During Build
-
-**Symptom**: Build fails with `401 Unauthorized` or `403 Forbidden` when resolving a private package.
-
-**Check**:
-- The `UV_INDEX_*` environment variable names match your index name exactly (uppercased, hyphens → underscores)
-- Credentials are set in AMP environment variables, not just in a local `.env`
-- Your token/PAT has the required read permissions for the package feed
-- The token hasn't expired (especially relevant for AWS CodeArtifact)
-
-### Package Not Found
-
-**Symptom**: `No matching distribution found for my-private-package`.
-
-**Check**:
-- The index URL in `pyproject.toml` ends with `/simple/`
-- The `[tool.uv.sources]` entry maps the correct package name to the correct index name
-- The package is actually published to your private registry
-- Run `uv lock` locally with the same credentials to verify resolution works
-
-### Lock File Conflicts
-
-**Symptom**: `uv lock` fails or produces unexpected results after adding a private index.
-
-**Solution**: Set the credentials locally and regenerate:
-
-```bash
-export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat
-uv lock
-```
-
-Then commit the updated `uv.lock`.
-
-## Related Guides
-
-<CardGroup cols={3}>
-  <Card title="Prepare for Deployment" icon="clipboard-check" href="/en/enterprise/guides/prepare-for-deployment">
-    Verify project structure and dependencies before deploying.
-  </Card>
-  <Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
-    Deploy your crew or flow and configure environment variables.
-  </Card>
-  <Card title="Update Your Crew" icon="arrows-rotate" href="/en/enterprise/guides/update-crew">
-    Update environment variables and push changes to a running deployment.
-  </Card>
-</CardGroup>

docs/en/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "Moving from LangGraph to CrewAI: A Practical Guide for Engineers"
-description: If you already have built with LangGraph, learn how to quickly port your projects to CrewAI
-icon: switch
-mode: "wide"
----
-
-You've built agents with LangGraph. You've wrestled with `StateGraph`, wired up conditional edges, and debugged state dictionaries at 2 AM. It works — but somewhere along the way, you started wondering if there's a better path to production.
-
-There is. **CrewAI Flows** gives you the same power — event-driven orchestration, conditional routing, shared state — with dramatically less boilerplate and a mental model that maps cleanly to how you actually think about multi-step AI workflows.
-
-This article walks through the core concepts side by side, shows real code comparisons, and demonstrates why CrewAI Flows is the framework you'll want to reach for next.
-
----
-
-## The Mental Model Shift
-
-LangGraph asks you to think in **graphs**: nodes, edges, and state dictionaries. Every workflow is a directed graph where you explicitly wire transitions between computation steps. It's powerful, but the abstraction carries overhead — especially when your workflow is fundamentally sequential with a few decision points.
-
-CrewAI Flows asks you to think in **events**: methods that start things, methods that listen for results, and methods that route execution. The topology of your workflow emerges from decorator annotations rather than explicit graph construction. This isn't just syntactic sugar — it changes how you design, read, and maintain your pipelines.
-
-Here's the core mapping:
-
-| LangGraph Concept | CrewAI Flows Equivalent |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-Let's see what this looks like in practice.
-
----
-
-## Demo 1: A Simple Sequential Pipeline
-
-Imagine you're building a pipeline that takes a topic, researches it, writes a summary, and formats the output. Here's how each framework handles it.
-
-### LangGraph Approach
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-You define functions, register them as nodes, and manually wire every transition. For a simple sequence like this, there's a lot of ceremony.
-
-### CrewAI Flows Approach
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-Notice what's different: no graph construction, no edge wiring, no compile step. The execution order is declared right where the logic lives. `@start()` marks the entry point, and `@listen(method_name)` chains steps together. The state is a proper Pydantic model with type safety, validation, and IDE auto-completion.
-
----
-
-## Demo 2: Conditional Routing
-
-This is where things get interesting. Say you're building a content pipeline that routes to different processing paths based on the type of content detected.
-
-### LangGraph Approach
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-You need a separate routing function, explicit conditional edge mapping, and termination edges for every branch. The routing logic is decoupled from the node that produces the routing decision.
-
-### CrewAI Flows Approach
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-The `@router()` decorator turns a method into a decision point. It returns a string that matches a listener — no mapping dictionaries, no separate routing functions. The branching logic reads like a Python `if` statement because it *is* one.
-
----
-
-## Demo 3: Integrating AI Agent Crews into Flows
-
-Here's where CrewAI's real power shines. Flows aren't just for chaining LLM calls — they orchestrate full **Crews** of autonomous agents. This is something LangGraph simply doesn't have a native equivalent for.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-This is the key insight: **Flows provide the orchestration layer, and Crews provide the intelligence layer.** Each step in a Flow can spin up a full team of collaborating agents, each with their own roles, goals, and tools. You get structured, predictable control flow *and* autonomous agent collaboration — the best of both worlds.
-
-In LangGraph, achieving something similar means manually implementing agent communication protocols, tool-calling loops, and delegation logic inside your node functions. It's possible, but it's plumbing you're building from scratch every time.
-
----
-
-## Demo 4: Parallel Execution and Synchronization
-
-Real-world pipelines often need to fan out work and join the results. CrewAI Flows handles this elegantly with `and_` and `or_` operators.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-Multiple `@start()` decorators fire in parallel. The `and_()` combinator on the `@listen` decorator ensures `synthesize_report` only executes after *all three* upstream methods complete. There's also `or_()` for when you want to proceed as soon as *any* upstream task finishes.
-
-In LangGraph, you'd need to build a fan-out/fan-in pattern with parallel branches, a synchronization node, and careful state merging — all wired explicitly through edges.
-
----
-
-## Why CrewAI Flows for Production
-
-Beyond cleaner syntax, Flows deliver several production-critical advantages:
-
-**Built-in state persistence.** Flow state is backed by LanceDB, meaning your workflows can survive crashes, be resumed, and accumulate knowledge across runs. LangGraph requires you to configure a separate checkpointer.
-
-**Type-safe state management.** Pydantic models give you validation, serialization, and IDE support out of the box. LangGraph's `TypedDict` states don't validate at runtime.
-
-**First-class agent orchestration.** Crews are a native primitive. You define agents with roles, goals, backstories, and tools — and they collaborate autonomously within the structured envelope of a Flow. No need to reinvent multi-agent coordination.
-
-**Simpler mental model.** Decorators declare intent. `@start` means "begin here." `@listen(x)` means "run after x." `@router(x)` means "decide where to go after x." The code reads like the workflow it describes.
-
-**CLI integration.** Run flows with `crewai run`. No separate compilation step, no graph serialization. Your Flow is a Python class, and it runs like one.
-
----
-
-## Migration Cheat Sheet
-
-If you're sitting on a LangGraph codebase and want to move to CrewAI Flows, here's a practical conversion guide:
-
-1. **Map your state.** Convert your `TypedDict` to a Pydantic `BaseModel`. Add default values for all fields.
-2. **Convert nodes to methods.** Each `add_node` function becomes a method on your `Flow` subclass. Replace `state["field"]` reads with `self.state.field`.
-3. **Replace edges with decorators.** Your `add_edge(START, "first_node")` becomes `@start()` on the first method. Sequential `add_edge("a", "b")` becomes `@listen(a)` on method `b`.
-4. **Replace conditional edges with `@router`.** Your routing function and `add_conditional_edges()` mapping become a single `@router()` method that returns a route string.
-5. **Replace compile + invoke with kickoff.** Drop `graph.compile()`. Call `flow.kickoff()` instead.
-6. **Consider where Crews fit.** Any node where you have complex multi-step agent logic is a candidate for extraction into a Crew. This is where you'll see the biggest quality improvement.
-
----
-
-## Getting Started
-
-Install CrewAI and scaffold a new Flow project:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-This generates a project structure with a ready-to-edit Flow class, configuration files, and a `pyproject.toml` with `type = "flow"` already set. Run it with:
-
-```bash
-crewai run
-```
-
-From there, add your agents, wire up your listeners, and ship it.
-
----
-
-## Final Thoughts
-
-LangGraph taught the ecosystem that AI workflows need structure. That was an important lesson. But CrewAI Flows takes that lesson and delivers it in a form that's faster to write, easier to read, and more powerful in production — especially when your workflows involve multiple collaborating agents.
-
-If you're building anything beyond a single-agent chain, give Flows a serious look. The decorator-driven model, native Crew integration, and built-in state management mean you'll spend less time on plumbing and more time on the problems that matter.
-
-Start with `crewai create flow`. You won't look back.

docs/en/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 When you specify `emit`, the decorator becomes a router. The human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="Do you approve this content for publication?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "Draft blog post content here..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "Draft blog post content here..."
+@listen("approved")
+def publish(self, result):
+    print(f"Publishing! User said: {result.feedback}")
 
-    @human_feedback(
-        message="Do you approve this content for publication?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Draft blog post content here..."
+@listen("rejected")
+def discard(self, result):
+    print(f"Discarding. Reason: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"Publishing! User said: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"Discarding. Reason: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"Revising based on: {result.feedback}")
 ```
 
-When the human says something like "needs more detail", the LLM collapses that to `"needs_revision"`, which triggers `review_content` again via `or_()` — creating a revision loop. The loop continues until the outcome is `"approved"` or `"rejected"`.
-
 <Tip>
 The LLM uses structured outputs (function calling) when available to guarantee the response is one of your specified outcomes. This makes routing reliable and predictable.
 </Tip>
 
-<Warning>
-A `@start()` method only runs once at the beginning of the flow. If you need a revision loop, separate the start method from the review method and use `@listen(or_("trigger", "revision_outcome"))` on the review method to enable the self-loop.
-</Warning>
-
 ## HumanFeedbackResult
 
 The `HumanFeedbackResult` dataclass contains all information about a human feedback interaction:
@@ -198,183 +188,127 @@ Each `HumanFeedbackResult` is appended to `human_feedback_history`, so multiple
 
 ## Complete Example: Content Approval Workflow
 
-Here's a full example implementing a content review and approval workflow with a revision loop:
+Here's a full example implementing a content review and approval workflow:
 
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """A flow that generates content and loops until the human approves."""
+    """A flow that generates content and gets human approval."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# AI Safety\n\nThis is a draft about AI Safety..."
+    def get_topic(self):
+        self.state.topic = input("What topic should I write about? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # In real use, this would call an LLM
+        self.state.draft = f"# {topic}\n\nThis is a draft about {topic}..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="Please review this draft. Approve, reject, or describe what needs changing:",
+        message="Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"Content approved and published! Reviewer said: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ Content approved and published!")
+        print(f"Reviewer comment: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"Content rejected. Reason: {result.feedback}")
+        print("\n❌ Content rejected")
+        print(f"Reason: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 Revision #{self.state.revision_count} requested")
+        print(f"Feedback: {result.feedback}")
 
+        # In a real flow, you might loop back to generate_draft
+        # For this example, we just acknowledge
+        return "revision_requested"
+
+
+# Run the flow
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow completed. Status: {flow.state.status}, Reviews: {flow.state.revision_count}")
+print(f"\nFlow completed. Revisions requested: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# AI Safety
-
-This is a draft about AI Safety... (v1)
-==================================================
-
-Please review this draft. Approve, reject, or describe what needs changing:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: Needs more detail on alignment research
+What topic should I write about? AI Safety
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
 # AI Safety
 
-This is a draft about AI Safety... (v2)
+This is a draft about AI Safety...
 ==================================================
 
-Please review this draft. Approve, reject, or describe what needs changing:
+Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: Looks good, approved!
 
-Content approved and published! Reviewer said: Looks good, approved!
+✅ Content approved and published!
+Reviewer comment: Looks good, approved!
 
-Flow completed. Status: published, Reviews: 2
+Flow completed. Revisions requested: 0
 ```
 
 </CodeGroup>
 
-The key pattern is `@listen(or_("generate_draft", "needs_revision"))` — the review method listens to both the initial trigger and its own revision outcome, creating a self-loop that repeats until the human approves or rejects.
-
 ## Combining with Other Decorators
 
-The `@human_feedback` decorator works with `@start()`, `@listen()`, and `or_()`. Both decorator orderings work — the framework propagates attributes in both directions — but the recommended patterns are:
+The `@human_feedback` decorator works with other flow decorators. Place it as the innermost decorator (closest to the function):
 
 ```python Code
-# One-shot review at the start of a flow (no self-loop)
+# Correct: @human_feedback is innermost (closest to the function)
 @start()
-@human_feedback(message="Review this:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="Review this:")
 def my_start_method(self):
     return "content"
 
-# Linear review on a listener (no self-loop)
 @listen(other_method)
-@human_feedback(message="Review this too:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="Review this too:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: review that can loop back for revisions
-@human_feedback(message="Approve or revise?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Self-loop pattern
-
-To create a revision loop, the review method must listen to **both** an upstream trigger and its own revision outcome using `or_()`:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="Approve or request changes?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-When the outcome is `"revise"`, the flow routes back to `review` (because it listens to `"revise"` via `or_()`). When the outcome is `"approved"`, the flow continues to `publish`. This works because the flow engine exempts routers from the "fire once" rule, allowing them to re-execute on each loop iteration.
-
-### Chained routers
-
-A listener triggered by one router's outcome can itself be a router:
-
-```python Code
-@start()
-def generate(self):
-    return "draft content"
-
-@human_feedback(message="First review:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-@listen("generate")
-def first_review(self):
-    return "draft content"
-
-@human_feedback(message="Final review:", emit=["publish", "hold"], llm="gpt-4o-mini")
-@listen("approved")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-
-@listen("hold")
-def on_hold(self, prev):
-    return "held for later"
-```
-
-### Limitations
-
-- **`@start()` methods run once**: A `@start()` method cannot self-loop. If you need a revision cycle, use a separate `@start()` method as the entry point and put the `@human_feedback` on a `@listen()` method.
-- **No `@start()` + `@listen()` on the same method**: This is a Flow framework constraint. A method is either a start point or a listener, not both.
+<Tip>
+Place `@human_feedback` as the innermost decorator (last/closest to the function) so it wraps the method directly and can capture the return value before passing to the flow system.
+</Tip>
 
 ## Best Practices
 
 ### 1. Write Clear Request Messages
 
-The `message` parameter is what the human sees. Make it actionable:
+The `request` parameter is what the human sees. Make it actionable:
 
 ```python Code
 # ✅ Good - clear and actionable
@@ -582,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="Approve this content for publication?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -600,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"Archived. Reason: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"Queued for revision: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Starting the flow (will pause and wait for Slack response)
 def start_content_pipeline():
@@ -655,22 +594,22 @@ Over time, the human sees progressively better pre-reviewed output because each
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
         message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
         learn=True,  # enable HITL learning
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **First run**: The human sees the raw output and says "Always include citations for factual claims." The lesson is distilled and stored in memory.

docs/en/learn/llm-connections.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## Connect CrewAI to LLMs
 
-CrewAI connects to LLMs through native SDK integrations for the most popular providers (OpenAI, Anthropic, Google Gemini, Azure, and AWS Bedrock), and uses LiteLLM as a flexible fallback for all other providers.
+CrewAI uses LiteLLM to connect to a wide variety of Language Models (LLMs). This integration provides extensive versatility, allowing you to use models from numerous providers with a simple, unified interface.
 
 <Note>
     By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set.
@@ -41,14 +41,6 @@ LiteLLM supports a wide range of providers, including but not limited to:
 
 For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).
 
-<Info>
-  To use any provider not covered by a native integration, add LiteLLM as a dependency to your project:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  Native providers (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) use their own SDK extras — see the [Provider Configuration Examples](/en/concepts/llms#provider-configuration-examples).
-</Info>
-
 ## Changing the LLM
 
 To use a different LLM with your CrewAI agents, you have several options:

docs/en/observability/tracing.mdx

@@ -35,7 +35,7 @@ Visit [app.crewai.com](https://app.crewai.com) and create your free account. Thi
 If you haven't already, install CrewAI with the CLI tools:
 
 ```bash
-uv add 'crewai[tools]'
+uv add crewai[tools]
 ```
 
 Then authenticate your CLI with your CrewAI AMP account:

docs/en/tools/automation/composiotool.mdx

@@ -18,46 +18,77 @@ Composio is an integration platform that allows you to connect your AI agents to
 To incorporate Composio tools into your project, follow the instructions below:
 
 ```shell
-pip install composio composio-crewai
+pip install composio-crewai
 pip install crewai
 ```
 
-After the installation is complete, set your Composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://platform.composio.dev)
+After the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://app.composio.dev)
 
 ## Example
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio with CrewAI Provider
+1. Initialize Composio toolset
 
 ```python Code
-from composio_crewai import ComposioProvider
-from composio import Composio
+from composio_crewai import ComposioToolSet, App, Action
 from crewai import Agent, Task, Crew
 
-composio = Composio(provider=ComposioProvider())
+toolset = ComposioToolSet()
 ```
 
-2. Create a new Composio Session and retrieve the tools
+2. Connect your GitHub account
 <CodeGroup>
-```python
-session = composio.create(
-    user_id="your-user-id",
-    toolkits=["gmail", "github"] # optional, default is all toolkits
-)
-tools = session.tools()
+```shell CLI
+composio add github
+```
+```python Code
+request = toolset.initiate_connection(app=App.GITHUB)
+print(f"Open this URL to authenticate: {request.redirectUrl}")
 ```
-Read more about sessions and user management [here](https://docs.composio.dev/docs/configuring-sessions)
 </CodeGroup>
 
-3. Authenticating users manually
+3. Get Tools
 
-Composio automatically authenticates the users during the agent chat session. However, you can also authenticate the user manually by calling the `authorize` method.
+- Retrieving all the tools from an app (not recommended for production):
 ```python Code
-connection_request = session.authorize("github")
-print(f"Open this URL to authenticate: {connection_request.redirect_url}")
+tools = toolset.get_tools(apps=[App.GITHUB])
 ```
 
+- Filtering tools based on tags:
+```python Code
+tag = "users"
+
+filtered_action_enums = toolset.find_actions_by_tags(
+    App.GITHUB,
+    tags=[tag], 
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+
+- Filtering tools based on use case:
+```python Code
+use_case = "Star a repository on GitHub"
+
+filtered_action_enums = toolset.find_actions_by_use_case(
+    App.GITHUB, use_case=use_case, advanced=False
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
+
+- Using specific tools:
+
+In this demo, we will use the `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` action from the GitHub app.
+```python Code
+tools = toolset.get_tools(
+    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
+)
+```
+Learn more about filtering actions [here](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)
+
 4. Define agent
 
 ```python Code
@@ -85,4 +116,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task])
 crew.kickoff()
 ```
 
-* More detailed list of tools can be found [here](https://docs.composio.dev/toolkits)
+* More detailed list of tools can be found [here](https://app.composio.dev)

docs/en/tools/search-research/bravesearchtool.mdx

@@ -1,316 +1,97 @@
 ---
-title: Brave Search Tools
-description: A suite of tools for querying the Brave Search API — covering web, news, image, and video search.
+title: Brave Search
+description: The `BraveSearchTool` is designed to search the internet using the Brave Search API.
 icon: searchengin
 mode: "wide"
 ---
 
-# Brave Search Tools
+# `BraveSearchTool`
 
 ## Description
 
-CrewAI offers a family of Brave Search tools, each targeting a specific [Brave Search API](https://brave.com/search/api/) endpoint.
-Rather than a single catch-all tool, you can pick exactly the tool that matches the kind of results your agent needs:
-
-| Tool | Endpoint | Use case |
-| --- | --- | --- |
-| `BraveWebSearchTool` | Web Search | General web results, snippets, and URLs |
-| `BraveNewsSearchTool` | News Search | Recent news articles and headlines |
-| `BraveImageSearchTool` | Image Search | Image results with dimensions and source URLs |
-| `BraveVideoSearchTool` | Video Search | Video results from across the web |
-| `BraveLocalPOIsTool` | Local POIs | Find points of interest (e.g., restaurants) |
-| `BraveLocalPOIsDescriptionTool` | Local POIs | Retrieve AI-generated location descriptions |
-| `BraveLLMContextTool` | LLM Context | Pre-extracted web content optimized for AI agents, LLM grounding, and RAG pipelines. |
-
-All tools share a common base class (`BraveSearchToolBase`) that provides consistent behavior — rate limiting, automatic retries on `429` responses, header and parameter validation, and optional file saving.
-
-<Note>
-  The older `BraveSearchTool` class is still available for backwards compatibility, but it is considered **legacy** and will not receive the same level of attention going forward. We recommend migrating to the specific tools listed above, which offer richer configuration and a more focused interface.
-</Note>
-
-<Note>
-    While many tools (e.g., _BraveWebSearchTool_, _BraveNewsSearchTool_, _BraveImageSearchTool_, and _BraveVideoSearchTool_) can be used with a free Brave Search API subscription/plan, some parameters (e.g., `enable_snippets`) and tools (e.g., _BraveLocalPOIsTool_ and _BraveLocalPOIsDescriptionTool_) require a paid plan. Consult your subscription plan's capabilities for clarification.
-</Note>
+This tool is designed to perform web searches using the Brave Search API. It allows you to search the internet with a specified query and retrieve relevant results. The tool supports customizable result counts and country-specific searches.
 
 ## Installation
 
+To incorporate this tool into your project, follow the installation instructions below:
+
 ```shell
 pip install 'crewai[tools]'
 ```
 
-## Getting Started
+## Steps to Get Started
 
-1. **Install the package** — confirm that `crewai[tools]` is installed in your Python environment.
-2. **Get an API key** — sign up at [api-dashboard.search.brave.com/login](https://api-dashboard.search.brave.com/login) to generate a key.
-3. **Set the environment variable** — store your key as `BRAVE_API_KEY`, or pass it directly via the `api_key` parameter.
+To effectively use the `BraveSearchTool`, follow these steps:
 
-## Quick Examples
+1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **API Key Acquisition**: Acquire a Brave Search API key at https://api.search.brave.com/app/keys (sign in to generate a key).
+3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool.
 
-### Web Search
+## Example
+
+The following example demonstrates how to initialize the tool and execute a search with a given query:
 
 ```python Code
-from crewai_tools import BraveWebSearchTool
+from crewai_tools import BraveSearchTool
 
-tool = BraveWebSearchTool()
-results = tool.run(q="CrewAI agent framework")
+# Initialize the tool for internet searching capabilities
+tool = BraveSearchTool()
+
+# Execute a search
+results = tool.run(search_query="CrewAI agent framework")
 print(results)
 ```
 
-### News Search
+## Parameters
+
+The `BraveSearchTool` accepts the following parameters:
+
+- **search_query**: Mandatory. The search query you want to use to search the internet.
+- **country**: Optional. Specify the country for the search results. Default is empty string.
+- **n_results**: Optional. Number of search results to return. Default is `10`.
+- **save_file**: Optional. Whether to save the search results to a file. Default is `False`.
+
+## Example with Parameters
+
+Here is an example demonstrating how to use the tool with additional parameters:
 
 ```python Code
-from crewai_tools import BraveNewsSearchTool
+from crewai_tools import BraveSearchTool
 
-tool = BraveNewsSearchTool()
-results = tool.run(q="latest AI breakthroughs")
-print(results)
-```
-
-### Image Search
-
-```python Code
-from crewai_tools import BraveImageSearchTool
-
-tool = BraveImageSearchTool()
-results = tool.run(q="northern lights photography")
-print(results)
-```
-
-### Video Search
-
-```python Code
-from crewai_tools import BraveVideoSearchTool
-
-tool = BraveVideoSearchTool()
-results = tool.run(q="how to build AI agents")
-print(results)
-```
-
-### Location POI Descriptions
-
-```python Code
-from crewai_tools import (
-    BraveWebSearchTool,
-    BraveLocalPOIsDescriptionTool,
+# Initialize the tool with custom parameters
+tool = BraveSearchTool(
+    country="US",
+    n_results=5,
+    save_file=True
 )
 
-web_search = BraveWebSearchTool(raw=True)
-poi_details = BraveLocalPOIsDescriptionTool()
-
-results = web_search.run(q="italian restaurants in pensacola, florida")
-
-if "locations" in results:
-    location_ids = [ loc["id"] for loc in results["locations"]["results"] ]
-    if location_ids:
-        descriptions = poi_details.run(ids=location_ids)
-        print(descriptions)
-```
-
-## Common Constructor Parameters
-
-Every Brave Search tool accepts the following parameters at initialization:
-
-| Parameter | Type | Default | Description |
-| --- | --- | --- | --- |
-| `api_key` | `str \| None` | `None` | Brave API key. Falls back to the `BRAVE_API_KEY` environment variable. |
-| `headers` | `dict \| None` | `None` | Additional HTTP headers to send with every request (e.g., `api-version`, geolocation headers). |
-| `requests_per_second` | `float` | `1.0` | Maximum request rate. The tool will sleep between calls to stay within this limit. |
-| `save_file` | `bool` | `False` | When `True`, each response is written to a timestamped `.txt` file. |
-| `raw` | `bool` | `False` | When `True`, the full API JSON response is returned without any refinement. |
-| `timeout` | `int` | `30` | HTTP request timeout in seconds. |
-| `country` | `str \| None` | `None` | Legacy shorthand for geo-targeting (e.g., `"US"`). Prefer using the `country` query parameter directly. |
-| `n_results` | `int` | `10` | Legacy shorthand for result count. Prefer using the `count` query parameter directly. |
-
-<Warning>
-  The `country` and `n_results` constructor parameters exist for backwards compatibility. They are applied as defaults when the corresponding query parameters (`country`, `count`) are not provided at call time. For new code, we recommend passing `country` and `count` directly as query parameters instead.
-</Warning>
-
-## Query Parameters
-
-Each tool validates its query parameters against a Pydantic schema before sending the request.
-The parameters vary slightly per endpoint — here is a summary of the most commonly used ones:
-
-### BraveWebSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting (e.g., `"US"`). |
-| `search_lang` | Two-letter language code for results (e.g., `"en"`). |
-| `count` | Max number of results to return (1–20). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter: `"pd"` (past day), `"pw"` (past week), `"pm"` (past month), `"py"` (past year), or a date range like `"2025-01-01to2025-06-01"`. |
-| `extra_snippets` | Include up to 5 additional text snippets per result. |
-| `goggles` | Brave Goggles URL(s) and/or source for custom re-ranking. |
-
-For the complete parameter and header reference, see the [Brave Web Search API documentation](https://api-dashboard.search.brave.com/api-reference/web/search/get).
-
-### BraveNewsSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–50). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter (same options as Web Search). |
-| `goggles` | Brave Goggles URL(s) and/or source for custom re-ranking. |
-
-For the complete parameter and header reference, see the [Brave News Search API documentation](https://api-dashboard.search.brave.com/api-reference/news/news_search/get).
-
-### BraveImageSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–200). |
-| `safesearch` | Content filter: `"off"` or `"strict"`. |
-| `spellcheck` | Attempt to correct spelling errors in the query. |
-
-For the complete parameter and header reference, see the [Brave Image Search API documentation](https://api-dashboard.search.brave.com/api-reference/images/image_search).
-
-### BraveVideoSearchTool
-
-| Parameter | Description |
-| --- | --- |
-| `q` | **(required)** Search query string (max 400 chars). |
-| `country` | Two-letter country code for geo-targeting. |
-| `search_lang` | Two-letter language code for results. |
-| `count` | Max number of results to return (1–50). |
-| `offset` | Skip the first N pages of results (0–9). |
-| `safesearch` | Content filter: `"off"`, `"moderate"`, or `"strict"`. |
-| `freshness` | Recency filter (same options as Web Search). |
-
-For the complete parameter and header reference, see the [Brave Video Search API documentation](https://api-dashboard.search.brave.com/api-reference/videos/video_search/get).
-
-### BraveLocalPOIsTool
-
-| Parameter | Description |
-| --- | --- |
-| `ids` | **(required)** A list of unique identifiers for the desired locations. |
-| `search_lang` | Two-letter language code for results. |
-
-For the complete parameter and header reference, see [Brave Local POIs API documentation](https://api-dashboard.search.brave.com/api-reference/web/local_pois).
-
-### BraveLocalPOIsDescriptionTool
-
-| Parameter | Description |
-| --- | --- |
-| `ids` | **(required)** A list of unique identifiers for the desired locations. |
-
-For the complete parameter and header reference, see [Brave POI Descriptions API documentation](https://api-dashboard.search.brave.com/api-reference/web/poi_descriptions).
-
-## Custom Headers
-
-All tools support custom HTTP request headers. The Web Search tool, for example, accepts geolocation headers for location-aware results:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(
-    headers={
-        "x-loc-lat": "37.7749",
-        "x-loc-long": "-122.4194",
-        "x-loc-city": "San Francisco",
-        "x-loc-state": "CA",
-        "x-loc-country": "US",
-    }
-)
-
-results = tool.run(q="best coffee shops nearby")
-```
-
-You can also update headers after initialization using the `set_headers()` method:
-
-```python Code
-tool.set_headers({"api-version": "2025-01-01"})
-```
-
-## Raw Mode
-
-By default, each tool refines the API response into a concise list of results. If you need the full, unprocessed API response, enable raw mode:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(raw=True)
-full_response = tool.run(q="Brave Search API")
+# Execute a search
+results = tool.run(search_query="Latest AI developments")
+print(results)
 ```
 
 ## Agent Integration Example
 
-Here's how to equip a CrewAI agent with multiple Brave Search tools:
+Here's how to integrate the `BraveSearchTool` with a CrewAI agent:
 
 ```python Code
 from crewai import Agent
 from crewai.project import agent
-from crewai_tools import BraveWebSearchTool, BraveNewsSearchTool
+from crewai_tools import BraveSearchTool
 
-web_search = BraveWebSearchTool()
-news_search = BraveNewsSearchTool()
+# Initialize the tool
+brave_search_tool = BraveSearchTool()
 
+# Define an agent with the BraveSearchTool
 @agent
 def researcher(self) -> Agent:
     return Agent(
         config=self.agents_config["researcher"],
-        tools=[web_search, news_search],
+        allow_delegation=False,
+        tools=[brave_search_tool]
     )
 ```
 
-## Advanced Example
-
-Combining multiple parameters for a targeted search:
-
-```python Code
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(
-    requests_per_second=0.5,  # conservative rate limit
-    save_file=True,
-)
-
-results = tool.run(
-    q="artificial intelligence news",
-    country="US",
-    search_lang="en",
-    count=5,
-    freshness="pm",           # past month only
-    extra_snippets=True,
-)
-print(results)
-```
-
-## Migrating from `BraveSearchTool` (Legacy)
-
-If you are currently using `BraveSearchTool`, switching to the new tools is straightforward:
-
-```python Code
-# Before (legacy)
-from crewai_tools import BraveSearchTool
-
-tool = BraveSearchTool(country="US", n_results=5, save_file=True)
-results = tool.run(search_query="AI agents")
-
-# After (recommended)
-from crewai_tools import BraveWebSearchTool
-
-tool = BraveWebSearchTool(save_file=True)
-results = tool.run(q="AI agents", country="US", count=5)
-```
-
-Key differences:
-- **Import**: Use `BraveWebSearchTool` (or the news/image/video variant) instead of `BraveSearchTool`.
-- **Query parameter**: Use `q` instead of `search_query`. (Both `search_query` and `query` are still accepted for convenience, but `q` is the preferred parameter.)
-- **Result count**: Pass `count` as a query parameter instead of `n_results` at init time.
-- **Country**: Pass `country` as a query parameter instead of at init time.
-- **API key**: Can now be passed directly via `api_key=` in addition to the `BRAVE_API_KEY` environment variable.
-- **Rate limiting**: Configurable via `requests_per_second` with automatic retry on `429` responses.
-
 ## Conclusion
 
-The Brave Search tool suite gives your CrewAI agents flexible, endpoint-specific access to the Brave Search API. Whether you need web pages, breaking news, images, or videos, there is a dedicated tool with validated parameters and built-in resilience. Pick the tool that fits your use case, and refer to the [Brave Search API documentation](https://brave.com/search/api/) for the full details on available parameters and response formats.
+By integrating the `BraveSearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. The tool provides a simple interface to the powerful Brave Search API, making it easy to retrieve and process search results programmatically. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward. 

docs/ko/changelog.mdx

@@ -4,214 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="2026년 3월 14일">
-  ## v1.10.2rc2
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc2)
-
-  ## 변경 사항
-
-  ### 버그 수정
-  - 읽기 전용 스토리지 작업에서 독점 잠금 제거
-
-  ### 문서
-  - v1.10.2rc1에 대한 변경 로그 및 버전 업데이트
-
-  ## 기여자
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="2026년 3월 13일">
-  ## v1.10.2rc1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc1)
-
-  ## 변경 사항
-
-  ### 기능
-  - 릴리스 명령 추가 및 PyPI 게시 트리거
-
-  ### 버그 수정
-  - 보호되지 않은 I/O에 대한 프로세스 간 및 스레드 안전 잠금 수정
-  - 모든 스레드 및 실행기 경계를 넘는 contextvars 전파
-  - async 작업 스레드로 ContextVars 전파
-
-  ### 문서
-  - v1.10.2a1에 대한 변경 로그 및 버전 업데이트
-
-  ## 기여자
-
-  @danglies007, @greysonlalonde
-
-</Update>
-
-<Update label="2026년 3월 11일">
-  ## v1.10.2a1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## 변경 사항
-
-  ### 기능
-  - Anthropics에 대한 도구 검색 지원 추가, 토큰 저장, 실행 중 적절한 도구를 동적으로 주입하는 기능 추가.
-  - 더 많은 Brave Search 도구 도입.
-  - 야간 릴리스를 위한 액션 생성.
-
-  ### 버그 수정
-  - 동시 다중 프로세스 실행 중 LockException 수정.
-  - 단일 사용자 메시지에서 병렬 도구 결과 그룹화 문제 해결.
-  - MCP 도구 해상도 문제 해결 및 모든 공유 가변 연결 제거.
-  - human_feedback 함수에서 LLM 매개변수 처리 업데이트.
-  - LockedListProxy 및 LockedDictProxy에 누락된 list/dict 메서드 추가.
-  - 병렬 도구 호출 스레드에 contextvars 컨텍스트 전파.
-  - CVE 경로 탐색 취약점을 해결하기 위해 gitpython 의존성을 >=3.1.41로 업데이트.
-
-  ### 리팩토링
-  - 메모리 클래스를 직렬화 가능하도록 리팩토링.
-
-  ### 문서
-  - v1.10.1에 대한 변경 로그 및 버전 업데이트.
-
-  ## 기여자
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
-<Update label="2026년 3월 4일">
-  ## v1.10.1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## 변경 사항
-
-  ### 기능
-  - Gemini GenAI 업그레이드
-
-  ### 버그 수정
-  - 재귀를 피하기 위해 실행기 리스너 값을 조정
-  - Gemini에서 병렬 함수 응답 부분을 단일 Content 객체로 그룹화
-  - Gemini에서 사고 모델의 사고 출력을 표시
-  - 에이전트 도구가 None일 때 MCP 및 플랫폼 도구 로드
-  - A2A에서 실행 이벤트 루프가 있는 Jupyter 환경 지원
-  - 일시적인 추적을 위해 익명 ID 사용
-  - 조건부로 플러스 헤더 전달
-  - 원격 측정을 위해 비주 스레드에서 신호 처리기 등록 건너뛰기
-  - 도구 오류를 관찰로 주입하고 이름 충돌 해결
-  - Dependabot 경고를 해결하기 위해 pypdf를 4.x에서 6.7.4로 업그레이드
-  - 심각 및 높은 Dependabot 보안 경고 해결
-
-  ### 문서
-  - Composio 도구 문서를 지역별로 동기화
-
-  ## 기여자
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
-<Update label="2026년 2월 27일">
-  ## v1.10.1a1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## 변경 사항
-
-  ### 기능
-  - 단계 콜백 메서드에서 비동기 호출 지원 구현
-  - 메모리 모듈의 무거운 의존성에 대한 지연 로딩 구현
-
-  ### 문서
-  - v1.10.0에 대한 변경 로그 및 버전 업데이트
-
-  ### 리팩토링
-  - 비동기 호출을 지원하기 위해 단계 콜백 메서드 리팩토링
-  - 메모리 모듈의 무거운 의존성에 대한 지연 로딩을 구현하기 위해 리팩토링
-
-  ### 버그 수정
-  - 릴리스 노트의 분기 수정
-
-  ## 기여자
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="2026년 2월 27일">
-  ## v1.10.1a1
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## 변경 사항
-
-  ### 리팩토링
-  - 비동기 호출을 지원하기 위해 단계 콜백 메서드 리팩토링
-  - 메모리 모듈의 무거운 의존성에 대해 지연 로딩 구현
-
-  ### 문서화
-  - v1.10.0에 대한 변경 로그 및 버전 업데이트
-
-  ### 버그 수정
-  - 릴리스 노트를 위한 브랜치 생성
-
-  ## 기여자
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="2026년 2월 26일">
-  ## v1.10.0
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0)
-
-  ## 변경 사항
-
-  ### 기능
-  - MCP 도구 해상도 및 관련 이벤트 개선
-  - lancedb 버전 업데이트 및 lance-namespace 패키지 추가
-  - CrewAgentExecutor 및 BaseTool에서 JSON 인수 파싱 및 검증 개선
-  - CLI HTTP 클라이언트를 requests에서 httpx로 마이그레이션
-  - 버전화된 문서 추가
-  - 버전 노트에 대한 yanked 감지 추가
-  - Flows에서 사용자 입력 처리 구현
-  - 인간 피드백 통합 테스트에서 HITL 자기 루프 기능 개선
-  - eventbus에 started_event_id 추가 및 설정
-  - tools.specs 자동 업데이트
-
-  ### 버그 수정
-  - 빈 경우에도 도구 kwargs를 검증하여 모호한 TypeError 방지
-  - LLM을 위한 도구 매개변수 스키마에서 null 타입 유지
-  - output_pydantic/output_json을 네이티브 구조화된 출력으로 매핑
-  - 약속이 있는 경우 콜백이 실행/대기되도록 보장
-  - 예외 컨텍스트에서 메서드 이름 캡처
-  - 라우터 결과에서 enum 타입 유지; 타입 개선
-  - 입력으로 지속성 ID가 전달될 때 조용히 깨지는 순환 흐름 수정
-  - CLI 플래그 형식을 --skip-provider에서 --skip_provider로 수정
-  - OpenAI 도구 호출 스트림이 완료되도록 보장
-  - MCP 도구에서 복잡한 스키마 $ref 포인터 해결
-  - 스키마에서 additionalProperties=false 강제 적용
-  - 크루 폴더에 대해 예약된 스크립트 이름 거부
-  - 가드레일 이벤트 방출 테스트에서 경쟁 조건 해결
-
-  ### 문서
-  - 비네이티브 LLM 공급자를 위한 litellm 종속성 노트 추가
-  - NL2SQL 보안 모델 및 강화 지침 명확화
-  - 9개 통합에서 96개의 누락된 작업 추가
-
-  ### 리팩토링
-  - crew를 provider로 리팩토링
-  - HITL을 provider 패턴으로 추출
-  - 훅 타이핑 및 등록 개선
-
-  ## 기여자
-
-  @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl
-
-</Update>
-
 <Update label="2026년 1월 26일">
   ## v1.9.0
 

docs/ko/concepts/llms.mdx

@@ -105,15 +105,6 @@ CrewAI 코드 내에는 사용할 모델을 지정할 수 있는 여러 위치
   </Tab>
 </Tabs>
 
-<Info>
-  CrewAI는 OpenAI, Anthropic, Google (Gemini API), Azure, AWS Bedrock에 대해 네이티브 SDK 통합을 제공합니다 — 제공자별 extras(예: `uv add "crewai[openai]"`) 외에 추가 설치가 필요하지 않습니다.
-
-  그 외 모든 제공자는 **LiteLLM**을 통해 지원됩니다. 이를 사용하려면 프로젝트에 의존성으로 추가하세요:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## 공급자 구성 예시
 
 CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양한 LLM 공급자를 지원합니다.
@@ -223,11 +214,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | 텍스트, 이미지     | 텍스트           |
     | `meta_llama/Llama-3.3-70B-Instruct`               | 128k | 4028       | 텍스트            | 텍스트           |
     | `meta_llama/Llama-3.3-8B-Instruct`                | 128k | 4028       | 텍스트            | 텍스트           |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Anthropic">
@@ -368,11 +354,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     | gemini-1.5-flash                 | 1M 토큰         | 밸런스 잡힌 멀티모달 모델, 대부분의 작업에 적합                         |
     | gemini-1.5-flash-8B              | 1M 토큰         | 가장 빠르고, 비용 효율적, 고빈도 작업에 적합                            |
     | gemini-1.5-pro                   | 2M 토큰         | 최고의 성능, 논리적 추론, 코딩, 창의적 협업 등 다양한 추론 작업에 적합   |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Azure">
@@ -458,11 +439,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         model="sagemaker/<my-endpoint>"
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Mistral">
@@ -478,11 +454,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         temperature=0.7
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Nvidia NIM">
@@ -569,11 +540,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     | rakuten/rakutenai-7b-instruct                                          | 1,024 토큰     | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM                         |
     | rakuten/rakutenai-7b-chat                                              | 1,024 토큰     | 언어 이해, 추론, 텍스트 생성이 탁월한 최첨단 LLM                         |
     | baichuan-inc/baichuan2-13b-chat                                        | 4,096 토큰     | 중국어 및 영어 대화, 코딩, 수학, 지시 따르기, 퀴즈 풀이 지원             |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -614,11 +580,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
 
         # ...
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Groq">
@@ -640,11 +601,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     | Llama 3.1 70B/8B| 131,072 토큰      | 고성능, 대용량 문맥 작업         |
     | Llama 3.2 Series| 8,192 토큰        | 범용 작업                        |
     | Mixtral 8x7B    | 32,768 토큰       | 성능과 문맥의 균형               |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="IBM watsonx.ai">
@@ -667,11 +623,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         base_url="https://api.watsonx.ai/v1"
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Ollama (Local LLMs)">
@@ -685,11 +636,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         base_url="http://localhost:11434"
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Fireworks AI">
@@ -705,11 +651,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         temperature=0.7
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Perplexity AI">
@@ -725,11 +666,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         base_url="https://api.perplexity.ai/"
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Hugging Face">
@@ -744,11 +680,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
         model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
     )
     ```
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="SambaNova">
@@ -772,11 +703,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
     | Llama 3.2 Series| 8,192 토큰         | 범용, 멀티모달 작업                  |
     | Llama 3.3 70B   | 최대 131,072 토큰   | 고성능, 높은 출력 품질               |
     | Qwen2 familly   | 8,192 토큰         | 고성능, 높은 출력 품질               |
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Cerebras">
@@ -802,11 +728,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
       - 속도와 품질의 우수한 밸런스
       - 긴 컨텍스트 윈도우 지원
     </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Open Router">
@@ -829,11 +750,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
       - openrouter/deepseek/deepseek-r1
       - openrouter/deepseek/deepseek-chat
     </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Nebius AI Studio">
@@ -856,11 +772,6 @@ CrewAI는 고유한 기능, 인증 방법, 모델 역량을 제공하는 다양
       - 경쟁력 있는 가격
       - 속도와 품질의 우수한 밸런스
     </Info>
-
-    **참고:** 이 제공자는 LiteLLM을 사용합니다. 프로젝트에 의존성으로 추가하세요:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 </AccordionGroup>
 

docs/ko/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ CrewAI Enterprise는 AI 워크플로우를 협업적인 인간-AI 프로세스
 `@human_feedback` 데코레이터를 사용하여 Flow 내에 인간 검토 체크포인트를 구성합니다. 실행이 검토 포인트에 도달하면 시스템이 일시 중지되고, 담당자에게 이메일로 알리며, 응답을 기다립니다.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # AI가 콘텐츠 생성
         return "Q1 캠페인용 마케팅 카피 생성..."
 
+    @listen(generate_content)
     @human_feedback(
         message="브랜드 준수를 위해 이 콘텐츠를 검토해 주세요:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "검토용 마케팅 카피..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"콘텐츠 거부됨. 사유: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"수정 요청: {result.feedback}")
 ```
 
 완전한 구현 세부 사항은 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요.

docs/ko/enterprise/guides/deploy-to-amp.mdx

@@ -176,11 +176,6 @@ Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않
       ![Set Environment Variables](/images/enterprise/set-env-variables.png)
     </Frame>
 
-    <Info>
-      프라이빗 Python 패키지를 사용하시나요? 여기에 레지스트리 자격 증명도 추가해야 합니다.
-      필요한 변수는 [프라이빗 패키지 레지스트리](/ko/enterprise/guides/private-package-registry)를 참조하세요.
-    </Info>
-
   </Step>
 
   <Step title="Crew 배포하기">

docs/ko/enterprise/guides/prepare-for-deployment.mdx

@@ -256,12 +256,6 @@ Crews와 Flows 모두 `src/project_name/main.py`에 진입점이 있습니다:
 1. **LLM API 키** (OpenAI, Anthropic, Google 등)
 2. **도구 API 키** - 외부 도구를 사용하는 경우 (Serper 등)
 
-<Info>
-  프로젝트가 **프라이빗 PyPI 레지스트리**의 패키지에 의존하는 경우, 레지스트리 인증 자격 증명도
-  환경 변수로 구성해야 합니다. 자세한 내용은
-  [프라이빗 패키지 레지스트리](/ko/enterprise/guides/private-package-registry) 가이드를 참조하세요.
-</Info>
-
 <Tip>
   구성 문제를 조기에 발견하기 위해 배포 전에 동일한 환경 변수로
   로컬에서 프로젝트를 테스트하세요.

docs/ko/enterprise/guides/private-package-registry.mdx

@@ -1,261 +0,0 @@
----
-title: "프라이빗 패키지 레지스트리"
-description: "CrewAI AMP에서 인증된 PyPI 레지스트리의 프라이빗 Python 패키지 설치하기"
-icon: "lock"
-mode: "wide"
----
-
-<Note>
-  이 가이드는 CrewAI AMP에 배포할 때 프라이빗 PyPI 레지스트리(Azure DevOps Artifacts, GitHub Packages,
-  GitLab, AWS CodeArtifact 등)에서 Python 패키지를 설치하도록 CrewAI 프로젝트를 구성하는 방법을 다룹니다.
-</Note>
-
-## 이 가이드가 필요한 경우
-
-프로젝트가 공개 PyPI가 아닌 프라이빗 레지스트리에 호스팅된 내부 또는 독점 Python 패키지에
-의존하는 경우, 다음을 수행해야 합니다:
-
-1. UV에 패키지를 **어디서** 찾을지 알려줍니다 (index URL)
-2. UV에 **어떤** 패키지가 해당 index에서 오는지 알려줍니다 (source 매핑)
-3. UV가 설치 중에 인증할 수 있도록 **자격 증명**을 제공합니다
-
-CrewAI AMP는 의존성 해결 및 설치에 [UV](https://docs.astral.sh/uv/)를 사용합니다.
-UV는 `pyproject.toml` 구성과 자격 증명용 환경 변수를 결합하여 인증된 프라이빗 레지스트리를 지원합니다.
-
-## 1단계: pyproject.toml 구성
-
-`pyproject.toml`에서 세 가지 요소가 함께 작동합니다:
-
-### 1a. 의존성 선언
-
-프라이빗 패키지를 다른 의존성과 마찬가지로 `[project.dependencies]`에 추가합니다:
-
-```toml
-[project]
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-```
-
-### 1b. index 정의
-
-프라이빗 레지스트리를 `[[tool.uv.index]]` 아래에 명명된 index로 등록합니다:
-
-```toml
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-```
-
-<Info>
-  `name` 필드는 중요합니다 — UV는 이를 사용하여 인증을 위한 환경 변수 이름을
-  구성합니다 (아래 [2단계](#2단계-인증-자격-증명-설정)를 참조하세요).
-
-  `explicit = true`를 설정하면 UV가 모든 패키지에 대해 이 index를 검색하지 않습니다 —
-  `[tool.uv.sources]`에서 명시적으로 매핑한 패키지만 검색합니다. 이렇게 하면 프라이빗
-  레지스트리에 대한 불필요한 쿼리를 방지하고 의존성 혼동 공격을 차단할 수 있습니다.
-</Info>
-
-### 1c. 패키지를 index에 매핑
-
-`[tool.uv.sources]`를 사용하여 프라이빗 index에서 해결해야 할 패키지를 UV에 알려줍니다:
-
-```toml
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-### 전체 예시
-
-```toml
-[project]
-name = "my-crew-project"
-version = "0.1.0"
-requires-python = ">=3.10,<=3.13"
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-
-[tool.crewai]
-type = "crew"
-
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-`pyproject.toml`을 업데이트한 후 lock 파일을 다시 생성합니다:
-
-```bash
-uv lock
-```
-
-<Warning>
-  업데이트된 `uv.lock`을 항상 `pyproject.toml` 변경 사항과 함께 커밋하세요.
-  lock 파일은 배포에 필수입니다 — [배포 준비하기](/ko/enterprise/guides/prepare-for-deployment)를 참조하세요.
-</Warning>
-
-## 2단계: 인증 자격 증명 설정
-
-UV는 `pyproject.toml`에서 정의한 index 이름을 기반으로 한 명명 규칙을 따르는
-환경 변수를 사용하여 프라이빗 index에 인증합니다:
-
-```
-UV_INDEX_{UPPER_NAME}_USERNAME
-UV_INDEX_{UPPER_NAME}_PASSWORD
-```
-
-여기서 `{UPPER_NAME}`은 index 이름을 **대문자**로 변환하고 **하이픈을 언더스코어로 대체**한 것입니다.
-
-예를 들어, `my-private-registry`라는 이름의 index는 다음을 사용합니다:
-
-| 변수 | 값 |
-|------|-----|
-| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | 레지스트리 사용자 이름 또는 토큰 이름 |
-| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | 레지스트리 비밀번호 또는 토큰/PAT |
-
-<Warning>
-  이 환경 변수는 CrewAI AMP **환경 변수** 설정을 통해 **반드시** 추가해야 합니다 —
-  전역적으로 또는 배포 수준에서. `.env` 파일에 설정하거나 프로젝트에 하드코딩할 수 없습니다.
-
-  아래 [AMP에서 환경 변수 설정](#amp에서-환경-변수-설정)을 참조하세요.
-</Warning>
-
-## 레지스트리 제공업체 참조
-
-아래 표는 일반적인 레지스트리 제공업체의 index URL 형식과 자격 증명 값을 보여줍니다.
-자리 표시자 값을 실제 조직 및 피드 세부 정보로 대체하세요.
-
-| 제공업체 | Index URL | 사용자 이름 | 비밀번호 |
-|---------|-----------|-----------|---------|
-| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | 비어 있지 않은 임의의 문자열 (예: `token`) | Packaging Read 범위의 Personal Access Token (PAT) |
-| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | GitHub 사용자 이름 | `read:packages` 범위의 Personal Access Token (classic) |
-| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | `read_api` 범위의 Project 또는 Personal Access Token |
-| **AWS CodeArtifact** | `aws codeartifact get-repository-endpoint`의 URL 사용 | `aws` | `aws codeartifact get-authorization-token`의 토큰 |
-| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Base64로 인코딩된 서비스 계정 키 |
-| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | 사용자 이름 또는 이메일 | API 키 또는 ID 토큰 |
-| **자체 호스팅 (devpi, Nexus 등)** | 레지스트리의 simple API URL | 레지스트리 사용자 이름 | 레지스트리 비밀번호 |
-
-<Tip>
-  **AWS CodeArtifact**의 경우 인증 토큰이 주기적으로 만료됩니다.
-  만료되면 `UV_INDEX_*_PASSWORD` 값을 갱신해야 합니다.
-  CI/CD 파이프라인에서 이를 자동화하는 것을 고려하세요.
-</Tip>
-
-## AMP에서 환경 변수 설정
-
-프라이빗 레지스트리 자격 증명은 CrewAI AMP에서 환경 변수로 구성해야 합니다.
-두 가지 옵션이 있습니다:
-
-<Tabs>
-  <Tab title="웹 인터페이스">
-    1. [CrewAI AMP](https://app.crewai.com)에 로그인합니다
-    2. 자동화로 이동합니다
-    3. **Environment Variables** 탭을 엽니다
-    4. 각 변수 (`UV_INDEX_*_USERNAME` 및 `UV_INDEX_*_PASSWORD`)에 값을 추가합니다
-
-    자세한 내용은 [AMP에 배포하기 — 환경 변수 설정하기](/ko/enterprise/guides/deploy-to-amp#환경-변수-설정하기) 단계를 참조하세요.
-  </Tab>
-  <Tab title="CLI 배포">
-    `crewai deploy create`를 실행하기 전에 로컬 `.env` 파일에 변수를 추가합니다.
-    CLI가 이를 안전하게 플랫폼으로 전송합니다:
-
-    ```bash
-    # .env
-    OPENAI_API_KEY=sk-...
-    UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-    UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here
-    ```
-
-    ```bash
-    crewai deploy create
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  자격 증명을 저장소에 **절대** 커밋하지 마세요. 모든 비밀 정보에는 AMP 환경 변수를 사용하세요.
-  `.env` 파일은 `.gitignore`에 포함되어야 합니다.
-</Warning>
-
-기존 배포의 자격 증명을 업데이트하려면 [Crew 업데이트하기 — 환경 변수](/ko/enterprise/guides/update-crew)를 참조하세요.
-
-## 전체 동작 흐름
-
-CrewAI AMP가 자동화를 빌드할 때, 해결 흐름은 다음과 같이 작동합니다:
-
-<Steps>
-  <Step title="빌드 시작">
-    AMP가 저장소를 가져오고 `pyproject.toml`과 `uv.lock`을 읽습니다.
-  </Step>
-  <Step title="UV가 의존성 해결">
-    UV가 `[tool.uv.sources]`를 읽어 각 패키지가 어떤 index에서 와야 하는지 결정합니다.
-  </Step>
-  <Step title="UV가 인증">
-    각 프라이빗 index에 대해 UV가 AMP에서 구성한 환경 변수에서
-    `UV_INDEX_{NAME}_USERNAME`과 `UV_INDEX_{NAME}_PASSWORD`를 조회합니다.
-  </Step>
-  <Step title="패키지 설치">
-    UV가 공개(PyPI) 및 프라이빗(레지스트리) 패키지를 모두 다운로드하고 설치합니다.
-  </Step>
-  <Step title="자동화 실행">
-    모든 의존성이 사용 가능한 상태에서 crew 또는 flow가 시작됩니다.
-  </Step>
-</Steps>
-
-## 문제 해결
-
-### 빌드 중 인증 오류
-
-**증상**: 프라이빗 패키지를 해결할 때 `401 Unauthorized` 또는 `403 Forbidden`으로 빌드가 실패합니다.
-
-**확인사항**:
-- `UV_INDEX_*` 환경 변수 이름이 index 이름과 정확히 일치하는지 확인합니다 (대문자, 하이픈 -> 언더스코어)
-- 자격 증명이 로컬 `.env`뿐만 아니라 AMP 환경 변수에 설정되어 있는지 확인합니다
-- 토큰/PAT에 패키지 피드에 필요한 읽기 권한이 있는지 확인합니다
-- 토큰이 만료되지 않았는지 확인합니다 (특히 AWS CodeArtifact의 경우)
-
-### 패키지를 찾을 수 없음
-
-**증상**: `No matching distribution found for my-private-package`.
-
-**확인사항**:
-- `pyproject.toml`의 index URL이 `/simple/`로 끝나는지 확인합니다
-- `[tool.uv.sources]` 항목이 올바른 패키지 이름을 올바른 index 이름에 매핑하는지 확인합니다
-- 패키지가 실제로 프라이빗 레지스트리에 게시되어 있는지 확인합니다
-- 동일한 자격 증명으로 로컬에서 `uv lock`을 실행하여 해결이 작동하는지 확인합니다
-
-### Lock 파일 충돌
-
-**증상**: 프라이빗 index를 추가한 후 `uv lock`이 실패하거나 예상치 못한 결과를 생성합니다.
-
-**해결책**: 로컬에서 자격 증명을 설정하고 다시 생성합니다:
-
-```bash
-export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat
-uv lock
-```
-
-그런 다음 업데이트된 `uv.lock`을 커밋합니다.
-
-## 관련 가이드
-
-<CardGroup cols={3}>
-  <Card title="배포 준비하기" icon="clipboard-check" href="/ko/enterprise/guides/prepare-for-deployment">
-    배포 전에 프로젝트 구조와 의존성을 확인합니다.
-  </Card>
-  <Card title="AMP에 배포하기" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
-    crew 또는 flow를 배포하고 환경 변수를 구성합니다.
-  </Card>
-  <Card title="Crew 업데이트하기" icon="arrows-rotate" href="/ko/enterprise/guides/update-crew">
-    환경 변수를 업데이트하고 실행 중인 배포에 변경 사항을 푸시합니다.
-  </Card>
-</CardGroup>

docs/ko/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "LangGraph에서 CrewAI로 옮기기: 엔지니어를 위한 실전 가이드"
-description: LangGraph로 이미 구축했다면, 프로젝트를 CrewAI로 빠르게 옮기는 방법을 알아보세요
-icon: switch
-mode: "wide"
----
-
-LangGraph로 에이전트를 구축해 왔습니다. `StateGraph`와 씨름하고, 조건부 에지를 연결하고, 새벽 2시에 상태 딕셔너리를 디버깅해 본 적도 있죠. 동작은 하지만 — 어느 순간부터 프로덕션으로 가는 더 나은 길이 없을까 고민하게 됩니다.
-
-있습니다. **CrewAI Flows**는 이벤트 기반 오케스트레이션, 조건부 라우팅, 공유 상태라는 동일한 힘을 훨씬 적은 보일러플레이트와 실제로 다단계 AI 워크플로우를 생각하는 방식에 잘 맞는 정신적 모델로 제공합니다.
-
-이 글은 핵심 개념을 나란히 비교하고 실제 코드 비교를 보여주며, 다음으로 손이 갈 프레임워크가 왜 CrewAI Flows인지 설명합니다.
-
----
-
-## 정신적 모델의 전환
-
-LangGraph는 **그래프**로 생각하라고 요구합니다: 노드, 에지, 그리고 상태 딕셔너리. 모든 워크플로우는 계산 단계 사이의 전이를 명시적으로 연결하는 방향 그래프입니다. 강력하지만, 특히 워크플로우가 몇 개의 결정 지점이 있는 순차적 흐름일 때 이 추상화는 오버헤드를 가져옵니다.
-
-CrewAI Flows는 **이벤트**로 생각하라고 요구합니다: 시작하는 메서드, 결과를 듣는 메서드, 실행을 라우팅하는 메서드. 워크플로우의 토폴로지는 명시적 그래프 구성 대신 데코레이터 어노테이션에서 드러납니다. 이것은 단순한 문법 설탕이 아니라 — 파이프라인을 설계하고 읽고 유지하는 방식을 바꿉니다.
-
-핵심 매핑은 다음과 같습니다:
-
-| LangGraph 개념 | CrewAI Flows 대응 |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-실제로 어떻게 보이는지 살펴보겠습니다.
-
----
-
-## 데모 1: 간단한 순차 파이프라인
-
-주제를 받아 조사하고, 요약을 작성한 뒤, 결과를 포맷팅하는 파이프라인을 만든다고 해봅시다. 각 프레임워크는 이렇게 처리합니다.
-
-### LangGraph 방식
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-함수를 정의하고 노드로 등록한 다음, 모든 전이를 수동으로 연결합니다. 이렇게 단순한 순서인데도 의례처럼 해야 할 작업이 많습니다.
-
-### CrewAI Flows 방식
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-눈에 띄는 차이점이 있습니다: 그래프 구성 없음, 에지 연결 없음, 컴파일 단계 없음. 실행 순서는 로직이 있는 곳에서 바로 선언됩니다. `@start()`는 진입점을 표시하고, `@listen(method_name)`은 단계들을 연결합니다. 상태는 타입 안전성, 검증, IDE 자동 완성까지 제공하는 제대로 된 Pydantic 모델입니다.
-
----
-
-## 데모 2: 조건부 라우팅
-
-여기서 흥미로워집니다. 콘텐츠 유형에 따라 서로 다른 처리 경로로 라우팅하는 파이프라인을 만든다고 해봅시다.
-
-### LangGraph 방식
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-별도의 라우팅 함수, 명시적 조건부 에지 매핑, 그리고 모든 분기에 대한 종료 에지가 필요합니다. 라우팅 결정 로직이 그 결정을 만들어 내는 노드와 분리됩니다.
-
-### CrewAI Flows 방식
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-`@router()` 데코레이터는 메서드를 결정 지점으로 만듭니다. 리스너와 매칭되는 문자열을 반환하므로, 매핑 딕셔너리도, 별도의 라우팅 함수도 필요 없습니다. 분기 로직이 Python `if` 문처럼 읽히는 이유는, 실제로 `if` 문이기 때문입니다.
-
----
-
-## 데모 3: AI 에이전트 Crew를 Flow에 통합하기
-
-여기서 CrewAI의 진짜 힘이 드러납니다. Flows는 LLM 호출을 연결하는 것에 그치지 않고 자율적인 에이전트 **Crew** 전체를 오케스트레이션합니다. 이는 LangGraph에 기본으로 대응되는 개념이 없습니다.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-핵심 인사이트는 다음과 같습니다: **Flows는 오케스트레이션 레이어를, Crews는 지능 레이어를 제공합니다.** Flow의 각 단계는 각자의 역할, 목표, 도구를 가진 협업 에이전트 팀을 띄울 수 있습니다. 구조화되고 예측 가능한 제어 흐름 *그리고* 자율적 에이전트 협업 — 두 세계의 장점을 모두 얻습니다.
-
-LangGraph에서 비슷한 것을 하려면 노드 함수 안에 에이전트 통신 프로토콜, 도구 호출 루프, 위임 로직을 직접 구현해야 합니다. 가능하긴 하지만, 매번 처음부터 배관을 만드는 셈입니다.
-
----
-
-## 데모 4: 병렬 실행과 동기화
-
-실제 파이프라인은 종종 작업을 병렬로 분기하고 결과를 합쳐야 합니다. CrewAI Flows는 `and_`와 `or_` 연산자로 이를 우아하게 처리합니다.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-여러 `@start()` 데코레이터는 병렬로 실행됩니다. `@listen` 데코레이터의 `and_()` 결합자는 `synthesize_report`가 *세 가지* 상위 메서드가 모두 완료된 뒤에만 실행되도록 보장합니다. *어떤* 상위 작업이든 끝나는 즉시 진행하고 싶다면 `or_()`도 사용할 수 있습니다.
-
-LangGraph에서는 병렬 분기, 동기화 노드, 신중한 상태 병합이 포함된 fan-out/fan-in 패턴을 만들어야 하며 — 모든 것을 에지로 명시적으로 연결해야 합니다.
-
----
-
-## 프로덕션에서 CrewAI Flows를 쓰는 이유
-
-깔끔한 문법을 넘어, Flows는 여러 프로덕션 핵심 이점을 제공합니다:
-
-**내장 상태 지속성.** Flow 상태는 LanceDB에 의해 백업되므로 워크플로우가 크래시에서 살아남고, 재개될 수 있으며, 실행 간에 지식을 축적할 수 있습니다. LangGraph는 별도의 체크포인터를 구성해야 합니다.
-
-**타입 안전한 상태 관리.** Pydantic 모델은 즉시 검증, 직렬화, IDE 지원을 제공합니다. LangGraph의 `TypedDict` 상태는 런타임 검증을 하지 않습니다.
-
-**일급 에이전트 오케스트레이션.** Crews는 기본 프리미티브입니다. 역할, 목표, 배경, 도구를 가진 에이전트를 정의하고, Flow의 구조적 틀 안에서 자율적으로 협업하게 합니다. 다중 에이전트 조율을 다시 만들 필요가 없습니다.
-
-**더 단순한 정신적 모델.** 데코레이터는 의도를 선언합니다. `@start`는 "여기서 시작", `@listen(x)`는 "x 이후 실행", `@router(x)`는 "x 이후 어디로 갈지 결정"을 의미합니다. 코드는 자신이 설명하는 워크플로우처럼 읽힙니다.
-
-**CLI 통합.** `crewai run`으로 Flows를 실행합니다. 별도의 컴파일 단계나 그래프 직렬화가 없습니다. Flow는 Python 클래스이며, 그대로 실행됩니다.
-
----
-
-## 마이그레이션 치트 시트
-
-LangGraph 코드베이스를 CrewAI Flows로 옮기고 싶다면, 다음의 실전 변환 가이드를 참고하세요:
-
-1. **상태를 매핑하세요.** `TypedDict`를 Pydantic `BaseModel`로 변환하고 모든 필드에 기본값을 추가하세요.
-2. **노드를 메서드로 변환하세요.** 각 `add_node` 함수는 `Flow` 서브클래스의 메서드가 됩니다. `state["field"]` 읽기는 `self.state.field`로 바꾸세요.
-3. **에지를 데코레이터로 교체하세요.** `add_edge(START, "first_node")`는 첫 메서드의 `@start()`가 됩니다. 순차적인 `add_edge("a", "b")`는 `b` 메서드의 `@listen(a)`가 됩니다.
-4. **조건부 에지는 `@router`로 교체하세요.** 라우팅 함수와 `add_conditional_edges()` 매핑은 하나의 `@router()` 메서드로 통합하고, 라우트 문자열을 반환하세요.
-5. **compile + invoke를 kickoff으로 교체하세요.** `graph.compile()`를 제거하고 `flow.kickoff()`를 호출하세요.
-6. **Crew가 들어갈 지점을 고려하세요.** 복잡한 다단계 에이전트 로직이 있는 노드는 Crew로 분리할 후보입니다. 이 부분에서 가장 큰 품질 향상을 체감할 수 있습니다.
-
----
-
-## 시작하기
-
-CrewAI를 설치하고 새 Flow 프로젝트를 스캐폴딩하세요:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-이렇게 하면 바로 편집 가능한 Flow 클래스, 설정 파일, 그리고 `type = "flow"`가 이미 설정된 `pyproject.toml`이 포함된 프로젝트 구조가 생성됩니다. 다음으로 실행하세요:
-
-```bash
-crewai run
-```
-
-그 다음부터는 에이전트를 추가하고 리스너를 연결한 뒤, 배포하면 됩니다.
-
----
-
-## 마무리
-
-LangGraph는 AI 워크플로우에 구조가 필요하다는 사실을 생태계에 일깨워 주었습니다. 중요한 교훈이었습니다. 하지만 CrewAI Flows는 그 교훈을 더 빠르게 쓰고, 더 쉽게 읽으며, 프로덕션에서 더 강력한 형태로 제공합니다 — 특히 워크플로우에 여러 에이전트의 협업이 포함될 때 그렇습니다.
-
-단일 에이전트 체인을 넘는 무엇인가를 만들고 있다면, Flows를 진지하게 검토해 보세요. 데코레이터 기반 모델, Crews의 네이티브 통합, 내장 상태 관리를 통해 배관 작업에 쓰는 시간을 줄이고, 중요한 문제에 더 많은 시간을 쓸 수 있습니다.
-
-`crewai create flow`로 시작하세요. 후회하지 않을 겁니다.

docs/ko/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 `emit`을 지정하면, 데코레이터는 라우터가 됩니다. 인간의 자유 형식 피드백이 LLM에 의해 해석되어 지정된 outcome 중 하나로 매핑됩니다:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="이 콘텐츠의 출판을 승인하시겠습니까?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "블로그 게시물 초안 내용..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "블로그 게시물 초안 내용..."
+@listen("approved")
+def publish(self, result):
+    print(f"출판 중! 사용자 의견: {result.feedback}")
 
-    @human_feedback(
-        message="이 콘텐츠의 출판을 승인하시겠습니까?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "블로그 게시물 초안 내용..."
+@listen("rejected")
+def discard(self, result):
+    print(f"폐기됨. 이유: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"출판 중! 사용자 의견: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"폐기됨. 이유: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"다음을 기반으로 수정 중: {result.feedback}")
 ```
 
-사용자가 "더 자세한 내용이 필요합니다"와 같이 말하면, LLM이 이를 `"needs_revision"`으로 매핑하고, `or_()`를 통해 `review_content`가 다시 트리거됩니다 — 수정 루프가 생성됩니다. outcome이 `"approved"` 또는 `"rejected"`가 될 때까지 루프가 계속됩니다.
-
 <Tip>
 LLM은 가능한 경우 구조화된 출력(function calling)을 사용하여 응답이 지정된 outcome 중 하나임을 보장합니다. 이로 인해 라우팅이 신뢰할 수 있고 예측 가능해집니다.
 </Tip>
 
-<Warning>
-`@start()` 메서드는 flow 시작 시 한 번만 실행됩니다. 수정 루프가 필요한 경우, start 메서드를 review 메서드와 분리하고 review 메서드에 `@listen(or_("trigger", "revision_outcome"))`를 사용하여 self-loop을 활성화하세요.
-</Warning>
-
 ## HumanFeedbackResult
 
 `HumanFeedbackResult` 데이터클래스는 인간 피드백 상호작용에 대한 모든 정보를 포함합니다:
@@ -203,162 +193,116 @@ def summarize(self):
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """콘텐츠를 생성하고 승인될 때까지 반복하는 Flow."""
+    """콘텐츠를 생성하고 인간의 승인을 받는 Flow입니다."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# AI 안전\n\nAI 안전에 대한 초안..."
+    def get_topic(self):
+        self.state.topic = input("어떤 주제에 대해 글을 쓸까요? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # 실제 사용에서는 LLM을 호출합니다
+        self.state.draft = f"# {topic}\n\n{topic}에 대한 초안입니다..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:",
+        message="이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"콘텐츠 승인 및 게시! 리뷰어 의견: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ 콘텐츠가 승인되어 출판되었습니다!")
+        print(f"검토자 코멘트: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"콘텐츠 거부됨. 이유: {result.feedback}")
+        print("\n❌ 콘텐츠가 거부되었습니다")
+        print(f"이유: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 수정 #{self.state.revision_count} 요청됨")
+        print(f"피드백: {result.feedback}")
 
+        # 실제 Flow에서는 generate_draft로 돌아갈 수 있습니다
+        # 이 예제에서는 단순히 확인합니다
+        return "revision_requested"
+
+
+# Flow 실행
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow 완료. 상태: {flow.state.status}, 검토 횟수: {flow.state.revision_count}")
+print(f"\nFlow 완료. 요청된 수정: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# AI 안전
-
-AI 안전에 대한 초안... (v1)
-==================================================
-
-이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: 더 자세한 내용이 필요합니다
+어떤 주제에 대해 글을 쓸까요? AI 안전
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
 # AI 안전
 
-AI 안전에 대한 초안... (v2)
+AI 안전에 대한 초안입니다...
 ==================================================
 
-이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:
+이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: 좋아 보입니다, 승인!
 
-콘텐츠 승인 및 게시! 리뷰어 의견: 좋아 보입니다, 승인!
+✅ 콘텐츠가 승인되어 출판되었습니다!
+검토자 코멘트: 좋아 보입니다, 승인!
 
-Flow 완료. 상태: published, 검토 횟수: 2
+Flow 완료. 요청된 수정: 0
 ```
 
 </CodeGroup>
 
 ## 다른 데코레이터와 결합하기
 
-`@human_feedback` 데코레이터는 `@start()`, `@listen()`, `or_()`와 함께 작동합니다. 데코레이터 순서는 두 가지 모두 동작합니다—프레임워크가 양방향으로 속성을 전파합니다—하지만 권장 패턴은 다음과 같습니다:
+`@human_feedback` 데코레이터는 다른 Flow 데코레이터와 함께 작동합니다. 가장 안쪽 데코레이터(함수에 가장 가까운)로 배치하세요:
 
 ```python Code
-# Flow 시작 시 일회성 검토 (self-loop 없음)
+# 올바름: @human_feedback이 가장 안쪽(함수에 가장 가까움)
 @start()
-@human_feedback(message="이것을 검토해 주세요:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="이것을 검토해 주세요:")
 def my_start_method(self):
     return "content"
 
-# 리스너에서 선형 검토 (self-loop 없음)
 @listen(other_method)
-@human_feedback(message="이것도 검토해 주세요:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="이것도 검토해 주세요:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: 수정을 위해 반복할 수 있는 검토
-@human_feedback(message="승인 또는 수정 요청?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Self-loop 패턴
-
-수정 루프를 만들려면 `or_()`를 사용하여 검토 메서드가 **상위 트리거**와 **자체 수정 outcome**을 모두 리스닝해야 합니다:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="승인하시겠습니까, 아니면 변경을 요청하시겠습니까?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-outcome이 `"revise"`이면 flow가 `review`로 다시 라우팅됩니다 (`or_()`를 통해 `"revise"`를 리스닝하기 때문). outcome이 `"approved"`이면 flow가 `publish`로 계속됩니다. flow 엔진이 라우터를 "한 번만 실행" 규칙에서 제외하여 각 루프 반복마다 재실행할 수 있기 때문에 이 패턴이 동작합니다.
-
-### 체인된 라우터
-
-한 라우터의 outcome으로 트리거된 리스너가 그 자체로 라우터가 될 수 있습니다:
-
-```python Code
-@start()
-@human_feedback(message="첫 번째 검토:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-def draft(self):
-    return "draft content"
-
-@listen("approved")
-@human_feedback(message="최종 검토:", emit=["publish", "revise"], llm="gpt-4o-mini")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-```
-
-### 제한 사항
-
-- **`@start()` 메서드는 한 번만 실행**: `@start()` 메서드는 self-loop할 수 없습니다. 수정 주기가 필요하면 별도의 `@start()` 메서드를 진입점으로 사용하고 `@listen()` 메서드에 `@human_feedback`를 배치하세요.
-- **동일 메서드에 `@start()` + `@listen()` 불가**: 이는 Flow 프레임워크 제약입니다. 메서드는 시작점이거나 리스너여야 하며, 둘 다일 수 없습니다.
+<Tip>
+`@human_feedback`를 가장 안쪽 데코레이터(마지막/함수에 가장 가까움)로 배치하여 메서드를 직접 래핑하고 Flow 시스템에 전달하기 전에 반환 값을 캡처할 수 있도록 하세요.
+</Tip>
 
 ## 모범 사례
 
@@ -572,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="이 콘텐츠의 출판을 승인하시겠습니까?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -590,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"보관됨. 이유: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"수정 대기열에 추가됨: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Flow 시작 (Slack 응답을 기다리며 일시 중지)
 def start_content_pipeline():
@@ -645,22 +594,22 @@ async def on_slack_feedback_async(flow_id: str, slack_message: str):
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
-        message="이 글 초안을 검토해 주세요:",
+        message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
-        learn=True,
+        learn=True,  # HITL 학습 활성화
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **첫 번째 실행**: 인간이 원시 출력을 보고 "사실에 대한 주장에는 항상 인용을 포함하세요."라고 말합니다. 교훈이 추출되어 메모리에 저장됩니다.

docs/ko/learn/llm-connections.mdx

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## CrewAI를 LLM에 연결하기
 
-CrewAI는 가장 인기 있는 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)에 대해 네이티브 SDK 통합을 통해 LLM에 연결하며, 그 외 모든 제공자에 대해서는 LiteLLM을 유연한 폴백으로 사용합니다.
+CrewAI는 LiteLLM을 사용하여 다양한 언어 모델(LLM)에 연결합니다. 이 통합은 높은 다양성을 제공하여, 여러 공급자의 모델을 간단하고 통합된 인터페이스로 사용할 수 있게 해줍니다.
 
 <Note>
     기본적으로 CrewAI는 `gpt-4o-mini` 모델을 사용합니다. 이는 `OPENAI_MODEL_NAME` 환경 변수에 의해 결정되며, 설정되지 않은 경우 기본값은 "gpt-4o-mini"입니다.
@@ -41,14 +41,6 @@ LiteLLM은 다음을 포함하되 이에 국한되지 않는 다양한 프로바
 
 지원되는 프로바이더의 전체 및 최신 목록은 [LiteLLM 프로바이더 문서](https://docs.litellm.ai/docs/providers)를 참조하세요.
 
-<Info>
-  네이티브 통합에서 지원하지 않는 제공자를 사용하려면 LiteLLM을 프로젝트에 의존성으로 추가하세요:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  네이티브 제공자(OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock)는 자체 SDK extras를 사용합니다 — [공급자 구성 예시](/ko/concepts/llms#공급자-구성-예시)를 참조하세요.
-</Info>
-
 ## LLM 변경하기
 
 CrewAI agent에서 다른 LLM을 사용하려면 여러 가지 방법이 있습니다:

docs/ko/observability/tracing.mdx

@@ -35,7 +35,7 @@ crewai login
 아직 설치하지 않았다면 CLI 도구와 함께 CrewAI를 설치하세요:
 
 ```bash
-uv add 'crewai[tools]'
+uv add crewai[tools]
 ```
 
 그런 다음 CrewAI AMP 계정으로 CLI를 인증하세요:

docs/ko/tools/automation/composiotool.mdx

@@ -18,46 +18,77 @@ Composio는 AI 에이전트를 250개 이상의 도구와 연결할 수 있는
 Composio 도구를 프로젝트에 통합하려면 아래 지침을 따르세요:
 
 ```shell
-pip install composio composio-crewai
+pip install composio-crewai
 pip install crewai
 ```
 
-설치가 완료되면 Composio API 키를 `COMPOSIO_API_KEY`로 설정하세요. Composio API 키는 [여기](https://platform.composio.dev)에서 받을 수 있습니다.
+설치가 완료된 후, `composio login`을 실행하거나 Composio API 키를 `COMPOSIO_API_KEY`로 export하세요. Composio API 키는 [여기](https://app.composio.dev)에서 받을 수 있습니다.
 
 ## 예시
 
-다음 예시는 도구를 초기화하고 GitHub 액션을 실행하는 방법을 보여줍니다:
+다음 예시는 도구를 초기화하고 github action을 실행하는 방법을 보여줍니다:
 
-1. CrewAI Provider와 함께 Composio 초기화
+1. Composio 도구 세트 초기화
 
 ```python Code
-from composio_crewai import ComposioProvider
-from composio import Composio
+from composio_crewai import ComposioToolSet, App, Action
 from crewai import Agent, Task, Crew
 
-composio = Composio(provider=ComposioProvider())
+toolset = ComposioToolSet()
 ```
 
-2. 새 Composio 세션을 만들고 도구 가져오기
+2. GitHub 계정 연결
 <CodeGroup>
-```python
-session = composio.create(
-    user_id="your-user-id",
-    toolkits=["gmail", "github"] # optional, default is all toolkits
-)
-tools = session.tools()
+```shell CLI
+composio add github
+```
+```python Code
+request = toolset.initiate_connection(app=App.GITHUB)
+print(f"Open this URL to authenticate: {request.redirectUrl}")
 ```
-세션 및 사용자 관리에 대한 자세한 내용은 [여기](https://docs.composio.dev/docs/configuring-sessions)를 참고하세요.
 </CodeGroup>
 
-3. 사용자 수동 인증하기
+3. 도구 가져오기
 
-Composio는 에이전트 채팅 세션 중에 사용자를 자동으로 인증합니다. 하지만 `authorize` 메서드를 호출해 사용자를 수동으로 인증할 수도 있습니다.
+- 앱에서 모든 도구를 가져오기 (프로덕션 환경에서는 권장하지 않음):
 ```python Code
-connection_request = session.authorize("github")
-print(f"Open this URL to authenticate: {connection_request.redirect_url}")
+tools = toolset.get_tools(apps=[App.GITHUB])
 ```
 
+- 태그를 기반으로 도구 필터링:
+```python Code
+tag = "users"
+
+filtered_action_enums = toolset.find_actions_by_tags(
+    App.GITHUB,
+    tags=[tag], 
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+
+- 사용 사례를 기반으로 도구 필터링:
+```python Code
+use_case = "Star a repository on GitHub"
+
+filtered_action_enums = toolset.find_actions_by_use_case(
+    App.GITHUB, use_case=use_case, advanced=False
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+<Tip>`advanced`를 True로 설정하면 복잡한 사용 사례를 위한 액션을 가져올 수 있습니다</Tip>
+
+- 특정 도구 사용하기:
+
+이 데모에서는 GitHub 앱의 `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` 액션을 사용합니다.
+```python Code
+tools = toolset.get_tools(
+    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
+)
+```
+액션 필터링에 대해 더 자세한 내용을 보려면 [여기](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)를 참고하세요.
+
 4. 에이전트 정의
 
 ```python Code
@@ -85,4 +116,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task])
 crew.kickoff()
 ```
 
-* 더욱 자세한 도구 목록은 [여기](https://docs.composio.dev/toolkits)에서 확인할 수 있습니다.
+* 더욱 자세한 도구 리스트는 [여기](https://app.composio.dev)에서 확인하실 수 있습니다.

docs/pt-BR/changelog.mdx

@@ -4,214 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="14 mar 2026">
-  ## v1.10.2rc2
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc2)
-
-  ## O que Mudou
-
-  ### Correções de Bugs
-  - Remover bloqueios exclusivos de operações de armazenamento somente leitura
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.10.2rc1
-
-  ## Contribuidores
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="13 mar 2026">
-  ## v1.10.2rc1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2rc1)
-
-  ## O que Mudou
-
-  ### Funcionalidades
-  - Adicionar comando de lançamento e acionar publicação no PyPI
-
-  ### Correções de Bugs
-  - Corrigir bloqueio seguro entre processos e threads para I/O não protegido
-  - Propagar contextvars através de todos os limites de thread e executor
-  - Propagar ContextVars para threads de tarefas assíncronas
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.10.2a1
-
-  ## Contribuidores
-
-  @danglies007, @greysonlalonde
-
-</Update>
-
-<Update label="11 mar 2026">
-  ## v1.10.2a1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
-
-  ## O que mudou
-
-  ### Recursos
-  - Adicionar suporte para busca de ferramentas, salvamento de tokens e injeção dinâmica de ferramentas apropriadas durante a execução para Anthropics.
-  - Introduzir mais ferramentas de Busca Brave.
-  - Criar ação para lançamentos noturnos.
-
-  ### Correções de Bugs
-  - Corrigir LockException durante a execução concorrente de múltiplos processos.
-  - Resolver problemas com a agrupação de resultados de ferramentas paralelas em uma única mensagem de usuário.
-  - Abordar resoluções de ferramentas MCP e eliminar todas as conexões mutáveis compartilhadas.
-  - Atualizar o manuseio de parâmetros LLM na função human_feedback.
-  - Adicionar métodos de lista/dicionário ausentes a LockedListProxy e LockedDictProxy.
-  - Propagar o contexto de contextvars para as threads de chamada de ferramentas paralelas.
-  - Atualizar a dependência gitpython para >=3.1.41 para resolver a vulnerabilidade de travessia de diretórios CVE.
-
-  ### Refatoração
-  - Refatorar classes de memória para serem serializáveis.
-
-  ### Documentação
-  - Atualizar o changelog e a versão para v1.10.1.
-
-  ## Contribuidores
-
-  @akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
-
-</Update>
-
-<Update label="04 mar 2026">
-  ## v1.10.1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1)
-
-  ## O que mudou
-
-  ### Recursos
-  - Atualizar Gemini GenAI
-
-  ### Correções de Bugs
-  - Ajustar o valor do listener do executor para evitar recursão
-  - Agrupar partes da resposta da função paralela em um único objeto Content no Gemini
-  - Exibir a saída de pensamento dos modelos de pensamento no Gemini
-  - Carregar ferramentas MCP e da plataforma quando as ferramentas do agente forem None
-  - Suportar ambientes Jupyter com loops de eventos em A2A
-  - Usar ID anônimo para rastreamentos efêmeros
-  - Passar condicionalmente o cabeçalho plus
-  - Ignorar o registro do manipulador de sinal em threads não principais para telemetria
-  - Injetar erros de ferramentas como observações e resolver colisões de nomes
-  - Atualizar pypdf de 4.x para 6.7.4 para resolver alertas do Dependabot
-  - Resolver alertas de segurança críticos e altos do Dependabot
-
-  ### Documentação
-  - Sincronizar a documentação da ferramenta Composio entre locais
-
-  ## Contribuidores
-
-  @giulio-leone, @greysonlalonde, @haxzie, @joaomdmoura, @lorenzejay, @mattatcha, @mplachta, @nicoferdi96
-
-</Update>
-
-<Update label="27 fev 2026">
-  ## v1.10.1a1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## O que Mudou
-
-  ### Funcionalidades
-  - Implementar suporte a invocação assíncrona em métodos de callback de etapas
-  - Implementar carregamento sob demanda para dependências pesadas no módulo de Memória
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.10.0
-
-  ### Refatoração
-  - Refatorar métodos de callback de etapas para suportar invocação assíncrona
-  - Refatorar para implementar carregamento sob demanda para dependências pesadas no módulo de Memória
-
-  ### Correções de Bugs
-  - Corrigir branch para notas de lançamento
-
-  ## Contribuidores
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="27 fev 2026">
-  ## v1.10.1a1
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.1a1)
-
-  ## O que Mudou
-
-  ### Refatoração
-  - Refatorar métodos de callback de etapas para suportar invocação assíncrona
-  - Implementar carregamento sob demanda para dependências pesadas no módulo de Memória
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.10.0
-
-  ### Correções de Bugs
-  - Criar branch para notas de lançamento
-
-  ## Contribuidores
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="26 fev 2026">
-  ## v1.10.0
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.0)
-
-  ## O que Mudou
-
-  ### Recursos
-  - Aprimorar a resolução da ferramenta MCP e eventos relacionados
-  - Atualizar a versão do lancedb e adicionar pacotes lance-namespace
-  - Aprimorar a análise e validação de argumentos JSON no CrewAgentExecutor e BaseTool
-  - Migrar o cliente HTTP da CLI de requests para httpx
-  - Adicionar documentação versionada
-  - Adicionar detecção de versões removidas para notas de versão
-  - Implementar tratamento de entrada do usuário em Flows
-  - Aprimorar a funcionalidade de auto-loop HITL nos testes de integração de feedback humano
-  - Adicionar started_event_id e definir no eventbus
-  - Atualizar automaticamente tools.specs
-
-  ### Correções de Bugs
-  - Validar kwargs da ferramenta mesmo quando vazios para evitar TypeError crípticos
-  - Preservar tipos nulos nos esquemas de parâmetros da ferramenta para LLM
-  - Mapear output_pydantic/output_json para saída estruturada nativa
-  - Garantir que callbacks sejam executados/aguardados se forem promessas
-  - Capturar o nome do método no contexto da exceção
-  - Preservar tipo enum no resultado do roteador; melhorar tipos
-  - Corrigir fluxos cíclicos que quebram silenciosamente quando o ID de persistência é passado nas entradas
-  - Corrigir o formato da flag da CLI de --skip-provider para --skip_provider
-  - Garantir que o fluxo de chamada da ferramenta OpenAI seja finalizado
-  - Resolver ponteiros $ref de esquema complexos nas ferramentas MCP
-  - Impor additionalProperties=false nos esquemas
-  - Rejeitar nomes de scripts reservados para pastas de equipe
-  - Resolver condição de corrida no teste de emissão de eventos de guardrail
-
-  ### Documentação
-  - Adicionar nota de dependência litellm para provedores de LLM não nativos
-  - Esclarecer o modelo de segurança NL2SQL e orientações de fortalecimento
-  - Adicionar 96 ações ausentes em 9 integrações
-
-  ### Refatoração
-  - Refatorar crew para provider
-  - Extrair HITL para padrão de provider
-  - Melhorar tipagem e registro de hooks
-
-  ## Contribuidores
-
-  @dependabot[bot], @github-actions[bot], @github-code-quality[bot], @greysonlalonde, @heitorado, @hobostay, @joaomdmoura, @johnvan7, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha, @mplachta, @nicoferdi96, @theCyberTech, @thiagomoretto, @vinibrsl
-
-</Update>
-
 <Update label="26 jan 2026">
   ## v1.9.0
 

docs/pt-BR/concepts/llms.mdx

@@ -105,15 +105,6 @@ Existem diferentes locais no código do CrewAI onde você pode especificar o mod
   </Tab>
 </Tabs>
 
-<Info>
-  O CrewAI oferece integrações nativas via SDK para OpenAI, Anthropic, Google (Gemini API), Azure e AWS Bedrock — sem necessidade de instalação extra além dos extras específicos do provedor (ex.: `uv add "crewai[openai]"`).
-
-  Todos os outros provedores são alimentados pelo **LiteLLM**. Se você planeja usar algum deles, adicione-o como dependência ao seu projeto:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-</Info>
-
 ## Exemplos de Configuração de Provedores
 
 O CrewAI suporta uma grande variedade de provedores de LLM, cada um com recursos, métodos de autenticação e capacidades de modelo únicos.
@@ -223,11 +214,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Texto, Imagem | Texto |
     | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Texto | Texto |
     | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Texto | Texto |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Anthropic">
@@ -368,11 +354,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     | gemini-1.5-flash                 | 1M tokens          | Modelo multimodal equilibrado, bom para maioria das tarefas         |
     | gemini-1.5-flash-8B              | 1M tokens          | Mais rápido, mais eficiente em custo, adequado para tarefas de alta frequência |
     | gemini-1.5-pro                   | 2M tokens          | Melhor desempenho para uma ampla variedade de tarefas de raciocínio, incluindo lógica, codificação e colaboração criativa |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Azure">
@@ -457,11 +438,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         model="sagemaker/<my-endpoint>"
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Mistral">
@@ -477,11 +453,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         temperature=0.7
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Nvidia NIM">
@@ -568,11 +539,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     | rakuten/rakutenai-7b-instruct                                            | 1.024 tokens       | LLM topo de linha, compreensão, raciocínio e geração textual.|
     | rakuten/rakutenai-7b-chat                                                | 1.024 tokens       | LLM topo de linha, compreensão, raciocínio e geração textual.|
     | baichuan-inc/baichuan2-13b-chat                                          | 4.096 tokens       | Suporte a chat em chinês/inglês, programação, matemática, seguir instruções, resolver quizzes.|
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
@@ -613,11 +579,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
 
         # ...
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Groq">
@@ -639,11 +600,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     | Llama 3.1 70B/8B  | 131.072 tokens      | Alta performance e tarefas de contexto grande|
     | Llama 3.2 Série   | 8.192 tokens        | Tarefas gerais                          |
     | Mixtral 8x7B      | 32.768 tokens       | Equilíbrio entre performance e contexto  |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="IBM watsonx.ai">
@@ -666,11 +622,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         base_url="https://api.watsonx.ai/v1"
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Ollama (LLMs Locais)">
@@ -684,11 +635,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         base_url="http://localhost:11434"
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Fireworks AI">
@@ -704,11 +650,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         temperature=0.7
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Perplexity AI">
@@ -724,11 +665,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         base_url="https://api.perplexity.ai/"
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Hugging Face">
@@ -743,11 +679,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
         model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
     )
     ```
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="SambaNova">
@@ -771,11 +702,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     | Llama 3.2 Série   | 8.192 tokens              | Tarefas gerais e multimodais                 |
     | Llama 3.3 70B     | Até 131.072 tokens        | Desempenho e qualidade de saída elevada      |
     | Família Qwen2     | 8.192 tokens              | Desempenho e qualidade de saída elevada      |
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Cerebras">
@@ -801,11 +727,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
       - Equilíbrio entre velocidade e qualidade
       - Suporte a longas janelas de contexto
     </Info>
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 
   <Accordion title="Open Router">
@@ -828,11 +749,6 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
       - openrouter/deepseek/deepseek-r1
       - openrouter/deepseek/deepseek-chat
     </Info>
-
-    **Nota:** Este provedor usa o LiteLLM. Adicione-o como dependência ao seu projeto:
-    ```bash
-    uv add 'crewai[litellm]'
-    ```
   </Accordion>
 </AccordionGroup>
 

docs/pt-BR/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ O CrewAI Enterprise oferece um sistema abrangente de gerenciamento Human-in-the-
 Configure checkpoints de revisão humana em seus Flows usando o decorador `@human_feedback`. Quando a execução atinge um ponto de revisão, o sistema pausa, notifica o responsável via email e aguarda uma resposta.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # IA gera conteúdo
         return "Texto de marketing gerado para campanha Q1..."
 
+    @listen(generate_content)
     @human_feedback(
         message="Por favor, revise este conteúdo para conformidade com a marca:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Texto de marketing para revisão..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"Conteúdo rejeitado. Motivo: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"Revisão solicitada: {result.feedback}")
 ```
 
 Para detalhes completos de implementação, consulte o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows).

docs/pt-BR/enterprise/guides/deploy-to-amp.mdx

@@ -176,11 +176,6 @@ Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não te
       ![Definir Variáveis de Ambiente](/images/enterprise/set-env-variables.png)
     </Frame>
 
-    <Info>
-      Usando pacotes Python privados? Você também precisará adicionar suas credenciais de registro aqui.
-      Consulte [Registros de Pacotes Privados](/pt-BR/enterprise/guides/private-package-registry) para as variáveis necessárias.
-    </Info>
-
   </Step>
 
   <Step title="Implante Seu Crew">

docs/pt-BR/enterprise/guides/prepare-for-deployment.mdx

@@ -256,12 +256,6 @@ Antes da implantação, certifique-se de ter:
 1. **Chaves de API de LLM** prontas (OpenAI, Anthropic, Google, etc.)
 2. **Chaves de API de ferramentas** se estiver usando ferramentas externas (Serper, etc.)
 
-<Info>
-  Se seu projeto depende de pacotes de um **registro PyPI privado**, você também precisará configurar
-  credenciais de autenticação do registro como variáveis de ambiente. Consulte o guia
-  [Registros de Pacotes Privados](/pt-BR/enterprise/guides/private-package-registry) para mais detalhes.
-</Info>
-
 <Tip>
   Teste seu projeto localmente com as mesmas variáveis de ambiente antes de implantar
   para detectar problemas de configuração antecipadamente.

docs/pt-BR/enterprise/guides/private-package-registry.mdx

@@ -1,263 +0,0 @@
----
-title: "Registros de Pacotes Privados"
-description: "Instale pacotes Python privados de registros PyPI autenticados no CrewAI AMP"
-icon: "lock"
-mode: "wide"
----
-
-<Note>
-  Este guia aborda como configurar seu projeto CrewAI para instalar pacotes Python
-  de registros PyPI privados (Azure DevOps Artifacts, GitHub Packages, GitLab, AWS CodeArtifact, etc.)
-  ao implantar no CrewAI AMP.
-</Note>
-
-## Quando Você Precisa Disso
-
-Se seu projeto depende de pacotes Python internos ou proprietários hospedados em um registro privado
-em vez do PyPI público, você precisará:
-
-1. Informar ao UV **onde** encontrar o pacote (uma URL de index)
-2. Informar ao UV **quais** pacotes vêm desse index (um mapeamento de source)
-3. Fornecer **credenciais** para que o UV possa autenticar durante a instalação
-
-O CrewAI AMP usa [UV](https://docs.astral.sh/uv/) para resolução e instalação de dependências.
-O UV suporta registros privados autenticados por meio da configuração do `pyproject.toml` combinada
-com variáveis de ambiente para credenciais.
-
-## Passo 1: Configurar o pyproject.toml
-
-Três elementos trabalham juntos no seu `pyproject.toml`:
-
-### 1a. Declarar a dependência
-
-Adicione o pacote privado ao seu `[project.dependencies]` como qualquer outra dependência:
-
-```toml
-[project]
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-```
-
-### 1b. Definir o index
-
-Registre seu registro privado como um index nomeado em `[[tool.uv.index]]`:
-
-```toml
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-```
-
-<Info>
-  O campo `name` é importante — o UV o utiliza para construir os nomes das variáveis de ambiente
-  para autenticação (veja o [Passo 2](#passo-2-configurar-credenciais-de-autenticação) abaixo).
-
-  Definir `explicit = true` significa que o UV não consultará esse index para todos os pacotes — apenas
-  os que você mapear explicitamente em `[tool.uv.sources]`. Isso evita consultas desnecessárias
-  ao seu registro privado e protege contra ataques de confusão de dependências.
-</Info>
-
-### 1c. Mapear o pacote para o index
-
-Informe ao UV quais pacotes devem ser resolvidos a partir do seu index privado usando `[tool.uv.sources]`:
-
-```toml
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-### Exemplo completo
-
-```toml
-[project]
-name = "my-crew-project"
-version = "0.1.0"
-requires-python = ">=3.10,<=3.13"
-dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
-    "my-private-package>=1.2.0",
-]
-
-[tool.crewai]
-type = "crew"
-
-[[tool.uv.index]]
-name = "my-private-registry"
-url = "https://pkgs.dev.azure.com/my-org/_packaging/my-feed/pypi/simple/"
-explicit = true
-
-[tool.uv.sources]
-my-private-package = { index = "my-private-registry" }
-```
-
-Após atualizar o `pyproject.toml`, regenere seu arquivo lock:
-
-```bash
-uv lock
-```
-
-<Warning>
-  Sempre faça commit do `uv.lock` atualizado junto com as alterações no `pyproject.toml`.
-  O arquivo lock é obrigatório para implantação — veja [Preparar para Implantação](/pt-BR/enterprise/guides/prepare-for-deployment).
-</Warning>
-
-## Passo 2: Configurar Credenciais de Autenticação
-
-O UV autentica em indexes privados usando variáveis de ambiente que seguem uma convenção de nomenclatura
-baseada no nome do index que você definiu no `pyproject.toml`:
-
-```
-UV_INDEX_{UPPER_NAME}_USERNAME
-UV_INDEX_{UPPER_NAME}_PASSWORD
-```
-
-Onde `{UPPER_NAME}` é o nome do seu index convertido para **maiúsculas** com **hifens substituídos por underscores**.
-
-Por exemplo, um index chamado `my-private-registry` usa:
-
-| Variável | Valor |
-|----------|-------|
-| `UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME` | Seu nome de usuário ou nome do token do registro |
-| `UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD` | Sua senha ou token/PAT do registro |
-
-<Warning>
-  Essas variáveis de ambiente **devem** ser adicionadas pelas configurações de **Variáveis de Ambiente** do CrewAI AMP —
-  globalmente ou no nível da implantação. Elas não podem ser definidas em arquivos `.env` ou codificadas no seu projeto.
-
-  Veja [Configurar Variáveis de Ambiente no AMP](#configurar-variáveis-de-ambiente-no-amp) abaixo.
-</Warning>
-
-## Referência de Provedores de Registro
-
-A tabela abaixo mostra o formato da URL de index e os valores de credenciais para provedores de registro comuns.
-Substitua os valores de exemplo pelos detalhes reais da sua organização e feed.
-
-| Provedor | URL do Index | Usuário | Senha |
-|----------|-------------|---------|-------|
-| **Azure DevOps Artifacts** | `https://pkgs.dev.azure.com/{org}/_packaging/{feed}/pypi/simple/` | Qualquer string não vazia (ex: `token`) | Personal Access Token (PAT) com escopo Packaging Read |
-| **GitHub Packages** | `https://pypi.pkg.github.com/{owner}/simple/` | Nome de usuário do GitHub | Personal Access Token (classic) com escopo `read:packages` |
-| **GitLab Package Registry** | `https://gitlab.com/api/v4/projects/{project_id}/packages/pypi/simple/` | `__token__` | Project ou Personal Access Token com escopo `read_api` |
-| **AWS CodeArtifact** | Use a URL de `aws codeartifact get-repository-endpoint` | `aws` | Token de `aws codeartifact get-authorization-token` |
-| **Google Artifact Registry** | `https://{region}-python.pkg.dev/{project}/{repo}/simple/` | `_json_key_base64` | Chave de conta de serviço codificada em Base64 |
-| **JFrog Artifactory** | `https://{instance}.jfrog.io/artifactory/api/pypi/{repo}/simple/` | Nome de usuário ou email | Chave API ou token de identidade |
-| **Auto-hospedado (devpi, Nexus, etc.)** | URL da API simple do seu registro | Nome de usuário do registro | Senha do registro |
-
-<Tip>
-  Para **AWS CodeArtifact**, o token de autorização expira periodicamente.
-  Você precisará atualizar o valor de `UV_INDEX_*_PASSWORD` quando ele expirar.
-  Considere automatizar isso no seu pipeline de CI/CD.
-</Tip>
-
-## Configurar Variáveis de Ambiente no AMP
-
-As credenciais do registro privado devem ser configuradas como variáveis de ambiente no CrewAI AMP.
-Você tem duas opções:
-
-<Tabs>
-  <Tab title="Interface Web">
-    1. Faça login no [CrewAI AMP](https://app.crewai.com)
-    2. Navegue até sua automação
-    3. Abra a aba **Environment Variables**
-    4. Adicione cada variável (`UV_INDEX_*_USERNAME` e `UV_INDEX_*_PASSWORD`) com seu valor
-
-    Veja o passo [Deploy para AMP — Definir Variáveis de Ambiente](/pt-BR/enterprise/guides/deploy-to-amp#definir-as-variáveis-de-ambiente) para detalhes.
-  </Tab>
-  <Tab title="Implantação via CLI">
-    Adicione as variáveis ao seu arquivo `.env` local antes de executar `crewai deploy create`.
-    A CLI as transferirá com segurança para a plataforma:
-
-    ```bash
-    # .env
-    OPENAI_API_KEY=sk-...
-    UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-    UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat-here
-    ```
-
-    ```bash
-    crewai deploy create
-    ```
-  </Tab>
-</Tabs>
-
-<Warning>
-  **Nunca** faça commit de credenciais no seu repositório. Use variáveis de ambiente do AMP para todos os segredos.
-  O arquivo `.env` deve estar listado no `.gitignore`.
-</Warning>
-
-Para atualizar credenciais em uma implantação existente, veja [Atualizar Seu Crew — Variáveis de Ambiente](/pt-BR/enterprise/guides/update-crew).
-
-## Como Tudo se Conecta
-
-Quando o CrewAI AMP faz o build da sua automação, o fluxo de resolução funciona assim:
-
-<Steps>
-  <Step title="Build inicia">
-    O AMP busca seu repositório e lê o `pyproject.toml` e o `uv.lock`.
-  </Step>
-  <Step title="UV resolve dependências">
-    O UV lê `[tool.uv.sources]` para determinar de qual index cada pacote deve vir.
-  </Step>
-  <Step title="UV autentica">
-    Para cada index privado, o UV busca `UV_INDEX_{NAME}_USERNAME` e `UV_INDEX_{NAME}_PASSWORD`
-    nas variáveis de ambiente que você configurou no AMP.
-  </Step>
-  <Step title="Pacotes são instalados">
-    O UV baixa e instala todos os pacotes — tanto públicos (do PyPI) quanto privados (do seu registro).
-  </Step>
-  <Step title="Automação executa">
-    Seu crew ou flow inicia com todas as dependências disponíveis.
-  </Step>
-</Steps>
-
-## Solução de Problemas
-
-### Erros de Autenticação Durante o Build
-
-**Sintoma**: Build falha com `401 Unauthorized` ou `403 Forbidden` ao resolver um pacote privado.
-
-**Verifique**:
-- Os nomes das variáveis de ambiente `UV_INDEX_*` correspondem exatamente ao nome do seu index (maiúsculas, hifens -> underscores)
-- As credenciais estão definidas nas variáveis de ambiente do AMP, não apenas em um `.env` local
-- Seu token/PAT tem as permissões de leitura necessárias para o feed de pacotes
-- O token não expirou (especialmente relevante para AWS CodeArtifact)
-
-### Pacote Não Encontrado
-
-**Sintoma**: `No matching distribution found for my-private-package`.
-
-**Verifique**:
-- A URL do index no `pyproject.toml` termina com `/simple/`
-- A entrada `[tool.uv.sources]` mapeia o nome correto do pacote para o nome correto do index
-- O pacote está realmente publicado no seu registro privado
-- Execute `uv lock` localmente com as mesmas credenciais para verificar se a resolução funciona
-
-### Conflitos no Arquivo Lock
-
-**Sintoma**: `uv lock` falha ou produz resultados inesperados após adicionar um index privado.
-
-**Solução**: Defina as credenciais localmente e regenere:
-
-```bash
-export UV_INDEX_MY_PRIVATE_REGISTRY_USERNAME=token
-export UV_INDEX_MY_PRIVATE_REGISTRY_PASSWORD=your-pat
-uv lock
-```
-
-Em seguida, faça commit do `uv.lock` atualizado.
-
-## Guias Relacionados
-
-<CardGroup cols={3}>
-  <Card title="Preparar para Implantação" icon="clipboard-check" href="/pt-BR/enterprise/guides/prepare-for-deployment">
-    Verifique a estrutura do projeto e as dependências antes de implantar.
-  </Card>
-  <Card title="Deploy para AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
-    Implante seu crew ou flow e configure variáveis de ambiente.
-  </Card>
-  <Card title="Atualizar Seu Crew" icon="arrows-rotate" href="/pt-BR/enterprise/guides/update-crew">
-    Atualize variáveis de ambiente e envie alterações para uma implantação em execução.
-  </Card>
-</CardGroup>

docs/pt-BR/guides/migration/migrating-from-langgraph.mdx

@@ -1,518 +0,0 @@
----
-title: "Migrando do LangGraph para o CrewAI: um guia prático para engenheiros"
-description: Se você já construiu com LangGraph, saiba como portar rapidamente seus projetos para o CrewAI
-icon: switch
-mode: "wide"
----
-
-Você construiu agentes com LangGraph. Já lutou com o `StateGraph`, ligou arestas condicionais e depurou dicionários de estado às 2 da manhã. Funciona — mas, em algum momento, você começou a se perguntar se existe um caminho melhor para produção.
-
-Existe. **CrewAI Flows** entrega o mesmo poder — orquestração orientada a eventos, roteamento condicional, estado compartilhado — com muito menos boilerplate e um modelo mental que se alinha a como você realmente pensa sobre fluxos de trabalho de IA em múltiplas etapas.
-
-Este artigo apresenta os conceitos principais lado a lado, mostra comparações reais de código e demonstra por que o CrewAI Flows é o framework que você vai querer usar a seguir.
-
----
-
-## A Mudança de Modelo Mental
-
-LangGraph pede que você pense em **grafos**: nós, arestas e dicionários de estado. Todo workflow é um grafo direcionado em que você conecta explicitamente as transições entre as etapas de computação. É poderoso, mas a abstração traz overhead — especialmente quando o seu fluxo é fundamentalmente sequencial com alguns pontos de decisão.
-
-CrewAI Flows pede que você pense em **eventos**: métodos que iniciam, métodos que escutam resultados e métodos que roteiam a execução. A topologia do workflow emerge de anotações com decorators, em vez de construção explícita do grafo. Isso não é apenas açúcar sintático — muda como você projeta, lê e mantém seus pipelines.
-
-Veja o mapeamento principal:
-
-| Conceito no LangGraph | Equivalente no CrewAI Flows |
-| --- | --- |
-| `StateGraph` class | `Flow` class |
-| `add_node()` | Methods decorated with `@start`, `@listen` |
-| `add_edge()` / `add_conditional_edges()` | `@listen()` / `@router()` decorators |
-| `TypedDict` state | Pydantic `BaseModel` state |
-| `START` / `END` constants | `@start()` decorator / natural method return |
-| `graph.compile()` | `flow.kickoff()` |
-| Checkpointer / persistence | Built-in memory (LanceDB-backed) |
-
-Vamos ver como isso fica na prática.
-
----
-
-## Demo 1: Um Pipeline Sequencial Simples
-
-Imagine que você está construindo um pipeline que recebe um tema, pesquisa, escreve um resumo e formata a saída. Veja como cada framework lida com isso.
-
-### Abordagem com LangGraph
-
-```python
-from typing import TypedDict
-from langgraph.graph import StateGraph, START, END
-
-class ResearchState(TypedDict):
-    topic: str
-    raw_research: str
-    summary: str
-    formatted_output: str
-
-def research_topic(state: ResearchState) -> dict:
-    # Call an LLM or search API
-    result = llm.invoke(f"Research the topic: {state['topic']}")
-    return {"raw_research": result}
-
-def write_summary(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Summarize this research:\n{state['raw_research']}"
-    )
-    return {"summary": result}
-
-def format_output(state: ResearchState) -> dict:
-    result = llm.invoke(
-        f"Format this summary as a polished article section:\n{state['summary']}"
-    )
-    return {"formatted_output": result}
-
-# Build the graph
-graph = StateGraph(ResearchState)
-graph.add_node("research", research_topic)
-graph.add_node("summarize", write_summary)
-graph.add_node("format", format_output)
-
-graph.add_edge(START, "research")
-graph.add_edge("research", "summarize")
-graph.add_edge("summarize", "format")
-graph.add_edge("format", END)
-
-# Compile and run
-app = graph.compile()
-result = app.invoke({"topic": "quantum computing advances in 2026"})
-print(result["formatted_output"])
-```
-
-Você define funções, registra-as como nós e conecta manualmente cada transição. Para uma sequência simples como essa, há muita cerimônia.
-
-### Abordagem com CrewAI Flows
-
-```python
-from crewai import LLM, Agent, Crew, Process, Task
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ResearchState(BaseModel):
-    topic: str = ""
-    raw_research: str = ""
-    summary: str = ""
-    formatted_output: str = ""
-
-class ResearchFlow(Flow[ResearchState]):
-    @start()
-    def research_topic(self):
-        # Option 1: Direct LLM call
-        result = llm.call(f"Research the topic: {self.state.topic}")
-        self.state.raw_research = result
-        return result
-
-    @listen(research_topic)
-    def write_summary(self, research_output):
-        # Option 2: A single agent
-        summarizer = Agent(
-            role="Research Summarizer",
-            goal="Produce concise, accurate summaries of research content",
-            backstory="You are an expert at distilling complex research into clear, "
-            "digestible summaries.",
-            llm=llm,
-            verbose=True,
-        )
-        result = summarizer.kickoff(
-            f"Summarize this research:\n{self.state.raw_research}"
-        )
-        self.state.summary = str(result)
-        return self.state.summary
-
-    @listen(write_summary)
-    def format_output(self, summary_output):
-        # Option 3: a complete crew (with one or more agents)
-        formatter = Agent(
-            role="Content Formatter",
-            goal="Transform research summaries into polished, publication-ready article sections",
-            backstory="You are a skilled editor with expertise in structuring and "
-            "presenting technical content for a general audience.",
-            llm=llm,
-            verbose=True,
-        )
-        format_task = Task(
-            description=f"Format this summary as a polished article section:\n{self.state.summary}",
-            expected_output="A well-structured, polished article section ready for publication.",
-            agent=formatter,
-        )
-        crew = Crew(
-            agents=[formatter],
-            tasks=[format_task],
-            process=Process.sequential,
-            verbose=True,
-        )
-        result = crew.kickoff()
-        self.state.formatted_output = str(result)
-        return self.state.formatted_output
-
-# Run the flow
-flow = ResearchFlow()
-flow.state.topic = "quantum computing advances in 2026"
-result = flow.kickoff()
-print(flow.state.formatted_output)
-
-```
-
-Repare a diferença: nada de construção de grafo, de ligação de arestas, nem de etapa de compilação. A ordem de execução é declarada exatamente onde a lógica vive. `@start()` marca o ponto de entrada, e `@listen(method_name)` encadeia as etapas. O estado é um modelo Pydantic de verdade, com segurança de tipos, validação e auto-complete na IDE.
-
----
-
-## Demo 2: Roteamento Condicional
-
-Aqui é que fica interessante. Digamos que você está construindo um pipeline de conteúdo que roteia para diferentes caminhos de processamento com base no tipo de conteúdo detectado.
-
-### Abordagem com LangGraph
-
-```python
-from typing import TypedDict, Literal
-from langgraph.graph import StateGraph, START, END
-
-class ContentState(TypedDict):
-    input_text: str
-    content_type: str
-    result: str
-
-def classify_content(state: ContentState) -> dict:
-    content_type = llm.invoke(
-        f"Classify this content as 'technical', 'creative', or 'business':\n{state['input_text']}"
-    )
-    return {"content_type": content_type.strip().lower()}
-
-def process_technical(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as technical doc:\n{state['input_text']}")
-    return {"result": result}
-
-def process_creative(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as creative writing:\n{state['input_text']}")
-    return {"result": result}
-
-def process_business(state: ContentState) -> dict:
-    result = llm.invoke(f"Process as business content:\n{state['input_text']}")
-    return {"result": result}
-
-# Routing function
-def route_content(state: ContentState) -> Literal["technical", "creative", "business"]:
-    return state["content_type"]
-
-# Build the graph
-graph = StateGraph(ContentState)
-graph.add_node("classify", classify_content)
-graph.add_node("technical", process_technical)
-graph.add_node("creative", process_creative)
-graph.add_node("business", process_business)
-
-graph.add_edge(START, "classify")
-graph.add_conditional_edges(
-    "classify",
-    route_content,
-    {
-        "technical": "technical",
-        "creative": "creative",
-        "business": "business",
-    }
-)
-graph.add_edge("technical", END)
-graph.add_edge("creative", END)
-graph.add_edge("business", END)
-
-app = graph.compile()
-result = app.invoke({"input_text": "Explain how TCP handshakes work"})
-```
-
-Você precisa de uma função de roteamento separada, de um mapeamento explícito de arestas condicionais e de arestas de término para cada ramificação. A lógica de roteamento fica desacoplada do nó que produz a decisão.
-
-### Abordagem com CrewAI Flows
-
-```python
-from crewai import LLM, Agent
-from crewai.flow.flow import Flow, listen, router, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class ContentState(BaseModel):
-    input_text: str = ""
-    content_type: str = ""
-    result: str = ""
-
-class ContentFlow(Flow[ContentState]):
-    @start()
-    def classify_content(self):
-        self.state.content_type = (
-            llm.call(
-                f"Classify this content as 'technical', 'creative', or 'business':\n"
-                f"{self.state.input_text}"
-            )
-            .strip()
-            .lower()
-        )
-        return self.state.content_type
-
-    @router(classify_content)
-    def route_content(self, classification):
-        if classification == "technical":
-            return "process_technical"
-        elif classification == "creative":
-            return "process_creative"
-        else:
-            return "process_business"
-
-    @listen("process_technical")
-    def handle_technical(self):
-        agent = Agent(
-            role="Technical Writer",
-            goal="Produce clear, accurate technical documentation",
-            backstory="You are an expert technical writer who specializes in "
-            "explaining complex technical concepts precisely.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as technical doc:\n{self.state.input_text}")
-        )
-
-    @listen("process_creative")
-    def handle_creative(self):
-        agent = Agent(
-            role="Creative Writer",
-            goal="Craft engaging and imaginative creative content",
-            backstory="You are a talented creative writer with a flair for "
-            "compelling storytelling and vivid expression.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as creative writing:\n{self.state.input_text}")
-        )
-
-    @listen("process_business")
-    def handle_business(self):
-        agent = Agent(
-            role="Business Writer",
-            goal="Produce professional, results-oriented business content",
-            backstory="You are an experienced business writer who communicates "
-            "strategy and value clearly to professional audiences.",
-            llm=llm,
-            verbose=True,
-        )
-        self.state.result = str(
-            agent.kickoff(f"Process as business content:\n{self.state.input_text}")
-        )
-
-flow = ContentFlow()
-flow.state.input_text = "Explain how TCP handshakes work"
-flow.kickoff()
-print(flow.state.result)
-
-```
-
-O decorator `@router()` transforma um método em um ponto de decisão. Ele retorna uma string que corresponde a um listener — sem dicionários de mapeamento, sem funções de roteamento separadas. A lógica de ramificação parece um `if` em Python porque *é* um.
-
----
-
-## Demo 3: Integrando Crews de Agentes de IA em Flows
-
-É aqui que o verdadeiro poder do CrewAI aparece. Flows não servem apenas para encadear chamadas de LLM — elas orquestram **Crews** completas de agentes autônomos. Isso é algo para o qual o LangGraph simplesmente não tem um equivalente nativo.
-
-```python
-from crewai import Agent, Task, Crew
-from crewai.flow.flow import Flow, listen, start
-from pydantic import BaseModel
-
-class ArticleState(BaseModel):
-    topic: str = ""
-    research: str = ""
-    draft: str = ""
-    final_article: str = ""
-
-class ArticleFlow(Flow[ArticleState]):
-
-    @start()
-    def run_research_crew(self):
-        """A full Crew of agents handles research."""
-        researcher = Agent(
-            role="Senior Research Analyst",
-            goal=f"Produce comprehensive research on: {self.state.topic}",
-            backstory="You're a veteran analyst known for thorough, "
-                       "well-sourced research reports.",
-            llm="gpt-4o"
-        )
-
-        research_task = Task(
-            description=f"Research '{self.state.topic}' thoroughly. "
-                        "Cover key trends, data points, and expert opinions.",
-            expected_output="A detailed research brief with sources.",
-            agent=researcher
-        )
-
-        crew = Crew(agents=[researcher], tasks=[research_task])
-        result = crew.kickoff()
-        self.state.research = result.raw
-        return result.raw
-
-    @listen(run_research_crew)
-    def run_writing_crew(self, research_output):
-        """A different Crew handles writing."""
-        writer = Agent(
-            role="Technical Writer",
-            goal="Write a compelling article based on provided research.",
-            backstory="You turn complex research into engaging, clear prose.",
-            llm="gpt-4o"
-        )
-
-        editor = Agent(
-            role="Senior Editor",
-            goal="Review and polish articles for publication quality.",
-            backstory="20 years of editorial experience at top tech publications.",
-            llm="gpt-4o"
-        )
-
-        write_task = Task(
-            description=f"Write an article based on this research:\n{self.state.research}",
-            expected_output="A well-structured draft article.",
-            agent=writer
-        )
-
-        edit_task = Task(
-            description="Review, fact-check, and polish the draft article.",
-            expected_output="A publication-ready article.",
-            agent=editor
-        )
-
-        crew = Crew(agents=[writer, editor], tasks=[write_task, edit_task])
-        result = crew.kickoff()
-        self.state.final_article = result.raw
-        return result.raw
-
-# Run the full pipeline
-flow = ArticleFlow()
-flow.state.topic = "The Future of Edge AI"
-flow.kickoff()
-print(flow.state.final_article)
-```
-
-Este é o insight-chave: **Flows fornecem a camada de orquestração, e Crews fornecem a camada de inteligência.** Cada etapa em um Flow pode subir uma equipe completa de agentes colaborativos, cada um com seus próprios papéis, objetivos e ferramentas. Você obtém fluxo de controle estruturado e previsível *e* colaboração autônoma de agentes — o melhor dos dois mundos.
-
-No LangGraph, alcançar algo similar significa implementar manualmente protocolos de comunicação entre agentes, loops de chamada de ferramentas e lógica de delegação dentro das funções dos nós. É possível, mas é encanamento que você constrói do zero todas as vezes.
-
----
-
-## Demo 4: Execução Paralela e Sincronização
-
-Pipelines do mundo real frequentemente precisam dividir o trabalho e juntar os resultados. O CrewAI Flows lida com isso de forma elegante com os operadores `and_` e `or_`.
-
-```python
-from crewai import LLM
-from crewai.flow.flow import Flow, and_, listen, start
-from pydantic import BaseModel
-
-llm = LLM(model="openai/gpt-5.2")
-
-class AnalysisState(BaseModel):
-    topic: str = ""
-    market_data: str = ""
-    tech_analysis: str = ""
-    competitor_intel: str = ""
-    final_report: str = ""
-
-class ParallelAnalysisFlow(Flow[AnalysisState]):
-    @start()
-    def start_method(self):
-        pass
-
-    @listen(start_method)
-    def gather_market_data(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def run_tech_analysis(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(start_method)
-    def gather_competitor_intel(self):
-        # Your agentic or deterministic code
-        pass
-
-    @listen(and_(gather_market_data, run_tech_analysis, gather_competitor_intel))
-    def synthesize_report(self):
-        # Your agentic or deterministic code
-        pass
-
-flow = ParallelAnalysisFlow()
-flow.state.topic = "AI-powered developer tools"
-flow.kickoff()
-
-```
-
-Vários decorators `@start()` disparam em paralelo. O combinador `and_()` no decorator `@listen` garante que `synthesize_report` só execute depois que *todos os três* métodos upstream forem concluídos. Também existe `or_()` para quando você quer prosseguir assim que *qualquer* tarefa upstream terminar.
-
-No LangGraph, você precisaria construir um padrão fan-out/fan-in com ramificações paralelas, um nó de sincronização e uma mesclagem de estado cuidadosa — tudo conectado explicitamente por arestas.
-
----
-
-## Por que CrewAI Flows em Produção
-
-Além de uma sintaxe mais limpa, Flows entrega várias vantagens críticas para produção:
-
-**Persistência de estado integrada.** O estado do Flow é respaldado pelo LanceDB, o que significa que seus workflows podem sobreviver a falhas, ser retomados e acumular conhecimento entre execuções. No LangGraph, você precisa configurar um checkpointer separado.
-
-**Gerenciamento de estado com segurança de tipos.** Modelos Pydantic oferecem validação, serialização e suporte de IDE prontos para uso. Estados `TypedDict` do LangGraph não validam em runtime.
-
-**Orquestração de agentes de primeira classe.** Crews são um primitivo nativo. Você define agentes com papéis, objetivos, histórias e ferramentas — e eles colaboram de forma autônoma dentro do envelope estruturado de um Flow. Não é preciso reinventar a coordenação multiagente.
-
-**Modelo mental mais simples.** Decorators declaram intenção. `@start` significa "comece aqui". `@listen(x)` significa "execute depois de x". `@router(x)` significa "decida para onde ir depois de x". O código lê como o workflow que ele descreve.
-
-**Integração com CLI.** Execute flows com `crewai run`. Sem etapa de compilação separada, sem serialização de grafo. Seu Flow é uma classe Python, e ele roda como tal.
-
----
-
-## Cheat Sheet de Migração
-
-Se você está com uma base de código LangGraph e quer migrar para o CrewAI Flows, aqui vai um guia prático de conversão:
-
-1. **Mapeie seu estado.** Converta seu `TypedDict` para um `BaseModel` do Pydantic. Adicione valores padrão para todos os campos.
-2. **Converta nós em métodos.** Cada função de `add_node` vira um método na sua subclasse de `Flow`. Substitua leituras `state["field"]` por `self.state.field`.
-3. **Substitua arestas por decorators.** `add_edge(START, "first_node")` vira `@start()` no primeiro método. A sequência `add_edge("a", "b")` vira `@listen(a)` no método `b`.
-4. **Substitua arestas condicionais por `@router`.** A função de roteamento e o mapeamento do `add_conditional_edges()` viram um único método `@router()` que retorna a string de rota.
-5. **Troque compile + invoke por kickoff.** Remova `graph.compile()`. Chame `flow.kickoff()`.
-6. **Considere onde as Crews se encaixam.** Qualquer nó com lógica complexa de agentes em múltiplas etapas é um candidato a extração para uma Crew. É aqui que você verá a maior melhoria de qualidade.
-
----
-
-## Primeiros Passos
-
-Instale o CrewAI e crie o scaffold de um novo projeto Flow:
-
-```bash
-pip install crewai
-crewai create flow my_first_flow
-cd my_first_flow
-```
-
-Isso gera uma estrutura de projeto com uma classe Flow pronta para edição, arquivos de configuração e um `pyproject.toml` com `type = "flow"` já definido. Execute com:
-
-```bash
-crewai run
-```
-
-A partir daí, adicione seus agentes, conecte seus listeners e publique.
-
----
-
-## Considerações Finais
-
-O LangGraph ensinou ao ecossistema que workflows de IA precisam de estrutura. Essa foi uma lição importante. Mas o CrewAI Flows pega essa lição e a entrega de um jeito mais rápido de escrever, mais fácil de ler e mais poderoso em produção — especialmente quando seus workflows envolvem múltiplos agentes colaborando.
-
-Se você está construindo algo além de uma cadeia de agente único, dê uma olhada séria no Flows. O modelo baseado em decorators, a integração nativa com Crews e o gerenciamento de estado embutido significam menos tempo com encanamento e mais tempo nos problemas que importam.
-
-Comece com `crewai create flow`. Você não vai olhar para trás.

docs/pt-BR/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 Quando você especifica `emit`, o decorador se torna um roteador. O feedback livre do humano é interpretado por um LLM e mapeado para um dos outcomes especificados:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="Você aprova este conteúdo para publicação?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "Rascunho do post do blog aqui..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "Rascunho do post do blog aqui..."
+@listen("approved")
+def publish(self, result):
+    print(f"Publicando! Usuário disse: {result.feedback}")
 
-    @human_feedback(
-        message="Você aprova este conteúdo para publicação?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Rascunho do post do blog aqui..."
+@listen("rejected")
+def discard(self, result):
+    print(f"Descartando. Motivo: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"Publicando! Usuário disse: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"Descartando. Motivo: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"Revisando baseado em: {result.feedback}")
 ```
 
-Quando o humano diz algo como "precisa de mais detalhes", o LLM mapeia para `"needs_revision"`, que dispara `review_content` novamente via `or_()` — criando um loop de revisão. O loop continua até que o outcome seja `"approved"` ou `"rejected"`.
-
 <Tip>
 O LLM usa saídas estruturadas (function calling) quando disponível para garantir que a resposta seja um dos seus outcomes especificados. Isso torna o roteamento confiável e previsível.
 </Tip>
 
-<Warning>
-Um método `@start()` só executa uma vez no início do flow. Se você precisa de um loop de revisão, separe o método start do método de revisão e use `@listen(or_("trigger", "revision_outcome"))` no método de revisão para habilitar o self-loop.
-</Warning>
-
 ## HumanFeedbackResult
 
 O dataclass `HumanFeedbackResult` contém todas as informações sobre uma interação de feedback humano:
@@ -203,162 +193,116 @@ Aqui está um exemplo completo implementando um fluxo de revisão e aprovação
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """Um flow que gera conteúdo e faz loop até o humano aprovar."""
+    """Um flow que gera conteúdo e obtém aprovação humana."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# IA Segura\n\nEste é um rascunho sobre IA Segura..."
+    def get_topic(self):
+        self.state.topic = input("Sobre qual tópico devo escrever? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # Em uso real, isso chamaria um LLM
+        self.state.draft = f"# {topic}\n\nEste é um rascunho sobre {topic}..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:",
+        message="Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"Conteúdo aprovado e publicado! Revisor disse: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ Conteúdo aprovado e publicado!")
+        print(f"Comentário do revisor: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"Conteúdo rejeitado. Motivo: {result.feedback}")
+        print("\n❌ Conteúdo rejeitado")
+        print(f"Motivo: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 Revisão #{self.state.revision_count} solicitada")
+        print(f"Feedback: {result.feedback}")
 
+        # Em um flow real, você pode voltar para generate_draft
+        # Para este exemplo, apenas reconhecemos
+        return "revision_requested"
+
+
+# Executar o flow
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow finalizado. Status: {flow.state.status}, Revisões: {flow.state.revision_count}")
+print(f"\nFlow concluído. Revisões solicitadas: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# IA Segura
-
-Este é um rascunho sobre IA Segura... (v1)
-==================================================
-
-Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: Preciso de mais detalhes sobre segurança em IA.
+Sobre qual tópico devo escrever? Segurança em IA
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
-# IA Segura
+# Segurança em IA
 
-Este é um rascunho sobre IA Segura... (v2)
+Este é um rascunho sobre Segurança em IA...
 ==================================================
 
-Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:
+Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: Parece bom, aprovado!
 
-Conteúdo aprovado e publicado! Revisor disse: Parece bom, aprovado!
+✅ Conteúdo aprovado e publicado!
+Comentário do revisor: Parece bom, aprovado!
 
-Flow finalizado. Status: published, Revisões: 2
+Flow concluído. Revisões solicitadas: 0
 ```
 
 </CodeGroup>
 
 ## Combinando com Outros Decoradores
 
-O decorador `@human_feedback` funciona com `@start()`, `@listen()` e `or_()`. Ambas as ordens de decoradores funcionam — o framework propaga atributos em ambas as direções — mas os padrões recomendados são:
+O decorador `@human_feedback` funciona com outros decoradores de flow. Coloque-o como o decorador mais interno (mais próximo da função):
 
 ```python Code
-# Revisão única no início do flow (sem self-loop)
+# Correto: @human_feedback é o mais interno (mais próximo da função)
 @start()
-@human_feedback(message="Revise isto:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="Revise isto:")
 def my_start_method(self):
     return "content"
 
-# Revisão linear em um listener (sem self-loop)
 @listen(other_method)
-@human_feedback(message="Revise isto também:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="Revise isto também:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: revisão que pode voltar para revisões
-@human_feedback(message="Aprovar ou revisar?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Padrão de self-loop
-
-Para criar um loop de revisão, o método de revisão deve escutar **ambos** um gatilho upstream e seu próprio outcome de revisão usando `or_()`:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="Aprovar ou solicitar alterações?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-Quando o outcome é `"revise"`, o flow roteia de volta para `review` (porque ele escuta `"revise"` via `or_()`). Quando o outcome é `"approved"`, o flow continua para `publish`. Isso funciona porque o engine de flow isenta roteadores da regra "fire once", permitindo que eles re-executem em cada iteração do loop.
-
-### Roteadores encadeados
-
-Um listener disparado pelo outcome de um roteador pode ser ele mesmo um roteador:
-
-```python Code
-@start()
-@human_feedback(message="Primeira revisão:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-def draft(self):
-    return "draft content"
-
-@listen("approved")
-@human_feedback(message="Revisão final:", emit=["publish", "revise"], llm="gpt-4o-mini")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-```
-
-### Limitações
-
-- **Métodos `@start()` executam uma vez**: Um método `@start()` não pode fazer self-loop. Se você precisa de um ciclo de revisão, use um método `@start()` separado como ponto de entrada e coloque o `@human_feedback` em um método `@listen()`.
-- **Sem `@start()` + `@listen()` no mesmo método**: Esta é uma restrição do framework de Flow. Um método é ou um ponto de início ou um listener, não ambos.
+<Tip>
+Coloque `@human_feedback` como o decorador mais interno (último/mais próximo da função) para que ele envolva o método diretamente e possa capturar o valor de retorno antes de passar para o sistema de flow.
+</Tip>
 
 ## Melhores Práticas
 
@@ -572,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="Aprova este conteúdo para publicação?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -590,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"Arquivado. Motivo: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"Na fila para revisão: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Iniciando o flow (vai pausar e aguardar resposta do Slack)
 def start_content_pipeline():
@@ -645,22 +594,22 @@ Com o tempo, o humano vê saídas pré-revisadas progressivamente melhores porqu
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
-        message="Revise este rascunho do artigo:",
+        message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
         learn=True,  # enable HITL learning
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **Primeira execução**: O humano vê a saída bruta e diz "Sempre inclua citações para afirmações factuais." A lição é destilada e armazenada na memória.

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

@@ -7,7 +7,7 @@ mode: "wide"
 
 ## Conecte o CrewAI a LLMs
 
-O CrewAI conecta-se a LLMs por meio de integrações nativas via SDK para os provedores mais populares (OpenAI, Anthropic, Google Gemini, Azure e AWS Bedrock), e usa o LiteLLM como alternativa flexível para todos os demais provedores.
+O CrewAI utiliza o LiteLLM para conectar-se a uma grande variedade de Modelos de Linguagem (LLMs). Essa integração proporciona grande versatilidade, permitindo que você utilize modelos de inúmeros provedores por meio de uma interface simples e unificada.
 
 <Note>
     Por padrão, o CrewAI usa o modelo `gpt-4o-mini`. Isso é determinado pela variável de ambiente `OPENAI_MODEL_NAME`, que tem como padrão "gpt-4o-mini" se não for definida.
@@ -40,14 +40,6 @@ O LiteLLM oferece suporte a uma ampla gama de provedores, incluindo, mas não se
 
 Para uma lista completa e sempre atualizada dos provedores suportados, consulte a [documentação de Provedores do LiteLLM](https://docs.litellm.ai/docs/providers).
 
-<Info>
-  Para usar qualquer provedor não coberto por uma integração nativa, adicione o LiteLLM como dependência ao seu projeto:
-  ```bash
-  uv add 'crewai[litellm]'
-  ```
-  Provedores nativos (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) usam seus próprios extras de SDK — consulte os [Exemplos de Configuração de Provedores](/pt-BR/concepts/llms#exemplos-de-configuração-de-provedores).
-</Info>
-
 ## Alterando a LLM
 
 Para utilizar uma LLM diferente com seus agentes CrewAI, você tem várias opções:

docs/pt-BR/tools/automation/composiotool.mdx

@@ -11,53 +11,84 @@ mode: "wide"
 Composio é uma plataforma de integração que permite conectar seus agentes de IA a mais de 250 ferramentas. Os principais recursos incluem:
 
 - **Autenticação de Nível Empresarial**: Suporte integrado para OAuth, Chaves de API, JWT com atualização automática de token
-- **Observabilidade Completa**: Logs detalhados de uso das ferramentas, carimbos de data/hora de execução e muito mais
+- **Observabilidade Completa**: Logs detalhados de uso das ferramentas, registros de execução, e muito mais
 
 ## Instalação
 
 Para incorporar as ferramentas Composio em seu projeto, siga as instruções abaixo:
 
 ```shell
-pip install composio composio-crewai
+pip install composio-crewai
 pip install crewai
 ```
 
-Após concluir a instalação, defina sua chave de API do Composio como `COMPOSIO_API_KEY`. Obtenha sua chave de API do Composio [aqui](https://platform.composio.dev)
+Após a conclusão da instalação, execute `composio login` ou exporte sua chave de API do composio como `COMPOSIO_API_KEY`. Obtenha sua chave de API Composio [aqui](https://app.composio.dev)
 
 ## Exemplo
 
-O exemplo a seguir demonstra como inicializar a ferramenta e executar uma ação do GitHub:
+O exemplo a seguir demonstra como inicializar a ferramenta e executar uma ação do github:
 
-1. Inicialize o Composio com o Provider do CrewAI
+1. Inicialize o conjunto de ferramentas Composio
 
 ```python Code
-from composio_crewai import ComposioProvider
-from composio import Composio
+from composio_crewai import ComposioToolSet, App, Action
 from crewai import Agent, Task, Crew
 
-composio = Composio(provider=ComposioProvider())
+toolset = ComposioToolSet()
 ```
 
-2. Crie uma nova sessão Composio e recupere as ferramentas
+2. Conecte sua conta do GitHub
 <CodeGroup>
-```python
-session = composio.create(
-    user_id="your-user-id",
-    toolkits=["gmail", "github"] # optional, default is all toolkits
-)
-tools = session.tools()
+```shell CLI
+composio add github
+```
+```python Code
+request = toolset.initiate_connection(app=App.GITHUB)
+print(f"Open this URL to authenticate: {request.redirectUrl}")
 ```
-Leia mais sobre sessões e gerenciamento de usuários [aqui](https://docs.composio.dev/docs/configuring-sessions)
 </CodeGroup>
 
-3. Autenticação manual dos usuários
+3. Obtenha ferramentas
 
-O Composio autentica automaticamente os usuários durante a sessão de chat do agente. No entanto, você também pode autenticar o usuário manualmente chamando o método `authorize`.
+- Recuperando todas as ferramentas de um app (não recomendado em produção):
 ```python Code
-connection_request = session.authorize("github")
-print(f"Open this URL to authenticate: {connection_request.redirect_url}")
+tools = toolset.get_tools(apps=[App.GITHUB])
 ```
 
+- Filtrando ferramentas com base em tags:
+```python Code
+tag = "users"
+
+filtered_action_enums = toolset.find_actions_by_tags(
+    App.GITHUB,
+    tags=[tag], 
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+
+- Filtrando ferramentas com base no caso de uso:
+```python Code
+use_case = "Star a repository on GitHub"
+
+filtered_action_enums = toolset.find_actions_by_use_case(
+    App.GITHUB, use_case=use_case, advanced=False
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+<Tip>Defina `advanced` como True para obter ações para casos de uso complexos</Tip>
+
+- Usando ferramentas específicas:
+
+Neste exemplo, usaremos a ação `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` do app GitHub.
+```python Code
+tools = toolset.get_tools(
+    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
+)
+```
+Saiba mais sobre como filtrar ações [aqui](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)
+
 4. Defina o agente
 
 ```python Code
@@ -85,4 +116,4 @@ crew = Crew(agents=[crewai_agent], tasks=[task])
 crew.kickoff()
 ```
 
-* Uma lista mais detalhada de ferramentas pode ser encontrada [aqui](https://docs.composio.dev/toolkits)
+* Uma lista mais detalhada de ferramentas pode ser encontrada [aqui](https://app.composio.dev)

lib/cli/README.md

@@ -1,15 +0,0 @@
-# crewai-cli
-
-CLI for CrewAI - scaffold, run, deploy and manage AI agent crews without installing the full framework.
-
-## Installation
-
-```bash
-pip install crewai-cli
-```
-
-Or install alongside the full framework:
-
-```bash
-pip install crewai[cli]
-```

lib/cli/pyproject.toml

@@ -1,39 +0,0 @@
-[project]
-name = "crewai-cli"
-version = "1.10.0"
-description = "CLI for CrewAI - scaffold, run, deploy and manage AI agent crews without installing the full framework."
-readme = "README.md"
-authors = [
-    { name = "Joao Moura", email = "joao@crewai.com" }
-]
-requires-python = ">=3.10, <3.14"
-dependencies = [
-    "click~=8.1.7",
-    "pydantic~=2.11.9",
-    "pydantic-settings~=2.10.1",
-    "appdirs~=1.4.4",
-    "httpx~=0.28.1",
-    "pyjwt>=2.9.0,<3",
-    "rich>=13.7.1",
-    "tomli~=2.0.2",
-    "tomli-w~=1.1.0",
-    "packaging>=23.0",
-    "python-dotenv~=1.1.1",
-    "uv~=0.9.13",
-    "portalocker~=2.7.0",
-]
-
-[project.urls]
-Homepage = "https://crewai.com"
-Documentation = "https://docs.crewai.com"
-Repository = "https://github.com/crewAIInc/crewAI"
-
-[project.scripts]
-crewai = "crewai_cli.cli:crewai"
-
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.hatch.build.targets.wheel]
-packages = ["src/crewai_cli"]

lib/cli/src/crewai_cli/__init__.py

@@ -1 +0,0 @@
-__version__ = "1.10.0"

lib/cli/src/crewai_cli/authentication/__init__.py

@@ -1,4 +0,0 @@
-from crewai_cli.authentication.main import AuthenticationCommand
-
-
-__all__ = ["AuthenticationCommand"]

lib/cli/src/crewai_cli/authentication/constants.py

@@ -1 +0,0 @@
-ALGORITHMS = ["RS256"]

lib/cli/src/crewai_cli/authentication/main.py

@@ -1,215 +0,0 @@
-import time
-from typing import TYPE_CHECKING, Any, TypeVar, cast
-import webbrowser
-
-import httpx
-from pydantic import BaseModel, Field
-from rich.console import Console
-
-from crewai_cli.authentication.utils import validate_jwt_token
-from crewai_cli.config import Settings
-from crewai_cli.shared.token_manager import TokenManager
-
-
-console = Console()
-
-TOauth2Settings = TypeVar("TOauth2Settings", bound="Oauth2Settings")
-
-
-class Oauth2Settings(BaseModel):
-    provider: str = Field(
-        description="OAuth2 provider used for authentication (e.g., workos, okta, auth0)."
-    )
-    client_id: str = Field(
-        description="OAuth2 client ID issued by the provider, used during authentication requests."
-    )
-    domain: str = Field(
-        description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens."
-    )
-    audience: str | None = Field(
-        description="OAuth2 audience value, typically used to identify the target API or resource.",
-        default=None,
-    )
-    extra: dict[str, Any] = Field(
-        description="Extra configuration for the OAuth2 provider.",
-        default={},
-    )
-
-    @classmethod
-    def from_settings(cls: type[TOauth2Settings]) -> TOauth2Settings:
-        """Create an Oauth2Settings instance from the CLI settings."""
-
-        settings = Settings()
-
-        return cls(
-            provider=settings.oauth2_provider,
-            domain=settings.oauth2_domain,
-            client_id=settings.oauth2_client_id,
-            audience=settings.oauth2_audience,
-            extra=settings.oauth2_extra,
-        )
-
-
-if TYPE_CHECKING:
-    from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class ProviderFactory:
-    @classmethod
-    def from_settings(
-        cls: type["ProviderFactory"],  # noqa: UP037
-        settings: Oauth2Settings | None = None,
-    ) -> "BaseProvider":  # noqa: UP037
-        settings = settings or Oauth2Settings.from_settings()
-
-        import importlib
-
-        module = importlib.import_module(
-            f"crewai_cli.authentication.providers.{settings.provider.lower()}"
-        )
-        # Converts from snake_case to CamelCase to obtain the provider class name.
-        provider = getattr(
-            module,
-            f"{''.join(word.capitalize() for word in settings.provider.split('_'))}Provider",
-        )
-
-        return cast("BaseProvider", provider(settings))
-
-
-class AuthenticationCommand:
-    def __init__(self) -> None:
-        self.token_manager = TokenManager()
-        self.oauth2_provider = ProviderFactory.from_settings()
-
-    def login(self) -> None:
-        """Sign up to CrewAI+"""
-        console.print("Signing in to CrewAI AMP...\n", style="bold blue")
-
-        device_code_data = self._get_device_code()
-        self._display_auth_instructions(device_code_data)
-
-        return self._poll_for_token(device_code_data)
-
-    def _get_device_code(self) -> dict[str, Any]:
-        """Get the device code to authenticate the user."""
-
-        device_code_payload = {
-            "client_id": self.oauth2_provider.get_client_id(),
-            "scope": " ".join(self.oauth2_provider.get_oauth_scopes()),
-            "audience": self.oauth2_provider.get_audience(),
-        }
-        response = httpx.post(
-            url=self.oauth2_provider.get_authorize_url(),
-            data=device_code_payload,
-            timeout=20,
-        )
-        response.raise_for_status()
-        return cast(dict[str, Any], response.json())
-
-    def _display_auth_instructions(self, device_code_data: dict[str, str]) -> None:
-        """Display the authentication instructions to the user."""
-
-        verification_uri = device_code_data.get(
-            "verification_uri_complete", device_code_data.get("verification_uri", "")
-        )
-
-        console.print("1. Navigate to: ", verification_uri)
-        console.print("2. Enter the following code: ", device_code_data["user_code"])
-        webbrowser.open(verification_uri)
-
-    def _poll_for_token(self, device_code_data: dict[str, Any]) -> None:
-        """Polls the server for the token until it is received, or max attempts are reached."""
-
-        token_payload = {
-            "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
-            "device_code": device_code_data["device_code"],
-            "client_id": self.oauth2_provider.get_client_id(),
-        }
-
-        console.print("\nWaiting for authentication... ", style="bold blue", end="")
-
-        attempts = 0
-        while True and attempts < 10:
-            response = httpx.post(
-                self.oauth2_provider.get_token_url(), data=token_payload, timeout=30
-            )
-            token_data = response.json()
-
-            if response.status_code == 200:
-                self._validate_and_save_token(token_data)
-
-                console.print(
-                    "Success!",
-                    style="bold green",
-                )
-
-                self._login_to_tool_repository()
-
-                console.print("\n[bold green]Welcome to CrewAI AMP![/bold green]\n")
-                return
-
-            if token_data["error"] not in ("authorization_pending", "slow_down"):
-                raise httpx.HTTPError(
-                    token_data.get("error_description") or token_data.get("error")
-                )
-
-            time.sleep(device_code_data["interval"])
-            attempts += 1
-
-        console.print(
-            "Timeout: Failed to get the token. Please try again.", style="bold red"
-        )
-
-    def _validate_and_save_token(self, token_data: dict[str, Any]) -> None:
-        """Validates the JWT token and saves the token to the token manager."""
-
-        jwt_token = token_data["access_token"]
-        issuer = self.oauth2_provider.get_issuer()
-        jwt_token_data = {
-            "jwt_token": jwt_token,
-            "jwks_url": self.oauth2_provider.get_jwks_url(),
-            "issuer": issuer,
-            "audience": self.oauth2_provider.get_audience(),
-        }
-
-        decoded_token = validate_jwt_token(**jwt_token_data)
-
-        expires_at = decoded_token.get("exp", 0)
-        self.token_manager.save_tokens(jwt_token, expires_at)
-
-    def _login_to_tool_repository(self) -> None:
-        """Login to the tool repository."""
-
-        from crewai_cli.tools.main import ToolCommand
-
-        try:
-            console.print(
-                "Now logging you in to the Tool Repository... ",
-                style="bold blue",
-                end="",
-            )
-
-            ToolCommand().login()
-
-            console.print(
-                "Success!\n",
-                style="bold green",
-            )
-
-            settings = Settings()
-
-            console.print(
-                f"You are now authenticated to the tool repository for organization [bold cyan]'{settings.org_name if settings.org_name else settings.org_uuid}'[/bold cyan]",
-                style="green",
-            )
-        except Exception:
-            console.print(
-                "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
-                style="yellow",
-            )
-            console.print(
-                "Other features will work normally, but you may experience limitations "
-                "with downloading and publishing tools."
-                "\nRun [bold]crewai login[/bold] to try logging in again.\n",
-                style="yellow",
-            )

lib/cli/src/crewai_cli/authentication/providers/auth0.py

@@ -1,34 +0,0 @@
-from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class Auth0Provider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"https://{self._get_domain()}/oauth/device/code"
-
-    def get_token_url(self) -> str:
-        return f"https://{self._get_domain()}/oauth/token"
-
-    def get_jwks_url(self) -> str:
-        return f"https://{self._get_domain()}/.well-known/jwks.json"
-
-    def get_issuer(self) -> str:
-        return f"https://{self._get_domain()}/"
-
-    def get_audience(self) -> str:
-        if self.settings.audience is None:
-            raise ValueError(
-                "Audience is required. Please set it in the configuration."
-            )
-        return self.settings.audience
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def _get_domain(self) -> str:
-        if self.settings.domain is None:
-            raise ValueError("Domain is required. Please set it in the configuration.")
-        return self.settings.domain

lib/cli/src/crewai_cli/authentication/providers/base_provider.py

@@ -1,33 +0,0 @@
-from abc import ABC, abstractmethod
-
-from crewai_cli.authentication.main import Oauth2Settings
-
-
-class BaseProvider(ABC):
-    def __init__(self, settings: Oauth2Settings):
-        self.settings = settings
-
-    @abstractmethod
-    def get_authorize_url(self) -> str: ...
-
-    @abstractmethod
-    def get_token_url(self) -> str: ...
-
-    @abstractmethod
-    def get_jwks_url(self) -> str: ...
-
-    @abstractmethod
-    def get_issuer(self) -> str: ...
-
-    @abstractmethod
-    def get_audience(self) -> str: ...
-
-    @abstractmethod
-    def get_client_id(self) -> str: ...
-
-    def get_required_fields(self) -> list[str]:
-        """Returns which provider-specific fields inside the "extra" dict will be required"""
-        return []
-
-    def get_oauth_scopes(self) -> list[str]:
-        return ["openid", "profile", "email"]

lib/cli/src/crewai_cli/authentication/providers/entra_id.py

@@ -1,43 +0,0 @@
-from typing import cast
-
-from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class EntraIdProvider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"{self._base_url()}/oauth2/v2.0/devicecode"
-
-    def get_token_url(self) -> str:
-        return f"{self._base_url()}/oauth2/v2.0/token"
-
-    def get_jwks_url(self) -> str:
-        return f"{self._base_url()}/discovery/v2.0/keys"
-
-    def get_issuer(self) -> str:
-        return f"{self._base_url()}/v2.0"
-
-    def get_audience(self) -> str:
-        if self.settings.audience is None:
-            raise ValueError(
-                "Audience is required. Please set it in the configuration."
-            )
-        return self.settings.audience
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def get_oauth_scopes(self) -> list[str]:
-        return [
-            *super().get_oauth_scopes(),
-            *cast(str, self.settings.extra.get("scope", "")).split(),
-        ]
-
-    def get_required_fields(self) -> list[str]:
-        return ["scope"]
-
-    def _base_url(self) -> str:
-        return f"https://login.microsoftonline.com/{self.settings.domain}"

lib/cli/src/crewai_cli/authentication/providers/keycloak.py

@@ -1,32 +0,0 @@
-from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class KeycloakProvider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/auth/device"
-
-    def get_token_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/token"
-
-    def get_jwks_url(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}/protocol/openid-connect/certs"
-
-    def get_issuer(self) -> str:
-        return f"{self._oauth2_base_url()}/realms/{self.settings.extra.get('realm')}"
-
-    def get_audience(self) -> str:
-        return self.settings.audience or "no-audience-provided"
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def get_required_fields(self) -> list[str]:
-        return ["realm"]
-
-    def _oauth2_base_url(self) -> str:
-        domain = self.settings.domain.removeprefix("https://").removeprefix("http://")
-        return f"https://{domain}"

lib/cli/src/crewai_cli/authentication/providers/okta.py

@@ -1,42 +0,0 @@
-from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class OktaProvider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/device/authorize"
-
-    def get_token_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/token"
-
-    def get_jwks_url(self) -> str:
-        return f"{self._oauth2_base_url()}/v1/keys"
-
-    def get_issuer(self) -> str:
-        return self._oauth2_base_url().removesuffix("/oauth2")
-
-    def get_audience(self) -> str:
-        if self.settings.audience is None:
-            raise ValueError(
-                "Audience is required. Please set it in the configuration."
-            )
-        return self.settings.audience
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def get_required_fields(self) -> list[str]:
-        return ["authorization_server_name", "using_org_auth_server"]
-
-    def _oauth2_base_url(self) -> str:
-        using_org_auth_server = self.settings.extra.get("using_org_auth_server", False)
-
-        if using_org_auth_server:
-            base_url = f"https://{self.settings.domain}/oauth2"
-        else:
-            base_url = f"https://{self.settings.domain}/oauth2/{self.settings.extra.get('authorization_server_name', 'default')}"
-
-        return f"{base_url}"

lib/cli/src/crewai_cli/authentication/providers/workos.py

@@ -1,30 +0,0 @@
-from crewai_cli.authentication.providers.base_provider import BaseProvider
-
-
-class WorkosProvider(BaseProvider):
-    def get_authorize_url(self) -> str:
-        return f"https://{self._get_domain()}/oauth2/device_authorization"
-
-    def get_token_url(self) -> str:
-        return f"https://{self._get_domain()}/oauth2/token"
-
-    def get_jwks_url(self) -> str:
-        return f"https://{self._get_domain()}/oauth2/jwks"
-
-    def get_issuer(self) -> str:
-        return f"https://{self._get_domain()}"
-
-    def get_audience(self) -> str:
-        return self.settings.audience or ""
-
-    def get_client_id(self) -> str:
-        if self.settings.client_id is None:
-            raise ValueError(
-                "Client ID is required. Please set it in the configuration."
-            )
-        return self.settings.client_id
-
-    def _get_domain(self) -> str:
-        if self.settings.domain is None:
-            raise ValueError("Domain is required. Please set it in the configuration.")
-        return self.settings.domain

lib/cli/src/crewai_cli/authentication/token.py

@@ -1,13 +0,0 @@
-from crewai_cli.shared.token_manager import TokenManager
-
-
-class AuthError(Exception):
-    pass
-
-
-def get_auth_token() -> str:
-    """Get the authentication token."""
-    access_token = TokenManager().get_token()
-    if not access_token:
-        raise AuthError("No token found, make sure you are logged in")
-    return access_token

lib/cli/src/crewai_cli/authentication/utils.py

@@ -1,63 +0,0 @@
-from typing import Any
-
-import jwt
-from jwt import PyJWKClient
-
-
-def validate_jwt_token(
-    jwt_token: str, jwks_url: str, issuer: str, audience: str
-) -> Any:
-    """
-    Verify the token's signature and claims using PyJWT.
-    :param jwt_token: The JWT (JWS) string to validate.
-    :param jwks_url: The URL of the JWKS endpoint.
-    :param issuer: The expected issuer of the token.
-    :param audience: The expected audience of the token.
-    :return: The decoded token.
-    :raises Exception: If the token is invalid for any reason (e.g., signature mismatch,
-                       expired, incorrect issuer/audience, JWKS fetching error,
-                       missing required claims).
-    """
-
-    try:
-        jwk_client = PyJWKClient(jwks_url)
-        signing_key = jwk_client.get_signing_key_from_jwt(jwt_token)
-
-        _unverified_decoded_token = jwt.decode(
-            jwt_token, options={"verify_signature": False}
-        )
-
-        return jwt.decode(
-            jwt_token,
-            signing_key.key,
-            algorithms=["RS256"],
-            audience=audience,
-            issuer=issuer,
-            leeway=10.0,
-            options={
-                "verify_signature": True,
-                "verify_exp": True,
-                "verify_nbf": True,
-                "verify_iat": True,
-                "require": ["exp", "iat", "iss", "aud", "sub"],
-            },
-        )
-
-    except jwt.ExpiredSignatureError as e:
-        raise Exception("Token has expired.") from e
-    except jwt.InvalidAudienceError as e:
-        actual_audience = _unverified_decoded_token.get("aud", "[no audience found]")
-        raise Exception(
-            f"Invalid token audience. Got: '{actual_audience}'. Expected: '{audience}'"
-        ) from e
-    except jwt.InvalidIssuerError as e:
-        actual_issuer = _unverified_decoded_token.get("iss", "[no issuer found]")
-        raise Exception(
-            f"Invalid token issuer. Got: '{actual_issuer}'. Expected: '{issuer}'"
-        ) from e
-    except jwt.MissingRequiredClaimError as e:
-        raise Exception(f"Token is missing required claims: {e!s}") from e
-    except jwt.exceptions.PyJWKClientError as e:
-        raise Exception(f"JWKS or key processing error: {e!s}") from e
-    except jwt.InvalidTokenError as e:
-        raise Exception(f"Invalid token: {e!s}") from e

lib/cli/src/crewai_cli/command.py

@@ -1,68 +0,0 @@
-from __future__ import annotations
-
-import json
-
-import httpx
-from rich.console import Console
-
-from crewai_cli.authentication.token import get_auth_token
-from crewai_cli.plus_api import PlusAPI
-
-
-console = Console()
-
-
-class BaseCommand:
-    def __init__(self) -> None:
-        pass
-
-
-class PlusAPIMixin:
-    def __init__(self) -> None:
-        try:
-            self.plus_api_client = PlusAPI(api_key=get_auth_token())
-        except Exception:
-            console.print(
-                "Please sign up/login to CrewAI+ before using the CLI.",
-                style="bold red",
-            )
-            console.print("Run 'crewai login' to sign up/login.", style="bold green")
-            raise SystemExit from None
-
-    def _validate_response(self, response: httpx.Response) -> None:
-        try:
-            json_response = response.json()
-        except (json.JSONDecodeError, ValueError):
-            console.print(
-                "Failed to parse response from Enterprise API failed. Details:",
-                style="bold red",
-            )
-            console.print(f"Status Code: {response.status_code}")
-            console.print(
-                f"Response:\n{response.content.decode('utf-8', errors='replace')}"
-            )
-            raise SystemExit from None
-
-        if response.status_code == 422:
-            console.print(
-                "Failed to complete operation. Please fix the following errors:",
-                style="bold red",
-            )
-            for field, messages in json_response.items():
-                for message in messages:
-                    console.print(
-                        f"* [bold red]{field.capitalize()}[/bold red] {message}"
-                    )
-            raise SystemExit
-
-        if not response.is_success:
-            console.print(
-                "Request to Enterprise API failed. Details:", style="bold red"
-            )
-            details = (
-                json_response.get("error")
-                or json_response.get("message")
-                or response.content.decode("utf-8", errors="replace")
-            )
-            console.print(f"{details}")
-            raise SystemExit

lib/cli/src/crewai_cli/config.py

@@ -1,221 +0,0 @@
-import json
-from logging import getLogger
-from pathlib import Path
-import tempfile
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-from crewai_cli.constants import (
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER,
-    DEFAULT_CREWAI_ENTERPRISE_URL,
-)
-from crewai_cli.shared.token_manager import TokenManager
-
-
-logger = getLogger(__name__)
-
-DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
-
-
-def get_writable_config_path() -> Path | None:
-    """
-    Find a writable location for the config file with fallback options.
-
-    Tries in order:
-    1. Default: ~/.config/crewai/settings.json
-    2. Temp directory: /tmp/crewai_settings.json (or OS equivalent)
-    3. Current directory: ./crewai_settings.json
-    4. In-memory only (returns None)
-
-    Returns:
-        Path object for writable config location, or None if no writable location found
-    """
-    fallback_paths = [
-        DEFAULT_CONFIG_PATH,  # Default location
-        Path(tempfile.gettempdir()) / "crewai_settings.json",  # Temporary directory
-        Path.cwd() / "crewai_settings.json",  # Current working directory
-    ]
-
-    for config_path in fallback_paths:
-        try:
-            config_path.parent.mkdir(parents=True, exist_ok=True)
-            test_file = config_path.parent / ".crewai_write_test"
-            try:
-                test_file.write_text("test")
-                test_file.unlink()  # Clean up test file
-                logger.info(f"Using config path: {config_path}")
-                return config_path
-            except Exception:  # noqa: S112
-                continue
-
-        except Exception:  # noqa: S112
-            continue
-
-    return None
-
-
-# Settings that are related to the user's account
-USER_SETTINGS_KEYS = [
-    "tool_repository_username",
-    "tool_repository_password",
-    "org_name",
-    "org_uuid",
-]
-
-# Settings that are related to the CLI
-CLI_SETTINGS_KEYS = [
-    "enterprise_base_url",
-    "oauth2_provider",
-    "oauth2_audience",
-    "oauth2_client_id",
-    "oauth2_domain",
-    "oauth2_extra",
-]
-
-# Default values for CLI settings
-DEFAULT_CLI_SETTINGS = {
-    "enterprise_base_url": DEFAULT_CREWAI_ENTERPRISE_URL,
-    "oauth2_provider": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER,
-    "oauth2_audience": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
-    "oauth2_client_id": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
-    "oauth2_domain": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-    "oauth2_extra": {},
-}
-
-# Readonly settings - cannot be set by the user
-READONLY_SETTINGS_KEYS = [
-    "org_name",
-    "org_uuid",
-]
-
-# Hidden settings - not displayed by the 'list' command and cannot be set by the user
-HIDDEN_SETTINGS_KEYS = [
-    "config_path",
-    "tool_repository_username",
-    "tool_repository_password",
-]
-
-
-class Settings(BaseModel):
-    enterprise_base_url: str | None = Field(
-        default=DEFAULT_CLI_SETTINGS["enterprise_base_url"],
-        description="Base URL of the CrewAI AMP instance",
-    )
-    tool_repository_username: str | None = Field(
-        None, description="Username for interacting with the Tool Repository"
-    )
-    tool_repository_password: str | None = Field(
-        None, description="Password for interacting with the Tool Repository"
-    )
-    org_name: str | None = Field(
-        None, description="Name of the currently active organization"
-    )
-    org_uuid: str | None = Field(
-        None, description="UUID of the currently active organization"
-    )
-    config_path: Path = Field(default=DEFAULT_CONFIG_PATH, frozen=True, exclude=True)
-
-    oauth2_provider: str = Field(
-        description="OAuth2 provider used for authentication (e.g., workos, okta, auth0).",
-        default=DEFAULT_CLI_SETTINGS["oauth2_provider"],
-    )
-
-    oauth2_audience: str | None = Field(
-        description="OAuth2 audience value, typically used to identify the target API or resource.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_audience"],
-    )
-
-    oauth2_client_id: str = Field(
-        default=DEFAULT_CLI_SETTINGS["oauth2_client_id"],
-        description="OAuth2 client ID issued by the provider, used during authentication requests.",
-    )
-
-    oauth2_domain: str = Field(
-        description="OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.",
-        default=DEFAULT_CLI_SETTINGS["oauth2_domain"],
-    )
-
-    oauth2_extra: dict[str, Any] = Field(
-        description="Extra configuration for the OAuth2 provider.",
-        default={},
-    )
-
-    def __init__(self, config_path: Path | None = None, **data: dict[str, Any]) -> None:
-        """Load Settings from config path with fallback support"""
-        if config_path is None:
-            config_path = get_writable_config_path()
-
-        # If config_path is None, we're in memory-only mode
-        if config_path is None:
-            merged_data = {**data}
-            # Dummy path for memory-only mode
-            super().__init__(config_path=Path("/dev/null"), **merged_data)
-            return
-
-        try:
-            config_path.parent.mkdir(parents=True, exist_ok=True)
-        except Exception:
-            merged_data = {**data}
-            # Dummy path for memory-only mode
-            super().__init__(config_path=Path("/dev/null"), **merged_data)
-            return
-
-        file_data = {}
-        if config_path.is_file():
-            try:
-                with config_path.open("r") as f:
-                    file_data = json.load(f)
-            except Exception:
-                file_data = {}
-
-        merged_data = {**file_data, **data}
-        super().__init__(config_path=config_path, **merged_data)
-
-    def clear_user_settings(self) -> None:
-        """Clear all user settings"""
-        self._reset_user_settings()
-        self.dump()
-
-    def reset(self) -> None:
-        """Reset all settings to default values"""
-        self._reset_user_settings()
-        self._reset_cli_settings()
-        self._clear_auth_tokens()
-        self.dump()
-
-    def dump(self) -> None:
-        """Save current settings to settings.json"""
-        if str(self.config_path) == "/dev/null":
-            return
-
-        try:
-            if self.config_path.is_file():
-                with self.config_path.open("r") as f:
-                    existing_data = json.load(f)
-            else:
-                existing_data = {}
-
-            updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
-            with self.config_path.open("w") as f:
-                json.dump(updated_data, f, indent=4)
-
-        except Exception:  # noqa: S110
-            pass
-
-    def _reset_user_settings(self) -> None:
-        """Reset all user settings to default values"""
-        for key in USER_SETTINGS_KEYS:
-            setattr(self, key, None)
-
-    def _reset_cli_settings(self) -> None:
-        """Reset all CLI settings to default values"""
-        for key in CLI_SETTINGS_KEYS:
-            setattr(self, key, DEFAULT_CLI_SETTINGS.get(key))
-
-    def _clear_auth_tokens(self) -> None:
-        """Clear all authentication tokens"""
-        TokenManager().clear_tokens()

lib/cli/src/crewai_cli/constants.py

@@ -1,333 +0,0 @@
-from typing import Any
-
-
-DEFAULT_CREWAI_ENTERPRISE_URL = "https://app.crewai.com"
-CREWAI_ENTERPRISE_DEFAULT_OAUTH2_PROVIDER = "workos"
-CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE = "client_01JNJQWBJ4SPFN3SWJM5T7BDG8"
-CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID = "client_01JYT06R59SP0NXYGD994NFXXX"
-CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN = "login.crewai.com"
-
-ENV_VARS: dict[str, list[dict[str, Any]]] = {
-    "openai": [
-        {
-            "prompt": "Enter your OPENAI API key (press Enter to skip)",
-            "key_name": "OPENAI_API_KEY",
-        }
-    ],
-    "anthropic": [
-        {
-            "prompt": "Enter your ANTHROPIC API key (press Enter to skip)",
-            "key_name": "ANTHROPIC_API_KEY",
-        }
-    ],
-    "gemini": [
-        {
-            "prompt": "Enter your GEMINI API key from https://ai.dev/apikey (press Enter to skip)",
-            "key_name": "GEMINI_API_KEY",
-        }
-    ],
-    "nvidia_nim": [
-        {
-            "prompt": "Enter your NVIDIA API key (press Enter to skip)",
-            "key_name": "NVIDIA_NIM_API_KEY",
-        }
-    ],
-    "groq": [
-        {
-            "prompt": "Enter your GROQ API key (press Enter to skip)",
-            "key_name": "GROQ_API_KEY",
-        }
-    ],
-    "watson": [
-        {
-            "prompt": "Enter your WATSONX URL (press Enter to skip)",
-            "key_name": "WATSONX_URL",
-        },
-        {
-            "prompt": "Enter your WATSONX API Key (press Enter to skip)",
-            "key_name": "WATSONX_APIKEY",
-        },
-        {
-            "prompt": "Enter your WATSONX Project Id (press Enter to skip)",
-            "key_name": "WATSONX_PROJECT_ID",
-        },
-    ],
-    "ollama": [
-        {
-            "default": True,
-            "API_BASE": "http://localhost:11434",
-        }
-    ],
-    "bedrock": [
-        {
-            "prompt": "Enter your AWS Access Key ID (press Enter to skip)",
-            "key_name": "AWS_ACCESS_KEY_ID",
-        },
-        {
-            "prompt": "Enter your AWS Secret Access Key (press Enter to skip)",
-            "key_name": "AWS_SECRET_ACCESS_KEY",
-        },
-        {
-            "prompt": "Enter your AWS Region Name (press Enter to skip)",
-            "key_name": "AWS_DEFAULT_REGION",
-        },
-    ],
-    "azure": [
-        {
-            "prompt": "Enter your Azure deployment name (must start with 'azure/')",
-            "key_name": "model",
-        },
-        {
-            "prompt": "Enter your AZURE API key (press Enter to skip)",
-            "key_name": "AZURE_API_KEY",
-        },
-        {
-            "prompt": "Enter your AZURE API base URL (press Enter to skip)",
-            "key_name": "AZURE_API_BASE",
-        },
-        {
-            "prompt": "Enter your AZURE API version (press Enter to skip)",
-            "key_name": "AZURE_API_VERSION",
-        },
-    ],
-    "cerebras": [
-        {
-            "prompt": "Enter your Cerebras model name (must start with 'cerebras/')",
-            "key_name": "model",
-        },
-        {
-            "prompt": "Enter your Cerebras API version (press Enter to skip)",
-            "key_name": "CEREBRAS_API_KEY",
-        },
-    ],
-    "huggingface": [
-        {
-            "prompt": "Enter your Huggingface API key (HF_TOKEN) (press Enter to skip)",
-            "key_name": "HF_TOKEN",
-        },
-    ],
-    "sambanova": [
-        {
-            "prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
-            "key_name": "SAMBANOVA_API_KEY",
-        }
-    ],
-}
-
-
-PROVIDERS: list[str] = [
-    "openai",
-    "anthropic",
-    "gemini",
-    "nvidia_nim",
-    "groq",
-    "huggingface",
-    "ollama",
-    "watson",
-    "bedrock",
-    "azure",
-    "cerebras",
-    "sambanova",
-]
-
-MODELS: dict[str, list[str]] = {
-    "openai": [
-        "gpt-4",
-        "gpt-4.1",
-        "gpt-4.1-mini-2025-04-14",
-        "gpt-4.1-nano-2025-04-14",
-        "gpt-4o",
-        "gpt-4o-mini",
-        "o1-mini",
-        "o1-preview",
-    ],
-    "anthropic": [
-        "claude-3-5-sonnet-20240620",
-        "claude-3-sonnet-20240229",
-        "claude-3-opus-20240229",
-        "claude-3-haiku-20240307",
-    ],
-    "gemini": [
-        "gemini/gemini-3-pro-preview",
-        "gemini/gemini-1.5-flash",
-        "gemini/gemini-1.5-pro",
-        "gemini/gemini-2.0-flash-lite-001",
-        "gemini/gemini-2.0-flash-001",
-        "gemini/gemini-2.0-flash-thinking-exp-01-21",
-        "gemini/gemini-2.5-flash-preview-04-17",
-        "gemini/gemini-2.5-pro-exp-03-25",
-        "gemini/gemini-gemma-2-9b-it",
-        "gemini/gemini-gemma-2-27b-it",
-        "gemini/gemma-3-1b-it",
-        "gemini/gemma-3-4b-it",
-        "gemini/gemma-3-12b-it",
-        "gemini/gemma-3-27b-it",
-    ],
-    "nvidia_nim": [
-        "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",
-        "nvidia_nim/nvidia/nemotron-4-mini-hindi-4b-instruct",
-        "nvidia_nim/nvidia/llama-3.1-nemotron-70b-instruct",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-8b",
-        "nvidia_nim/nvidia/llama3-chatqa-1.5-70b",
-        "nvidia_nim/nvidia/vila",
-        "nvidia_nim/nvidia/neva-22",
-        "nvidia_nim/nvidia/nemotron-mini-4b-instruct",
-        "nvidia_nim/nvidia/usdcode-llama3-70b-instruct",
-        "nvidia_nim/nvidia/nemotron-4-340b-instruct",
-        "nvidia_nim/meta/codellama-70b",
-        "nvidia_nim/meta/llama2-70b",
-        "nvidia_nim/meta/llama3-8b-instruct",
-        "nvidia_nim/meta/llama3-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-8b-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/meta/llama-3.1-405b-instruct",
-        "nvidia_nim/meta/llama-3.2-1b-instruct",
-        "nvidia_nim/meta/llama-3.2-3b-instruct",
-        "nvidia_nim/meta/llama-3.2-11b-vision-instruct",
-        "nvidia_nim/meta/llama-3.2-90b-vision-instruct",
-        "nvidia_nim/meta/llama-3.1-70b-instruct",
-        "nvidia_nim/google/gemma-7b",
-        "nvidia_nim/google/gemma-2b",
-        "nvidia_nim/google/codegemma-7b",
-        "nvidia_nim/google/codegemma-1.1-7b",
-        "nvidia_nim/google/recurrentgemma-2b",
-        "nvidia_nim/google/gemma-2-9b-it",
-        "nvidia_nim/google/gemma-2-27b-it",
-        "nvidia_nim/google/gemma-2-2b-it",
-        "nvidia_nim/google/deplot",
-        "nvidia_nim/google/paligemma",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.2",
-        "nvidia_nim/mistralai/mixtral-8x7b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-large",
-        "nvidia_nim/mistralai/mixtral-8x22b-instruct-v0.1",
-        "nvidia_nim/mistralai/mistral-7b-instruct-v0.3",
-        "nvidia_nim/nv-mistralai/mistral-nemo-12b-instruct",
-        "nvidia_nim/mistralai/mamba-codestral-7b-v0.1",
-        "nvidia_nim/microsoft/phi-3-mini-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-mini-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-8k-instruct",
-        "nvidia_nim/microsoft/phi-3-small-128k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-4k-instruct",
-        "nvidia_nim/microsoft/phi-3-medium-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-mini-instruct",
-        "nvidia_nim/microsoft/phi-3.5-moe-instruct",
-        "nvidia_nim/microsoft/kosmos-2",
-        "nvidia_nim/microsoft/phi-3-vision-128k-instruct",
-        "nvidia_nim/microsoft/phi-3.5-vision-instruct",
-        "nvidia_nim/databricks/dbrx-instruct",
-        "nvidia_nim/snowflake/arctic",
-        "nvidia_nim/aisingapore/sea-lion-7b-instruct",
-        "nvidia_nim/ibm/granite-8b-code-instruct",
-        "nvidia_nim/ibm/granite-34b-code-instruct",
-        "nvidia_nim/ibm/granite-3.0-8b-instruct",
-        "nvidia_nim/ibm/granite-3.0-3b-a800m-instruct",
-        "nvidia_nim/mediatek/breeze-7b-instruct",
-        "nvidia_nim/upstage/solar-10.7b-instruct",
-        "nvidia_nim/writer/palmyra-med-70b-32k",
-        "nvidia_nim/writer/palmyra-med-70b",
-        "nvidia_nim/writer/palmyra-fin-70b-32k",
-        "nvidia_nim/01-ai/yi-large",
-        "nvidia_nim/deepseek-ai/deepseek-coder-6.7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-instruct",
-        "nvidia_nim/rakuten/rakutenai-7b-chat",
-        "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
-    ],
-    "groq": [
-        "groq/llama-3.1-8b-instant",
-        "groq/llama-3.1-70b-versatile",
-        "groq/llama-3.1-405b-reasoning",
-        "groq/gemma2-9b-it",
-        "groq/gemma-7b-it",
-    ],
-    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
-    "watson": [
-        "watsonx/meta-llama/llama-3-1-70b-instruct",
-        "watsonx/meta-llama/llama-3-1-8b-instruct",
-        "watsonx/meta-llama/llama-3-2-11b-vision-instruct",
-        "watsonx/meta-llama/llama-3-2-1b-instruct",
-        "watsonx/meta-llama/llama-3-2-90b-vision-instruct",
-        "watsonx/meta-llama/llama-3-405b-instruct",
-        "watsonx/mistral/mistral-large",
-        "watsonx/ibm/granite-3-8b-instruct",
-    ],
-    "bedrock": [
-        "bedrock/us.amazon.nova-pro-v1:0",
-        "bedrock/us.amazon.nova-micro-v1:0",
-        "bedrock/us.amazon.nova-lite-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0",
-        "bedrock/us.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-opus-20240229-v1:0",
-        "bedrock/us.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/us.meta.llama3-2-11b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-90b-instruct-v1:0",
-        "bedrock/us.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-8b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-3-70b-instruct-v1:0",
-        "bedrock/us.meta.llama3-1-405b-instruct-v1:0",
-        "bedrock/eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/eu.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/eu.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/eu.meta.llama3-2-3b-instruct-v1:0",
-        "bedrock/eu.meta.llama3-2-1b-instruct-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/apac.anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/apac.anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/apac.anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/amazon.nova-pro-v1:0",
-        "bedrock/amazon.nova-micro-v1:0",
-        "bedrock/amazon.nova-lite-v1:0",
-        "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
-        "bedrock/anthropic.claude-3-5-haiku-20241022-v1:0",
-        "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
-        "bedrock/anthropic.claude-3-7-sonnet-20250219-v1:0",
-        "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
-        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
-        "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
-        "bedrock/anthropic.claude-v2:1",
-        "bedrock/anthropic.claude-v2",
-        "bedrock/anthropic.claude-instant-v1",
-        "bedrock/meta.llama3-1-405b-instruct-v1:0",
-        "bedrock/meta.llama3-1-70b-instruct-v1:0",
-        "bedrock/meta.llama3-1-8b-instruct-v1:0",
-        "bedrock/meta.llama3-70b-instruct-v1:0",
-        "bedrock/meta.llama3-8b-instruct-v1:0",
-        "bedrock/amazon.titan-text-lite-v1",
-        "bedrock/amazon.titan-text-express-v1",
-        "bedrock/cohere.command-text-v14",
-        "bedrock/ai21.j2-mid-v1",
-        "bedrock/ai21.j2-ultra-v1",
-        "bedrock/ai21.jamba-instruct-v1:0",
-        "bedrock/mistral.mistral-7b-instruct-v0:2",
-        "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
-    ],
-    "huggingface": [
-        "huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
-        "huggingface/mistralai/Mixtral-8x7B-Instruct-v0.1",
-        "huggingface/tiiuae/falcon-180B-chat",
-        "huggingface/google/gemma-7b-it",
-    ],
-    "sambanova": [
-        "sambanova/Meta-Llama-3.3-70B-Instruct",
-        "sambanova/QwQ-32B-Preview",
-        "sambanova/Qwen2.5-72B-Instruct",
-        "sambanova/Qwen2.5-Coder-32B-Instruct",
-        "sambanova/Meta-Llama-3.1-405B-Instruct",
-        "sambanova/Meta-Llama-3.1-70B-Instruct",
-        "sambanova/Meta-Llama-3.1-8B-Instruct",
-        "sambanova/Llama-3.2-90B-Vision-Instruct",
-        "sambanova/Llama-3.2-11B-Vision-Instruct",
-        "sambanova/Meta-Llama-3.2-3B-Instruct",
-        "sambanova/Meta-Llama-3.2-1B-Instruct",
-    ],
-}
-
-DEFAULT_LLM_MODEL = "gpt-4.1-mini"
-
-JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-
-LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

lib/cli/src/crewai_cli/crew_chat.py

@@ -1,23 +0,0 @@
-"""Wrapper for the crew chat command.
-
-Delegates to ``crewai.cli.crew_chat.run_chat`` when the full crewai package is
-installed, otherwise prints a helpful error message.
-"""
-
-from __future__ import annotations
-
-import click
-
-
-def run_chat() -> None:
-    try:
-        from crewai.cli.crew_chat import run_chat as _run_chat
-    except ImportError:
-        click.secho(
-            "The 'chat' command requires the full crewai package.\n"
-            "Install it with: pip install crewai",
-            fg="red",
-        )
-        raise SystemExit(1) from None
-
-    _run_chat()

lib/cli/src/crewai_cli/git.py

@@ -1,89 +0,0 @@
-from functools import lru_cache
-import subprocess
-
-
-class Repository:
-    def __init__(self, path: str = ".") -> None:
-        self.path = path
-
-        if not self.is_git_installed():
-            raise ValueError("Git is not installed or not found in your PATH.")
-
-        if not self.is_git_repo():
-            raise ValueError(f"{self.path} is not a Git repository.")
-
-        self.fetch()
-
-    @staticmethod
-    def is_git_installed() -> bool:
-        """Check if Git is installed and available in the system."""
-        try:
-            subprocess.run(
-                ["git", "--version"],  # noqa: S607
-                capture_output=True,
-                check=True,
-                text=True,
-            )
-            return True
-        except (subprocess.CalledProcessError, FileNotFoundError):
-            return False
-
-    def fetch(self) -> None:
-        """Fetch latest updates from the remote."""
-        subprocess.run(["git", "fetch"], cwd=self.path, check=True)  # noqa: S607
-
-    def status(self) -> str:
-        """Get the git status in porcelain format."""
-        return subprocess.check_output(
-            ["git", "status", "--branch", "--porcelain"],  # noqa: S607
-            cwd=self.path,
-            encoding="utf-8",
-        ).strip()
-
-    @lru_cache(maxsize=None)  # noqa: B019
-    def is_git_repo(self) -> bool:
-        """Check if the current directory is a git repository.
-
-        Notes:
-          - TODO: This method is cached to avoid redundant checks, but using lru_cache on methods can lead to memory leaks
-        """
-        try:
-            subprocess.check_output(
-                ["git", "rev-parse", "--is-inside-work-tree"],  # noqa: S607
-                cwd=self.path,
-                encoding="utf-8",
-            )
-            return True
-        except subprocess.CalledProcessError:
-            return False
-
-    def has_uncommitted_changes(self) -> bool:
-        """Check if the repository has uncommitted changes."""
-        return len(self.status().splitlines()) > 1
-
-    def is_ahead_or_behind(self) -> bool:
-        """Check if the repository is ahead or behind the remote."""
-        for line in self.status().splitlines():
-            if line.startswith("##") and ("ahead" in line or "behind" in line):
-                return True
-        return False
-
-    def is_synced(self) -> bool:
-        """Return True if the Git repository is fully synced with the remote, False otherwise."""
-        if self.has_uncommitted_changes() or self.is_ahead_or_behind():
-            return False
-        return True
-
-    def origin_url(self) -> str | None:
-        """Get the Git repository's remote URL."""
-        try:
-            result = subprocess.run(
-                ["git", "remote", "get-url", "origin"],  # noqa: S607
-                cwd=self.path,
-                capture_output=True,
-                text=True,
-                check=True,
-            )
-            return result.stdout.strip()
-        except subprocess.CalledProcessError:
-            return None

lib/cli/src/crewai_cli/plus_api.py

@@ -1,210 +0,0 @@
-import os
-from typing import Any
-from urllib.parse import urljoin
-
-import httpx
-
-from crewai_cli.config import Settings
-from crewai_cli.constants import DEFAULT_CREWAI_ENTERPRISE_URL
-from crewai_cli.version import get_crewai_version
-
-
-class PlusAPI:
-    """
-    This class exposes methods for working with the CrewAI+ API.
-    """
-
-    TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
-    ORGANIZATIONS_RESOURCE = "/crewai_plus/api/v1/me/organizations"
-    CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
-    AGENTS_RESOURCE = "/crewai_plus/api/v1/agents"
-    TRACING_RESOURCE = "/crewai_plus/api/v1/tracing"
-    EPHEMERAL_TRACING_RESOURCE = "/crewai_plus/api/v1/tracing/ephemeral"
-    INTEGRATIONS_RESOURCE = "/crewai_plus/api/v1/integrations"
-
-    def __init__(self, api_key: str) -> None:
-        self.api_key = api_key
-        self.headers = {
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json",
-            "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
-            "X-Crewai-Version": get_crewai_version(),
-        }
-        settings = Settings()
-        if settings.org_uuid:
-            self.headers["X-Crewai-Organization-Id"] = settings.org_uuid
-
-        self.base_url = (
-            os.getenv("CREWAI_PLUS_URL")
-            or str(settings.enterprise_base_url)
-            or DEFAULT_CREWAI_ENTERPRISE_URL
-        )
-
-    def _make_request(
-        self, method: str, endpoint: str, **kwargs: Any
-    ) -> httpx.Response:
-        url = urljoin(self.base_url, endpoint)
-        verify = kwargs.pop("verify", True)
-        with httpx.Client(trust_env=False, verify=verify) as client:
-            return client.request(method, url, headers=self.headers, **kwargs)
-
-    def login_to_tool_repository(self) -> httpx.Response:
-        return self._make_request("POST", f"{self.TOOLS_RESOURCE}/login")
-
-    def get_tool(self, handle: str) -> httpx.Response:
-        return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
-
-    async def get_agent(self, handle: str) -> httpx.Response:
-        url = urljoin(self.base_url, f"{self.AGENTS_RESOURCE}/{handle}")
-        async with httpx.AsyncClient() as client:
-            return await client.get(url, headers=self.headers)
-
-    def publish_tool(
-        self,
-        handle: str,
-        is_public: bool,
-        version: str,
-        description: str | None,
-        encoded_file: str,
-        available_exports: list[dict[str, Any]] | None = None,
-    ) -> httpx.Response:
-        params = {
-            "handle": handle,
-            "public": is_public,
-            "version": version,
-            "file": encoded_file,
-            "description": description,
-            "available_exports": available_exports,
-        }
-        return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params)
-
-    def deploy_by_name(self, project_name: str) -> httpx.Response:
-        return self._make_request(
-            "POST", f"{self.CREWS_RESOURCE}/by-name/{project_name}/deploy"
-        )
-
-    def deploy_by_uuid(self, uuid: str) -> httpx.Response:
-        return self._make_request("POST", f"{self.CREWS_RESOURCE}/{uuid}/deploy")
-
-    def crew_status_by_name(self, project_name: str) -> httpx.Response:
-        return self._make_request(
-            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/status"
-        )
-
-    def crew_status_by_uuid(self, uuid: str) -> httpx.Response:
-        return self._make_request("GET", f"{self.CREWS_RESOURCE}/{uuid}/status")
-
-    def crew_by_name(
-        self, project_name: str, log_type: str = "deployment"
-    ) -> httpx.Response:
-        return self._make_request(
-            "GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/logs/{log_type}"
-        )
-
-    def crew_by_uuid(self, uuid: str, log_type: str = "deployment") -> httpx.Response:
-        return self._make_request(
-            "GET", f"{self.CREWS_RESOURCE}/{uuid}/logs/{log_type}"
-        )
-
-    def delete_crew_by_name(self, project_name: str) -> httpx.Response:
-        return self._make_request(
-            "DELETE", f"{self.CREWS_RESOURCE}/by-name/{project_name}"
-        )
-
-    def delete_crew_by_uuid(self, uuid: str) -> httpx.Response:
-        return self._make_request("DELETE", f"{self.CREWS_RESOURCE}/{uuid}")
-
-    def list_crews(self) -> httpx.Response:
-        return self._make_request("GET", self.CREWS_RESOURCE)
-
-    def create_crew(self, payload: dict[str, Any]) -> httpx.Response:
-        return self._make_request("POST", self.CREWS_RESOURCE, json=payload)
-
-    def get_organizations(self) -> httpx.Response:
-        return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
-
-    def initialize_trace_batch(self, payload: dict[str, Any]) -> httpx.Response:
-        return self._make_request(
-            "POST",
-            f"{self.TRACING_RESOURCE}/batches",
-            json=payload,
-            timeout=30,
-        )
-
-    def initialize_ephemeral_trace_batch(
-        self, payload: dict[str, Any]
-    ) -> httpx.Response:
-        return self._make_request(
-            "POST",
-            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches",
-            json=payload,
-        )
-
-    def send_trace_events(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> httpx.Response:
-        return self._make_request(
-            "POST",
-            f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
-            json=payload,
-            timeout=30,
-        )
-
-    def send_ephemeral_trace_events(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> httpx.Response:
-        return self._make_request(
-            "POST",
-            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
-            json=payload,
-            timeout=30,
-        )
-
-    def finalize_trace_batch(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> httpx.Response:
-        return self._make_request(
-            "PATCH",
-            f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
-            json=payload,
-            timeout=30,
-        )
-
-    def finalize_ephemeral_trace_batch(
-        self, trace_batch_id: str, payload: dict[str, Any]
-    ) -> httpx.Response:
-        return self._make_request(
-            "PATCH",
-            f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/finalize",
-            json=payload,
-            timeout=30,
-        )
-
-    def mark_trace_batch_as_failed(
-        self, trace_batch_id: str, error_message: str
-    ) -> httpx.Response:
-        return self._make_request(
-            "PATCH",
-            f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}",
-            json={"status": "failed", "failure_reason": error_message},
-            timeout=30,
-        )
-
-    def get_mcp_configs(self, slugs: list[str]) -> httpx.Response:
-        """Get MCP server configurations for the given slugs."""
-        return self._make_request(
-            "GET",
-            f"{self.INTEGRATIONS_RESOURCE}/mcp_configs",
-            params={"slugs": ",".join(slugs)},
-            timeout=30,
-        )
-
-    def get_triggers(self) -> httpx.Response:
-        """Get all available triggers from integrations."""
-        return self._make_request("GET", f"{self.INTEGRATIONS_RESOURCE}/apps")
-
-    def get_trigger_payload(self, app_slug: str, trigger_slug: str) -> httpx.Response:
-        """Get sample payload for a specific trigger."""
-        return self._make_request(
-            "GET", f"{self.INTEGRATIONS_RESOURCE}/{app_slug}/{trigger_slug}/payload"
-        )

lib/cli/src/crewai_cli/provider.py

@@ -1,231 +0,0 @@
-from collections import defaultdict
-from collections.abc import Sequence
-import json
-import os
-from pathlib import Path
-import time
-from typing import Any
-
-import certifi
-import click
-import httpx
-
-from crewai_cli.constants import JSON_URL, MODELS, PROVIDERS
-
-
-def select_choice(prompt_message: str, choices: Sequence[str]) -> str | None:
-    """Presents a list of choices to the user and prompts them to select one.
-
-    Args:
-        prompt_message: The message to display to the user before presenting the choices.
-        choices: A list of options to present to the user.
-
-    Returns:
-        The selected choice from the list, or None if the user chooses to quit.
-    """
-
-    provider_models = get_provider_data()
-    if not provider_models:
-        return None
-    click.secho(prompt_message, fg="cyan")
-    for idx, choice in enumerate(choices, start=1):
-        click.secho(f"{idx}. {choice}", fg="cyan")
-    click.secho("q. Quit", fg="cyan")
-
-    while True:
-        choice = click.prompt(
-            "Enter the number of your choice or 'q' to quit", type=str
-        )
-
-        if choice.lower() == "q":
-            return None
-
-        try:
-            selected_index = int(choice) - 1
-            if 0 <= selected_index < len(choices):
-                return choices[selected_index]
-        except ValueError:
-            pass
-
-        click.secho(
-            "Invalid selection. Please select a number between 1 and 6 or 'q' to quit.",
-            fg="red",
-        )
-
-
-def select_provider(provider_models: dict[str, list[str]]) -> str | None | bool:
-    """Presents a list of providers to the user and prompts them to select one.
-
-    Args:
-        provider_models: A dictionary of provider models.
-
-    Returns:
-        The selected provider, None if user explicitly quits, or False if no selection.
-    """
-    predefined_providers = [p.lower() for p in PROVIDERS]
-    all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
-
-    provider = select_choice(
-        "Select a provider to set up:", [*predefined_providers, "other"]
-    )
-    if provider is None:  # User typed 'q'
-        return None
-
-    if provider == "other":
-        provider = select_choice("Select a provider from the full list:", all_providers)
-        if provider is None:  # User typed 'q'
-            return None
-
-    return provider.lower() if provider else False
-
-
-def select_model(provider: str, provider_models: dict[str, list[str]]) -> str | None:
-    """Presents a list of models for a given provider to the user and prompts them to select one.
-
-    Args:
-        provider: The provider for which to select a model.
-        provider_models: A dictionary of provider models.
-
-    Returns:
-        The selected model, or None if the operation is aborted or an invalid selection is made.
-    """
-    predefined_providers = [p.lower() for p in PROVIDERS]
-
-    if provider in predefined_providers:
-        available_models = MODELS.get(provider, [])
-    else:
-        available_models = provider_models.get(provider, [])
-
-    if not available_models:
-        click.secho(f"No models available for provider '{provider}'.", fg="red")
-        return None
-
-    return select_choice(
-        f"Select a model to use for {provider.capitalize()}:", available_models
-    )
-
-
-def load_provider_data(cache_file: Path, cache_expiry: int) -> dict[str, Any] | None:
-    """Loads provider data from a cache file if it exists and is not expired.
-
-    If the cache is expired or corrupted, it fetches the data from the web.
-
-    Args:
-        cache_file: The path to the cache file.
-        cache_expiry: The cache expiry time in seconds.
-
-    Returns:
-        The loaded provider data or None if the operation fails.
-    """
-    current_time = time.time()
-    if (
-        cache_file.exists()
-        and (current_time - cache_file.stat().st_mtime) < cache_expiry
-    ):
-        data = read_cache_file(cache_file)
-        if data:
-            return data
-        click.secho(
-            "Cache is corrupted. Fetching provider data from the web...", fg="yellow"
-        )
-    else:
-        click.secho(
-            "Cache expired or not found. Fetching provider data from the web...",
-            fg="cyan",
-        )
-    return fetch_provider_data(cache_file)
-
-
-def read_cache_file(cache_file: Path) -> dict[str, Any] | None:
-    """Reads and returns the JSON content from a cache file.
-
-    Args:
-        cache_file: The path to the cache file.
-
-    Returns:
-        The JSON content of the cache file or None if the JSON is invalid.
-    """
-    try:
-        with open(cache_file, "r") as f:
-            data: dict[str, Any] = json.load(f)
-            return data
-    except json.JSONDecodeError:
-        return None
-
-
-def fetch_provider_data(cache_file: Path) -> dict[str, Any] | None:
-    """Fetches provider data from a specified URL and caches it to a file.
-
-    Args:
-        cache_file: The path to the cache file.
-
-    Returns:
-        The fetched provider data or None if the operation fails.
-    """
-    ssl_config = os.environ["SSL_CERT_FILE"] = certifi.where()
-
-    try:
-        with httpx.stream("GET", JSON_URL, timeout=60, verify=ssl_config) as response:
-            response.raise_for_status()
-            data = download_data(response)
-            with open(cache_file, "w") as f:
-                json.dump(data, f)
-            return data
-    except httpx.HTTPError as e:
-        click.secho(f"Error fetching provider data: {e}", fg="red")
-    except json.JSONDecodeError:
-        click.secho("Error parsing provider data. Invalid JSON format.", fg="red")
-    return None
-
-
-def download_data(response: httpx.Response) -> dict[str, Any]:
-    """Downloads data from a given HTTP response and returns the JSON content.
-
-    Args:
-        response: The HTTP response object.
-
-    Returns:
-        The JSON content of the response.
-    """
-    total_size = int(response.headers.get("content-length", 0))
-    block_size = 8192
-    data_chunks: list[bytes] = []
-    bar: Any
-    with click.progressbar(
-        length=total_size, label="Downloading", show_pos=True
-    ) as bar:
-        for chunk in response.iter_bytes(block_size):
-            if chunk:
-                data_chunks.append(chunk)
-                bar.update(len(chunk))
-    data_content = b"".join(data_chunks)
-    result: dict[str, Any] = json.loads(data_content.decode("utf-8"))
-    return result
-
-
-def get_provider_data() -> dict[str, list[str]] | None:
-    """Retrieves provider data from a cache file.
-
-    Filters out models based on provider criteria, and returns a dictionary of providers
-    mapped to their models.
-
-    Returns:
-        A dictionary of providers mapped to their models or None if the operation fails.
-    """
-    cache_dir = Path.home() / ".crewai"
-    cache_dir.mkdir(exist_ok=True)
-    cache_file = cache_dir / "provider_cache.json"
-    cache_expiry = 24 * 3600
-
-    data = load_provider_data(cache_file, cache_expiry)
-    if not data:
-        return None
-
-    provider_models = defaultdict(list)
-    for model_name, properties in data.items():
-        provider = properties.get("litellm_provider", "").strip().lower()
-        if "http" in provider or provider == "other":
-            continue
-        if provider:
-            provider_models[provider].append(model_name)
-    return provider_models

lib/cli/src/crewai_cli/reset_memories_command.py

@@ -1,31 +0,0 @@
-"""Wrapper for the reset-memories command.
-
-Delegates to ``crewai.cli.reset_memories_command`` when the full crewai
-package is installed, otherwise prints a helpful error message.
-"""
-
-from __future__ import annotations
-
-import click
-
-
-def reset_memories_command(
-    memory: bool,
-    knowledge: bool,
-    agent_knowledge: bool,
-    kickoff_outputs: bool,
-    all: bool,
-) -> None:
-    try:
-        from crewai.cli.reset_memories_command import (
-            reset_memories_command as _reset,
-        )
-    except ImportError:
-        click.secho(
-            "The 'reset-memories' command requires the full crewai package.\n"
-            "Install it with: pip install crewai",
-            fg="red",
-        )
-        raise SystemExit(1) from None
-
-    _reset(memory, knowledge, agent_knowledge, kickoff_outputs, all)

lib/cli/src/crewai_cli/shared/token_manager.py

@@ -1,186 +0,0 @@
-from datetime import datetime
-import json
-import os
-from pathlib import Path
-import sys
-import tempfile
-from typing import Final, Literal, cast
-
-from cryptography.fernet import Fernet
-
-
-_FERNET_KEY_LENGTH: Final[Literal[44]] = 44
-
-
-class TokenManager:
-    """Manages encrypted token storage."""
-
-    def __init__(self, file_path: str = "tokens.enc") -> None:
-        """Initialize the TokenManager.
-
-        Args:
-            file_path: The file path to store encrypted tokens.
-        """
-        self.file_path = file_path
-        self.key = self._get_or_create_key()
-        self.fernet = Fernet(self.key)
-
-    def _get_or_create_key(self) -> bytes:
-        """Get or create the encryption key.
-
-        Returns:
-            The encryption key as bytes.
-        """
-        key_filename: str = "secret.key"
-
-        key = self._read_secure_file(key_filename)
-        if key is not None and len(key) == _FERNET_KEY_LENGTH:
-            return key
-
-        new_key = Fernet.generate_key()
-        if self._atomic_create_secure_file(key_filename, new_key):
-            return new_key
-
-        key = self._read_secure_file(key_filename)
-        if key is not None and len(key) == _FERNET_KEY_LENGTH:
-            return key
-
-        raise RuntimeError("Failed to create or read encryption key")
-
-    def save_tokens(self, access_token: str, expires_at: int) -> None:
-        """Save the access token and its expiration time.
-
-        Args:
-            access_token: The access token to save.
-            expires_at: The UNIX timestamp of the expiration time.
-        """
-        expiration_time = datetime.fromtimestamp(expires_at)
-        data = {
-            "access_token": access_token,
-            "expiration": expiration_time.isoformat(),
-        }
-        encrypted_data = self.fernet.encrypt(json.dumps(data).encode())
-        self._atomic_write_secure_file(self.file_path, encrypted_data)
-
-    def get_token(self) -> str | None:
-        """Get the access token if it is valid and not expired.
-
-        Returns:
-            The access token if valid and not expired, otherwise None.
-        """
-        encrypted_data = self._read_secure_file(self.file_path)
-        if encrypted_data is None:
-            return None
-
-        decrypted_data = self.fernet.decrypt(encrypted_data)
-        data = json.loads(decrypted_data)
-
-        expiration = datetime.fromisoformat(data["expiration"])
-        if expiration <= datetime.now():
-            return None
-
-        return cast(str | None, data.get("access_token"))
-
-    def clear_tokens(self) -> None:
-        """Clear the stored tokens."""
-        self._delete_secure_file(self.file_path)
-
-    @staticmethod
-    def _get_secure_storage_path() -> Path:
-        """Get the secure storage path based on the operating system.
-
-        Returns:
-            The secure storage path.
-        """
-        if sys.platform == "win32":
-            base_path = os.environ.get("LOCALAPPDATA")
-        elif sys.platform == "darwin":
-            base_path = os.path.expanduser("~/Library/Application Support")
-        else:
-            base_path = os.path.expanduser("~/.local/share")
-
-        app_name = "crewai/credentials"
-        storage_path = Path(base_path) / app_name
-
-        storage_path.mkdir(parents=True, exist_ok=True)
-
-        return storage_path
-
-    def _atomic_create_secure_file(self, filename: str, content: bytes) -> bool:
-        """Create a file only if it doesn't exist.
-
-        Args:
-            filename: The name of the file.
-            content: The content to write.
-
-        Returns:
-            True if file was created, False if it already exists.
-        """
-        storage_path = self._get_secure_storage_path()
-        file_path = storage_path / filename
-
-        try:
-            fd = os.open(file_path, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0o600)
-            try:
-                os.write(fd, content)
-            finally:
-                os.close(fd)
-            return True
-        except FileExistsError:
-            return False
-
-    def _atomic_write_secure_file(self, filename: str, content: bytes) -> None:
-        """Write content to a secure file.
-
-        Args:
-            filename: The name of the file.
-            content: The content to write.
-        """
-        storage_path = self._get_secure_storage_path()
-        file_path = storage_path / filename
-
-        fd, temp_path = tempfile.mkstemp(dir=storage_path, prefix=f".{filename}.")
-        fd_closed = False
-        try:
-            os.write(fd, content)
-            os.close(fd)
-            fd_closed = True
-            os.chmod(temp_path, 0o600)
-            os.replace(temp_path, file_path)
-        except Exception:
-            if not fd_closed:
-                os.close(fd)
-            if os.path.exists(temp_path):
-                os.unlink(temp_path)
-            raise
-
-    def _read_secure_file(self, filename: str) -> bytes | None:
-        """Read the content of a secure file.
-
-        Args:
-            filename: The name of the file.
-
-        Returns:
-            The content of the file if it exists, otherwise None.
-        """
-        storage_path = self._get_secure_storage_path()
-        file_path = storage_path / filename
-
-        try:
-            with open(file_path, "rb") as f:
-                return f.read()
-        except FileNotFoundError:
-            return None
-
-    def _delete_secure_file(self, filename: str) -> None:
-        """Delete a secure file.
-
-        Args:
-            filename: The name of the file.
-        """
-        storage_path = self._get_secure_storage_path()
-        file_path = storage_path / filename
-        try:
-            file_path.unlink()
-        except FileNotFoundError:
-            pass

lib/cli/src/crewai_cli/task_outputs.py

@@ -1,54 +0,0 @@
-"""Lightweight SQLite reader for kickoff task outputs.
-
-Only used by the ``crewai log-tasks-outputs`` CLI command.  Depends solely on
-the standard library + *appdirs* so crewai-cli can read stored outputs without
-importing the full crewai framework.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from pathlib import Path
-import sqlite3
-from typing import Any
-
-from crewai_cli.user_data import _db_storage_path
-
-
-logger = logging.getLogger(__name__)
-
-
-def load_task_outputs(db_path: str | None = None) -> list[dict[str, Any]]:
-    """Return all rows from the kickoff task outputs database."""
-    if db_path is None:
-        db_path = str(Path(_db_storage_path()) / "latest_kickoff_task_outputs.db")
-
-    if not Path(db_path).exists():
-        return []
-
-    try:
-        with sqlite3.connect(db_path) as conn:
-            cursor = conn.cursor()
-            cursor.execute("""
-                SELECT *
-                FROM latest_kickoff_task_outputs
-                ORDER BY task_index
-            """)
-            rows = cursor.fetchall()
-            results: list[dict[str, Any]] = [
-                {
-                    "task_id": row[0],
-                    "expected_output": row[1],
-                    "output": json.loads(row[2]),
-                    "task_index": row[3],
-                    "inputs": json.loads(row[4]),
-                    "was_replayed": row[5],
-                    "timestamp": row[6],
-                }
-                for row in rows
-            ]
-            return results
-    except sqlite3.Error as e:
-        logger.error("Failed to load task outputs: %s", e)
-        return []

lib/cli/src/crewai_cli/user_data.py

@@ -1,66 +0,0 @@
-"""Standalone user-data helpers for the CLI package.
-
-These mirror the functions in ``crewai.events.listeners.tracing.utils`` but
-depend only on the standard library + *appdirs* so that crewai-cli can work
-without importing the full crewai framework.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-import os
-from pathlib import Path
-from typing import Any, cast
-
-import appdirs
-
-
-logger = logging.getLogger(__name__)
-
-
-def _get_project_directory_name() -> str:
-    return os.environ.get("CREWAI_STORAGE_DIR", Path.cwd().name)
-
-
-def _db_storage_path() -> str:
-    app_name = _get_project_directory_name()
-    app_author = "CrewAI"
-    data_dir = Path(appdirs.user_data_dir(app_name, app_author))
-    data_dir.mkdir(parents=True, exist_ok=True)
-    return str(data_dir)
-
-
-def _user_data_file() -> Path:
-    base = Path(_db_storage_path())
-    base.mkdir(parents=True, exist_ok=True)
-    return base / ".crewai_user.json"
-
-
-def _load_user_data() -> dict[str, Any]:
-    p = _user_data_file()
-    if p.exists():
-        try:
-            return cast(dict[str, Any], json.loads(p.read_text()))
-        except (json.JSONDecodeError, OSError, PermissionError) as e:
-            logger.warning("Failed to load user data: %s", e)
-    return {}
-
-
-def _save_user_data(data: dict[str, Any]) -> None:
-    try:
-        p = _user_data_file()
-        p.write_text(json.dumps(data, indent=2))
-    except (OSError, PermissionError) as e:
-        logger.warning("Failed to save user data: %s", e)
-
-
-def is_tracing_enabled() -> bool:
-    """Check if tracing is enabled (mirrors crewai core logic)."""
-    data = _load_user_data()
-    if (
-        data.get("first_execution_done", False)
-        and data.get("trace_consent", False) is False
-    ):
-        return False
-    return os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true"

lib/cli/src/crewai_cli/utils.py

@@ -1,369 +0,0 @@
-from __future__ import annotations
-
-from functools import reduce
-from inspect import getmro, isclass
-import os
-from pathlib import Path
-import shutil
-import sys
-from typing import Any, cast
-
-import click
-from rich.console import Console
-import tomli
-
-from crewai_cli.config import Settings
-from crewai_cli.constants import ENV_VARS
-
-
-if sys.version_info >= (3, 11):
-    import tomllib
-
-console = Console()
-
-
-def copy_template(
-    src: Path, dst: Path, name: str, class_name: str, folder_name: str
-) -> None:
-    """Copy a file from src to dst."""
-    with open(src, "r") as file:
-        content = file.read()
-
-    content = content.replace("{{name}}", name)
-    content = content.replace("{{crew_name}}", class_name)
-    content = content.replace("{{folder_name}}", folder_name)
-
-    with open(dst, "w") as file:
-        file.write(content)
-
-    click.secho(f"  - Created {dst}", fg="green")
-
-
-def read_toml(file_path: str = "pyproject.toml") -> dict[str, Any]:
-    """Read the content of a TOML file and return it as a dictionary."""
-    with open(file_path, "rb") as f:
-        return tomli.load(f)
-
-
-def parse_toml(content: str) -> dict[str, Any]:
-    if sys.version_info >= (3, 11):
-        return tomllib.loads(content)
-    return tomli.loads(content)
-
-
-def get_project_name(
-    pyproject_path: str = "pyproject.toml", require: bool = False
-) -> str | None:
-    """Get the project name from the pyproject.toml file."""
-    return _get_project_attribute(pyproject_path, ["project", "name"], require=require)
-
-
-def get_project_version(
-    pyproject_path: str = "pyproject.toml", require: bool = False
-) -> str | None:
-    """Get the project version from the pyproject.toml file."""
-    return _get_project_attribute(
-        pyproject_path, ["project", "version"], require=require
-    )
-
-
-def get_project_description(
-    pyproject_path: str = "pyproject.toml", require: bool = False
-) -> str | None:
-    """Get the project description from the pyproject.toml file."""
-    return _get_project_attribute(
-        pyproject_path, ["project", "description"], require=require
-    )
-
-
-def _get_project_attribute(
-    pyproject_path: str, keys: list[str], require: bool
-) -> Any | None:
-    """Get an attribute from the pyproject.toml file."""
-    attribute = None
-
-    try:
-        with open(pyproject_path, "r") as f:
-            pyproject_content = parse_toml(f.read())
-
-        dependencies = (
-            _get_nested_value(pyproject_content, ["project", "dependencies"]) or []
-        )
-        if not any(True for dep in dependencies if "crewai" in dep):
-            raise Exception("crewai is not in the dependencies.")
-
-        attribute = _get_nested_value(pyproject_content, keys)
-    except FileNotFoundError:
-        console.print(f"Error: {pyproject_path} not found.", style="bold red")
-    except KeyError:
-        console.print(
-            f"Error: {pyproject_path} is not a valid pyproject.toml file.",
-            style="bold red",
-        )
-    except Exception as e:
-        if sys.version_info >= (3, 11) and isinstance(e, tomllib.TOMLDecodeError):
-            console.print(
-                f"Error: {pyproject_path} is not a valid TOML file.", style="bold red"
-            )
-        else:
-            console.print(
-                f"Error reading the pyproject.toml file: {e}", style="bold red"
-            )
-
-    if require and not attribute:
-        console.print(
-            f"Unable to read '{'.'.join(keys)}' in the pyproject.toml file. Please verify that the file exists and contains the specified attribute.",
-            style="bold red",
-        )
-        raise SystemExit
-
-    return attribute
-
-
-def _get_nested_value(data: dict[str, Any], keys: list[str]) -> Any:
-    return reduce(dict.__getitem__, keys, data)
-
-
-def fetch_and_json_env_file(env_file_path: str = ".env") -> dict[str, Any]:
-    """Fetch the environment variables from a .env file and return them as a dictionary."""
-    try:
-        with open(env_file_path, "r") as f:
-            env_content = f.read()
-
-        env_dict = {}
-        for line in env_content.splitlines():
-            if line.strip() and not line.strip().startswith("#"):
-                key, value = line.split("=", 1)
-                env_dict[key.strip()] = value.strip()
-
-        return env_dict
-
-    except FileNotFoundError:
-        console.print(f"Error: {env_file_path} not found.", style="bold red")
-    except Exception as e:
-        console.print(f"Error reading the .env file: {e}", style="bold red")
-
-    return {}
-
-
-def tree_copy(source: Path, destination: Path) -> None:
-    """Copies the entire directory structure from the source to the destination."""
-    for item in os.listdir(source):
-        source_item = os.path.join(source, item)
-        destination_item = os.path.join(destination, item)
-        if os.path.isdir(source_item):
-            shutil.copytree(source_item, destination_item)
-        else:
-            shutil.copy2(source_item, destination_item)
-
-
-def tree_find_and_replace(directory: Path, find: str, replace: str) -> None:
-    """Recursively searches through a directory, replacing a target string in
-    both file contents and filenames with a specified replacement string.
-    """
-    for path, dirs, files in os.walk(os.path.abspath(directory), topdown=False):
-        for filename in files:
-            filepath = os.path.join(path, filename)
-
-            with open(filepath, "r", encoding="utf-8", errors="ignore") as file:
-                contents = file.read()
-            with open(filepath, "w") as file:
-                file.write(contents.replace(find, replace))
-
-            if find in filename:
-                new_filename = filename.replace(find, replace)
-                new_filepath = os.path.join(path, new_filename)
-                os.rename(filepath, new_filepath)
-
-        for dirname in dirs:
-            if find in dirname:
-                new_dirname = dirname.replace(find, replace)
-                new_dirpath = os.path.join(path, new_dirname)
-                old_dirpath = os.path.join(path, dirname)
-                os.rename(old_dirpath, new_dirpath)
-
-
-def load_env_vars(folder_path: Path) -> dict[str, Any]:
-    """Loads environment variables from a .env file in the specified folder path."""
-    env_file_path = folder_path / ".env"
-    env_vars = {}
-    if env_file_path.exists():
-        with open(env_file_path, "r") as file:
-            for line in file:
-                key, _, value = line.strip().partition("=")
-                if key and value:
-                    env_vars[key] = value
-    return env_vars
-
-
-def update_env_vars(
-    env_vars: dict[str, Any], provider: str, model: str
-) -> dict[str, Any] | None:
-    """Updates environment variables with the API key for the selected provider and model."""
-    provider_config = cast(
-        list[str],
-        ENV_VARS.get(
-            provider,
-            [
-                click.prompt(
-                    f"Enter the environment variable name for your {provider.capitalize()} API key",
-                    type=str,
-                )
-            ],
-        ),
-    )
-
-    api_key_var = provider_config[0]
-
-    if api_key_var not in env_vars:
-        try:
-            env_vars[api_key_var] = click.prompt(
-                f"Enter your {provider.capitalize()} API key", type=str, hide_input=True
-            )
-        except click.exceptions.Abort:
-            click.secho("Operation aborted by the user.", fg="red")
-            return None
-    else:
-        click.secho(f"API key already exists for {provider.capitalize()}.", fg="yellow")
-
-    env_vars["MODEL"] = model
-    click.secho(f"Selected model: {model}", fg="green")
-    return env_vars
-
-
-def write_env_file(folder_path: Path, env_vars: dict[str, Any]) -> None:
-    """Writes environment variables to a .env file in the specified folder."""
-    env_file_path = folder_path / ".env"
-    with open(env_file_path, "w") as file:
-        for key, value in env_vars.items():
-            file.write(f"{key.upper()}={value}\n")
-
-
-def is_valid_tool(obj: Any) -> bool:
-    """Check if an object is a valid tool class.
-
-    Works without importing crewai by checking MRO class names.
-    Falls back to crewai's ``is_valid_tool`` when available.
-    """
-    try:
-        from crewai.cli.utils import is_valid_tool as _core_is_valid_tool
-
-        return _core_is_valid_tool(obj)
-    except ImportError:
-        pass
-
-    if isclass(obj):
-        try:
-            return any(base.__name__ == "BaseTool" for base in getmro(obj))
-        except (TypeError, AttributeError):
-            return False
-    return False
-
-
-def extract_available_exports(dir_path: str = "src") -> list[dict[str, Any]]:
-    """Extract available tool classes from the project's __init__.py files."""
-    try:
-        init_files = Path(dir_path).glob("**/__init__.py")
-        available_exports: list[dict[str, Any]] = []
-
-        for init_file in init_files:
-            tools = _load_tools_from_init(init_file)
-            available_exports.extend(tools)
-
-        if not available_exports:
-            _print_no_tools_warning()
-            raise SystemExit(1)
-
-        return available_exports
-
-    except SystemExit:
-        raise
-    except Exception as e:
-        console.print(f"[red]Error: Could not extract tool classes: {e!s}[/red]")
-        console.print(
-            "Please ensure your project contains valid tools (classes inheriting from BaseTool or functions with @tool decorator)."
-        )
-        raise SystemExit(1) from e
-
-
-def _load_tools_from_init(init_file: Path) -> list[dict[str, Any]]:
-    """Load and validate tools from a given __init__.py file."""
-    import importlib.util as _importlib_util
-
-    spec = _importlib_util.spec_from_file_location("temp_module", init_file)
-
-    if not spec or not spec.loader:
-        return []
-
-    module = _importlib_util.module_from_spec(spec)
-    sys.modules["temp_module"] = module
-
-    try:
-        spec.loader.exec_module(module)
-
-        if not hasattr(module, "__all__"):
-            console.print(
-                f"Warning: No __all__ defined in {init_file}",
-                style="bold yellow",
-            )
-            raise SystemExit(1)
-
-        return [
-            {"name": name}
-            for name in module.__all__
-            if hasattr(module, name) and is_valid_tool(getattr(module, name))
-        ]
-
-    except SystemExit:
-        raise
-    except Exception as e:
-        console.print(f"[red]Warning: Could not load {init_file}: {e!s}[/red]")
-        raise SystemExit(1) from e
-
-    finally:
-        sys.modules.pop("temp_module", None)
-
-
-def _print_no_tools_warning() -> None:
-    """Display warning and usage instructions if no tools were found."""
-    console.print(
-        "\n[bold yellow]Warning: No valid tools were exposed in your __init__.py file![/bold yellow]"
-    )
-    console.print(
-        "Your __init__.py file must contain all classes that inherit from [bold]BaseTool[/bold] "
-        "or functions decorated with [bold]@tool[/bold]."
-    )
-    console.print(
-        "\nExample:\n[dim]# In your __init__.py file[/dim]\n"
-        "[green]__all__ = ['YourTool', 'your_tool_function'][/green]\n\n"
-        "[dim]# In your tool.py file[/dim]\n"
-        "[green]from crewai.tools import BaseTool, tool\n\n"
-        "# Tool class example\n"
-        "class YourTool(BaseTool):\n"
-        '    name = "your_tool"\n'
-        '    description = "Your tool description"\n'
-        "    # ... rest of implementation\n\n"
-        "# Decorated function example\n"
-        "@tool\n"
-        "def your_tool_function(text: str) -> str:\n"
-        '    """Your tool description"""\n'
-        "    # ... implementation\n"
-        "    return result\n"
-    )
-
-
-def build_env_with_tool_repository_credentials(
-    repository_handle: str,
-) -> dict[str, Any]:
-    repository_handle = repository_handle.upper().replace("-", "_")
-    settings = Settings()
-
-    env = os.environ.copy()
-    env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
-        settings.tool_repository_username or ""
-    )
-    env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
-        settings.tool_repository_password or ""
-    )
-
-    return env

lib/cli/src/crewai_cli/version.py

@@ -1,215 +0,0 @@
-"""Version utilities for CrewAI CLI."""
-
-from collections.abc import Mapping
-from datetime import datetime, timedelta
-from functools import lru_cache
-import importlib.metadata
-import json
-from pathlib import Path
-from typing import Any
-from urllib import request
-from urllib.error import URLError
-
-import appdirs
-from packaging.version import InvalidVersion, Version, parse
-
-
-@lru_cache(maxsize=1)
-def _get_cache_file() -> Path:
-    """Get the path to the version cache file.
-
-    Cached to avoid repeated filesystem operations.
-    """
-    cache_dir = Path(appdirs.user_cache_dir("crewai"))
-    cache_dir.mkdir(parents=True, exist_ok=True)
-    return cache_dir / "version_cache.json"
-
-
-def get_crewai_version() -> str:
-    """Get the version number of CrewAI running the CLI."""
-    return importlib.metadata.version("crewai")
-
-
-def _is_cache_valid(cache_data: Mapping[str, Any]) -> bool:
-    """Check if the cache is still valid, less than 24 hours old."""
-    if "timestamp" not in cache_data:
-        return False
-
-    try:
-        cache_time = datetime.fromisoformat(str(cache_data["timestamp"]))
-        return datetime.now() - cache_time < timedelta(hours=24)
-    except (ValueError, TypeError):
-        return False
-
-
-def _find_latest_non_yanked_version(
-    releases: Mapping[str, list[dict[str, Any]]],
-) -> str | None:
-    """Find the latest non-yanked version from PyPI releases data.
-
-    Args:
-        releases: PyPI releases dict mapping version strings to file info lists.
-
-    Returns:
-        The latest non-yanked version string, or None if all versions are yanked.
-    """
-    best_version: Version | None = None
-    best_version_str: str | None = None
-
-    for version_str, files in releases.items():
-        try:
-            v = parse(version_str)
-        except InvalidVersion:
-            continue
-
-        if v.is_prerelease or v.is_devrelease:
-            continue
-
-        if not files:
-            continue
-
-        all_yanked = all(f.get("yanked", False) for f in files)
-        if all_yanked:
-            continue
-
-        if best_version is None or v > best_version:
-            best_version = v
-            best_version_str = version_str
-
-    return best_version_str
-
-
-def _is_version_yanked(
-    version_str: str,
-    releases: Mapping[str, list[dict[str, Any]]],
-) -> tuple[bool, str]:
-    """Check if a specific version is yanked.
-
-    Args:
-        version_str: The version string to check.
-        releases: PyPI releases dict mapping version strings to file info lists.
-
-    Returns:
-        Tuple of (is_yanked, yanked_reason).
-    """
-    files = releases.get(version_str, [])
-    if not files:
-        return False, ""
-
-    all_yanked = all(f.get("yanked", False) for f in files)
-    if not all_yanked:
-        return False, ""
-
-    for f in files:
-        reason = f.get("yanked_reason", "")
-        if reason:
-            return True, str(reason)
-
-    return True, ""
-
-
-def get_latest_version_from_pypi(timeout: int = 2) -> str | None:
-    """Get the latest non-yanked version of CrewAI from PyPI.
-
-    Args:
-        timeout: Request timeout in seconds.
-
-    Returns:
-        Latest non-yanked version string or None if unable to fetch.
-    """
-    cache_file = _get_cache_file()
-    if cache_file.exists():
-        try:
-            cache_data = json.loads(cache_file.read_text())
-            if _is_cache_valid(cache_data) and "current_version" in cache_data:
-                version: str | None = cache_data.get("version")
-                return version
-        except (json.JSONDecodeError, OSError):
-            pass
-
-    try:
-        with request.urlopen(
-            "https://pypi.org/pypi/crewai/json", timeout=timeout
-        ) as response:
-            data = json.loads(response.read())
-            releases: dict[str, list[dict[str, Any]]] = data["releases"]
-            latest_version = _find_latest_non_yanked_version(releases)
-
-            current_version = get_crewai_version()
-            is_yanked, yanked_reason = _is_version_yanked(current_version, releases)
-
-            cache_data = {
-                "version": latest_version,
-                "timestamp": datetime.now().isoformat(),
-                "current_version": current_version,
-                "current_version_yanked": is_yanked,
-                "current_version_yanked_reason": yanked_reason,
-            }
-            cache_file.write_text(json.dumps(cache_data))
-
-            return latest_version
-    except (URLError, json.JSONDecodeError, KeyError, OSError):
-        return None
-
-
-def is_current_version_yanked() -> tuple[bool, str]:
-    """Check if the currently installed version has been yanked on PyPI.
-
-    Reads from cache if available, otherwise triggers a fetch.
-
-    Returns:
-        Tuple of (is_yanked, yanked_reason).
-    """
-    cache_file = _get_cache_file()
-    if cache_file.exists():
-        try:
-            cache_data = json.loads(cache_file.read_text())
-            if _is_cache_valid(cache_data) and "current_version" in cache_data:
-                current = get_crewai_version()
-                if cache_data.get("current_version") == current:
-                    return (
-                        bool(cache_data.get("current_version_yanked", False)),
-                        str(cache_data.get("current_version_yanked_reason", "")),
-                    )
-        except (json.JSONDecodeError, OSError):
-            pass
-
-    get_latest_version_from_pypi()
-
-    try:
-        cache_data = json.loads(cache_file.read_text())
-        return (
-            bool(cache_data.get("current_version_yanked", False)),
-            str(cache_data.get("current_version_yanked_reason", "")),
-        )
-    except (json.JSONDecodeError, OSError):
-        return False, ""
-
-
-def check_version() -> tuple[str, str | None]:
-    """Check current and latest versions.
-
-    Returns:
-        Tuple of (current_version, latest_version).
-        latest_version is None if unable to fetch from PyPI.
-    """
-    current = get_crewai_version()
-    latest = get_latest_version_from_pypi()
-    return current, latest
-
-
-def is_newer_version_available() -> tuple[bool, str, str | None]:
-    """Check if a newer version is available.
-
-    Returns:
-        Tuple of (is_newer, current_version, latest_version).
-    """
-    current, latest = check_version()
-
-    if latest is None:
-        return False, current, None
-
-    try:
-        return parse(latest) > parse(current), current, latest
-    except (InvalidVersion, TypeError):
-        return False, current, latest

lib/cli/tests/authentication/providers/test_auth0.py

@@ -1,91 +0,0 @@
-import pytest
-from crewai_cli.authentication.main import Oauth2Settings
-from crewai_cli.authentication.providers.auth0 import Auth0Provider
-
-
-
-class TestAuth0Provider:
-
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="auth0",
-            domain="test-domain.auth0.com",
-            client_id="test-client-id",
-            audience="test-audience"
-        )
-        self.provider = Auth0Provider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = Auth0Provider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "auth0"
-        assert provider.settings.domain == "test-domain.auth0.com"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://test-domain.auth0.com/oauth/device/code"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="auth0",
-            domain="my-company.auth0.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = Auth0Provider(settings)
-        expected_url = "https://my-company.auth0.com/oauth/device/code"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_token_url(self):
-        expected_url = "https://test-domain.auth0.com/oauth/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="auth0",
-            domain="another-domain.auth0.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = Auth0Provider(settings)
-        expected_url = "https://another-domain.auth0.com/oauth/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://test-domain.auth0.com/.well-known/jwks.json"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="auth0",
-            domain="dev.auth0.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = Auth0Provider(settings)
-        expected_url = "https://dev.auth0.com/.well-known/jwks.json"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://test-domain.auth0.com/"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="auth0",
-            domain="prod.auth0.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = Auth0Provider(settings)
-        expected_issuer = "https://prod.auth0.com/"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"

lib/cli/tests/authentication/providers/test_entra_id.py

@@ -1,141 +0,0 @@
-import pytest
-
-from crewai_cli.authentication.main import Oauth2Settings
-from crewai_cli.authentication.providers.entra_id import EntraIdProvider
-
-
-class TestEntraIdProvider:
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="entra_id",
-            domain="tenant-id-abcdef123456",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "scope": "openid profile email api://crewai-cli-dev/read"
-            }
-        )
-        self.provider = EntraIdProvider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = EntraIdProvider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "entra_id"
-        assert provider.settings.domain == "tenant-id-abcdef123456"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://login.microsoftonline.com/tenant-id-abcdef123456/oauth2/v2.0/devicecode"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        # For EntraID, the domain is the tenant ID.
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="my-company.entra.id",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = EntraIdProvider(settings)
-        expected_url = "https://login.microsoftonline.com/my-company.entra.id/oauth2/v2.0/devicecode"
-        assert provider.get_authorize_url() == expected_url
-    
-    def test_get_token_url(self):
-        expected_url = "https://login.microsoftonline.com/tenant-id-abcdef123456/oauth2/v2.0/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        # For EntraID, the domain is the tenant ID.
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="another-domain.entra.id",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = EntraIdProvider(settings)
-        expected_url = "https://login.microsoftonline.com/another-domain.entra.id/oauth2/v2.0/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://login.microsoftonline.com/tenant-id-abcdef123456/discovery/v2.0/keys"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        # For EntraID, the domain is the tenant ID.
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="dev.entra.id",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = EntraIdProvider(settings)
-        expected_url = "https://login.microsoftonline.com/dev.entra.id/discovery/v2.0/keys"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://login.microsoftonline.com/tenant-id-abcdef123456/v2.0"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        # For EntraID, the domain is the tenant ID.
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="other-tenant-id-xpto",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = EntraIdProvider(settings)
-        expected_issuer = "https://login.microsoftonline.com/other-tenant-id-xpto/v2.0"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_audience_assertion_error_when_none(self):
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="test-tenant-id",
-            client_id="test-client-id",
-            audience=None,
-        )
-        provider = EntraIdProvider(settings)
-
-        with pytest.raises(ValueError, match="Audience is required"):
-            provider.get_audience()
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"
-
-    def test_get_required_fields(self):
-        assert set(self.provider.get_required_fields()) == set(["scope"]) 
-
-    def test_get_oauth_scopes(self):
-        settings = Oauth2Settings(
-            provider="entra_id",
-            domain="tenant-id-abcdef123456",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "scope": "api://crewai-cli-dev/read"
-            }
-        )
-        provider = EntraIdProvider(settings)
-        assert provider.get_oauth_scopes() == ["openid", "profile", "email", "api://crewai-cli-dev/read"]
-    
-    def test_get_oauth_scopes_with_multiple_custom_scopes(self):
-        settings = Oauth2Settings(  
-            provider="entra_id",
-            domain="tenant-id-abcdef123456",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "scope": "api://crewai-cli-dev/read api://crewai-cli-dev/write custom-scope1 custom-scope2"
-            }
-        )
-        provider = EntraIdProvider(settings)
-        assert provider.get_oauth_scopes() == ["openid", "profile", "email", "api://crewai-cli-dev/read", "api://crewai-cli-dev/write", "custom-scope1", "custom-scope2"]
-
-    def test_base_url(self):
-        assert self.provider._base_url() == "https://login.microsoftonline.com/tenant-id-abcdef123456"

lib/cli/tests/authentication/providers/test_keycloak.py

@@ -1,138 +0,0 @@
-import pytest
-
-from crewai_cli.authentication.main import Oauth2Settings
-from crewai_cli.authentication.providers.keycloak import KeycloakProvider
-
-
-class TestKeycloakProvider:
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="keycloak",
-            domain="keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        self.provider = KeycloakProvider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = KeycloakProvider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "keycloak"
-        assert provider.settings.domain == "keycloak.example.com"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-        assert provider.settings.extra.get("realm") == "test-realm"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/auth/device"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="auth.company.com",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "my-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://auth.company.com/realms/my-realm/protocol/openid-connect/auth/device"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_token_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="sso.enterprise.com",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "enterprise-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://sso.enterprise.com/realms/enterprise-realm/protocol/openid-connect/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://keycloak.example.com/realms/test-realm/protocol/openid-connect/certs"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="identity.org",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "org-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_url = "https://identity.org/realms/org-realm/protocol/openid-connect/certs"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://keycloak.example.com/realms/test-realm"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="login.myapp.io",
-            client_id="test-client",
-            audience="test-audience",
-            extra={
-                "realm": "app-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        expected_issuer = "https://login.myapp.io/realms/app-realm"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"
-
-    def test_get_required_fields(self):
-        assert self.provider.get_required_fields() == ["realm"]
-
-    def test_oauth2_base_url(self):
-        assert self.provider._oauth2_base_url() == "https://keycloak.example.com"
-
-    def test_oauth2_base_url_strips_https_prefix(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="https://keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        assert provider._oauth2_base_url() == "https://keycloak.example.com"
-
-    def test_oauth2_base_url_strips_http_prefix(self):
-        settings = Oauth2Settings(
-            provider="keycloak",
-            domain="http://keycloak.example.com",
-            client_id="test-client-id",
-            audience="test-audience",
-            extra={
-                "realm": "test-realm"
-            }
-        )
-        provider = KeycloakProvider(settings)
-        assert provider._oauth2_base_url() == "https://keycloak.example.com"

lib/cli/tests/authentication/providers/test_okta.py

@@ -1,257 +0,0 @@
-import pytest
-
-from crewai_cli.authentication.main import Oauth2Settings
-from crewai_cli.authentication.providers.okta import OktaProvider
-
-
-class TestOktaProvider:
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience="test-audience",
-        )
-        self.provider = OktaProvider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = OktaProvider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "okta"
-        assert provider.settings.domain == "test-domain.okta.com"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://test-domain.okta.com/oauth2/default/v1/device/authorize"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="my-company.okta.com",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://my-company.okta.com/oauth2/default/v1/device/authorize"
-        assert provider.get_authorize_url() == expected_url
-    
-    def test_get_authorize_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/device/authorize"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/device/authorize"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_token_url(self):
-        expected_url = "https://test-domain.okta.com/oauth2/default/v1/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="another-domain.okta.com",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://another-domain.okta.com/oauth2/default/v1/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_token_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://test-domain.okta.com/oauth2/default/v1/keys"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="dev.okta.com",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://dev.okta.com/oauth2/default/v1/keys"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777/v1/keys"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_url = "https://test-domain.okta.com/oauth2/v1/keys"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://test-domain.okta.com/oauth2/default"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="prod.okta.com",
-            client_id="test-client",
-            audience="test-audience",
-        )
-        provider = OktaProvider(settings)
-        expected_issuer = "https://prod.okta.com/oauth2/default"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_issuer = "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        expected_issuer = "https://test-domain.okta.com"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_audience_assertion_error_when_none(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-        )
-        provider = OktaProvider(settings)
-
-        with pytest.raises(ValueError, match="Audience is required"):
-            provider.get_audience()
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"
-
-    def test_get_required_fields(self):
-        assert set(self.provider.get_required_fields()) == set(["authorization_server_name", "using_org_auth_server"]) 
-    
-    def test_oauth2_base_url(self):
-        assert self.provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2/default"
-    
-    def test_oauth2_base_url_with_custom_authorization_server_name(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": False,
-                "authorization_server_name": "my_auth_server_xxxAAA777"
-            }
-        )
-
-        provider = OktaProvider(settings)
-        assert provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2/my_auth_server_xxxAAA777"
-
-    def test_oauth2_base_url_when_using_org_auth_server(self):
-        settings = Oauth2Settings(
-            provider="okta",
-            domain="test-domain.okta.com",
-            client_id="test-client-id",
-            audience=None,
-            extra={
-                "using_org_auth_server": True,
-                "authorization_server_name": None
-            }
-        )
-        provider = OktaProvider(settings)
-        assert provider._oauth2_base_url() == "https://test-domain.okta.com/oauth2"

lib/cli/tests/authentication/providers/test_workos.py

@@ -1,100 +0,0 @@
-import pytest
-from crewai_cli.authentication.main import Oauth2Settings
-from crewai_cli.authentication.providers.workos import WorkosProvider
-
-
-class TestWorkosProvider:
-
-    @pytest.fixture(autouse=True)
-    def setup_method(self):
-        self.valid_settings = Oauth2Settings(
-            provider="workos",
-            domain="login.company.com",
-            client_id="test-client-id",
-            audience="test-audience"
-        )
-        self.provider = WorkosProvider(self.valid_settings)
-
-    def test_initialization_with_valid_settings(self):
-        provider = WorkosProvider(self.valid_settings)
-        assert provider.settings == self.valid_settings
-        assert provider.settings.provider == "workos"
-        assert provider.settings.domain == "login.company.com"
-        assert provider.settings.client_id == "test-client-id"
-        assert provider.settings.audience == "test-audience"
-
-    def test_get_authorize_url(self):
-        expected_url = "https://login.company.com/oauth2/device_authorization"
-        assert self.provider.get_authorize_url() == expected_url
-
-    def test_get_authorize_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="workos",
-            domain="login.example.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = WorkosProvider(settings)
-        expected_url = "https://login.example.com/oauth2/device_authorization"
-        assert provider.get_authorize_url() == expected_url
-
-    def test_get_token_url(self):
-        expected_url = "https://login.company.com/oauth2/token"
-        assert self.provider.get_token_url() == expected_url
-
-    def test_get_token_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="workos",
-            domain="api.workos.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = WorkosProvider(settings)
-        expected_url = "https://api.workos.com/oauth2/token"
-        assert provider.get_token_url() == expected_url
-
-    def test_get_jwks_url(self):
-        expected_url = "https://login.company.com/oauth2/jwks"
-        assert self.provider.get_jwks_url() == expected_url
-
-    def test_get_jwks_url_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="workos",
-            domain="auth.enterprise.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = WorkosProvider(settings)
-        expected_url = "https://auth.enterprise.com/oauth2/jwks"
-        assert provider.get_jwks_url() == expected_url
-
-    def test_get_issuer(self):
-        expected_issuer = "https://login.company.com"
-        assert self.provider.get_issuer() == expected_issuer
-
-    def test_get_issuer_with_different_domain(self):
-        settings = Oauth2Settings(
-            provider="workos",
-            domain="sso.company.com",
-            client_id="test-client",
-            audience="test-audience"
-        )
-        provider = WorkosProvider(settings)
-        expected_issuer = "https://sso.company.com"
-        assert provider.get_issuer() == expected_issuer
-
-    def test_get_audience(self):
-        assert self.provider.get_audience() == "test-audience"
-
-    def test_get_audience_fallback_to_default(self):
-        settings = Oauth2Settings(
-            provider="workos",
-            domain="login.company.com",
-            client_id="test-client-id",
-            audience=None
-        )
-        provider = WorkosProvider(settings)
-        assert provider.get_audience() == ""
-
-    def test_get_client_id(self):
-        assert self.provider.get_client_id() == "test-client-id"

lib/cli/tests/authentication/test_auth_main.py

@@ -1,348 +0,0 @@
-from datetime import datetime, timedelta
-from unittest.mock import MagicMock, call, patch
-
-import pytest
-import httpx
-from crewai_cli.authentication.main import AuthenticationCommand
-from crewai_cli.constants import (
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
-    CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-)
-
-
-class TestAuthenticationCommand:
-    def setup_method(self):
-        # Mock Settings so we always use default constants regardless of local config.
-        with patch("crewai_cli.authentication.main.Settings") as mock_settings:
-            instance = mock_settings.return_value
-            instance.oauth2_provider = "workos"
-            instance.oauth2_domain = CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN
-            instance.oauth2_client_id = CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID
-            instance.oauth2_audience = CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE
-            instance.oauth2_extra = {}
-            self.auth_command = AuthenticationCommand()
-
-    @pytest.mark.parametrize(
-        "user_provider,expected_urls",
-        [
-            (
-                "workos",
-                {
-                    "device_code_url": f"https://{CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN}/oauth2/device_authorization",
-                    "token_url": f"https://{CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN}/oauth2/token",
-                    "client_id": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_CLIENT_ID,
-                    "audience": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
-                    "domain": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-                },
-            ),
-        ],
-    )
-    @patch("crewai_cli.authentication.main.AuthenticationCommand._get_device_code")
-    @patch(
-        "crewai_cli.authentication.main.AuthenticationCommand._display_auth_instructions"
-    )
-    @patch("crewai_cli.authentication.main.AuthenticationCommand._poll_for_token")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_login(
-        self,
-        mock_console_print,
-        mock_poll,
-        mock_display,
-        mock_get_device,
-        user_provider,
-        expected_urls,
-    ):
-        mock_get_device.return_value = {
-            "device_code": "test_code",
-            "user_code": "123456",
-        }
-
-        self.auth_command.login()
-
-        mock_console_print.assert_called_once_with(
-            "Signing in to CrewAI AMP...\n", style="bold blue"
-        )
-        mock_get_device.assert_called_once()
-        mock_display.assert_called_once_with(
-            {"device_code": "test_code", "user_code": "123456"}
-        )
-        mock_poll.assert_called_once_with(
-            {"device_code": "test_code", "user_code": "123456"},
-        )
-        assert (
-            self.auth_command.oauth2_provider.get_client_id()
-            == expected_urls["client_id"]
-        )
-        assert (
-            self.auth_command.oauth2_provider.get_audience()
-            == expected_urls["audience"]
-        )
-        assert (
-            self.auth_command.oauth2_provider._get_domain() == expected_urls["domain"]
-        )
-
-    @patch("crewai_cli.authentication.main.webbrowser")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_display_auth_instructions(self, mock_console_print, mock_webbrowser):
-        device_code_data = {
-            "verification_uri_complete": "https://example.com/auth",
-            "user_code": "123456",
-        }
-
-        self.auth_command._display_auth_instructions(device_code_data)
-
-        expected_calls = [
-            call("1. Navigate to: ", "https://example.com/auth"),
-            call("2. Enter the following code: ", "123456"),
-        ]
-        mock_console_print.assert_has_calls(expected_calls)
-        mock_webbrowser.open.assert_called_once_with("https://example.com/auth")
-
-    @pytest.mark.parametrize(
-        "user_provider,jwt_config",
-        [
-            (
-                "workos",
-                {
-                    "jwks_url": f"https://{CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN}/oauth2/jwks",
-                    "issuer": f"https://{CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN}",
-                    "audience": CREWAI_ENTERPRISE_DEFAULT_OAUTH2_AUDIENCE,
-                },
-            ),
-        ],
-    )
-    @pytest.mark.parametrize("has_expiration", [True, False])
-    @patch("crewai_cli.authentication.main.validate_jwt_token")
-    @patch("crewai_cli.authentication.main.TokenManager.save_tokens")
-    def test_validate_and_save_token(
-        self,
-        mock_save_tokens,
-        mock_validate_jwt,
-        user_provider,
-        jwt_config,
-        has_expiration,
-    ):
-        from crewai_cli.authentication.main import Oauth2Settings
-        from crewai_cli.authentication.providers.workos import WorkosProvider
-
-        if user_provider == "workos":
-            self.auth_command.oauth2_provider = WorkosProvider(
-                settings=Oauth2Settings(
-                    provider=user_provider,
-                    client_id="test-client-id",
-                    domain=CREWAI_ENTERPRISE_DEFAULT_OAUTH2_DOMAIN,
-                    audience=jwt_config["audience"],
-                )
-            )
-
-        token_data = {"access_token": "test_access_token", "id_token": "test_id_token"}
-
-        if has_expiration:
-            future_timestamp = int((datetime.now() + timedelta(days=100)).timestamp())
-            decoded_token = {"exp": future_timestamp}
-        else:
-            decoded_token = {}
-
-        mock_validate_jwt.return_value = decoded_token
-
-        self.auth_command._validate_and_save_token(token_data)
-
-        mock_validate_jwt.assert_called_once_with(
-            jwt_token="test_access_token",
-            jwks_url=jwt_config["jwks_url"],
-            issuer=jwt_config["issuer"],
-            audience=jwt_config["audience"],
-        )
-
-        if has_expiration:
-            mock_save_tokens.assert_called_once_with(
-                "test_access_token", future_timestamp
-            )
-        else:
-            mock_save_tokens.assert_called_once_with("test_access_token", 0)
-
-    @patch("crewai_cli.tools.main.ToolCommand")
-    @patch("crewai_cli.authentication.main.Settings")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_login_to_tool_repository_success(
-        self, mock_console_print, mock_settings, mock_tool_command
-    ):
-        mock_tool_instance = MagicMock()
-        mock_tool_command.return_value = mock_tool_instance
-
-        mock_settings_instance = MagicMock()
-        mock_settings_instance.org_name = "Test Org"
-        mock_settings_instance.org_uuid = "test-uuid-123"
-        mock_settings.return_value = mock_settings_instance
-
-        self.auth_command._login_to_tool_repository()
-
-        mock_tool_command.assert_called_once()
-        mock_tool_instance.login.assert_called_once()
-
-        expected_calls = [
-            call(
-                "Now logging you in to the Tool Repository... ",
-                style="bold blue",
-                end="",
-            ),
-            call("Success!\n", style="bold green"),
-            call(
-                "You are now authenticated to the tool repository for organization [bold cyan]'Test Org'[/bold cyan]",
-                style="green",
-            ),
-        ]
-        mock_console_print.assert_has_calls(expected_calls)
-
-    @patch("crewai_cli.tools.main.ToolCommand")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_login_to_tool_repository_error(
-        self, mock_console_print, mock_tool_command
-    ):
-        mock_tool_instance = MagicMock()
-        mock_tool_instance.login.side_effect = Exception("Tool repository error")
-        mock_tool_command.return_value = mock_tool_instance
-
-        self.auth_command._login_to_tool_repository()
-
-        mock_tool_command.assert_called_once()
-        mock_tool_instance.login.assert_called_once()
-
-        expected_calls = [
-            call(
-                "Now logging you in to the Tool Repository... ",
-                style="bold blue",
-                end="",
-            ),
-            call(
-                "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.",
-                style="yellow",
-            ),
-            call(
-                "Other features will work normally, but you may experience limitations with downloading and publishing tools.\nRun [bold]crewai login[/bold] to try logging in again.\n",
-                style="yellow",
-            ),
-        ]
-        mock_console_print.assert_has_calls(expected_calls)
-
-    @patch("crewai_cli.authentication.main.httpx.post")
-    def test_get_device_code(self, mock_post):
-        mock_response = MagicMock()
-        mock_response.json.return_value = {
-            "device_code": "test_device_code",
-            "user_code": "123456",
-            "verification_uri_complete": "https://example.com/auth",
-        }
-        mock_post.return_value = mock_response
-
-        self.auth_command.oauth2_provider = MagicMock()
-        self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
-        self.auth_command.oauth2_provider.get_authorize_url.return_value = (
-            "https://example.com/device"
-        )
-        self.auth_command.oauth2_provider.get_audience.return_value = "test_audience"
-        self.auth_command.oauth2_provider.get_oauth_scopes.return_value = ["openid", "profile", "email"]
-
-        result = self.auth_command._get_device_code()
-
-        mock_post.assert_called_once_with(
-            url="https://example.com/device",
-            data={
-                "client_id": "test_client",
-                "scope": "openid profile email",
-                "audience": "test_audience",
-            },
-            timeout=20,
-        )
-
-        assert result == {
-            "device_code": "test_device_code",
-            "user_code": "123456",
-            "verification_uri_complete": "https://example.com/auth",
-        }
-
-    @patch("crewai_cli.authentication.main.httpx.post")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_poll_for_token_success(self, mock_console_print, mock_post):
-        mock_response_success = MagicMock()
-        mock_response_success.status_code = 200
-        mock_response_success.json.return_value = {
-            "access_token": "test_access_token",
-            "id_token": "test_id_token",
-        }
-        mock_post.return_value = mock_response_success
-
-        device_code_data = {"device_code": "test_device_code", "interval": 1}
-
-        with (
-            patch.object(
-                self.auth_command, "_validate_and_save_token"
-            ) as mock_validate,
-            patch.object(
-                self.auth_command, "_login_to_tool_repository"
-            ) as mock_tool_login,
-        ):
-            self.auth_command.oauth2_provider = MagicMock()
-            self.auth_command.oauth2_provider.get_token_url.return_value = (
-                "https://example.com/token"
-            )
-            self.auth_command.oauth2_provider.get_client_id.return_value = "test_client"
-
-            self.auth_command._poll_for_token(device_code_data)
-
-            mock_post.assert_called_once_with(
-                "https://example.com/token",
-                data={
-                    "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
-                    "device_code": "test_device_code",
-                    "client_id": "test_client",
-                },
-                timeout=30,
-            )
-
-            mock_validate.assert_called_once()
-            mock_tool_login.assert_called_once()
-
-            expected_calls = [
-                call("\nWaiting for authentication... ", style="bold blue", end=""),
-                call("Success!", style="bold green"),
-                call("\n[bold green]Welcome to CrewAI AMP![/bold green]\n"),
-            ]
-            mock_console_print.assert_has_calls(expected_calls)
-
-    @patch("crewai_cli.authentication.main.httpx.post")
-    @patch("crewai_cli.authentication.main.console.print")
-    def test_poll_for_token_timeout(self, mock_console_print, mock_post):
-        mock_response_pending = MagicMock()
-        mock_response_pending.status_code = 400
-        mock_response_pending.json.return_value = {"error": "authorization_pending"}
-        mock_post.return_value = mock_response_pending
-
-        device_code_data = {
-            "device_code": "test_device_code",
-            "interval": 0.1,  # Short interval for testing
-        }
-
-        self.auth_command._poll_for_token(device_code_data)
-
-        mock_console_print.assert_any_call(
-            "Timeout: Failed to get the token. Please try again.", style="bold red"
-        )
-
-    @patch("crewai_cli.authentication.main.httpx.post")
-    def test_poll_for_token_error(self, mock_post):
-        """Test the method to poll for token (error path)."""
-        # Setup mock to return error
-        mock_response_error = MagicMock()
-        mock_response_error.status_code = 400
-        mock_response_error.json.return_value = {
-            "error": "access_denied",
-            "error_description": "User denied access",
-        }
-        mock_post.return_value = mock_response_error
-
-        device_code_data = {"device_code": "test_device_code", "interval": 1}
-
-        with pytest.raises(httpx.HTTPError):
-            self.auth_command._poll_for_token(device_code_data)

lib/cli/tests/authentication/test_utils.py

@@ -1,107 +0,0 @@
-import unittest
-from unittest.mock import MagicMock, patch
-
-import jwt
-
-from crewai_cli.authentication.utils import validate_jwt_token
-
-
-@patch("crewai_cli.authentication.utils.PyJWKClient", return_value=MagicMock())
-@patch("crewai_cli.authentication.utils.jwt")
-class TestUtils(unittest.TestCase):
-    def test_validate_jwt_token(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.return_value = {"exp": 1719859200}
-
-        # Create signing key object mock with a .key attribute
-        mock_pyjwkclient.return_value.get_signing_key_from_jwt.return_value = MagicMock(
-            key="mock_signing_key"
-        )
-
-        jwt_token = "aaaaa.bbbbbb.cccccc"  # noqa: S105
-
-        decoded_token = validate_jwt_token(
-            jwt_token=jwt_token,
-            jwks_url="https://mock_jwks_url",
-            issuer="https://mock_issuer",
-            audience="app_id_xxxx",
-        )
-
-        mock_jwt.decode.assert_called_with(
-            jwt_token,
-            "mock_signing_key",
-            algorithms=["RS256"],
-            audience="app_id_xxxx",
-            issuer="https://mock_issuer",
-            leeway=10.0,
-            options={
-                "verify_signature": True,
-                "verify_exp": True,
-                "verify_nbf": True,
-                "verify_iat": True,
-                "require": ["exp", "iat", "iss", "aud", "sub"],
-            },
-        )
-        mock_pyjwkclient.assert_called_once_with("https://mock_jwks_url")
-        self.assertEqual(decoded_token, {"exp": 1719859200})
-
-    def test_validate_jwt_token_expired(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.side_effect = jwt.ExpiredSignatureError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )
-
-    def test_validate_jwt_token_invalid_audience(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.side_effect = jwt.InvalidAudienceError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )
-
-    def test_validate_jwt_token_invalid_issuer(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.side_effect = jwt.InvalidIssuerError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )
-
-    def test_validate_jwt_token_missing_required_claims(
-        self, mock_jwt, mock_pyjwkclient
-    ):
-        mock_jwt.decode.side_effect = jwt.MissingRequiredClaimError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )
-
-    def test_validate_jwt_token_jwks_error(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.side_effect = jwt.exceptions.PyJWKClientError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )
-
-    def test_validate_jwt_token_invalid_token(self, mock_jwt, mock_pyjwkclient):
-        mock_jwt.decode.side_effect = jwt.InvalidTokenError
-        with self.assertRaises(Exception):  # noqa: B017
-            validate_jwt_token(
-                jwt_token="aaaaa.bbbbbb.cccccc",  # noqa: S106
-                jwks_url="https://mock_jwks_url",
-                issuer="https://mock_issuer",
-                audience="app_id_xxxx",
-            )

lib/cli/tests/test_cli.py

@@ -1,255 +0,0 @@
-from pathlib import Path
-from unittest import mock
-
-import pytest
-from click.testing import CliRunner
-from crewai_cli.cli import (
-    deploy_create,
-    deploy_list,
-    deploy_logs,
-    deploy_push,
-    deploy_remove,
-    deply_status,
-    flow_add_crew,
-    login,
-    reset_memories,
-    test,
-    train,
-    version,
-)
-
-
-@pytest.fixture
-def runner():
-    return CliRunner()
-
-
-@mock.patch("crewai_cli.cli.train_crew")
-def test_train_default_iterations(train_crew, runner):
-    result = runner.invoke(train)
-
-    train_crew.assert_called_once_with(5, "trained_agents_data.pkl")
-    assert result.exit_code == 0
-    assert "Training the Crew for 5 iterations" in result.output
-
-
-@mock.patch("crewai_cli.cli.train_crew")
-def test_train_custom_iterations(train_crew, runner):
-    result = runner.invoke(train, ["--n_iterations", "10"])
-
-    train_crew.assert_called_once_with(10, "trained_agents_data.pkl")
-    assert result.exit_code == 0
-    assert "Training the Crew for 10 iterations" in result.output
-
-
-@mock.patch("crewai_cli.cli.train_crew")
-def test_train_invalid_string_iterations(train_crew, runner):
-    result = runner.invoke(train, ["--n_iterations", "invalid"])
-
-    train_crew.assert_not_called()
-    assert result.exit_code == 2
-    assert (
-        "Usage: train [OPTIONS]\nTry 'train --help' for help.\n\nError: Invalid value for '-n' / '--n_iterations': 'invalid' is not a valid integer.\n"
-        in result.output
-    )
-
-
-def test_reset_no_memory_flags(runner):
-    result = runner.invoke(
-        reset_memories,
-    )
-    assert (
-        result.output
-        == "Please specify at least one memory type to reset using the appropriate flags.\n"
-    )
-
-
-def test_version_flag(runner):
-    result = runner.invoke(version)
-
-    assert result.exit_code == 0
-    assert "crewai version:" in result.output
-
-
-def test_version_command(runner):
-    result = runner.invoke(version)
-
-    assert result.exit_code == 0
-    assert "crewai version:" in result.output
-
-
-def test_version_command_with_tools(runner):
-    result = runner.invoke(version, ["--tools"])
-
-    assert result.exit_code == 0
-    assert "crewai version:" in result.output
-    assert (
-        "crewai tools version:" in result.output
-        or "crewai tools not installed" in result.output
-    )
-
-
-@mock.patch("crewai_cli.cli.evaluate_crew")
-def test_test_default_iterations(evaluate_crew, runner):
-    result = runner.invoke(test)
-
-    evaluate_crew.assert_called_once_with(3, "gpt-4o-mini")
-    assert result.exit_code == 0
-    assert "Testing the crew for 3 iterations with model gpt-4o-mini" in result.output
-
-
-@mock.patch("crewai_cli.cli.evaluate_crew")
-def test_test_custom_iterations(evaluate_crew, runner):
-    result = runner.invoke(test, ["--n_iterations", "5", "--model", "gpt-4o"])
-
-    evaluate_crew.assert_called_once_with(5, "gpt-4o")
-    assert result.exit_code == 0
-    assert "Testing the crew for 5 iterations with model gpt-4o" in result.output
-
-
-@mock.patch("crewai_cli.cli.evaluate_crew")
-def test_test_invalid_string_iterations(evaluate_crew, runner):
-    result = runner.invoke(test, ["--n_iterations", "invalid"])
-
-    evaluate_crew.assert_not_called()
-    assert result.exit_code == 2
-    assert (
-        "Usage: test [OPTIONS]\nTry 'test --help' for help.\n\nError: Invalid value for '-n' / '--n_iterations': 'invalid' is not a valid integer.\n"
-        in result.output
-    )
-
-
-@mock.patch("crewai_cli.cli.AuthenticationCommand")
-def test_login(command, runner):
-    mock_auth = command.return_value
-    result = runner.invoke(login)
-
-    assert result.exit_code == 0
-    mock_auth.login.assert_called_once()
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_create(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deploy_create)
-
-    assert result.exit_code == 0
-    mock_deploy.create_crew.assert_called_once()
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_list(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deploy_list)
-
-    assert result.exit_code == 0
-    mock_deploy.list_crews.assert_called_once()
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_push(command, runner):
-    mock_deploy = command.return_value
-    uuid = "test-uuid"
-    result = runner.invoke(deploy_push, ["-u", uuid])
-
-    assert result.exit_code == 0
-    mock_deploy.deploy.assert_called_once_with(uuid=uuid)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_push_no_uuid(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deploy_push)
-
-    assert result.exit_code == 0
-    mock_deploy.deploy.assert_called_once_with(uuid=None)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_status(command, runner):
-    mock_deploy = command.return_value
-    uuid = "test-uuid"
-    result = runner.invoke(deply_status, ["-u", uuid])
-
-    assert result.exit_code == 0
-    mock_deploy.get_crew_status.assert_called_once_with(uuid=uuid)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_status_no_uuid(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deply_status)
-
-    assert result.exit_code == 0
-    mock_deploy.get_crew_status.assert_called_once_with(uuid=None)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_logs(command, runner):
-    mock_deploy = command.return_value
-    uuid = "test-uuid"
-    result = runner.invoke(deploy_logs, ["-u", uuid])
-
-    assert result.exit_code == 0
-    mock_deploy.get_crew_logs.assert_called_once_with(uuid=uuid)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_logs_no_uuid(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deploy_logs)
-
-    assert result.exit_code == 0
-    mock_deploy.get_crew_logs.assert_called_once_with(uuid=None)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_remove(command, runner):
-    mock_deploy = command.return_value
-    uuid = "test-uuid"
-    result = runner.invoke(deploy_remove, ["-u", uuid])
-
-    assert result.exit_code == 0
-    mock_deploy.remove_crew.assert_called_once_with(uuid=uuid)
-
-
-@mock.patch("crewai_cli.cli.DeployCommand")
-def test_deploy_remove_no_uuid(command, runner):
-    mock_deploy = command.return_value
-    result = runner.invoke(deploy_remove)
-
-    assert result.exit_code == 0
-    mock_deploy.remove_crew.assert_called_once_with(uuid=None)
-
-
-@mock.patch("crewai_cli.add_crew_to_flow.create_embedded_crew")
-@mock.patch("pathlib.Path.exists", return_value=True)
-def test_flow_add_crew(mock_path_exists, mock_create_embedded_crew, runner):
-    crew_name = "new_crew"
-    result = runner.invoke(flow_add_crew, [crew_name])
-
-    assert result.exit_code == 0, f"Command failed with output: {result.output}"
-    assert f"Adding crew {crew_name} to the flow" in result.output
-
-    mock_create_embedded_crew.assert_called_once()
-    call_args, call_kwargs = mock_create_embedded_crew.call_args
-    assert call_args[0] == crew_name
-    assert "parent_folder" in call_kwargs
-    assert isinstance(call_kwargs["parent_folder"], Path)
-
-
-def test_add_crew_to_flow_not_in_root(runner):
-    with mock.patch("pathlib.Path.exists", autospec=True) as mock_exists:
-        def exists_side_effect(self):
-            if self.name == "pyproject.toml":
-                return False
-            return True
-
-        mock_exists.side_effect = exists_side_effect
-
-        result = runner.invoke(flow_add_crew, ["new_crew"])
-
-        assert result.exit_code != 0
-        assert "This command must be run from the root of a flow project." in str(
-            result.output
-        )

lib/cli/tests/test_config.py

@@ -1,148 +0,0 @@
-import json
-import shutil
-import tempfile
-import unittest
-from datetime import datetime, timedelta
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-from crewai_cli.config import (
-    CLI_SETTINGS_KEYS,
-    DEFAULT_CLI_SETTINGS,
-    USER_SETTINGS_KEYS,
-    Settings,
-)
-from crewai_cli.shared.token_manager import TokenManager
-
-
-class TestSettings(unittest.TestCase):
-    def setUp(self):
-        self.test_dir = Path(tempfile.mkdtemp())
-        self.config_path = self.test_dir / "settings.json"
-
-    def tearDown(self):
-        shutil.rmtree(self.test_dir)
-
-    def test_empty_initialization(self):
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_data(self):
-        settings = Settings(
-            config_path=self.config_path, tool_repository_username="user1"
-        )
-        self.assertEqual(settings.tool_repository_username, "user1")
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_existing_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"tool_repository_username": "file_user"}, f)
-
-        settings = Settings(config_path=self.config_path)
-        self.assertEqual(settings.tool_repository_username, "file_user")
-
-    def test_merge_file_and_input_data(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump(
-                {
-                    "tool_repository_username": "file_user",
-                    "tool_repository_password": "file_pass",
-                },
-                f,
-            )
-
-        settings = Settings(
-            config_path=self.config_path, tool_repository_username="new_user"
-        )
-        self.assertEqual(settings.tool_repository_username, "new_user")
-        self.assertEqual(settings.tool_repository_password, "file_pass")
-
-    def test_clear_user_settings(self):
-        user_settings = {key: f"value_for_{key}" for key in USER_SETTINGS_KEYS}
-
-        settings = Settings(config_path=self.config_path, **user_settings)
-        settings.clear_user_settings()
-
-        for key in user_settings.keys():
-            self.assertEqual(getattr(settings, key), None)
-
-    @patch("crewai_cli.config.TokenManager")
-    def test_reset_settings(self, mock_token_manager):
-        user_settings = {key: f"value_for_{key}" for key in USER_SETTINGS_KEYS}
-        cli_settings = {key: f"value_for_{key}" for key in CLI_SETTINGS_KEYS if key != "oauth2_extra"}
-        cli_settings["oauth2_extra"] = {"scope": "xxx", "other": "yyy"}
-
-        settings = Settings(
-            config_path=self.config_path, **user_settings, **cli_settings
-        )
-
-        mock_token_manager.return_value = MagicMock()
-        TokenManager().save_tokens(
-            "aaa.bbb.ccc", (datetime.now() + timedelta(seconds=36000)).timestamp()
-        )
-
-        settings.reset()
-
-        for key in user_settings.keys():
-            self.assertEqual(getattr(settings, key), None)
-        for key in cli_settings.keys():
-            self.assertEqual(getattr(settings, key), DEFAULT_CLI_SETTINGS.get(key))
-
-        mock_token_manager.return_value.clear_tokens.assert_called_once()
-
-    def test_dump_new_settings(self):
-        settings = Settings(
-            config_path=self.config_path, tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_update_existing_settings(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"existing_setting": "value"}, f)
-
-        settings = Settings(
-            config_path=self.config_path, tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["existing_setting"], "value")
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_none_values(self):
-        settings = Settings(config_path=self.config_path, tool_repository_username=None)
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertIsNone(saved_data.get("tool_repository_username"))
-
-    def test_invalid_json_in_config(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            f.write("invalid json")
-
-        try:
-            settings = Settings(config_path=self.config_path)
-            self.assertIsNone(settings.tool_repository_username)
-        except json.JSONDecodeError:
-            self.fail("Settings initialization should handle invalid JSON")
-
-    def test_empty_config_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        self.config_path.touch()
-
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)

lib/cli/tests/test_constants.py

@@ -1,20 +0,0 @@
-from crewai_cli.constants import ENV_VARS, MODELS, PROVIDERS
-
-
-def test_huggingface_in_providers():
-    """Test that Huggingface is in the PROVIDERS list."""
-    assert "huggingface" in PROVIDERS
-
-
-def test_huggingface_env_vars():
-    """Test that Huggingface environment variables are properly configured."""
-    assert "huggingface" in ENV_VARS
-    assert any(
-        detail.get("key_name") == "HF_TOKEN" for detail in ENV_VARS["huggingface"]
-    )
-
-
-def test_huggingface_models():
-    """Test that Huggingface models are properly configured."""
-    assert "huggingface" in MODELS
-    assert len(MODELS["huggingface"]) > 0

lib/cli/tests/test_git.py

@@ -1,101 +0,0 @@
-import pytest
-from crewai_cli.git import Repository
-
-
-@pytest.fixture()
-def repository(fp):
-    fp.register(["git", "--version"], stdout="git version 2.30.0\n")
-    fp.register(["git", "rev-parse", "--is-inside-work-tree"], stdout="true\n")
-    fp.register(["git", "fetch"], stdout="")
-    return Repository(path=".")
-
-
-def test_init_with_invalid_git_repo(fp):
-    fp.register(["git", "--version"], stdout="git version 2.30.0\n")
-    fp.register(
-        ["git", "rev-parse", "--is-inside-work-tree"],
-        returncode=1,
-        stderr="fatal: not a git repository\n",
-    )
-
-    with pytest.raises(ValueError):
-        Repository(path="invalid/path")
-
-
-def test_is_git_not_installed(fp):
-    fp.register(["git", "--version"], returncode=1)
-
-    with pytest.raises(
-        ValueError, match="Git is not installed or not found in your PATH."
-    ):
-        Repository(path=".")
-
-
-def test_status(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main [ahead 1]\n",
-    )
-    assert repository.status() == "## main...origin/main [ahead 1]"
-
-
-def test_has_uncommitted_changes(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main\n M somefile.txt\n",
-    )
-    assert repository.has_uncommitted_changes() is True
-
-
-def test_is_ahead_or_behind(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main [ahead 1]\n",
-    )
-    assert repository.is_ahead_or_behind() is True
-
-
-def test_is_synced_when_synced(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"], stdout="## main...origin/main\n"
-    )
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"], stdout="## main...origin/main\n"
-    )
-    assert repository.is_synced() is True
-
-
-def test_is_synced_with_uncommitted_changes(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main\n M somefile.txt\n",
-    )
-    assert repository.is_synced() is False
-
-
-def test_is_synced_when_ahead_or_behind(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main [ahead 1]\n",
-    )
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main [ahead 1]\n",
-    )
-    assert repository.is_synced() is False
-
-
-def test_is_synced_with_uncommitted_changes_and_ahead(fp, repository):
-    fp.register(
-        ["git", "status", "--branch", "--porcelain"],
-        stdout="## main...origin/main [ahead 1]\n M somefile.txt\n",
-    )
-    assert repository.is_synced() is False
-
-
-def test_origin_url(fp, repository):
-    fp.register(
-        ["git", "remote", "get-url", "origin"],
-        stdout="https://github.com/user/repo.git\n",
-    )
-    assert repository.origin_url() == "https://github.com/user/repo.git"

lib/cli/tests/test_plus_api.py

@@ -1,356 +0,0 @@
-import os
-import unittest
-from unittest.mock import ANY, AsyncMock, MagicMock, patch
-
-import pytest
-
-from crewai_cli.plus_api import PlusAPI
-
-
-class TestPlusAPI(unittest.TestCase):
-    def setUp(self):
-        self.api_key = "test_api_key"
-        self.api = PlusAPI(self.api_key)
-        self.org_uuid = "test-org-uuid"
-
-    def test_init(self):
-        self.assertEqual(self.api.api_key, self.api_key)
-        self.assertEqual(self.api.headers["Authorization"], f"Bearer {self.api_key}")
-        self.assertEqual(self.api.headers["Content-Type"], "application/json")
-        self.assertTrue("CrewAI-CLI/" in self.api.headers["User-Agent"])
-        self.assertTrue(self.api.headers["X-Crewai-Version"])
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_login_to_tool_repository(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-
-        response = self.api.login_to_tool_repository()
-
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/tools/login"
-        )
-        self.assertEqual(response, mock_response)
-
-    def assert_request_with_org_id(
-        self, mock_client_instance, method: str, endpoint: str, **kwargs
-    ):
-        mock_client_instance.request.assert_called_once_with(
-            method,
-            f"{os.getenv('CREWAI_PLUS_URL')}{endpoint}",
-            headers={
-                "Authorization": ANY,
-                "Content-Type": ANY,
-                "User-Agent": ANY,
-                "X-Crewai-Version": ANY,
-                "X-Crewai-Organization-Id": self.org_uuid,
-            },
-            **kwargs,
-        )
-
-    @patch("crewai_cli.plus_api.Settings")
-    @patch("crewai_cli.plus_api.httpx.Client")
-    def test_login_to_tool_repository_with_org_uuid(
-        self, mock_client_class, mock_settings_class
-    ):
-        mock_settings = MagicMock()
-        mock_settings.org_uuid = self.org_uuid
-        mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL')
-        mock_settings_class.return_value = mock_settings
-        self.api = PlusAPI(self.api_key)
-
-        mock_client_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_client_instance.request.return_value = mock_response
-        mock_client_class.return_value.__enter__.return_value = mock_client_instance
-
-        response = self.api.login_to_tool_repository()
-
-        self.assert_request_with_org_id(
-            mock_client_instance, "POST", "/crewai_plus/api/v1/tools/login"
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_get_tool(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-
-        response = self.api.get_tool("test_tool_handle")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/tools/test_tool_handle"
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.Settings")
-    @patch("crewai_cli.plus_api.httpx.Client")
-    def test_get_tool_with_org_uuid(self, mock_client_class, mock_settings_class):
-        mock_settings = MagicMock()
-        mock_settings.org_uuid = self.org_uuid
-        mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL')
-        mock_settings_class.return_value = mock_settings
-        self.api = PlusAPI(self.api_key)
-
-        mock_client_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_client_instance.request.return_value = mock_response
-        mock_client_class.return_value.__enter__.return_value = mock_client_instance
-
-        response = self.api.get_tool("test_tool_handle")
-
-        self.assert_request_with_org_id(
-            mock_client_instance, "GET", "/crewai_plus/api/v1/tools/test_tool_handle"
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_publish_tool(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-        handle = "test_tool_handle"
-        public = True
-        version = "1.0.0"
-        description = "Test tool description"
-        encoded_file = "encoded_test_file"
-
-        response = self.api.publish_tool(
-            handle, public, version, description, encoded_file
-        )
-
-        params = {
-            "handle": handle,
-            "public": public,
-            "version": version,
-            "file": encoded_file,
-            "description": description,
-            "available_exports": None,
-        }
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/tools", json=params
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.Settings")
-    @patch("crewai_cli.plus_api.httpx.Client")
-    def test_publish_tool_with_org_uuid(self, mock_client_class, mock_settings_class):
-        mock_settings = MagicMock()
-        mock_settings.org_uuid = self.org_uuid
-        mock_settings.enterprise_base_url = os.getenv('CREWAI_PLUS_URL')
-        mock_settings_class.return_value = mock_settings
-        self.api = PlusAPI(self.api_key)
-
-        mock_client_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_client_instance.request.return_value = mock_response
-        mock_client_class.return_value.__enter__.return_value = mock_client_instance
-
-        handle = "test_tool_handle"
-        public = True
-        version = "1.0.0"
-        description = "Test tool description"
-        encoded_file = "encoded_test_file"
-
-        response = self.api.publish_tool(
-            handle, public, version, description, encoded_file
-        )
-
-        expected_params = {
-            "handle": handle,
-            "public": public,
-            "version": version,
-            "file": encoded_file,
-            "description": description,
-            "available_exports": None,
-        }
-
-        self.assert_request_with_org_id(
-            mock_client_instance, "POST", "/crewai_plus/api/v1/tools", json=expected_params
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_publish_tool_without_description(self, mock_make_request):
-        mock_response = MagicMock()
-        mock_make_request.return_value = mock_response
-        handle = "test_tool_handle"
-        public = False
-        version = "2.0.0"
-        description = None
-        encoded_file = "encoded_test_file"
-
-        response = self.api.publish_tool(
-            handle, public, version, description, encoded_file
-        )
-
-        params = {
-            "handle": handle,
-            "public": public,
-            "version": version,
-            "file": encoded_file,
-            "description": description,
-            "available_exports": None,
-        }
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/tools", json=params
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.httpx.Client")
-    def test_make_request(self, mock_client_class):
-        mock_client_instance = MagicMock()
-        mock_response = MagicMock()
-        mock_client_instance.request.return_value = mock_response
-        mock_client_class.return_value.__enter__.return_value = mock_client_instance
-
-        response = self.api._make_request("GET", "test_endpoint")
-
-        mock_client_class.assert_called_once_with(trust_env=False, verify=True)
-        mock_client_instance.request.assert_called_once_with(
-            "GET", f"{self.api.base_url}/test_endpoint", headers=self.api.headers
-        )
-        self.assertEqual(response, mock_response)
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_deploy_by_name(self, mock_make_request):
-        self.api.deploy_by_name("test_project")
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/crews/by-name/test_project/deploy"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_deploy_by_uuid(self, mock_make_request):
-        self.api.deploy_by_uuid("test_uuid")
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/crews/test_uuid/deploy"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_crew_status_by_name(self, mock_make_request):
-        self.api.crew_status_by_name("test_project")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/crews/by-name/test_project/status"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_crew_status_by_uuid(self, mock_make_request):
-        self.api.crew_status_by_uuid("test_uuid")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/crews/test_uuid/status"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_crew_by_name(self, mock_make_request):
-        self.api.crew_by_name("test_project")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/crews/by-name/test_project/logs/deployment"
-        )
-
-        self.api.crew_by_name("test_project", "custom_log")
-        mock_make_request.assert_called_with(
-            "GET", "/crewai_plus/api/v1/crews/by-name/test_project/logs/custom_log"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_crew_by_uuid(self, mock_make_request):
-        self.api.crew_by_uuid("test_uuid")
-        mock_make_request.assert_called_once_with(
-            "GET", "/crewai_plus/api/v1/crews/test_uuid/logs/deployment"
-        )
-
-        self.api.crew_by_uuid("test_uuid", "custom_log")
-        mock_make_request.assert_called_with(
-            "GET", "/crewai_plus/api/v1/crews/test_uuid/logs/custom_log"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_delete_crew_by_name(self, mock_make_request):
-        self.api.delete_crew_by_name("test_project")
-        mock_make_request.assert_called_once_with(
-            "DELETE", "/crewai_plus/api/v1/crews/by-name/test_project"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_delete_crew_by_uuid(self, mock_make_request):
-        self.api.delete_crew_by_uuid("test_uuid")
-        mock_make_request.assert_called_once_with(
-            "DELETE", "/crewai_plus/api/v1/crews/test_uuid"
-        )
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_list_crews(self, mock_make_request):
-        self.api.list_crews()
-        mock_make_request.assert_called_once_with("GET", "/crewai_plus/api/v1/crews")
-
-    @patch("crewai_cli.plus_api.PlusAPI._make_request")
-    def test_create_crew(self, mock_make_request):
-        payload = {"name": "test_crew"}
-        self.api.create_crew(payload)
-        mock_make_request.assert_called_once_with(
-            "POST", "/crewai_plus/api/v1/crews", json=payload
-        )
-
-    @patch("crewai_cli.plus_api.Settings")
-    @patch.dict(os.environ, {"CREWAI_PLUS_URL": ""})
-    def test_custom_base_url(self, mock_settings_class):
-        mock_settings = MagicMock()
-        mock_settings.enterprise_base_url = "https://custom-url.com/api"
-        mock_settings_class.return_value = mock_settings
-        custom_api = PlusAPI("test_key")
-        self.assertEqual(
-            custom_api.base_url,
-            "https://custom-url.com/api",
-        )
-
-    @patch.dict(os.environ, {"CREWAI_PLUS_URL": "https://custom-url-from-env.com"})
-    def test_custom_base_url_from_env(self):
-        custom_api = PlusAPI("test_key")
-        self.assertEqual(
-            custom_api.base_url,
-            "https://custom-url-from-env.com",
-        )
-
-
-@pytest.mark.asyncio
-@patch("httpx.AsyncClient")
-async def test_get_agent(mock_async_client_class):
-    api = PlusAPI("test_api_key")
-    mock_response = MagicMock()
-    mock_client_instance = AsyncMock()
-    mock_client_instance.get.return_value = mock_response
-    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
-
-    response = await api.get_agent("test_agent_handle")
-
-    mock_client_instance.get.assert_called_once_with(
-        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
-        headers=api.headers,
-    )
-    assert response == mock_response
-
-
-@pytest.mark.asyncio
-@patch("httpx.AsyncClient")
-@patch("crewai_cli.plus_api.Settings")
-async def test_get_agent_with_org_uuid(mock_settings_class, mock_async_client_class):
-    org_uuid = "test-org-uuid"
-    mock_settings = MagicMock()
-    mock_settings.org_uuid = org_uuid
-    mock_settings.enterprise_base_url = os.getenv("CREWAI_PLUS_URL")
-    mock_settings_class.return_value = mock_settings
-
-    api = PlusAPI("test_api_key")
-
-    mock_response = MagicMock()
-    mock_client_instance = AsyncMock()
-    mock_client_instance.get.return_value = mock_response
-    mock_async_client_class.return_value.__aenter__.return_value = mock_client_instance
-
-    response = await api.get_agent("test_agent_handle")
-
-    mock_client_instance.get.assert_called_once_with(
-        f"{api.base_url}/crewai_plus/api/v1/agents/test_agent_handle",
-        headers=api.headers,
-    )
-    assert "X-Crewai-Organization-Id" in api.headers
-    assert api.headers["X-Crewai-Organization-Id"] == org_uuid
-    assert response == mock_response

lib/cli/tests/test_token_manager.py

@@ -1,294 +0,0 @@
-"""Tests for TokenManager with atomic file operations."""
-
-import json
-import os
-import tempfile
-import unittest
-from datetime import datetime, timedelta
-from pathlib import Path
-from unittest.mock import patch
-
-from cryptography.fernet import Fernet
-
-from crewai_cli.shared.token_manager import TokenManager
-
-
-class TestTokenManager(unittest.TestCase):
-    """Test cases for TokenManager."""
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def setUp(self, mock_get_key: unittest.mock.MagicMock) -> None:
-        """Set up test fixtures."""
-        mock_get_key.return_value = Fernet.generate_key()
-        self.token_manager = TokenManager()
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._read_secure_file")
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_get_or_create_key_existing(
-        self,
-        mock_get_or_create: unittest.mock.MagicMock,
-        mock_read: unittest.mock.MagicMock,
-    ) -> None:
-        """Test that existing key is returned when present."""
-        mock_key = Fernet.generate_key()
-        mock_get_or_create.return_value = mock_key
-
-        token_manager = TokenManager()
-        result = token_manager.key
-
-        self.assertEqual(result, mock_key)
-
-    def test_get_or_create_key_new(self) -> None:
-        """Test that new key is created when none exists."""
-        mock_key = Fernet.generate_key()
-
-        with (
-            patch.object(self.token_manager, "_read_secure_file", return_value=None) as mock_read,
-            patch.object(self.token_manager, "_atomic_create_secure_file", return_value=True) as mock_atomic_create,
-            patch("crewai_cli.shared.token_manager.Fernet.generate_key", return_value=mock_key) as mock_generate,
-        ):
-            result = self.token_manager._get_or_create_key()
-
-            self.assertEqual(result, mock_key)
-            mock_read.assert_called_with("secret.key")
-            mock_generate.assert_called_once()
-            mock_atomic_create.assert_called_once_with("secret.key", mock_key)
-
-    def test_get_or_create_key_race_condition(self) -> None:
-        """Test that another process's key is used when atomic create fails."""
-        our_key = Fernet.generate_key()
-        their_key = Fernet.generate_key()
-
-        with (
-            patch.object(self.token_manager, "_read_secure_file", side_effect=[None, their_key]) as mock_read,
-            patch.object(self.token_manager, "_atomic_create_secure_file", return_value=False) as mock_atomic_create,
-            patch("crewai_cli.shared.token_manager.Fernet.generate_key", return_value=our_key),
-        ):
-            result = self.token_manager._get_or_create_key()
-
-            self.assertEqual(result, their_key)
-            self.assertEqual(mock_read.call_count, 2)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._atomic_write_secure_file")
-    def test_save_tokens(
-        self, mock_write: unittest.mock.MagicMock
-    ) -> None:
-        """Test saving tokens encrypts and writes atomically."""
-        access_token = "test_token"
-        expires_at = int((datetime.now() + timedelta(seconds=3600)).timestamp())
-
-        self.token_manager.save_tokens(access_token, expires_at)
-
-        mock_write.assert_called_once()
-        args = mock_write.call_args[0]
-        self.assertEqual(args[0], "tokens.enc")
-        decrypted_data = self.token_manager.fernet.decrypt(args[1])
-        data = json.loads(decrypted_data)
-        self.assertEqual(data["access_token"], access_token)
-        expiration = datetime.fromisoformat(data["expiration"])
-        self.assertEqual(expiration, datetime.fromtimestamp(expires_at))
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._read_secure_file")
-    def test_get_token_valid(
-        self, mock_read: unittest.mock.MagicMock
-    ) -> None:
-        """Test getting a valid non-expired token."""
-        access_token = "test_token"
-        expiration = (datetime.now() + timedelta(hours=1)).isoformat()
-        data = {"access_token": access_token, "expiration": expiration}
-        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
-        mock_read.return_value = encrypted_data
-
-        result = self.token_manager.get_token()
-
-        self.assertEqual(result, access_token)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._read_secure_file")
-    def test_get_token_expired(
-        self, mock_read: unittest.mock.MagicMock
-    ) -> None:
-        """Test that expired token returns None."""
-        access_token = "test_token"
-        expiration = (datetime.now() - timedelta(hours=1)).isoformat()
-        data = {"access_token": access_token, "expiration": expiration}
-        encrypted_data = self.token_manager.fernet.encrypt(json.dumps(data).encode())
-        mock_read.return_value = encrypted_data
-
-        result = self.token_manager.get_token()
-
-        self.assertIsNone(result)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._read_secure_file")
-    def test_get_token_not_found(
-        self, mock_read: unittest.mock.MagicMock
-    ) -> None:
-        """Test that missing token file returns None."""
-        mock_read.return_value = None
-
-        result = self.token_manager.get_token()
-
-        self.assertIsNone(result)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._delete_secure_file")
-    def test_clear_tokens(
-        self, mock_delete: unittest.mock.MagicMock
-    ) -> None:
-        """Test clearing tokens deletes the token file."""
-        self.token_manager.clear_tokens()
-
-        mock_delete.assert_called_once_with("tokens.enc")
-
-
-class TestAtomicFileOperations(unittest.TestCase):
-    """Test atomic file operations directly."""
-
-    def setUp(self) -> None:
-        """Set up test fixtures with temp directory."""
-        self.temp_dir = tempfile.mkdtemp()
-        self.original_get_path = TokenManager._get_secure_storage_path
-
-        # Patch to use temp directory
-        def mock_get_path() -> Path:
-            return Path(self.temp_dir)
-
-        TokenManager._get_secure_storage_path = staticmethod(mock_get_path)
-
-    def tearDown(self) -> None:
-        """Clean up temp directory."""
-        TokenManager._get_secure_storage_path = staticmethod(self.original_get_path)
-        import shutil
-        shutil.rmtree(self.temp_dir, ignore_errors=True)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_atomic_create_new_file(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test atomic create succeeds for new file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        result = tm._atomic_create_secure_file("test.txt", b"content")
-
-        self.assertTrue(result)
-        file_path = Path(self.temp_dir) / "test.txt"
-        self.assertTrue(file_path.exists())
-        self.assertEqual(file_path.read_bytes(), b"content")
-        self.assertEqual(file_path.stat().st_mode & 0o777, 0o600)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_atomic_create_existing_file(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test atomic create fails for existing file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        # Create file first
-        file_path = Path(self.temp_dir) / "test.txt"
-        file_path.write_bytes(b"original")
-
-        result = tm._atomic_create_secure_file("test.txt", b"new content")
-
-        self.assertFalse(result)
-        self.assertEqual(file_path.read_bytes(), b"original")
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_atomic_write_new_file(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test atomic write creates new file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        tm._atomic_write_secure_file("test.txt", b"content")
-
-        file_path = Path(self.temp_dir) / "test.txt"
-        self.assertTrue(file_path.exists())
-        self.assertEqual(file_path.read_bytes(), b"content")
-        self.assertEqual(file_path.stat().st_mode & 0o777, 0o600)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_atomic_write_overwrites(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test atomic write overwrites existing file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        file_path = Path(self.temp_dir) / "test.txt"
-        file_path.write_bytes(b"original")
-
-        tm._atomic_write_secure_file("test.txt", b"new content")
-
-        self.assertEqual(file_path.read_bytes(), b"new content")
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_atomic_write_no_temp_file_on_success(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test that temp file is cleaned up after successful write."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        tm._atomic_write_secure_file("test.txt", b"content")
-
-        # Check no temp files remain
-        temp_files = list(Path(self.temp_dir).glob(".test.txt.*"))
-        self.assertEqual(len(temp_files), 0)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_read_secure_file_exists(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test reading existing file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        file_path = Path(self.temp_dir) / "test.txt"
-        file_path.write_bytes(b"content")
-
-        result = tm._read_secure_file("test.txt")
-
-        self.assertEqual(result, b"content")
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_read_secure_file_not_exists(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test reading non-existent file returns None."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        result = tm._read_secure_file("nonexistent.txt")
-
-        self.assertIsNone(result)
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_delete_secure_file_exists(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test deleting existing file."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        file_path = Path(self.temp_dir) / "test.txt"
-        file_path.write_bytes(b"content")
-
-        tm._delete_secure_file("test.txt")
-
-        self.assertFalse(file_path.exists())
-
-    @patch("crewai_cli.shared.token_manager.TokenManager._get_or_create_key")
-    def test_delete_secure_file_not_exists(
-        self, mock_get_key: unittest.mock.MagicMock
-    ) -> None:
-        """Test deleting non-existent file doesn't raise."""
-        mock_get_key.return_value = Fernet.generate_key()
-        tm = TokenManager()
-
-        # Should not raise
-        tm._delete_secure_file("nonexistent.txt")
-
-
-if __name__ == "__main__":
-    unittest.main()

lib/cli/tests/test_utils.py

@@ -1,146 +0,0 @@
-import os
-import shutil
-import tempfile
-from pathlib import Path
-
-import pytest
-from crewai_cli import utils
-
-
-@pytest.fixture
-def temp_tree():
-    root_dir = tempfile.mkdtemp()
-
-    create_file(os.path.join(root_dir, "file1.txt"), "Hello, world!")
-    create_file(os.path.join(root_dir, "file2.txt"), "Another file")
-    os.mkdir(os.path.join(root_dir, "empty_dir"))
-    nested_dir = os.path.join(root_dir, "nested_dir")
-    os.mkdir(nested_dir)
-    create_file(os.path.join(nested_dir, "nested_file.txt"), "Nested content")
-
-    yield root_dir
-
-    shutil.rmtree(root_dir)
-
-
-def create_file(path, content):
-    with open(path, "w") as f:
-        f.write(content)
-
-
-def test_tree_find_and_replace_file_content(temp_tree):
-    utils.tree_find_and_replace(temp_tree, "world", "universe")
-    with open(os.path.join(temp_tree, "file1.txt"), "r") as f:
-        assert f.read() == "Hello, universe!"
-
-
-def test_tree_find_and_replace_file_name(temp_tree):
-    old_path = os.path.join(temp_tree, "file2.txt")
-    new_path = os.path.join(temp_tree, "file2_renamed.txt")
-    os.rename(old_path, new_path)
-    utils.tree_find_and_replace(temp_tree, "renamed", "modified")
-    assert os.path.exists(os.path.join(temp_tree, "file2_modified.txt"))
-    assert not os.path.exists(new_path)
-
-
-def test_tree_find_and_replace_directory_name(temp_tree):
-    utils.tree_find_and_replace(temp_tree, "empty", "renamed")
-    assert os.path.exists(os.path.join(temp_tree, "renamed_dir"))
-    assert not os.path.exists(os.path.join(temp_tree, "empty_dir"))
-
-
-def test_tree_find_and_replace_nested_content(temp_tree):
-    utils.tree_find_and_replace(temp_tree, "Nested", "Updated")
-    with open(os.path.join(temp_tree, "nested_dir", "nested_file.txt"), "r") as f:
-        assert f.read() == "Updated content"
-
-
-def test_tree_find_and_replace_no_matches(temp_tree):
-    utils.tree_find_and_replace(temp_tree, "nonexistent", "replacement")
-    assert set(os.listdir(temp_tree)) == {
-        "file1.txt",
-        "file2.txt",
-        "empty_dir",
-        "nested_dir",
-    }
-
-
-def test_tree_copy_full_structure(temp_tree):
-    dest_dir = tempfile.mkdtemp()
-    try:
-        utils.tree_copy(temp_tree, dest_dir)
-        assert set(os.listdir(dest_dir)) == set(os.listdir(temp_tree))
-        assert os.path.isfile(os.path.join(dest_dir, "file1.txt"))
-        assert os.path.isfile(os.path.join(dest_dir, "file2.txt"))
-        assert os.path.isdir(os.path.join(dest_dir, "empty_dir"))
-        assert os.path.isdir(os.path.join(dest_dir, "nested_dir"))
-        assert os.path.isfile(os.path.join(dest_dir, "nested_dir", "nested_file.txt"))
-    finally:
-        shutil.rmtree(dest_dir)
-
-
-def test_tree_copy_preserve_content(temp_tree):
-    dest_dir = tempfile.mkdtemp()
-    try:
-        utils.tree_copy(temp_tree, dest_dir)
-        with open(os.path.join(dest_dir, "file1.txt"), "r") as f:
-            assert f.read() == "Hello, world!"
-        with open(os.path.join(dest_dir, "nested_dir", "nested_file.txt"), "r") as f:
-            assert f.read() == "Nested content"
-    finally:
-        shutil.rmtree(dest_dir)
-
-
-def test_tree_copy_to_existing_directory(temp_tree):
-    dest_dir = tempfile.mkdtemp()
-    try:
-        create_file(os.path.join(dest_dir, "existing_file.txt"), "I was here first")
-        utils.tree_copy(temp_tree, dest_dir)
-        assert os.path.isfile(os.path.join(dest_dir, "existing_file.txt"))
-        assert os.path.isfile(os.path.join(dest_dir, "file1.txt"))
-    finally:
-        shutil.rmtree(dest_dir)
-
-
-@pytest.fixture
-def temp_project_dir():
-    """Create a temporary directory for testing tool extraction."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        yield Path(temp_dir)
-
-
-def create_init_file(directory, content):
-    return create_file(directory / "__init__.py", content)
-
-
-def test_extract_available_exports_empty_project(temp_project_dir, capsys):
-    with pytest.raises(SystemExit):
-        utils.extract_available_exports(dir_path=temp_project_dir)
-    captured = capsys.readouterr()
-
-    assert "No valid tools were exposed in your __init__.py file" in captured.out
-
-
-def test_extract_available_exports_no_init_file(temp_project_dir, capsys):
-    (temp_project_dir / "some_file.py").write_text("print('hello')")
-    with pytest.raises(SystemExit):
-        utils.extract_available_exports(dir_path=temp_project_dir)
-    captured = capsys.readouterr()
-
-    assert "No valid tools were exposed in your __init__.py file" in captured.out
-
-
-def test_extract_available_exports_empty_init_file(temp_project_dir, capsys):
-    create_init_file(temp_project_dir, "")
-    with pytest.raises(SystemExit):
-        utils.extract_available_exports(dir_path=temp_project_dir)
-    captured = capsys.readouterr()
-
-    assert "Warning: No __all__ defined in" in captured.out
-
-
-# Tests for extract_available_exports with crewai.tools (BaseTool, @tool)
-# remain in lib/crewai/tests/cli/test_utils.py as they require the crewai core package.
-
-# Tests for get_crews, get_flows, fetch_crews, is_valid_tool
-# remain in lib/crewai/tests/cli/test_utils.py as they require the crewai core package.

lib/cli/tests/test_version.py

@@ -1,372 +0,0 @@
-"""Test for version management."""
-
-import json
-from datetime import datetime, timedelta
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-from crewai_cli.version import get_crewai_version as _get_ver
-from crewai_cli.version import (
-    _find_latest_non_yanked_version,
-    _get_cache_file,
-    _is_cache_valid,
-    _is_version_yanked,
-    get_crewai_version,
-    get_latest_version_from_pypi,
-    is_current_version_yanked,
-    is_newer_version_available,
-)
-
-
-def test_dynamic_versioning_consistency() -> None:
-    """Test that dynamic versioning provides consistent version across all access methods."""
-    cli_version = get_crewai_version()
-    package_version = _get_ver()
-
-    assert cli_version == package_version
-
-    assert package_version is not None
-    assert len(package_version.strip()) > 0
-
-
-class TestVersionChecking:
-    """Test version checking utilities."""
-
-    def test_get_crewai_version(self) -> None:
-        """Test getting current crewai version."""
-        version = get_crewai_version()
-        assert isinstance(version, str)
-        assert len(version) > 0
-
-    def test_get_cache_file(self) -> None:
-        """Test cache file path generation."""
-        cache_file = _get_cache_file()
-        assert isinstance(cache_file, Path)
-        assert cache_file.name == "version_cache.json"
-
-    def test_is_cache_valid_with_fresh_cache(self) -> None:
-        """Test cache validation with fresh cache."""
-        cache_data = {"timestamp": datetime.now().isoformat(), "version": "1.0.0"}
-        assert _is_cache_valid(cache_data) is True
-
-    def test_is_cache_valid_with_stale_cache(self) -> None:
-        """Test cache validation with stale cache."""
-        old_time = datetime.now() - timedelta(hours=25)
-        cache_data = {"timestamp": old_time.isoformat(), "version": "1.0.0"}
-        assert _is_cache_valid(cache_data) is False
-
-    def test_is_cache_valid_with_missing_timestamp(self) -> None:
-        """Test cache validation with missing timestamp."""
-        cache_data = {"version": "1.0.0"}
-        assert _is_cache_valid(cache_data) is False
-
-    @patch("crewai_cli.version.Path.exists")
-    @patch("crewai_cli.version.request.urlopen")
-    def test_get_latest_version_from_pypi_success(
-        self, mock_urlopen: MagicMock, mock_exists: MagicMock
-    ) -> None:
-        """Test successful PyPI version fetch uses releases data."""
-        mock_exists.return_value = False
-
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "2.0.0": [{"yanked": False}],
-            "2.1.0": [{"yanked": True, "yanked_reason": "bad release"}],
-        }
-        mock_response = MagicMock()
-        mock_response.read.return_value = json.dumps(
-            {"info": {"version": "2.1.0"}, "releases": releases}
-        ).encode()
-        mock_urlopen.return_value.__enter__.return_value = mock_response
-
-        version = get_latest_version_from_pypi()
-        assert version == "2.0.0"
-
-    @patch("crewai_cli.version.Path.exists")
-    @patch("crewai_cli.version.request.urlopen")
-    def test_get_latest_version_from_pypi_failure(
-        self, mock_urlopen: MagicMock, mock_exists: MagicMock
-    ) -> None:
-        """Test PyPI version fetch failure."""
-        from urllib.error import URLError
-
-        mock_exists.return_value = False
-
-        mock_urlopen.side_effect = URLError("Network error")
-
-        version = get_latest_version_from_pypi()
-        assert version is None
-
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version.get_latest_version_from_pypi")
-    def test_is_newer_version_available_true(
-        self, mock_latest: MagicMock, mock_current: MagicMock
-    ) -> None:
-        """Test when newer version is available."""
-        mock_current.return_value = "1.0.0"
-        mock_latest.return_value = "2.0.0"
-
-        is_newer, current, latest = is_newer_version_available()
-        assert is_newer is True
-        assert current == "1.0.0"
-        assert latest == "2.0.0"
-
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version.get_latest_version_from_pypi")
-    def test_is_newer_version_available_false(
-        self, mock_latest: MagicMock, mock_current: MagicMock
-    ) -> None:
-        """Test when no newer version is available."""
-        mock_current.return_value = "2.0.0"
-        mock_latest.return_value = "2.0.0"
-
-        is_newer, current, latest = is_newer_version_available()
-        assert is_newer is False
-        assert current == "2.0.0"
-        assert latest == "2.0.0"
-
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version.get_latest_version_from_pypi")
-    def test_is_newer_version_available_with_none_latest(
-        self, mock_latest: MagicMock, mock_current: MagicMock
-    ) -> None:
-        """Test when PyPI fetch fails."""
-        mock_current.return_value = "1.0.0"
-        mock_latest.return_value = None
-
-        is_newer, current, latest = is_newer_version_available()
-        assert is_newer is False
-        assert current == "1.0.0"
-        assert latest is None
-
-
-class TestFindLatestNonYankedVersion:
-    """Test _find_latest_non_yanked_version helper."""
-
-    def test_skips_yanked_versions(self) -> None:
-        """Test that yanked versions are skipped."""
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "2.0.0": [{"yanked": True}],
-        }
-        assert _find_latest_non_yanked_version(releases) == "1.0.0"
-
-    def test_returns_highest_non_yanked(self) -> None:
-        """Test that the highest non-yanked version is returned."""
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "1.5.0": [{"yanked": False}],
-            "2.0.0": [{"yanked": True}],
-        }
-        assert _find_latest_non_yanked_version(releases) == "1.5.0"
-
-    def test_returns_none_when_all_yanked(self) -> None:
-        """Test that None is returned when all versions are yanked."""
-        releases = {
-            "1.0.0": [{"yanked": True}],
-            "2.0.0": [{"yanked": True}],
-        }
-        assert _find_latest_non_yanked_version(releases) is None
-
-    def test_skips_prerelease_versions(self) -> None:
-        """Test that pre-release versions are skipped."""
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "2.0.0a1": [{"yanked": False}],
-            "2.0.0rc1": [{"yanked": False}],
-        }
-        assert _find_latest_non_yanked_version(releases) == "1.0.0"
-
-    def test_skips_versions_with_empty_files(self) -> None:
-        """Test that versions with no files are skipped."""
-        releases: dict[str, list[dict[str, bool]]] = {
-            "1.0.0": [{"yanked": False}],
-            "2.0.0": [],
-        }
-        assert _find_latest_non_yanked_version(releases) == "1.0.0"
-
-    def test_handles_invalid_version_strings(self) -> None:
-        """Test that invalid version strings are skipped."""
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "not-a-version": [{"yanked": False}],
-        }
-        assert _find_latest_non_yanked_version(releases) == "1.0.0"
-
-    def test_partially_yanked_files_not_considered_yanked(self) -> None:
-        """Test that a version with some non-yanked files is not yanked."""
-        releases = {
-            "1.0.0": [{"yanked": False}],
-            "2.0.0": [{"yanked": True}, {"yanked": False}],
-        }
-        assert _find_latest_non_yanked_version(releases) == "2.0.0"
-
-
-class TestIsVersionYanked:
-    """Test _is_version_yanked helper."""
-
-    def test_non_yanked_version(self) -> None:
-        """Test a non-yanked version returns False."""
-        releases = {"1.0.0": [{"yanked": False}]}
-        is_yanked, reason = _is_version_yanked("1.0.0", releases)
-        assert is_yanked is False
-        assert reason == ""
-
-    def test_yanked_version_with_reason(self) -> None:
-        """Test a yanked version returns True with reason."""
-        releases = {
-            "1.0.0": [{"yanked": True, "yanked_reason": "critical bug"}],
-        }
-        is_yanked, reason = _is_version_yanked("1.0.0", releases)
-        assert is_yanked is True
-        assert reason == "critical bug"
-
-    def test_yanked_version_without_reason(self) -> None:
-        """Test a yanked version returns True with empty reason."""
-        releases = {"1.0.0": [{"yanked": True}]}
-        is_yanked, reason = _is_version_yanked("1.0.0", releases)
-        assert is_yanked is True
-        assert reason == ""
-
-    def test_unknown_version(self) -> None:
-        """Test an unknown version returns False."""
-        releases = {"1.0.0": [{"yanked": False}]}
-        is_yanked, reason = _is_version_yanked("9.9.9", releases)
-        assert is_yanked is False
-        assert reason == ""
-
-    def test_partially_yanked_files(self) -> None:
-        """Test a version with mixed yanked/non-yanked files is not yanked."""
-        releases = {
-            "1.0.0": [{"yanked": True}, {"yanked": False}],
-        }
-        is_yanked, reason = _is_version_yanked("1.0.0", releases)
-        assert is_yanked is False
-        assert reason == ""
-
-    def test_multiple_yanked_files_picks_first_reason(self) -> None:
-        """Test that the first available reason is returned."""
-        releases = {
-            "1.0.0": [
-                {"yanked": True, "yanked_reason": ""},
-                {"yanked": True, "yanked_reason": "second reason"},
-            ],
-        }
-        is_yanked, reason = _is_version_yanked("1.0.0", releases)
-        assert is_yanked is True
-        assert reason == "second reason"
-
-
-class TestIsCurrentVersionYanked:
-    """Test is_current_version_yanked public function."""
-
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version._get_cache_file")
-    def test_reads_from_valid_cache(
-        self, mock_cache_file: MagicMock, mock_version: MagicMock, tmp_path: Path
-    ) -> None:
-        """Test reading yanked status from a valid cache."""
-        mock_version.return_value = "1.0.0"
-        cache_file = tmp_path / "version_cache.json"
-        cache_data = {
-            "version": "2.0.0",
-            "timestamp": datetime.now().isoformat(),
-            "current_version": "1.0.0",
-            "current_version_yanked": True,
-            "current_version_yanked_reason": "bad release",
-        }
-        cache_file.write_text(json.dumps(cache_data))
-        mock_cache_file.return_value = cache_file
-
-        is_yanked, reason = is_current_version_yanked()
-        assert is_yanked is True
-        assert reason == "bad release"
-
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version._get_cache_file")
-    def test_not_yanked_from_cache(
-        self, mock_cache_file: MagicMock, mock_version: MagicMock, tmp_path: Path
-    ) -> None:
-        """Test non-yanked status from a valid cache."""
-        mock_version.return_value = "2.0.0"
-        cache_file = tmp_path / "version_cache.json"
-        cache_data = {
-            "version": "2.0.0",
-            "timestamp": datetime.now().isoformat(),
-            "current_version": "2.0.0",
-            "current_version_yanked": False,
-            "current_version_yanked_reason": "",
-        }
-        cache_file.write_text(json.dumps(cache_data))
-        mock_cache_file.return_value = cache_file
-
-        is_yanked, reason = is_current_version_yanked()
-        assert is_yanked is False
-        assert reason == ""
-
-    @patch("crewai_cli.version.get_latest_version_from_pypi")
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version._get_cache_file")
-    def test_triggers_fetch_on_stale_cache(
-        self,
-        mock_cache_file: MagicMock,
-        mock_version: MagicMock,
-        mock_fetch: MagicMock,
-        tmp_path: Path,
-    ) -> None:
-        """Test that a stale cache triggers a re-fetch."""
-        mock_version.return_value = "1.0.0"
-        cache_file = tmp_path / "version_cache.json"
-        old_time = datetime.now() - timedelta(hours=25)
-        cache_data = {
-            "version": "2.0.0",
-            "timestamp": old_time.isoformat(),
-            "current_version": "1.0.0",
-            "current_version_yanked": True,
-            "current_version_yanked_reason": "old reason",
-        }
-        cache_file.write_text(json.dumps(cache_data))
-        mock_cache_file.return_value = cache_file
-
-        fresh_cache = {
-            "version": "2.0.0",
-            "timestamp": datetime.now().isoformat(),
-            "current_version": "1.0.0",
-            "current_version_yanked": False,
-            "current_version_yanked_reason": "",
-        }
-
-        def write_fresh_cache() -> str:
-            cache_file.write_text(json.dumps(fresh_cache))
-            return "2.0.0"
-
-        mock_fetch.side_effect = lambda: write_fresh_cache()
-
-        is_yanked, reason = is_current_version_yanked()
-        assert is_yanked is False
-        mock_fetch.assert_called_once()
-
-    @patch("crewai_cli.version.get_latest_version_from_pypi")
-    @patch("crewai_cli.version.get_crewai_version")
-    @patch("crewai_cli.version._get_cache_file")
-    def test_returns_false_on_fetch_failure(
-        self,
-        mock_cache_file: MagicMock,
-        mock_version: MagicMock,
-        mock_fetch: MagicMock,
-        tmp_path: Path,
-    ) -> None:
-        """Test that fetch failure returns not yanked."""
-        mock_version.return_value = "1.0.0"
-        cache_file = tmp_path / "version_cache.json"
-        mock_cache_file.return_value = cache_file
-        mock_fetch.return_value = None
-
-        is_yanked, reason = is_current_version_yanked()
-        assert is_yanked is False
-        assert reason == ""
-
-
-
-# TestConsoleFormatterVersionCheck tests remain in lib/crewai/tests/cli/test_version.py
-# as they depend on crewai.events.utils.console_formatter (core package).

lib/crewai-files/pyproject.toml

@@ -8,8 +8,8 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
-    "Pillow~=12.1.1",
-    "pypdf~=6.7.5",
+    "Pillow~=10.4.0",
+    "pypdf~=4.0.0",
     "python-magic>=0.4.27",
     "aiocache~=0.12.3",
     "aiofiles~=24.1.0",

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.10.2rc2"
+__version__ = "1.9.3"

lib/crewai-tools/pyproject.toml

@@ -8,10 +8,12 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
+    "lancedb~=0.5.4",
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.10.2rc2",
+    "crewai==1.9.3",
+    "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -108,7 +110,7 @@ stagehand = [
     "stagehand>=0.4.1",
 ]
 github = [
-    "gitpython>=3.1.41,<4",
+    "gitpython==3.1.38",
     "PyGithub==1.59.1",
 ]
 rag = [

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -10,18 +10,7 @@ from crewai_tools.aws.s3.writer_tool import S3WriterTool
 from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool
 from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool
 from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool
-from crewai_tools.tools.brave_search_tool.brave_image_tool import BraveImageSearchTool
-from crewai_tools.tools.brave_search_tool.brave_llm_context_tool import (
-    BraveLLMContextTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_local_pois_tool import (
-    BraveLocalPOIsDescriptionTool,
-    BraveLocalPOIsTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_news_tool import BraveNewsSearchTool
 from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
-from crewai_tools.tools.brave_search_tool.brave_video_tool import BraveVideoSearchTool
-from crewai_tools.tools.brave_search_tool.brave_web_tool import BraveWebSearchTool
 from crewai_tools.tools.brightdata_tool.brightdata_dataset import (
     BrightDataDatasetTool,
 )
@@ -211,14 +200,7 @@ __all__ = [
     "ArxivPaperTool",
     "BedrockInvokeAgentTool",
     "BedrockKBRetrieverTool",
-    "BraveImageSearchTool",
-    "BraveLLMContextTool",
-    "BraveLocalPOIsDescriptionTool",
-    "BraveLocalPOIsTool",
-    "BraveNewsSearchTool",
     "BraveSearchTool",
-    "BraveVideoSearchTool",
-    "BraveWebSearchTool",
     "BrightDataDatasetTool",
     "BrightDataSearchTool",
     "BrightDataWebUnlockerTool",
@@ -309,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.10.2rc2"
+__version__ = "1.9.3"

lib/crewai-tools/src/crewai_tools/adapters/lancedb_adapter.py

@@ -1,9 +1,7 @@
 from collections.abc import Callable
-import os
 from pathlib import Path
 from typing import Any
 
-from crewai.utilities.lock_store import lock as store_lock
 from lancedb import (  # type: ignore[import-untyped]
     DBConnection as LanceDBConnection,
     connect as lancedb_connect,
@@ -35,12 +33,10 @@ class LanceDBAdapter(Adapter):
 
     _db: LanceDBConnection = PrivateAttr()
     _table: LanceDBTable = PrivateAttr()
-    _lock_name: str = PrivateAttr(default="")
 
     def model_post_init(self, __context: Any) -> None:
         self._db = lancedb_connect(self.uri)
         self._table = self._db.open_table(self.table_name)
-        self._lock_name = f"lancedb:{os.path.realpath(str(self.uri))}"
 
         super().model_post_init(__context)
 
@@ -60,5 +56,4 @@ class LanceDBAdapter(Adapter):
         *args: Any,
         **kwargs: Any,
     ) -> None:
-        with store_lock(self._lock_name):
-            self._table.add(*args, **kwargs)
+        self._table.add(*args, **kwargs)

lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_session_manager.py

@@ -1,9 +1,6 @@
 from __future__ import annotations
 
-import asyncio
-import contextvars
 import logging
-import threading
 from typing import TYPE_CHECKING
 
 
@@ -21,9 +18,6 @@ class BrowserSessionManager:
     This class maintains separate browser sessions for different threads,
     enabling concurrent usage of browsers in multi-threaded environments.
     Browsers are created lazily only when needed by tools.
-
-    Uses per-key events to serialize creation for the same thread_id without
-    blocking unrelated callers or wasting resources on duplicate sessions.
     """
 
     def __init__(self, region: str = "us-west-2"):
@@ -33,10 +27,8 @@ class BrowserSessionManager:
             region: AWS region for browser client
         """
         self.region = region
-        self._lock = threading.Lock()
         self._async_sessions: dict[str, tuple[BrowserClient, AsyncBrowser]] = {}
         self._sync_sessions: dict[str, tuple[BrowserClient, SyncBrowser]] = {}
-        self._creating: dict[str, threading.Event] = {}
 
     async def get_async_browser(self, thread_id: str) -> AsyncBrowser:
         """Get or create an async browser for the specified thread.
@@ -47,29 +39,10 @@ class BrowserSessionManager:
         Returns:
             An async browser instance specific to the thread
         """
-        loop = asyncio.get_event_loop()
-        while True:
-            with self._lock:
-                if thread_id in self._async_sessions:
-                    return self._async_sessions[thread_id][1]
-                if thread_id not in self._creating:
-                    self._creating[thread_id] = threading.Event()
-                    break
-                event = self._creating[thread_id]
-            ctx = contextvars.copy_context()
-            await loop.run_in_executor(None, ctx.run, event.wait)
+        if thread_id in self._async_sessions:
+            return self._async_sessions[thread_id][1]
 
-        try:
-            browser_client, browser = await self._create_async_browser_session(
-                thread_id
-            )
-            with self._lock:
-                self._async_sessions[thread_id] = (browser_client, browser)
-            return browser
-        finally:
-            with self._lock:
-                evt = self._creating.pop(thread_id)
-            evt.set()
+        return await self._create_async_browser_session(thread_id)
 
     def get_sync_browser(self, thread_id: str) -> SyncBrowser:
         """Get or create a sync browser for the specified thread.
@@ -80,33 +53,19 @@ class BrowserSessionManager:
         Returns:
             A sync browser instance specific to the thread
         """
-        while True:
-            with self._lock:
-                if thread_id in self._sync_sessions:
-                    return self._sync_sessions[thread_id][1]
-                if thread_id not in self._creating:
-                    self._creating[thread_id] = threading.Event()
-                    break
-                event = self._creating[thread_id]
-            event.wait()
+        if thread_id in self._sync_sessions:
+            return self._sync_sessions[thread_id][1]
 
-        try:
-            return self._create_sync_browser_session(thread_id)
-        finally:
-            with self._lock:
-                evt = self._creating.pop(thread_id)
-            evt.set()
+        return self._create_sync_browser_session(thread_id)
 
-    async def _create_async_browser_session(
-        self, thread_id: str
-    ) -> tuple[BrowserClient, AsyncBrowser]:
+    async def _create_async_browser_session(self, thread_id: str) -> AsyncBrowser:
         """Create a new async browser session for the specified thread.
 
         Args:
             thread_id: Unique identifier for the thread
 
         Returns:
-            Tuple of (BrowserClient, AsyncBrowser).
+            The newly created async browser instance
 
         Raises:
             Exception: If browser session creation fails
@@ -116,8 +75,10 @@ class BrowserSessionManager:
         browser_client = BrowserClient(region=self.region)
 
         try:
+            # Start browser session
             browser_client.start()
 
+            # Get WebSocket connection info
             ws_url, headers = browser_client.generate_ws_headers()
 
             logger.info(
@@ -126,6 +87,7 @@ class BrowserSessionManager:
 
             from playwright.async_api import async_playwright
 
+            # Connect to browser using Playwright
             playwright = await async_playwright().start()
             browser = await playwright.chromium.connect_over_cdp(
                 endpoint_url=ws_url, headers=headers, timeout=30000
@@ -134,13 +96,17 @@ class BrowserSessionManager:
                 f"Successfully connected to async browser for thread {thread_id}"
             )
 
-            return browser_client, browser
+            # Store session resources
+            self._async_sessions[thread_id] = (browser_client, browser)
+
+            return browser
 
         except Exception as e:
             logger.error(
                 f"Failed to create async browser session for thread {thread_id}: {e}"
             )
 
+            # Clean up resources if session creation fails
             if browser_client:
                 try:
                     browser_client.stop()
@@ -166,8 +132,10 @@ class BrowserSessionManager:
         browser_client = BrowserClient(region=self.region)
 
         try:
+            # Start browser session
             browser_client.start()
 
+            # Get WebSocket connection info
             ws_url, headers = browser_client.generate_ws_headers()
 
             logger.info(
@@ -176,6 +144,7 @@ class BrowserSessionManager:
 
             from playwright.sync_api import sync_playwright
 
+            # Connect to browser using Playwright
             playwright = sync_playwright().start()
             browser = playwright.chromium.connect_over_cdp(
                 endpoint_url=ws_url, headers=headers, timeout=30000
@@ -184,8 +153,8 @@ class BrowserSessionManager:
                 f"Successfully connected to sync browser for thread {thread_id}"
             )
 
-            with self._lock:
-                self._sync_sessions[thread_id] = (browser_client, browser)
+            # Store session resources
+            self._sync_sessions[thread_id] = (browser_client, browser)
 
             return browser
 
@@ -194,6 +163,7 @@ class BrowserSessionManager:
                 f"Failed to create sync browser session for thread {thread_id}: {e}"
             )
 
+            # Clean up resources if session creation fails
             if browser_client:
                 try:
                     browser_client.stop()
@@ -208,13 +178,13 @@ class BrowserSessionManager:
         Args:
             thread_id: Unique identifier for the thread
         """
-        with self._lock:
-            if thread_id not in self._async_sessions:
-                logger.warning(f"No async browser session found for thread {thread_id}")
-                return
+        if thread_id not in self._async_sessions:
+            logger.warning(f"No async browser session found for thread {thread_id}")
+            return
 
-            browser_client, browser = self._async_sessions.pop(thread_id)
+        browser_client, browser = self._async_sessions[thread_id]
 
+        # Close browser
         if browser:
             try:
                 await browser.close()
@@ -223,6 +193,7 @@ class BrowserSessionManager:
                     f"Error closing async browser for thread {thread_id}: {e}"
                 )
 
+        # Stop browser client
         if browser_client:
             try:
                 browser_client.stop()
@@ -231,6 +202,8 @@ class BrowserSessionManager:
                     f"Error stopping browser client for thread {thread_id}: {e}"
                 )
 
+        # Remove session from dictionary
+        del self._async_sessions[thread_id]
         logger.info(f"Async browser session cleaned up for thread {thread_id}")
 
     def close_sync_browser(self, thread_id: str) -> None:
@@ -239,13 +212,13 @@ class BrowserSessionManager:
         Args:
             thread_id: Unique identifier for the thread
         """
-        with self._lock:
-            if thread_id not in self._sync_sessions:
-                logger.warning(f"No sync browser session found for thread {thread_id}")
-                return
+        if thread_id not in self._sync_sessions:
+            logger.warning(f"No sync browser session found for thread {thread_id}")
+            return
 
-            browser_client, browser = self._sync_sessions.pop(thread_id)
+        browser_client, browser = self._sync_sessions[thread_id]
 
+        # Close browser
         if browser:
             try:
                 browser.close()
@@ -254,6 +227,7 @@ class BrowserSessionManager:
                     f"Error closing sync browser for thread {thread_id}: {e}"
                 )
 
+        # Stop browser client
         if browser_client:
             try:
                 browser_client.stop()
@@ -262,17 +236,19 @@ class BrowserSessionManager:
                     f"Error stopping browser client for thread {thread_id}: {e}"
                 )
 
+        # Remove session from dictionary
+        del self._sync_sessions[thread_id]
         logger.info(f"Sync browser session cleaned up for thread {thread_id}")
 
     async def close_all_browsers(self) -> None:
         """Close all browser sessions."""
-        with self._lock:
-            async_thread_ids = list(self._async_sessions.keys())
-            sync_thread_ids = list(self._sync_sessions.keys())
-
+        # Close all async browsers
+        async_thread_ids = list(self._async_sessions.keys())
         for thread_id in async_thread_ids:
             await self.close_async_browser(thread_id)
 
+        # Close all sync browsers
+        sync_thread_ids = list(self._sync_sessions.keys())
         for thread_id in sync_thread_ids:
             self.close_sync_browser(thread_id)
 

lib/crewai-tools/src/crewai_tools/rag/core.py

@@ -1,11 +1,9 @@
 import logging
-import os
 from pathlib import Path
 from typing import Any
 from uuid import uuid4
 
 import chromadb
-from crewai.utilities.lock_store import lock as store_lock
 from pydantic import BaseModel, Field, PrivateAttr
 
 from crewai_tools.rag.base_loader import BaseLoader
@@ -40,32 +38,22 @@ class RAG(Adapter):
     _client: Any = PrivateAttr()
     _collection: Any = PrivateAttr()
     _embedding_service: EmbeddingService = PrivateAttr()
-    _lock_name: str = PrivateAttr(default="")
 
     def model_post_init(self, __context: Any) -> None:
         try:
-            self._lock_name = (
-                f"chromadb:{os.path.realpath(self.persist_directory)}"
-                if self.persist_directory
-                else "chromadb:ephemeral"
+            if self.persist_directory:
+                self._client = chromadb.PersistentClient(path=self.persist_directory)
+            else:
+                self._client = chromadb.Client()
+
+            self._collection = self._client.get_or_create_collection(
+                name=self.collection_name,
+                metadata={
+                    "hnsw:space": "cosine",
+                    "description": "CrewAI Knowledge Base",
+                },
             )
 
-            with store_lock(self._lock_name):
-                if self.persist_directory:
-                    self._client = chromadb.PersistentClient(
-                        path=self.persist_directory
-                    )
-                else:
-                    self._client = chromadb.Client()
-
-                self._collection = self._client.get_or_create_collection(
-                    name=self.collection_name,
-                    metadata={
-                        "hnsw:space": "cosine",
-                        "description": "CrewAI Knowledge Base",
-                    },
-                )
-
             self._embedding_service = EmbeddingService(
                 provider=self.embedding_provider,
                 model=self.embedding_model,
@@ -99,8 +87,29 @@ class RAG(Adapter):
         loader_result = loader.load(source_content)
         doc_id = loader_result.doc_id
 
-        chunks = chunker.chunk(loader_result.content)
+        existing_doc = self._collection.get(
+            where={"source": source_content.source_ref}, limit=1
+        )
+        existing_doc_id = (
+            existing_doc and existing_doc["metadatas"][0]["doc_id"]
+            if existing_doc["metadatas"]
+            else None
+        )
+
+        if existing_doc_id == doc_id:
+            logger.warning(
+                f"Document with source {loader_result.source} already exists"
+            )
+            return
+
+        # Document with same source ref does exists but the content has changed, deleting the oldest reference
+        if existing_doc_id and existing_doc_id != loader_result.doc_id:
+            logger.warning(f"Deleting old document with doc_id {existing_doc_id}")
+            self._collection.delete(where={"doc_id": existing_doc_id})
+
         documents = []
+
+        chunks = chunker.chunk(loader_result.content)
         for i, chunk in enumerate(chunks):
             doc_metadata = (metadata or {}).copy()
             doc_metadata["chunk_index"] = i
@@ -127,6 +136,7 @@ class RAG(Adapter):
 
         ids = [doc.id for doc in documents]
         metadatas = []
+
         for doc in documents:
             doc_metadata = doc.metadata.copy()
             doc_metadata.update(
@@ -138,36 +148,16 @@ class RAG(Adapter):
             )
             metadatas.append(doc_metadata)
 
-        with store_lock(self._lock_name):
-            existing_doc = self._collection.get(
-                where={"source": source_content.source_ref}, limit=1
+        try:
+            self._collection.add(
+                ids=ids,
+                embeddings=embeddings,
+                documents=contents,
+                metadatas=metadatas,
             )
-            existing_doc_id = (
-                existing_doc and existing_doc["metadatas"][0]["doc_id"]
-                if existing_doc["metadatas"]
-                else None
-            )
-
-            if existing_doc_id == doc_id:
-                logger.warning(
-                    f"Document with source {loader_result.source} already exists"
-                )
-                return
-
-            if existing_doc_id and existing_doc_id != loader_result.doc_id:
-                logger.warning(f"Deleting old document with doc_id {existing_doc_id}")
-                self._collection.delete(where={"doc_id": existing_doc_id})
-
-            try:
-                self._collection.add(
-                    ids=ids,
-                    embeddings=embeddings,
-                    documents=contents,
-                    metadatas=metadatas,
-                )
-                logger.info(f"Added {len(documents)} documents to knowledge base")
-            except Exception as e:
-                logger.error(f"Failed to add documents to ChromaDB: {e}")
+            logger.info(f"Added {len(documents)} documents to knowledge base")
+        except Exception as e:
+            logger.error(f"Failed to add documents to ChromaDB: {e}")
 
     def query(self, question: str, where: dict[str, Any] | None = None) -> str:  # type: ignore
         try:
@@ -211,8 +201,7 @@ class RAG(Adapter):
 
     def delete_collection(self) -> None:
         try:
-            with store_lock(self._lock_name):
-                self._client.delete_collection(self.collection_name)
+            self._client.delete_collection(self.collection_name)
             logger.info(f"Deleted collection: {self.collection_name}")
         except Exception as e:
             logger.error(f"Failed to delete collection: {e}")

lib/crewai-tools/src/crewai_tools/tools/__init__.py

@@ -1,18 +1,7 @@
 from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool
 from crewai_tools.tools.apify_actors_tool.apify_actors_tool import ApifyActorsTool
 from crewai_tools.tools.arxiv_paper_tool.arxiv_paper_tool import ArxivPaperTool
-from crewai_tools.tools.brave_search_tool.brave_image_tool import BraveImageSearchTool
-from crewai_tools.tools.brave_search_tool.brave_llm_context_tool import (
-    BraveLLMContextTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_local_pois_tool import (
-    BraveLocalPOIsDescriptionTool,
-    BraveLocalPOIsTool,
-)
-from crewai_tools.tools.brave_search_tool.brave_news_tool import BraveNewsSearchTool
 from crewai_tools.tools.brave_search_tool.brave_search_tool import BraveSearchTool
-from crewai_tools.tools.brave_search_tool.brave_video_tool import BraveVideoSearchTool
-from crewai_tools.tools.brave_search_tool.brave_web_tool import BraveWebSearchTool
 from crewai_tools.tools.brightdata_tool import (
     BrightDataDatasetTool,
     BrightDataSearchTool,
@@ -196,14 +185,7 @@ __all__ = [
     "AIMindTool",
     "ApifyActorsTool",
     "ArxivPaperTool",
-    "BraveImageSearchTool",
-    "BraveLLMContextTool",
-    "BraveLocalPOIsDescriptionTool",
-    "BraveLocalPOIsTool",
-    "BraveNewsSearchTool",
     "BraveSearchTool",
-    "BraveVideoSearchTool",
-    "BraveWebSearchTool",
     "BrightDataDatasetTool",
     "BrightDataSearchTool",
     "BrightDataWebUnlockerTool",

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/base.py

@@ -1,322 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from datetime import datetime
-import json
-import logging
-import os
-import threading
-import time
-from typing import Any, ClassVar
-
-from crewai.tools import BaseTool, EnvVar
-from pydantic import BaseModel, Field
-import requests
-
-
-logger = logging.getLogger(__name__)
-
-# Brave API error codes that indicate non-retryable quota/usage exhaustion.
-_QUOTA_CODES = frozenset({"QUOTA_LIMITED", "USAGE_LIMIT_EXCEEDED"})
-
-
-def _save_results_to_file(content: str) -> None:
-    """Saves the search results to a file."""
-    filename = f"search_results_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt"
-    with open(filename, "w") as file:
-        file.write(content)
-
-
-def _parse_error_body(resp: requests.Response) -> dict[str, Any] | None:
-    """Extract the structured "error" object from a Brave API error response."""
-    try:
-        body = resp.json()
-        error = body.get("error")
-        return error if isinstance(error, dict) else None
-    except (ValueError, KeyError):
-        return None
-
-
-def _raise_for_error(resp: requests.Response) -> None:
-    """Brave Search API error responses contain helpful JSON payloads"""
-    status = resp.status_code
-    try:
-        body = json.dumps(resp.json())
-    except (ValueError, KeyError):
-        body = resp.text[:500]
-
-    raise RuntimeError(f"Brave Search API error (HTTP {status}): {body}")
-
-
-def _is_retryable(resp: requests.Response) -> bool:
-    """Return True for transient failures that are worth retrying.
-
-    * 429 + RATE_LIMITED — the per-second sliding window is full.
-    * 5xx — transient server-side errors.
-
-    Quota exhaustion (QUOTA_LIMITED, USAGE_LIMIT_EXCEEDED) is
-    explicitly excluded: retrying will never succeed until the billing
-    period resets.
-    """
-    if resp.status_code == 429:
-        error = _parse_error_body(resp) or {}
-        return error.get("code") not in _QUOTA_CODES
-    return 500 <= resp.status_code < 600
-
-
-def _retry_delay(resp: requests.Response, attempt: int) -> float:
-    """Compute wait time before the next retry attempt.
-
-    Prefers the server-supplied Retry-After header when available;
-    falls back to exponential backoff (1s, 2s, 4s, ...).
-    """
-    retry_after = resp.headers.get("Retry-After")
-    if retry_after is not None:
-        try:
-            return max(0.0, float(retry_after))
-        except (ValueError, TypeError):
-            pass
-    return float(2**attempt)
-
-
-class BraveSearchToolBase(BaseTool, ABC):
-    """
-    Base class for Brave Search API interactions.
-
-    Individual tool subclasses must provide the following:
-      - search_url
-      - header_schema (pydantic model)
-      - args_schema (pydantic model)
-      - _refine_payload() -> dict[str, Any]
-    """
-
-    search_url: str
-    raw: bool = False
-    args_schema: type[BaseModel]
-    header_schema: type[BaseModel]
-
-    # Tool options (legacy parameters)
-    country: str | None = None
-    save_file: bool = False
-    n_results: int = 10
-
-    env_vars: list[EnvVar] = Field(
-        default_factory=lambda: [
-            EnvVar(
-                name="BRAVE_API_KEY",
-                description="API key for Brave Search",
-                required=True,
-            ),
-        ]
-    )
-
-    def __init__(
-        self,
-        *,
-        api_key: str | None = None,
-        headers: dict[str, Any] | None = None,
-        requests_per_second: float = 1.0,
-        save_file: bool = False,
-        raw: bool = False,
-        timeout: int = 30,
-        **kwargs: Any,
-    ):
-        super().__init__(**kwargs)
-
-        self._api_key = api_key or os.environ.get("BRAVE_API_KEY")
-        if not self._api_key:
-            raise ValueError("BRAVE_API_KEY environment variable is required")
-
-        self.raw = bool(raw)
-        self._timeout = int(timeout)
-        self.save_file = bool(save_file)
-        self._requests_per_second = float(requests_per_second)
-        self._headers = self._build_and_validate_headers(headers or {})
-        # Per-instance rate limiting: each instance has its own clock and lock.
-        # Total process rate is the sum of limits of instances you create.
-        self._last_request_time: float = 0
-        self._rate_limit_lock = threading.Lock()
-
-    @property
-    def api_key(self) -> str:
-        return self._api_key
-
-    @property
-    def headers(self) -> dict[str, Any]:
-        return self._headers
-
-    def set_headers(self, headers: dict[str, Any]) -> BraveSearchToolBase:
-        merged = {**self._headers, **{k.lower(): v for k, v in headers.items()}}
-        self._headers = self._build_and_validate_headers(merged)
-        return self
-
-    def _build_and_validate_headers(self, headers: dict[str, Any]) -> dict[str, Any]:
-        normalized = {k.lower(): v for k, v in headers.items()}
-        normalized.setdefault("x-subscription-token", self._api_key)
-        normalized.setdefault("accept", "application/json")
-
-        try:
-            self.header_schema(**normalized)
-        except Exception as e:
-            raise ValueError(f"Invalid headers: {e}") from e
-
-        return normalized
-
-    def _rate_limit(self) -> None:
-        """Enforce minimum interval between requests for this instance. Thread-safe."""
-        if self._requests_per_second <= 0:
-            return
-
-        min_interval = 1.0 / self._requests_per_second
-        with self._rate_limit_lock:
-            now = time.time()
-            next_allowed = self._last_request_time + min_interval
-            if now < next_allowed:
-                time.sleep(next_allowed - now)
-                now = time.time()
-            self._last_request_time = now
-
-    def _make_request(
-        self, params: dict[str, Any], *, _max_retries: int = 3
-    ) -> dict[str, Any]:
-        """Execute an HTTP GET against the Brave Search API with retry logic."""
-        last_resp: requests.Response | None = None
-
-        # Retry the request up to _max_retries times
-        for attempt in range(_max_retries):
-            self._rate_limit()
-
-            # Make the request
-            try:
-                resp = requests.get(
-                    self.search_url,
-                    headers=self._headers,
-                    params=params,
-                    timeout=self._timeout,
-                )
-            except requests.ConnectionError as exc:
-                raise RuntimeError(
-                    f"Brave Search API connection failed: {exc}"
-                ) from exc
-            except requests.Timeout as exc:
-                raise RuntimeError(
-                    f"Brave Search API request timed out after {self._timeout}s: {exc}"
-                ) from exc
-
-            # Log the rate limit headers and request details
-            logger.debug(
-                "Brave Search API request: %s %s -> %d",
-                "GET",
-                resp.url,
-                resp.status_code,
-            )
-
-            # Response was OK, return the JSON body
-            if resp.ok:
-                try:
-                    return resp.json()
-                except ValueError as exc:
-                    raise RuntimeError(
-                        f"Brave Search API returned invalid JSON (HTTP {resp.status_code}): {exc}"
-                    ) from exc
-
-            # Response was not OK, but is retryable
-            # (e.g., 429 Too Many Requests, 500 Internal Server Error)
-            if _is_retryable(resp) and attempt < _max_retries - 1:
-                delay = _retry_delay(resp, attempt)
-                logger.warning(
-                    "Brave Search API returned %d. Retrying in %.1fs (attempt %d/%d)",
-                    resp.status_code,
-                    delay,
-                    attempt + 1,
-                    _max_retries,
-                )
-                time.sleep(delay)
-                last_resp = resp
-                continue
-
-            # Response was not OK, nor was it retryable
-            # (e.g., 422 Unprocessable Entity, 400 Bad Request (OPTION_NOT_IN_PLAN))
-            _raise_for_error(resp)
-
-        # All retries exhausted
-        _raise_for_error(last_resp or resp)  # type: ignore[possibly-undefined]
-        return {}  # unreachable (here to satisfy the type checker and linter)
-
-    def _run(self, q: str | None = None, **params: Any) -> Any:
-        # Allow positional usage: tool.run("latest Brave browser features")
-        if q is not None:
-            params["q"] = q
-
-        params = self._common_payload_refinement(params)
-
-        # Validate only schema fields
-        schema_keys = self.args_schema.model_fields
-        payload_in = {k: v for k, v in params.items() if k in schema_keys}
-
-        try:
-            validated = self.args_schema(**payload_in)
-        except Exception as e:
-            raise ValueError(f"Invalid parameters: {e}") from e
-
-        # The subclass may have additional refinements to apply to the payload, such as goggles or other parameters
-        payload = self._refine_request_payload(validated.model_dump(exclude_none=True))
-        response = self._make_request(payload)
-
-        if not self.raw:
-            response = self._refine_response(response)
-
-        if self.save_file:
-            _save_results_to_file(json.dumps(response, indent=2))
-
-        return response
-
-    @abstractmethod
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        """Subclass must implement: transform validated params dict into API request params."""
-        raise NotImplementedError
-
-    @abstractmethod
-    def _refine_response(self, response: dict[str, Any]) -> Any:
-        """Subclass must implement: transform response dict into a more useful format."""
-        raise NotImplementedError
-
-    _EMPTY_VALUES: ClassVar[tuple[None, str, str, list[Any]]] = (None, "", "null", [])
-
-    def _common_payload_refinement(self, params: dict[str, Any]) -> dict[str, Any]:
-        """Common payload refinement for all tools."""
-        # crewAI's schema pipeline (ensure_all_properties_required in
-        # pydantic_schema_utils.py) marks every property as required so
-        # that OpenAI strict-mode structured outputs work correctly.
-        # The side-effect is that the LLM fills in *every* parameter —
-        # even truly optional ones — using placeholder values such as
-        # None, "", "null", or [].  Only optional fields are affected,
-        # so we limit the check to those.
-        fields = self.args_schema.model_fields
-        params = {
-            k: v
-            for k, v in params.items()
-            # Permit custom and required fields, and fields with non-empty values
-            if k not in fields or fields[k].is_required() or v not in self._EMPTY_VALUES
-        }
-
-        # Make sure params has "q" for query instead of "query" or "search_query"
-        query = params.get("query") or params.get("search_query")
-        if query is not None and "q" not in params:
-            params["q"] = query
-        params.pop("query", None)
-        params.pop("search_query", None)
-
-        # If "count" was not explicitly provided, use n_results
-        # (only when the schema actually supports a "count" field)
-        if "count" in self.args_schema.model_fields:
-            if "count" not in params and self.n_results is not None:
-                params["count"] = self.n_results
-
-        # If "country" was not explicitly provided, but self.country is set, use it
-        # (only when the schema actually supports a "country" field)
-        if "country" in self.args_schema.model_fields:
-            if "country" not in params and self.country is not None:
-                params["country"] = self.country
-
-        return params

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_image_tool.py

@@ -1,42 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.schemas import (
-    ImageSearchHeaders,
-    ImageSearchParams,
-)
-
-
-class BraveImageSearchTool(BraveSearchToolBase):
-    """A tool that performs image searches using the Brave Search API."""
-
-    name: str = "Brave Image Search"
-    args_schema: type[BaseModel] = ImageSearchParams
-    header_schema: type[BaseModel] = ImageSearchHeaders
-
-    description: str = (
-        "A tool that performs image searches using the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/images/search"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
-        results = response.get("results", [])
-        return [
-            {
-                "title": result.get("title"),
-                "url": result.get("properties", {}).get("url"),
-                "dimensions": f"{w}x{h}"
-                if (w := result.get("properties", {}).get("width"))
-                and (h := result.get("properties", {}).get("height"))
-                else None,
-            }
-            for result in results
-        ]

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_llm_context_tool.py

@@ -1,32 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-
-from crewai_tools.tools.brave_search_tool.base import BraveSearchToolBase
-from crewai_tools.tools.brave_search_tool.response_types import LLMContext
-from crewai_tools.tools.brave_search_tool.schemas import (
-    LLMContextHeaders,
-    LLMContextParams,
-)
-
-
-class BraveLLMContextTool(BraveSearchToolBase):
-    """A tool that retrieves context for LLM usage from the Brave Search API."""
-
-    name: str = "Brave LLM Context"
-    args_schema: type[BaseModel] = LLMContextParams
-    header_schema: type[BaseModel] = LLMContextHeaders
-
-    description: str = (
-        "A tool that retrieves context for LLM usage from the Brave Search API. "
-        "Results are returned as structured JSON data."
-    )
-
-    search_url: str = "https://api.search.brave.com/res/v1/llm/context"
-
-    def _refine_request_payload(self, params: dict[str, Any]) -> dict[str, Any]:
-        return params
-
-    def _refine_response(self, response: LLMContext.Response) -> LLMContext.Response:
-        """The LLM Context response schema is fairly simple. Return as is."""
-        return response