Files
crewAI/docs/ko/enterprise/integrations/zendesk.mdx
Lorenze Jay d1343b96ed Release/v1.0.0 (#3618)
* feat: add `apps` & `actions` attributes to Agent (#3504)

* feat: add app attributes to Agent

* feat: add actions attribute to Agent

* chore: resolve linter issues

* refactor: merge the apps and actions parameters into a single one

* fix: remove unnecessary print

* feat: logging error when CrewaiPlatformTools fails

* chore: export CrewaiPlatformTools directly from crewai_tools

* style: resolver linter issues

* test: fix broken tests

* style: solve linter issues

* fix: fix broken test

* feat: monorepo restructure and test/ci updates

- Add crewai workspace member
- Fix vcr cassette paths and restore test dirs
- Resolve ci failures and update linter/pytest rules

* chore: update python version to 3.13 and package metadata

* feat: add crewai-tools workspace and fix tests/dependencies

* feat: add crewai-tools workspace structure

* Squashed 'temp-crewai-tools/' content from commit 9bae5633

git-subtree-dir: temp-crewai-tools
git-subtree-split: 9bae56339096cb70f03873e600192bd2cd207ac9

* feat: configure crewai-tools workspace package with dependencies

* fix: apply ruff auto-formatting to crewai-tools code

* chore: update lockfile

* fix: don't allow tool tests yet

* fix: comment out extra pytest flags for now

* fix: remove conflicting conftest.py from crewai-tools tests

* fix: resolve dependency conflicts and test issues

- Pin vcrpy to 7.0.0 to fix pytest-recording compatibility
- Comment out types-requests to resolve urllib3 conflict
- Update requests requirement in crewai-tools to >=2.32.0

* chore: update CI workflows and docs for monorepo structure

* chore: update CI workflows and docs for monorepo structure

* fix: actions syntax

* chore: ci publish and pin versions

* fix: add permission to action

* chore: bump version to 1.0.0a1 across all packages

- Updated version to 1.0.0a1 in pyproject.toml for crewai and crewai-tools
- Adjusted version in __init__.py files for consistency

* WIP: v1 docs (#3626)

(cherry picked from commit d46e20fa09bcd2f5916282f5553ddeb7183bd92c)

* docs: parity for all translations

* docs: full name of acronym AMP

* docs: fix lingering unused code

* docs: expand contextual options in docs.json

* docs: add contextual action to request feature on GitHub (#3635)

* chore: apply linting fixes to crewai-tools

* feat: add required env var validation for brightdata

Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>

* fix: handle properly anyOf oneOf allOf schema's props

Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>

* feat: bump version to 1.0.0a2

* Lorenze/native inference sdks (#3619)

* ruff linted

* using native sdks with litellm fallback

* drop exa

* drop print on completion

* Refactor LLM and utility functions for type consistency

- Updated `max_tokens` parameter in `LLM` class to accept `float` in addition to `int`.
- Modified `create_llm` function to ensure consistent type hints and return types, now returning `LLM | BaseLLM | None`.
- Adjusted type hints for various parameters in `create_llm` and `_llm_via_environment_or_fallback` functions for improved clarity and type safety.
- Enhanced test cases to reflect changes in type handling and ensure proper instantiation of LLM instances.

* fix agent_tests

* fix litellm tests and usagemetrics fix

* drop print

* Refactor LLM event handling and improve test coverage

- Removed commented-out event emission for LLM call failures in `llm.py`.
- Added `from_agent` parameter to `CrewAgentExecutor` for better context in LLM responses.
- Enhanced test for LLM call failure to simulate OpenAI API failure and updated assertions for clarity.
- Updated agent and task ID assertions in tests to ensure they are consistently treated as strings.

* fix test_converter

* fixed tests/agents/test_agent.py

* Refactor LLM context length exception handling and improve provider integration

- Renamed `LLMContextLengthExceededException` to `LLMContextLengthExceededExceptionError` for clarity and consistency.
- Updated LLM class to pass the provider parameter correctly during initialization.
- Enhanced error handling in various LLM provider implementations to raise the new exception type.
- Adjusted tests to reflect the updated exception name and ensure proper error handling in context length scenarios.

* Enhance LLM context window handling across providers

- Introduced CONTEXT_WINDOW_USAGE_RATIO to adjust context window sizes dynamically for Anthropic, Azure, Gemini, and OpenAI LLMs.
- Added validation for context window sizes in Azure and Gemini providers to ensure they fall within acceptable limits.
- Updated context window size calculations to use the new ratio, improving consistency and adaptability across different models.
- Removed hardcoded context window sizes in favor of ratio-based calculations for better flexibility.

* fix test agent again

* fix test agent

* feat: add native LLM providers for Anthropic, Azure, and Gemini

- Introduced new completion implementations for Anthropic, Azure, and Gemini, integrating their respective SDKs.
- Added utility functions for tool validation and extraction to support function calling across LLM providers.
- Enhanced context window management and token usage extraction for each provider.
- Created a common utility module for shared functionality among LLM providers.

* chore: update dependencies and improve context management

- Removed direct dependency on `litellm` from the main dependencies and added it under extras for better modularity.
- Updated the `litellm` dependency specification to allow for greater flexibility in versioning.
- Refactored context length exception handling across various LLM providers to use a consistent error class.
- Enhanced platform-specific dependency markers for NVIDIA packages to ensure compatibility across different systems.

* refactor(tests): update LLM instantiation to include is_litellm flag in test cases

- Modified multiple test cases in test_llm.py to set the is_litellm parameter to True when instantiating the LLM class.
- This change ensures that the tests are aligned with the latest LLM configuration requirements and improves consistency across test scenarios.
- Adjusted relevant assertions and comments to reflect the updated LLM behavior.

* linter

* linted

* revert constants

* fix(tests): correct type hint in expected model description

- Updated the expected description in the test_generate_model_description_dict_field function to use 'Dict' instead of 'dict' for consistency with type hinting conventions.
- This change ensures that the test accurately reflects the expected output format for model descriptions.

* refactor(llm): enhance LLM instantiation and error handling

- Updated the LLM class to include validation for the model parameter, ensuring it is a non-empty string.
- Improved error handling by logging warnings when the native SDK fails, allowing for a fallback to LiteLLM.
- Adjusted the instantiation of LLM in test cases to consistently include the is_litellm flag, aligning with recent changes in LLM configuration.
- Modified relevant tests to reflect these updates, ensuring better coverage and accuracy in testing scenarios.

* fixed test

* refactor(llm): enhance token usage tracking and add copy methods

- Updated the LLM class to track token usage and log callbacks in streaming mode, improving monitoring capabilities.
- Introduced shallow and deep copy methods for the LLM instance, allowing for better management of LLM configurations and parameters.
- Adjusted test cases to instantiate LLM with the is_litellm flag, ensuring alignment with recent changes in LLM configuration.

* refactor(tests): reorganize imports and enhance error messages in test cases

- Cleaned up import statements in test_crew.py for better organization and readability.
- Enhanced error messages in test cases to use `re.escape` for improved regex matching, ensuring more robust error handling.
- Adjusted comments for clarity and consistency across test scenarios.
- Ensured that all necessary modules are imported correctly to avoid potential runtime issues.

* feat: add base devtooling

* fix: ensure dep refs are updated for devtools

* fix: allow pre-release

* feat: allow release after tag

* feat: bump versions to 1.0.0a3 

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>

* fix: match tag and release title, ignore devtools build for pypi

* fix: allow failed pypi publish

* feat: introduce trigger listing and execution commands for local development (#3643)

* chore: exclude tests from ruff linting

* chore: exclude tests from GitHub Actions linter

* fix: replace print statements with logger in agent and memory handling

* chore: add noqa for intentional print in printer utility

* fix: resolve linting errors across codebase

* feat: update docs with new approach to consume Platform Actions (#3675)

* fix: remove duplicate line and add explicit env var

* feat: bump versions to 1.0.0a4 (#3686)

* Update triggers docs (#3678)

* docs: introduce triggers list & triggers run command

* docs: add KO triggers docs

* docs: ensure CREWAI_PLATFORM_INTEGRATION_TOKEN is mentioned on docs (#3687)

* Lorenze/bedrock llm (#3693)

* feat: add AWS Bedrock support and update dependencies

- Introduced BedrockCompletion class for AWS Bedrock integration in LLM.
- Added boto3 as a new dependency in both pyproject.toml and uv.lock.
- Updated LLM class to support Bedrock provider.
- Created new files for Bedrock provider implementation.

* using converse api

* converse

* linted

* refactor: update BedrockCompletion class to improve parameter handling

- Changed max_tokens from a fixed integer to an optional integer.
- Simplified model ID assignment by removing the inference profile mapping method.
- Cleaned up comments and unnecessary code related to tool specifications and model-specific parameters.

* feat: improve event bus thread safety and async support

Add thread-safe, async-compatible event bus with read–write locking and
handler dependency ordering. Remove blinker dependency and implement
direct dispatch. Improve type safety, error handling, and deterministic
event synchronization.

Refactor tests to auto-wait for async handlers, ensure clean teardown,
and add comprehensive concurrency coverage. Replace thread-local state
in AgentEvaluator with instance-based locking for correct cross-thread
access. Enhance tracing reliability and event finalization.

* feat: enhance OpenAICompletion class with additional client parameters (#3701)

* feat: enhance OpenAICompletion class with additional client parameters

- Added support for default_headers, default_query, and client_params in the OpenAICompletion class.
- Refactored client initialization to use a dedicated method for client parameter retrieval.
- Introduced new test cases to validate the correct usage of OpenAICompletion with various parameters.

* fix: correct test case for unsupported OpenAI model

- Updated the test_openai.py to ensure that the LLM instance is created before calling the method, maintaining proper error handling for unsupported models.
- This change ensures that the test accurately checks for the NotFoundError when an invalid model is specified.

* fix: enhance error handling in OpenAICompletion class

- Added specific exception handling for NotFoundError and APIConnectionError in the OpenAICompletion class to provide clearer error messages and improve logging.
- Updated the test case for unsupported models to ensure it raises a ValueError with the appropriate message when a non-existent model is specified.
- This change improves the robustness of the OpenAI API integration and enhances the clarity of error reporting.

* fix: improve test for unsupported OpenAI model handling

- Refactored the test case in test_openai.py to create the LLM instance after mocking the OpenAI client, ensuring proper error handling for unsupported models.
- This change enhances the clarity of the test by accurately checking for ValueError when a non-existent model is specified, aligning with recent improvements in error handling for the OpenAICompletion class.

* feat: bump versions to 1.0.0b1 (#3706)

* Lorenze/tools drop litellm (#3710)

* completely drop litellm and correctly pass config for qdrant

* feat: add support for additional embedding models in EmbeddingService

- Expanded the list of supported embedding models to include Google Vertex, Hugging Face, Jina, Ollama, OpenAI, Roboflow, Watson X, custom embeddings, Sentence Transformers, Text2Vec, OpenClip, and Instructor.
- This enhancement improves the versatility of the EmbeddingService by allowing integration with a wider range of embedding providers.

* fix: update collection parameter handling in CrewAIRagAdapter

- Changed the condition for setting vectors_config in the CrewAIRagAdapter to check for QdrantConfig instance instead of using hasattr. This improves type safety and ensures proper configuration handling for Qdrant integration.

* moved stagehand as optional dep (#3712)

* feat: bump versions to 1.0.0b2 (#3713)

* feat: enhance AnthropicCompletion class with additional client parame… (#3707)

* feat: enhance AnthropicCompletion class with additional client parameters and tool handling

- Added support for client_params in the AnthropicCompletion class to allow for additional client configuration.
- Refactored client initialization to use a dedicated method for retrieving client parameters.
- Implemented a new method to handle tool use conversation flow, ensuring proper execution and response handling.
- Introduced comprehensive test cases to validate the functionality of the AnthropicCompletion class, including tool use scenarios and parameter handling.

* drop print statements

* test: add fixture to mock ANTHROPIC_API_KEY for tests

- Introduced a pytest fixture to automatically mock the ANTHROPIC_API_KEY environment variable for all tests in the test_anthropic.py module.
- This change ensures that tests can run without requiring a real API key, improving test isolation and reliability.

* refactor: streamline streaming message handling in AnthropicCompletion class

- Removed the 'stream' parameter from the API call as it is set internally by the SDK.
- Simplified the handling of tool use events and response construction by extracting token usage from the final message.
- Enhanced the flow for managing tool use conversation, ensuring proper integration with the streaming API response.

* fix streaming here too

* fix: improve error handling in tool conversion for AnthropicCompletion class

- Enhanced exception handling during tool conversion by catching KeyError and ValueError.
- Added logging for conversion errors to aid in debugging and maintain robustness in tool integration.

* feat: enhance GeminiCompletion class with client parameter support (#3717)

* feat: enhance GeminiCompletion class with client parameter support

- Added support for client_params in the GeminiCompletion class to allow for additional client configuration.
- Refactored client initialization into a dedicated method for improved parameter handling.
- Introduced a new method to retrieve client parameters, ensuring compatibility with the base class.
- Enhanced error handling during client initialization to provide clearer messages for missing configuration.
- Updated documentation to reflect the changes in client parameter usage.

* add optional dependancies

* refactor: update test fixture to mock GOOGLE_API_KEY

- Renamed the fixture from `mock_anthropic_api_key` to `mock_google_api_key` to reflect the change in the environment variable being mocked.
- This update ensures that all tests in the module can run with a mocked GOOGLE_API_KEY, improving test isolation and reliability.

* fix tests

* feat: enhance BedrockCompletion class with advanced features

* feat: enhance BedrockCompletion class with advanced features and error handling

- Added support for guardrail configuration, additional model request fields, and custom response field paths in the BedrockCompletion class.
- Improved error handling for AWS exceptions and added token usage tracking with stop reason logging.
- Enhanced streaming response handling with comprehensive event management, including tool use and content block processing.
- Updated documentation to reflect new features and initialization parameters.
- Introduced a new test suite for BedrockCompletion to validate functionality and ensure robust integration with AWS Bedrock APIs.

* chore: add boto typing

* fix: use typing_extensions.Required for Python 3.10 compatibility

---------

Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>

* feat: azure native tests

* feat: add Azure AI Inference support and related tests

- Introduced the `azure-ai-inference` package with version `1.0.0b9` and its dependencies in `uv.lock` and `pyproject.toml`.
- Added new test files for Azure LLM functionality, including tests for Azure completion and tool handling.
- Implemented comprehensive test cases to validate Azure-specific behavior and integration with the CrewAI framework.
- Enhanced the testing framework to mock Azure credentials and ensure proper isolation during tests.

* feat: enhance AzureCompletion class with Azure OpenAI support

- Added support for the Azure OpenAI endpoint in the AzureCompletion class, allowing for flexible endpoint configurations.
- Implemented endpoint validation and correction to ensure proper URL formats for Azure OpenAI deployments.
- Enhanced error handling to provide clearer messages for common HTTP errors, including authentication and rate limit issues.
- Updated tests to validate the new endpoint handling and error messaging, ensuring robust integration with Azure AI Inference.
- Refactored parameter preparation to conditionally include the model parameter based on the endpoint type.

* refactor: convert project module to metaclass with full typing

* Lorenze/OpenAI base url backwards support (#3723)

* fix: enhance OpenAICompletion class base URL handling

- Updated the base URL assignment in the OpenAICompletion class to prioritize the new `api_base` attribute and fallback to the environment variable `OPENAI_BASE_URL` if both are not set.
- Added `api_base` to the list of parameters in the OpenAICompletion class to ensure proper configuration and flexibility in API endpoint management.

* feat: enhance OpenAICompletion class with api_base support

- Added the `api_base` parameter to the OpenAICompletion class to allow for flexible API endpoint configuration.
- Updated the `_get_client_params` method to prioritize `base_url` over `api_base`, ensuring correct URL handling.
- Introduced comprehensive tests to validate the behavior of `api_base` and `base_url` in various scenarios, including environment variable fallback.
- Enhanced test coverage for client parameter retrieval, ensuring robust integration with the OpenAI API.

* fix: improve OpenAICompletion class configuration handling

- Added a debug print statement to log the client configuration parameters during initialization for better traceability.
- Updated the base URL assignment logic to ensure it defaults to None if no valid base URL is provided, enhancing robustness in API endpoint configuration.
- Refined the retrieval of the `api_base` environment variable to streamline the configuration process.

* drop print

* feat: improvements on import native sdk support (#3725)

* feat: add support for Anthropic provider and enhance logging

- Introduced the `anthropic` package with version `0.69.0` in `pyproject.toml` and `uv.lock`, allowing for integration with the Anthropic API.
- Updated logging in the LLM class to provide clearer error messages when importing native providers, enhancing debugging capabilities.
- Improved error handling in the AnthropicCompletion class to guide users on installation via the updated error message format.
- Refactored import error handling in other provider classes to maintain consistency in error messaging and installation instructions.

* feat: enhance LLM support with Bedrock provider and update dependencies

- Added support for the `bedrock` provider in the LLM class, allowing integration with AWS Bedrock APIs.
- Updated `uv.lock` to replace `boto3` with `bedrock` in the dependencies, reflecting the new provider structure.
- Introduced `SUPPORTED_NATIVE_PROVIDERS` to include `bedrock` and ensure proper error handling when instantiating native providers.
- Enhanced error handling in the LLM class to raise informative errors when native provider instantiation fails.
- Added tests to validate the behavior of the new Bedrock provider and ensure fallback mechanisms work correctly for unsupported providers.

* test: update native provider fallback tests to expect ImportError

* adjust the test with the expected bevaior - raising ImportError

* this is exoecting the litellm format, all gemini native tests are in test_google.py

---------

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>

* fix: remove stdout prints, improve test determinism, and update trace handling

Removed `print` statements from the `LLMStreamChunkEvent` handler to prevent
LLM response chunks from being written directly to stdout. The listener now
only tracks chunks internally.

Fixes #3715

Added explicit return statements for trace-related tests.

Updated cassette for `test_failed_evaluation` to reflect new behavior where
an empty trace dict is used instead of returning early.

Ensured deterministic cleanup order in test fixtures by making
`clear_event_bus_handlers` depend on `setup_test_environment`. This guarantees
event bus shutdown and file handle cleanup occur before temporary directory
deletion, resolving intermittent “Directory not empty” errors in CI.

* chore: remove lib/crewai exclusion from pre-commit hooks

* feat: enhance task guardrail functionality and validation

* feat: enhance task guardrail functionality and validation

- Introduced support for multiple guardrails in the Task class, allowing for sequential processing of guardrails.
- Added a new `guardrails` field to the Task model to accept a list of callable guardrails or string descriptions.
- Implemented validation to ensure guardrails are processed correctly, including handling of retries and error messages.
- Enhanced the `_invoke_guardrail_function` method to manage guardrail execution and integrate with existing task output processing.
- Updated tests to cover various scenarios involving multiple guardrails, including success, failure, and retry mechanisms.

This update improves the flexibility and robustness of task execution by allowing for more complex validation scenarios.

* refactor: enhance guardrail type handling in Task model

- Updated the Task class to improve guardrail type definitions, introducing GuardrailType and GuardrailsType for better clarity and type safety.
- Simplified the validation logic for guardrails, ensuring that both single and multiple guardrails are processed correctly.
- Enhanced error messages for guardrail validation to provide clearer feedback when incorrect types are provided.
- This refactor improves the maintainability and robustness of task execution by standardizing guardrail handling.

* feat: implement per-guardrail retry tracking in Task model

- Introduced a new private attribute `_guardrail_retry_counts` to the Task class for tracking retry attempts on a per-guardrail basis.
- Updated the guardrail processing logic to utilize the new retry tracking, allowing for independent retry counts for each guardrail.
- Enhanced error handling to provide clearer feedback when guardrails fail validation after exceeding retry limits.
- Modified existing tests to validate the new retry tracking behavior, ensuring accurate assertions on guardrail retries.

This update improves the robustness and flexibility of task execution by allowing for more granular control over guardrail validation and retry mechanisms.

* chore: 1.0.0b3 bump (#3734)

* chore: full ruff and mypy

improved linting, pre-commit setup, and internal architecture. Configured Ruff to respect .gitignore, added stricter rules, and introduced a lock pre-commit hook with virtualenv activation. Fixed type shadowing in EXASearchTool using a type_ alias to avoid PEP 563 conflicts and resolved circular imports in agent executor and guardrail modules. Removed agent-ops attributes, deprecated watson alias, and dropped crewai-enterprise tools with corresponding test updates. Refactored cache and memoization for thread safety and cleaned up structured output adapters and related logic.

* New MCL DSL (#3738)

* Adding MCP implementation

* New tests for MCP implementation

* fix tests

* update docs

* Revert "New tests for MCP implementation"

This reverts commit 0bbe6dee90.

* linter

* linter

* fix

* verify mcp pacakge exists

* adjust docs to be clear only remote servers are supported

* reverted

* ensure args schema generated properly

* properly close out

---------

Co-authored-by: lorenzejay <lorenzejaytech@gmail.com>
Co-authored-by: Greyson Lalonde <greyson.r.lalonde@gmail.com>

* feat: a2a experimental

experimental a2a support

---------

Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>
Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>
Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>
Co-authored-by: Mike Plachta <mplachta@users.noreply.github.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
2025-10-20 14:10:19 -07:00

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---
title: Zendesk 통합
description: "CrewAI를 위한 Zendesk 통합으로 고객 지원 및 헬프데스크 관리."
icon: "headset"
mode: "wide"
---
## 개요
에이전트가 Zendesk를 통해 고객 지원 운영을 관리할 수 있도록 지원합니다. 티켓 생성 및 업데이트, 사용자 관리, 지원 지표 추적, 그리고 AI 기반 자동화를 통해 고객 서비스 워크플로우를 간소화할 수 있습니다.
## 사전 준비 사항
Zendesk 통합을 사용하기 전에 다음을 확인하세요.
- 활성 구독이 있는 [CrewAI AMP](https://app.crewai.com) 계정
- 적절한 API 권한이 있는 Zendesk 계정
- [통합 페이지](https://app.crewai.com/integrations)를 통해 Zendesk 계정 연결
## 사용 가능한 도구
### **티켓 관리**
<AccordionGroup>
<Accordion title="zendesk/create_ticket">
**설명:** Zendesk에 새로운 지원 티켓을 생성합니다.
**매개변수:**
- `ticketSubject` (string, 필수): 티켓 제목 줄 (예: "도와주세요, 프린터에 불이 났어요!")
- `ticketDescription` (string, 필수): 티켓에 표시될 첫 번째 댓글 (예: "연기가 정말 화려하네요.")
- `requesterName` (string, 필수): 지원 요청자의 이름 (예: "Jane Customer")
- `requesterEmail` (string, 필수): 지원 요청자의 이메일 (예: "jane@example.com")
- `assigneeId` (string, 선택): 이 티켓에 할당된 Zendesk 에이전트 ID - 사용자가 담당자를 선택할 수 있도록 Connect Portal Workflow Settings 를 사용하세요
- `ticketType` (string, 선택): 티켓 유형 - 옵션: problem, incident, question, task
- `ticketPriority` (string, 선택): 우선순위 수준 - 옵션: urgent, high, normal, low
- `ticketStatus` (string, 선택): 티켓 상태 - 옵션: new, open, pending, hold, solved, closed
- `ticketDueAt` (string, 선택): task 유형 티켓의 마감일 (ISO 8601 타임스탬프)
- `ticketTags` (string, 선택): 적용할 태그 배열 (예: `["enterprise", "other_tag"]`)
- `ticketExternalId` (string, 선택): 티켓을 로컬 레코드와 연결할 외부 ID
- `ticketCustomFields` (object, 선택): JSON 형식의 사용자 정의 필드 값
</Accordion>
<Accordion title="zendesk/update_ticket">
**설명:** Zendesk의 기존 지원 티켓을 업데이트합니다.
**매개변수:**
- `ticketId` (string, 필수): 업데이트할 티켓의 ID (예: "35436")
- `ticketSubject` (string, 선택): 업데이트된 티켓 제목
- `requesterName` (string, 필수): 이 티켓을 요청한 사용자의 이름
- `requesterEmail` (string, 필수): 이 티켓을 요청한 사용자의 이메일
- `assigneeId` (string, 선택): 업데이트된 담당자 ID - Connect Portal Workflow Settings 를 사용하세요
- `ticketType` (string, 선택): 업데이트된 티켓 유형 - 옵션: problem, incident, question, task
- `ticketPriority` (string, 선택): 업데이트된 우선순위 - 옵션: urgent, high, normal, low
- `ticketStatus` (string, 선택): 업데이트된 상태 - 옵션: new, open, pending, hold, solved, closed
- `ticketDueAt` (string, 선택): 업데이트된 마감일 (ISO 8601 타임스탬프)
- `ticketTags` (string, 선택): 업데이트된 태그 배열
- `ticketExternalId` (string, 선택): 업데이트된 외부 ID
- `ticketCustomFields` (object, 선택): 업데이트된 사용자 정의 필드 값
</Accordion>
<Accordion title="zendesk/get_ticket_by_id">
**설명:** ID로 특정 티켓을 조회합니다.
**매개변수:**
- `ticketId` (string, 필수): 조회할 티켓의 ID (예: "35436")
</Accordion>
<Accordion title="zendesk/add_comment_to_ticket">
**설명:** 기존 티켓에 댓글이나 내부 노트를 추가합니다.
**매개변수:**
- `ticketId` (string, 필수): 댓글을 추가할 티켓의 ID (예: "35436")
- `commentBody` (string, 필수): 댓글 메시지 (일반 텍스트 또는 HTML 지원, 예: "도움을 주셔서 감사합니다!")
- `isInternalNote` (boolean, 선택): 공개 답글 대신 내부 노트로 설정하려면 true (기본값: false)
- `isPublic` (boolean, 선택): 공개 댓글이면 true, 내부 노트이면 false
</Accordion>
<Accordion title="zendesk/search_tickets">
**설명:** 다양한 필터 및 조건을 사용하여 티켓을 검색합니다.
**매개변수:**
- `ticketSubject` (string, 선택): 티켓 제목 내 텍스트로 필터링
- `ticketDescription` (string, 선택): 티켓 설명 및 댓글 내 텍스트로 필터링
- `ticketStatus` (string, 선택): 상태로 필터링 - 옵션: new, open, pending, hold, solved, closed
- `ticketType` (string, 선택): 유형으로 필터링 - 옵션: problem, incident, question, task, no_type
- `ticketPriority` (string, 선택): 우선순위로 필터링 - 옵션: urgent, high, normal, low, no_priority
- `requesterId` (string, 선택): 요청자 사용자 ID로 필터링
- `assigneeId` (string, 선택): 담당 에이전트 ID로 필터링
- `recipientEmail` (string, 선택): 원래 수신자 이메일 주소로 필터링
- `ticketTags` (string, 선택): 티켓 태그로 필터링
- `ticketExternalId` (string, 선택): 외부 ID로 필터링
- `createdDate` (object, 선택): 생성일로 필터링 (연산자: EQUALS, LESS_THAN_EQUALS, GREATER_THAN_EQUALS, 값)
- `updatedDate` (object, 선택): 업데이트 날짜로 필터링 (연산자와 값)
- `dueDate` (object, 선택): 마감일로 필터링 (연산자와 값)
- `sort_by` (string, 선택): 정렬 필드 - 옵션: created_at, updated_at, priority, status, ticket_type
- `sort_order` (string, 선택): 정렬 방향 - 옵션: asc, desc
</Accordion>
</AccordionGroup>
### **사용자 관리**
<AccordionGroup>
<Accordion title="zendesk/create_user">
**설명:** Zendesk에서 새로운 사용자를 생성합니다.
**매개변수:**
- `name` (string, 필수): 사용자의 전체 이름
- `email` (string, 선택): 사용자의 이메일 주소 (예: "jane@example.com")
- `phone` (string, 선택): 사용자의 전화번호
- `role` (string, 선택): 사용자 역할 - 옵션: admin, agent, end-user
- `externalId` (string, 선택): 다른 시스템의 고유 식별자
- `details` (string, 선택): 추가 사용자 정보
- `notes` (string, 선택): 사용자에 대한 내부 메모
</Accordion>
<Accordion title="zendesk/update_user">
**설명:** 기존 사용자의 정보를 업데이트합니다.
**매개변수:**
- `userId` (string, 필수): 업데이트할 사용자의 ID
- `name` (string, 선택): 업데이트할 사용자 이름
- `email` (string, 선택): 업데이트할 이메일 (업데이트 시 보조 이메일로 추가됨)
- `phone` (string, 선택): 업데이트할 전화번호
- `role` (string, 선택): 업데이트할 역할 - 옵션: admin, agent, end-user
- `externalId` (string, 선택): 업데이트된 외부 ID
- `details` (string, 선택): 업데이트된 사용자 상세 정보
- `notes` (string, 선택): 업데이트된 내부 메모
</Accordion>
<Accordion title="zendesk/get_user_by_id">
**설명:** ID로 특정 사용자를 조회합니다.
**매개변수:**
- `userId` (string, 필수): 조회할 사용자 ID
</Accordion>
<Accordion title="zendesk/search_users">
**설명:** 다양한 기준으로 사용자를 검색합니다.
**매개변수:**
- `name` (string, 선택): 사용자 이름으로 필터링
- `email` (string, 선택): 사용자 이메일로 필터링 (예: "jane@example.com")
- `role` (string, 선택): 역할로 필터링 - 옵션: admin, agent, end-user
- `externalId` (string, 선택): 외부 ID로 필터링
- `sort_by` (string, 선택): 정렬 필드 - 옵션: created_at, updated_at
- `sort_order` (string, 선택): 정렬 방향 - 옵션: asc, desc
</Accordion>
</AccordionGroup>
### **관리 도구**
<AccordionGroup>
<Accordion title="zendesk/get_ticket_fields">
**설명:** 티켓에 사용할 수 있는 모든 표준 및 맞춤 필드를 검색합니다.
**파라미터:**
- `paginationParameters` (object, 선택 사항): 페이지네이션 설정
- `pageCursor` (string, 선택 사항): 페이지네이션을 위한 페이지 커서
</Accordion>
<Accordion title="zendesk/get_ticket_audits">
**설명:** 티켓의 감사 기록(읽기 전용 이력)을 가져옵니다.
**파라미터:**
- `ticketId` (string, 선택 사항): 특정 티켓의 감사를 조회합니다(비워두면 모든 비보관된 티켓의 감사를 조회, 예: "1234")
- `paginationParameters` (object, 선택 사항): 페이지네이션 설정
- `pageCursor` (string, 선택 사항): 페이지네이션을 위한 페이지 커서
</Accordion>
</AccordionGroup>
## 커스텀 필드
커스텀 필드를 사용하면 조직에 특화된 추가 정보를 저장할 수 있습니다:
```json
[
{ "id": 27642, "value": "745" },
{ "id": 27648, "value": "yes" }
]
```
## 티켓 우선순위 레벨
우선순위 레벨 이해하기:
- **긴급** - 즉각적인 조치가 필요한 치명적 이슈
- **높음** - 신속하게 해결해야 하는 중요한 이슈
- **보통** - 대부분의 티켓에 해당하는 표준 우선순위
- **낮음** - 여유가 있을 때 처리해도 되는 사소한 이슈
## 티켓 상태 워크플로우
표준 티켓 상태 진행:
- **new** - 최근에 생성됨, 아직 할당되지 않음
- **open** - 현재 작업 중
- **pending** - 고객 응답 또는 외부 조치 대기 중
- **hold** - 일시 중지됨
- **solved** - 문제가 해결되어 고객 확인 대기 중
- **closed** - 티켓이 완료되어 종료됨
## 사용 예시
### 기본 Zendesk 에이전트 설정
```python
from crewai import Agent, Task, Crew
# Create an agent with Zendesk capabilities
zendesk_agent = Agent(
role="Support Manager",
goal="Manage customer support tickets and provide excellent customer service",
backstory="An AI assistant specialized in customer support operations and ticket management.",
apps=['zendesk']
)
# Task to create a new support ticket
create_ticket_task = Task(
description="Create a high-priority support ticket for John Smith who is unable to access his account after password reset",
agent=zendesk_agent,
expected_output="Support ticket created successfully with ticket ID"
)
# Run the task
crew = Crew(
agents=[zendesk_agent],
tasks=[create_ticket_task]
)
crew.kickoff()
```
### 특정 Zendesk 도구 필터링
```python
support_agent = Agent(
role="Customer Support Agent",
goal="Handle customer inquiries and resolve support issues efficiently",
backstory="An experienced support agent who specializes in ticket resolution and customer communication.",
apps=['zendesk']
)
# Task to manage support workflow
support_task = Task(
description="Create a ticket for login issues, add troubleshooting comments, and update status to resolved",
agent=support_agent,
expected_output="Support ticket managed through complete resolution workflow"
)
crew = Crew(
agents=[support_agent],
tasks=[support_task]
)
crew.kickoff()
```
### 고급 티켓 관리
```python
from crewai import Agent, Task, Crew
ticket_manager = Agent(
role="Ticket Manager",
goal="Manage support ticket workflows and ensure timely resolution",
backstory="An AI assistant that specializes in support ticket triage and workflow optimization.",
apps=['zendesk']
)
# Task to manage ticket lifecycle
ticket_workflow = Task(
description="""
1. 계정 접근 문제에 대한 새로운 지원 티켓을 생성합니다.
2. 문제 해결 단계를 내부 노트에 추가합니다.
3. 고객 등급에 따라 티켓 우선순위를 업데이트합니다.
4. 해결 코멘트를 추가하고 티켓을 종료합니다.
""",
agent=ticket_manager,
expected_output="티켓 생성부터 해결까지 전체 생명주기 관리"
)
crew = Crew(
agents=[ticket_manager],
tasks=[ticket_workflow]
)
crew.kickoff()
```
### 지원 분석 및 보고
```python
from crewai import Agent, Task, Crew
support_analyst = Agent(
role="Support Analyst",
goal="Analyze support metrics and generate insights for team performance",
backstory="An analytical AI that excels at extracting insights from support data and ticket patterns.",
apps=['zendesk']
)
# Complex task involving analytics and reporting
analytics_task = Task(
description="""
1. Search for all open tickets from the last 30 days
2. Analyze ticket resolution times and customer satisfaction
3. Identify common issues and support patterns
4. Generate weekly support performance report
""",
agent=support_analyst,
expected_output="Comprehensive support analytics report with performance insights and recommendations"
)
crew = Crew(
agents=[support_analyst],
tasks=[analytics_task]
)
crew.kickoff()
```