From bf9e0423f200ce34384b56813249541c5cf72f4a Mon Sep 17 00:00:00 2001 From: Tony Kipkemboi Date: Thu, 2 Oct 2025 11:36:04 -0400 Subject: [PATCH] chore(docs): bring AMP doc refresh from release/v1.0.0 into main (#3637) * 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 * chore: tidy docs formatting --- README.md | 28 +- docs/docs.json | 210 +++- docs/en/api-reference/introduction.mdx | 24 +- docs/en/changelog.mdx | 934 +++++++++-------- docs/en/concepts/agents.mdx | 2 +- docs/en/concepts/cli.mdx | 32 +- docs/en/concepts/event-listener.mdx | 2 +- docs/en/concepts/flows.mdx | 11 +- docs/en/concepts/tasks.mdx | 13 +- docs/en/concepts/tools.mdx | 4 +- .../features/agent-repositories.mdx | 27 +- docs/en/enterprise/features/automations.mdx | 104 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3d3eb3324..a448e6355 100644 --- a/README.md +++ b/README.md @@ -62,9 +62,9 @@ With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the standard for enterprise-ready AI automation. -# CrewAI Enterprise Suite +# CrewAI AMP Suite -CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation. +CrewAI AMP Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation. You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com) @@ -76,9 +76,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app. - **Advanced Security**: Built-in robust security and compliance measures ensuring safe deployment and management. - **Actionable Insights**: Real-time analytics and reporting to optimize performance and decision-making. - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues. -- **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements. +- **On-premise and Cloud Deployment Options**: Deploy CrewAI AMP on-premise or in the cloud, depending on your security and compliance requirements. -CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient, +CrewAI AMP is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient, intelligent automations. ## Table of contents @@ -674,9 +674,9 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b ### Enterprise Features -- [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer) -- [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments) -- [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free) +- [What additional features does CrewAI AMP offer?](#q-what-additional-features-does-crewai-amp-offer) +- [Is CrewAI AMP available for cloud and on-premise deployments?](#q-is-crewai-amp-available-for-cloud-and-on-premise-deployments) +- [Can I try CrewAI AMP for free?](#q-can-i-try-crewai-amp-for-free) ### Q: What exactly is CrewAI? @@ -732,17 +732,17 @@ A: Check out practical examples in the [CrewAI-examples repository](https://gith A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines. -### Q: What additional features does CrewAI Enterprise offer? +### Q: What additional features does CrewAI AMP offer? -A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support. +A: CrewAI AMP provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support. -### Q: Is CrewAI Enterprise available for cloud and on-premise deployments? +### Q: Is CrewAI AMP available for cloud and on-premise deployments? -A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements. +A: Yes, CrewAI AMP supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements. -### Q: Can I try CrewAI Enterprise for free? +### Q: Can I try CrewAI AMP for free? -A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free. +A: Yes, you can explore part of the CrewAI AMP Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free. ### Q: Does CrewAI support fine-tuning or training custom models? @@ -762,7 +762,7 @@ A: CrewAI is highly scalable, supporting simple automations and large-scale ente ### Q: Does CrewAI offer debugging and monitoring tools? -A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations. +A: Yes, CrewAI AMP includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations. ### Q: What programming languages does CrewAI support? diff --git a/docs/docs.json b/docs/docs.json index dd2d0f302..37c7679f0 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -9,7 +9,22 @@ }, "favicon": "/images/favicon.svg", "contextual": { - "options": ["copy", "view", "chatgpt", "claude"] + "options": [ + "copy", + "view", + "chatgpt", + "claude", + "perplexity", + "mcp", + "cursor", + "vscode", + { + "title": "Request a feature", + "description": "Join the discussion on GitHub to request a new feature", + "icon": "plus", + "href": "https://github.com/crewAIInc/crewAI/issues/new/choose" + } + ] }, "navigation": { "languages": [ @@ -40,6 +55,16 @@ ] }, "tabs": [ + { + "tab": "Home", + "icon": "house", + "groups": [ + { + "group": "Welcome", + "pages": ["index"] + } + ] + }, { "tab": "Documentation", "icon": "book-open", @@ -225,9 +250,9 @@ "group": "Integrations", "icon": "plug", "pages": [ - "en/tools/tool-integrations/overview", - "en/tools/tool-integrations/bedrockinvokeagenttool", - "en/tools/tool-integrations/crewaiautomationtool" + "en/tools/integration/overview", + "en/tools/integration/bedrockinvokeagenttool", + "en/tools/integration/crewaiautomationtool" ] }, { @@ -279,6 +304,7 @@ "en/learn/force-tool-output-as-result", "en/learn/hierarchical-process", "en/learn/human-input-on-execution", + "en/learn/human-in-the-loop", "en/learn/kickoff-async", "en/learn/kickoff-for-each", "en/learn/llm-connections", @@ -295,7 +321,7 @@ ] }, { - "tab": "Enterprise", + "tab": "AMP", "icon": "briefcase", "groups": [ { @@ -303,15 +329,27 @@ "pages": ["en/enterprise/introduction"] }, { - "group": "Features", + "group": "Build", + "pages": [ + "en/enterprise/features/automations", + "en/enterprise/features/crew-studio", + "en/enterprise/features/marketplace", + "en/enterprise/features/agent-repositories", + "en/enterprise/features/tools-and-integrations" + ] + }, + { + "group": "Operate", "pages": [ - "en/enterprise/features/rbac", - "en/enterprise/features/tool-repository", - "en/enterprise/features/webhook-streaming", "en/enterprise/features/traces", - "en/enterprise/features/hallucination-guardrail", - "en/enterprise/features/integrations", - "en/enterprise/features/agent-repositories" + "en/enterprise/features/webhook-streaming", + "en/enterprise/features/hallucination-guardrail" + ] + }, + { + "group": "Manage", + "pages": [ + "en/enterprise/features/rbac" ] }, { @@ -335,6 +373,22 @@ "en/enterprise/integrations/zendesk" ] }, + { + "group": "Triggers", + "pages": [ + "en/enterprise/guides/automation-triggers", + "en/enterprise/guides/gmail-trigger", + "en/enterprise/guides/google-calendar-trigger", + "en/enterprise/guides/google-drive-trigger", + "en/enterprise/guides/outlook-trigger", + "en/enterprise/guides/onedrive-trigger", + "en/enterprise/guides/microsoft-teams-trigger", + "en/enterprise/guides/slack-trigger", + "en/enterprise/guides/hubspot-trigger", + "en/enterprise/guides/salesforce-trigger", + "en/enterprise/guides/zapier-trigger" + ] + }, { "group": "How-To Guides", "pages": [ @@ -344,15 +398,11 @@ "en/enterprise/guides/update-crew", "en/enterprise/guides/enable-crew-studio", "en/enterprise/guides/azure-openai-setup", - "en/enterprise/guides/automation-triggers", - "en/enterprise/guides/hubspot-trigger", + "en/enterprise/guides/tool-repository", "en/enterprise/guides/react-component-export", - "en/enterprise/guides/salesforce-trigger", - "en/enterprise/guides/slack-trigger", "en/enterprise/guides/team-management", - "en/enterprise/guides/webhook-automation", "en/enterprise/guides/human-in-the-loop", - "en/enterprise/guides/zapier-trigger" + "en/enterprise/guides/webhook-automation" ] }, { @@ -425,6 +475,16 @@ ] }, "tabs": [ + { + "tab": "Início", + "icon": "house", + "groups": [ + { + "group": "Bem-vindo", + "pages": ["pt-BR/index"] + } + ] + }, { "tab": "Documentação", "icon": "book-open", @@ -600,12 +660,12 @@ ] }, { - "group": "Integrações", + "group": "Integrations", "icon": "plug", "pages": [ - "pt-BR/tools/tool-integrations/overview", - "pt-BR/tools/tool-integrations/bedrockinvokeagenttool", - "pt-BR/tools/tool-integrations/crewaiautomationtool" + "pt-BR/tools/integration/overview", + "pt-BR/tools/integration/bedrockinvokeagenttool", + "pt-BR/tools/integration/crewaiautomationtool" ] }, { @@ -654,6 +714,7 @@ "pt-BR/learn/force-tool-output-as-result", "pt-BR/learn/hierarchical-process", "pt-BR/learn/human-input-on-execution", + "pt-BR/learn/human-in-the-loop", "pt-BR/learn/kickoff-async", "pt-BR/learn/kickoff-for-each", "pt-BR/learn/llm-connections", @@ -670,7 +731,7 @@ ] }, { - "tab": "Enterprise", + "tab": "AMP", "icon": "briefcase", "groups": [ { @@ -678,14 +739,27 @@ "pages": ["pt-BR/enterprise/introduction"] }, { - "group": "Funcionalidades", + "group": "Construir", + "pages": [ + "pt-BR/enterprise/features/automations", + "pt-BR/enterprise/features/crew-studio", + "pt-BR/enterprise/features/marketplace", + "pt-BR/enterprise/features/agent-repositories", + "pt-BR/enterprise/features/tools-and-integrations" + ] + }, + { + "group": "Operar", "pages": [ - "pt-BR/enterprise/features/rbac", - "pt-BR/enterprise/features/tool-repository", - "pt-BR/enterprise/features/webhook-streaming", "pt-BR/enterprise/features/traces", - "pt-BR/enterprise/features/hallucination-guardrail", - "pt-BR/enterprise/features/integrations" + "pt-BR/enterprise/features/webhook-streaming", + "pt-BR/enterprise/features/hallucination-guardrail" + ] + }, + { + "group": "Gerenciar", + "pages": [ + "pt-BR/enterprise/features/rbac" ] }, { @@ -718,14 +792,20 @@ "pt-BR/enterprise/guides/update-crew", "pt-BR/enterprise/guides/enable-crew-studio", "pt-BR/enterprise/guides/azure-openai-setup", - "pt-BR/enterprise/guides/automation-triggers", - "pt-BR/enterprise/guides/hubspot-trigger", + "pt-BR/enterprise/guides/tool-repository", "pt-BR/enterprise/guides/react-component-export", - "pt-BR/enterprise/guides/salesforce-trigger", - "pt-BR/enterprise/guides/slack-trigger", "pt-BR/enterprise/guides/team-management", - "pt-BR/enterprise/guides/webhook-automation", "pt-BR/enterprise/guides/human-in-the-loop", + "pt-BR/enterprise/guides/webhook-automation" + ] + }, + { + "group": "Triggers", + "pages": [ + "pt-BR/enterprise/guides/automation-triggers", + "pt-BR/enterprise/guides/slack-trigger", + "pt-BR/enterprise/guides/hubspot-trigger", + "pt-BR/enterprise/guides/salesforce-trigger", "pt-BR/enterprise/guides/zapier-trigger" ] }, @@ -801,6 +881,16 @@ ] }, "tabs": [ + { + "tab": "홈", + "icon": "house", + "groups": [ + { + "group": "환영합니다", + "pages": ["ko/index"] + } + ] + }, { "tab": "기술 문서", "icon": "book-open", @@ -979,17 +1069,16 @@ "ko/tools/cloud-storage/overview", "ko/tools/cloud-storage/s3readertool", "ko/tools/cloud-storage/s3writertool", - "ko/tools/cloud-storage/bedrockinvokeagenttool", "ko/tools/cloud-storage/bedrockkbretriever" ] }, { - "group": "통합", + "group": "Integrations", "icon": "plug", "pages": [ - "ko/tools/tool-integrations/overview", - "ko/tools/tool-integrations/bedrockinvokeagenttool", - "ko/tools/tool-integrations/crewaiautomationtool" + "ko/tools/integration/overview", + "ko/tools/integration/bedrockinvokeagenttool", + "ko/tools/integration/crewaiautomationtool" ] }, { @@ -1039,6 +1128,7 @@ "ko/learn/force-tool-output-as-result", "ko/learn/hierarchical-process", "ko/learn/human-input-on-execution", + "ko/learn/human-in-the-loop", "ko/learn/kickoff-async", "ko/learn/kickoff-for-each", "ko/learn/llm-connections", @@ -1063,15 +1153,27 @@ "pages": ["ko/enterprise/introduction"] }, { - "group": "특징", + "group": "빌드", + "pages": [ + "ko/enterprise/features/automations", + "ko/enterprise/features/crew-studio", + "ko/enterprise/features/marketplace", + "ko/enterprise/features/agent-repositories", + "ko/enterprise/features/tools-and-integrations" + ] + }, + { + "group": "운영", "pages": [ - "ko/enterprise/features/rbac", - "ko/enterprise/features/tool-repository", - "ko/enterprise/features/webhook-streaming", "ko/enterprise/features/traces", - "ko/enterprise/features/hallucination-guardrail", - "ko/enterprise/features/integrations", - "ko/enterprise/features/agent-repositories" + "ko/enterprise/features/webhook-streaming", + "ko/enterprise/features/hallucination-guardrail" + ] + }, + { + "group": "관리", + "pages": [ + "ko/enterprise/features/rbac" ] }, { @@ -1104,14 +1206,20 @@ "ko/enterprise/guides/update-crew", "ko/enterprise/guides/enable-crew-studio", "ko/enterprise/guides/azure-openai-setup", - "ko/enterprise/guides/automation-triggers", - "ko/enterprise/guides/hubspot-trigger", + "ko/enterprise/guides/tool-repository", "ko/enterprise/guides/react-component-export", - "ko/enterprise/guides/salesforce-trigger", - "ko/enterprise/guides/slack-trigger", "ko/enterprise/guides/team-management", - "ko/enterprise/guides/webhook-automation", "ko/enterprise/guides/human-in-the-loop", + "ko/enterprise/guides/webhook-automation" + ] + }, + { + "group": "트리거", + "pages": [ + "ko/enterprise/guides/automation-triggers", + "ko/enterprise/guides/slack-trigger", + "ko/enterprise/guides/hubspot-trigger", + "ko/enterprise/guides/salesforce-trigger", "ko/enterprise/guides/zapier-trigger" ] }, diff --git a/docs/en/api-reference/introduction.mdx b/docs/en/api-reference/introduction.mdx index 3ee25b14a..3e952574a 100644 --- a/docs/en/api-reference/introduction.mdx +++ b/docs/en/api-reference/introduction.mdx @@ -1,29 +1,29 @@ --- title: "Introduction" -description: "Complete reference for the CrewAI Enterprise REST API" +description: "Complete reference for the CrewAI AMP REST API" icon: "code" mode: "wide" --- -# CrewAI Enterprise API +# CrewAI AMP API -Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services. +Welcome to the CrewAI AMP API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services. ## Quick Start - Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab. + Navigate to your crew's detail page in the CrewAI AMP dashboard and copy your Bearer Token from the Status tab. - + Use the `GET /inputs` endpoint to see what parameters your crew expects. - + Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`. - + Use `GET /status/{kickoff_id}` to check execution status and retrieve results. @@ -46,7 +46,7 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \ | **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations | -You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard. +You can find both token types in the Status tab of your crew's detail page in the CrewAI AMP dashboard. ## Base URL @@ -62,7 +62,7 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard. ## Typical Workflow 1. **Discovery**: Call `GET /inputs` to understand what your crew needs -2. **Execution**: Submit inputs via `POST /kickoff` to start processing +2. **Execution**: Submit inputs via `POST /kickoff` to start processing 3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion 4. **Results**: Extract the final output from the completed response @@ -82,12 +82,12 @@ The API uses standard HTTP status codes: ## Interactive Testing -**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons. +**Why no "Send" button?** Since each CrewAI AMP user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons. Each endpoint page shows you: - ✅ **Exact request format** with all parameters -- ✅ **Response examples** for success and error cases +- ✅ **Response examples** for success and error cases - ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.) - ✅ **Authentication examples** with proper Bearer token format @@ -104,7 +104,7 @@ Each endpoint page shows you: **Example workflow:** 1. **Copy this cURL example** from any endpoint page -2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL +2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL 3. **Replace the Bearer token** with your real token from the dashboard 4. **Run the request** in your terminal or API client diff --git a/docs/en/changelog.mdx b/docs/en/changelog.mdx index 64674c71d..ed194bebc 100644 --- a/docs/en/changelog.mdx +++ b/docs/en/changelog.mdx @@ -4,6 +4,84 @@ description: "Product updates, improvements, and bug fixes for CrewAI" icon: "clock" mode: "wide" --- + + ## v1.0.0a1 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.0.0a1) + + ## What's Changed + + ### Core Improvements & Fixes + - Fixed permission handling for `actions` in agent configuration + - Updated CI workflows and release publishing to support the new monorepo structure + - Bumped Python support to 3.13 and refreshed workspace metadata + + ### New Features & Enhancements + - Added `apps` and `actions` attributes to `Agent` for richer runtime control + - Merged the `crewai-tools` repository into the main workspace (monorepo) + - Bumped all packages to 1.0.0a1 to mark the alpha milestone + + ### Cleanup & Infrastructure + - Delivered a new CI pipeline with version pinning and publishing strategy + - Unified internal code to manage multiple packages coherently + + + + + ## v0.201.1 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.201.1) + + ## What's Changed + + ### Core Improvements & Fixes + - Renamed Watson embedding provider to `watsonx` and refreshed environment variable prefixes + - Added ChromaDB compatibility for `watsonx` and `voyageai` embedding providers + + ### Cleanup & Deprecations + - Standardized environment variable prefixes for all embedding providers + - Bumped CrewAI to 0.201.1 and updated internal dependencies + + + + + ## v0.201.0 + + [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.201.0) + + ## What's Changed + + ### Core Improvements & Fixes + - Made the `ready` parameter optional in `_create_reasoning_plan` + - Fixed nested config handling for embedder configuration + - Added `batch_size` support to avoid token limit errors + - Corrected Quickstart documentation directory naming + - Resolved test duration cache issues and event exports + - Added fallback logic to crew settings + + ### New Features & Enhancements + - Introduced thread-safe platform context management + - Added `crewai uv` wrapper command to run `uv` from the CLI + - Enabled marking traces as failed for observability workflows + - Added custom embedding types and provider migration support + - Upgraded ChromaDB to v1.1.0 with compatibility fixes and type improvements + - Added Pydantic-compatible import validation and reorganized dependency groups + + ### Documentation & Guides + - Updated changelog coverage for recent releases (0.193.x series) + - Documented metadata support for LLM Guardrail events + - Added guidance for fallback behavior and configuration visibility + + ### Cleanup & Deprecations + - Resolved Ruff and MyPy issues across modules + - Improved type annotations and consolidated utilities + - Deprecated legacy utilities in favor of Pydantic-compatible imports + + ### Contributors + - @qizwiz (first contribution) + + + ## v0.193.2 @@ -33,28 +111,28 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.193.0) ## Core Improvements & Fixes - - - Fixed handling of the `model` parameter during OpenAI adapter initialization - - Resolved test duration cache issues in CI workflows - - Fixed flaky test related to repeated tool usage by agents - - Added missing event exports to `__init__.py` for consistent module behavior - - Dropped message storage from metadata in Mem0 to reduce bloat - - Fixed L2 distance metric support for backward compatibility in vector search + + - Fixed handling of the `model` parameter during OpenAI adapter initialization + - Resolved test duration cache issues in CI workflows + - Fixed flaky test related to repeated tool usage by agents + - Added missing event exports to `__init__.py` for consistent module behavior + - Dropped message storage from metadata in Mem0 to reduce bloat + - Fixed L2 distance metric support for backward compatibility in vector search ## New Features & Enhancements - - - Introduced thread-safe platform context management - - Added test duration caching for optimized `pytest-split` runs - - Added ephemeral trace improvements for better trace control - - Made search parameters for RAG, knowledge, and memory fully configurable - - Enabled ChromaDB to use OpenAI API for embedding functions - - Added deeper observability tools for user-level insights - - Unified RAG storage system with instance-specific client support + + - Introduced thread-safe platform context management + - Added test duration caching for optimized `pytest-split` runs + - Added ephemeral trace improvements for better trace control + - Made search parameters for RAG, knowledge, and memory fully configurable + - Enabled ChromaDB to use OpenAI API for embedding functions + - Added deeper observability tools for user-level insights + - Unified RAG storage system with instance-specific client support ## Documentation & Guides - - - Updated `RagTool` references to reflect CrewAI native RAG implementation - - Improved internal docs for `langgraph` and `openai` agent adapters with type annotations and docstrings + + - Updated `RagTool` references to reflect CrewAI native RAG implementation + - Improved internal docs for `langgraph` and `openai` agent adapters with type annotations and docstrings @@ -65,8 +143,8 @@ mode: "wide" ## What's Changed - - Fixed version not being found and silently failing reversion - - Bumped CrewAI version to 0.186.1 and updated dependencies in the CLI + - Fixed version not being found and silently failing reversion + - Bumped CrewAI version to 0.186.1 and updated dependencies in the CLI @@ -86,31 +164,31 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.177.0) - + ## Core Improvements & Fixes - - - Achieved parity between `rag` package and current implementation - - Enhanced LLM event handling with task and agent metadata - - Fixed mutable default arguments by replacing them with `None` - - Suppressed Pydantic deprecation warnings during initialization - - Fixed broken example link in `README.md` - - Removed Python 3.12+ only Ruff rules for compatibility - - Migrated CI workflows to use `uv` and updated dev tooling - + + - Achieved parity between `rag` package and current implementation + - Enhanced LLM event handling with task and agent metadata + - Fixed mutable default arguments by replacing them with `None` + - Suppressed Pydantic deprecation warnings during initialization + - Fixed broken example link in `README.md` + - Removed Python 3.12+ only Ruff rules for compatibility + - Migrated CI workflows to use `uv` and updated dev tooling + ## New Features & Enhancements - - - Added tracing improvements and cleanup - - Centralized event logic by moving `events` module to `crewai.events` - + + - Added tracing improvements and cleanup + - Centralized event logic by moving `events` module to `crewai.events` + ## Documentation & Guides - - - Updated Enterprise Action Auth Token section documentation - - Published documentation updates for `v0.175.0` release - + + - Updated Enterprise Action Auth Token section documentation + - Published documentation updates for `v0.175.0` release + ## Cleanup & Refactoring - - - Refactored parser into modular functions for better structure - + + - Refactored parser into modular functions for better structure + @@ -118,40 +196,40 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.175.0) - + ## Core Improvements & Fixes - - - Fixed migration of the `tool` section during `crewai update` - - Reverted OpenAI pin: now requires `openai >=1.13.3` due to fixed import issues - - Fixed flaky tests and improved test stability - - Improved `Flow` listener resumability for HITL and cyclic flows - - Enhanced timeout handling in `PlusAPI` and `TraceBatchManager` - - Batched entity memory items to reduce redundant operations - + + - Fixed migration of the `tool` section during `crewai update` + - Reverted OpenAI pin: now requires `openai >=1.13.3` due to fixed import issues + - Fixed flaky tests and improved test stability + - Improved `Flow` listener resumability for HITL and cyclic flows + - Enhanced timeout handling in `PlusAPI` and `TraceBatchManager` + - Batched entity memory items to reduce redundant operations + ## New Features & Enhancements - - - Added support for additional parameters in `Flow.start()` methods - - Displayed task names in verbose CLI output - - Added centralized embedding types and introduced a base embedding client - - Introduced generic clients for ChromaDB and Qdrant - - Added support for `crewai config reset` to clear tokens - - Enabled `crewai_trigger_payload` auto-injection - - Simplified RAG client initialization and introduced RAG configuration system - - Added Qdrant RAG provider support - - Improved tracing with better event data - - Added support to remove Auth0 and email entry on `crewai login` - + + - Added support for additional parameters in `Flow.start()` methods + - Displayed task names in verbose CLI output + - Added centralized embedding types and introduced a base embedding client + - Introduced generic clients for ChromaDB and Qdrant + - Added support for `crewai config reset` to clear tokens + - Enabled `crewai_trigger_payload` auto-injection + - Simplified RAG client initialization and introduced RAG configuration system + - Added Qdrant RAG provider support + - Improved tracing with better event data + - Added support to remove Auth0 and email entry on `crewai login` + ## Documentation & Guides - - - Added documentation for automation triggers - - Fixed API Reference OpenAPI sources and redirects - - Added hybrid search alpha parameter to the docs - + + - Added documentation for automation triggers + - Fixed API Reference OpenAPI sources and redirects + - Added hybrid search alpha parameter to the docs + ## Cleanup & Deprecations - - - Added deprecation notice for `Task.max_retries` - - Removed Auth0 dependency from login flow - + + - Added deprecation notice for `Task.max_retries` + - Removed Auth0 dependency from login flow + @@ -159,35 +237,35 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.165.1) - + ## Core Improvements & Fixes - - - Fixed compatibility in `XMLSearchTool` by converting config values to strings for `configparser` - - Fixed flaky Pytest test involving `PytestUnraisableExceptionWarning` - - Mocked telemetry in test suite for more stable CI runs - - Moved Chroma lockfile handling to `db_storage_path` - - Ignored deprecation warnings from `chromadb` - - Pinned OpenAI version `<1.100.0` due to `ResponseTextConfigParam` import issue - + + - Fixed compatibility in `XMLSearchTool` by converting config values to strings for `configparser` + - Fixed flaky Pytest test involving `PytestUnraisableExceptionWarning` + - Mocked telemetry in test suite for more stable CI runs + - Moved Chroma lockfile handling to `db_storage_path` + - Ignored deprecation warnings from `chromadb` + - Pinned OpenAI version `<1.100.0` due to `ResponseTextConfigParam` import issue + ## New Features & Enhancements - - - Included exchanged agent messages into `ExternalMemory` metadata - - Automatically injected `crewai_trigger_payload` - - Renamed internal flag `inject_trigger_input` to `allow_crewai_trigger_context` - - Continued tracing improvements and ephemeral tracing logic - - Consolidated tracing logic conditions - - Added support for `agent_id`-linked memory entries in `Mem0` - + + - Included exchanged agent messages into `ExternalMemory` metadata + - Automatically injected `crewai_trigger_payload` + - Renamed internal flag `inject_trigger_input` to `allow_crewai_trigger_context` + - Continued tracing improvements and ephemeral tracing logic + - Consolidated tracing logic conditions + - Added support for `agent_id`-linked memory entries in `Mem0` + ## Documentation & Guides - - - Added example to Tool Repository docs - - Updated Mem0 documentation for Short-Term and Entity Memory integration - - Revised Korean translations and improved sentence structures - + + - Added example to Tool Repository docs + - Updated Mem0 documentation for Short-Term and Entity Memory integration + - Revised Korean translations and improved sentence structures + ## Cleanup & Chores - - - Removed deprecated AgentOps integration - + + - Removed deprecated AgentOps integration + @@ -195,35 +273,35 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.165.0) - + ## Core Improvements & Fixes - - - Fixed compatibility in `XMLSearchTool` by converting config values to strings for `configparser` - - Fixed flaky Pytest test involving `PytestUnraisableExceptionWarning` - - Mocked telemetry in test suite for more stable CI runs - - Moved Chroma lockfile handling to `db_storage_path` - - Ignored deprecation warnings from `chromadb` - - Pinned OpenAI version `<1.100.0` due to `ResponseTextConfigParam` import issue - + + - Fixed compatibility in `XMLSearchTool` by converting config values to strings for `configparser` + - Fixed flaky Pytest test involving `PytestUnraisableExceptionWarning` + - Mocked telemetry in test suite for more stable CI runs + - Moved Chroma lockfile handling to `db_storage_path` + - Ignored deprecation warnings from `chromadb` + - Pinned OpenAI version `<1.100.0` due to `ResponseTextConfigParam` import issue + ## New Features & Enhancements - - - Included exchanged agent messages into `ExternalMemory` metadata - - Automatically injected `crewai_trigger_payload` - - Renamed internal flag `inject_trigger_input` to `allow_crewai_trigger_context` - - Continued tracing improvements and ephemeral tracing logic - - Consolidated tracing logic conditions - - Added support for `agent_id`-linked memory entries in `Mem0` - + + - Included exchanged agent messages into `ExternalMemory` metadata + - Automatically injected `crewai_trigger_payload` + - Renamed internal flag `inject_trigger_input` to `allow_crewai_trigger_context` + - Continued tracing improvements and ephemeral tracing logic + - Consolidated tracing logic conditions + - Added support for `agent_id`-linked memory entries in `Mem0` + ## Documentation & Guides - - - Added example to Tool Repository docs - - Updated Mem0 documentation for Short-Term and Entity Memory integration - - Revised Korean translations and improved sentence structures - + + - Added example to Tool Repository docs + - Updated Mem0 documentation for Short-Term and Entity Memory integration + - Revised Korean translations and improved sentence structures + ## Cleanup & Chores - - - Removed deprecated AgentOps integration - + + - Removed deprecated AgentOps integration + @@ -231,26 +309,26 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.159.0) - + ## Core Improvements & Fixes - - - Improved LLM message formatting performance for better runtime efficiency - - Fixed use of incorrect endpoint in enterprise configuration auth/parameters - - Commented out listener resumability check for stability during partial flow resumption - + + - Improved LLM message formatting performance for better runtime efficiency + - Fixed use of incorrect endpoint in enterprise configuration auth/parameters + - Commented out listener resumability check for stability during partial flow resumption + ## New Features & Enhancements - - - Added `enterprise configure` command to CLI for streamlined enterprise setup - - Introduced partial flow resumability support - + + - Added `enterprise configure` command to CLI for streamlined enterprise setup + - Introduced partial flow resumability support + ## Documentation & Guides - - - Added documentation for new tools - - Added Korean translations - - Updated documentation with TrueFoundry integration details - - Added RBAC documentation and general cleanup - - Fixed API reference and revamped examples/cookbooks across EN, PT-BR, and KO - + + - Added documentation for new tools + - Added Korean translations + - Updated documentation with TrueFoundry integration details + - Added RBAC documentation and general cleanup + - Fixed API reference and revamped examples/cookbooks across EN, PT-BR, and KO + @@ -259,32 +337,32 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.157.0) ## v0.157.0 What's Changed - + ## Core Improvements & Fixes - - - Enabled word wrapping for long input tool - - Allowed persisting Flow state with `BaseModel` entries - - Optimized string operations using `partition()` for performance - - Dropped support for deprecated User Memory system - - Bumped LiteLLM version to `1.74.9` - - Fixed CLI to show missing modules more clearly during import - - Supported device authorization with Okta - + + - Enabled word wrapping for long input tool + - Allowed persisting Flow state with `BaseModel` entries + - Optimized string operations using `partition()` for performance + - Dropped support for deprecated User Memory system + - Bumped LiteLLM version to `1.74.9` + - Fixed CLI to show missing modules more clearly during import + - Supported device authorization with Okta + ## New Features & Enhancements - - - Added `crewai config` CLI command group with tests - - Added default value support for `crew.name` - - Introduced initial tracing capabilities - - Added support for LangDB integration - - Added support for CLI configuration documentation - + + - Added `crewai config` CLI command group with tests + - Added default value support for `crew.name` + - Introduced initial tracing capabilities + - Added support for LangDB integration + - Added support for CLI configuration documentation + ## Documentation & Guides - - - Updated MCP documentation with `connect_timeout` attribute - - Added LangDB integration documentation - - Added CLI config documentation - - General feature doc updates and cleanup - + + - Updated MCP documentation with `connect_timeout` attribute + - Added LangDB integration documentation + - Added CLI config documentation + - General feature doc updates and cleanup + @@ -292,24 +370,24 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.152.0) - + ## Core Improvements & Fixes - - - Removed `crewai signup` references and replaced them with `crewai login` - - Fixed support for adding memories to Mem0 using `agent_id` - - Changed the default value in Mem0 configuration - - Updated import error to show missing module files clearly - - Added timezone support to event timestamps - + + - Removed `crewai signup` references and replaced them with `crewai login` + - Fixed support for adding memories to Mem0 using `agent_id` + - Changed the default value in Mem0 configuration + - Updated import error to show missing module files clearly + - Added timezone support to event timestamps + ## New Features & Enhancements - - - Enhanced `Flow` class to support custom flow names - - Refactored RAG components into a dedicated top-level module - + + - Enhanced `Flow` class to support custom flow names + - Refactored RAG components into a dedicated top-level module + ## Documentation & Guides - - - Fixed incorrect model naming in Google Vertex AI documentation - + + - Fixed incorrect model naming in Google Vertex AI documentation + @@ -317,42 +395,42 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.150.0) - + ## Core Improvements & Fixes - - - Used file lock around Chroma client initialization - - Removed workaround related to SQLite without FTS5 - - Dropped unsupported `stop` parameter for LLM models automatically - - Fixed `save` method and updated related test cases - - Fixed message handling for Ollama models when last message is from assistant - - Removed duplicate print on LLM call error - - Added deprecation notice to `UserMemory` - - Upgraded LiteLLM to version 1.74.3 - + + - Used file lock around Chroma client initialization + - Removed workaround related to SQLite without FTS5 + - Dropped unsupported `stop` parameter for LLM models automatically + - Fixed `save` method and updated related test cases + - Fixed message handling for Ollama models when last message is from assistant + - Removed duplicate print on LLM call error + - Added deprecation notice to `UserMemory` + - Upgraded LiteLLM to version 1.74.3 + ## New Features & Enhancements - - - Added support for ad-hoc tool calling via internal LLM class - - Updated Mem0 Storage from v1.1 to v2 - + + - Added support for ad-hoc tool calling via internal LLM class + - Updated Mem0 Storage from v1.1 to v2 + ## Documentation & Guides - - - Fixed neatlogs documentation - - Added Tavily Search & Extractor tools to the Search-Research suite - - Added documentation for `SerperScrapeWebsiteTool` and reorganized Serper section - - General documentation updates and improvements - + + - Fixed neatlogs documentation + - Added Tavily Search & Extractor tools to the Search-Research suite + - Added documentation for `SerperScrapeWebsiteTool` and reorganized Serper section + - General documentation updates and improvements + ## crewai-tools v0.58.0 ### New Tools / Enhancements - - **SerperScrapeWebsiteTool**: Added a tool for extracting clean content from URLs - - **Bedrock AgentCore**: Integrated browser and code interpreter toolkits for Bedrock agents - - **Stagehand Update**: Refactored and updated Stagehand integration - + - **SerperScrapeWebsiteTool**: Added a tool for extracting clean content from URLs + - **Bedrock AgentCore**: Integrated browser and code interpreter toolkits for Bedrock agents + - **Stagehand Update**: Refactored and updated Stagehand integration + ### Fixes & Cleanup - - **FTS5 Support**: Enabled SQLite FTS5 for improved text search in test workflows - - **Test Speedups**: Parallelized GitHub Actions test suite for faster CI runs - - **Cleanup**: Removed SQLite workaround due to FTS5 support being available - **MongoDBVectorSearchTool**: Fixed serialization and schema handling - + - **FTS5 Support**: Enabled SQLite FTS5 for improved text search in test workflows + - **Test Speedups**: Parallelized GitHub Actions test suite for faster CI runs + - **Cleanup**: Removed SQLite workaround due to FTS5 support being available + **MongoDBVectorSearchTool**: Fixed serialization and schema handling + @@ -360,31 +438,31 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.148.0) - + ## Core Improvements & Fixes - - - Used production WorkOS environment ID - - Added SQLite FTS5 support to test workflow - - Fixed agent knowledge handling - - Compared using `BaseLLM` class instead of `LLM` - - Fixed missing `create_directory` parameter in `Task` class - + + - Used production WorkOS environment ID + - Added SQLite FTS5 support to test workflow + - Fixed agent knowledge handling + - Compared using `BaseLLM` class instead of `LLM` + - Fixed missing `create_directory` parameter in `Task` class + ## New Features & Enhancements - - - Introduced Agent evaluation functionality - - Added Evaluator experiment and regression testing methods - - Implemented thread-safe `AgentEvaluator` - - Enabled event emission for Agent evaluation - - Supported evaluation of single `Agent` and `LiteAgent` - - Added integration with `neatlogs` - - Added crew context tracking for LLM guardrail events - + + - Introduced Agent evaluation functionality + - Added Evaluator experiment and regression testing methods + - Implemented thread-safe `AgentEvaluator` + - Enabled event emission for Agent evaluation + - Supported evaluation of single `Agent` and `LiteAgent` + - Added integration with `neatlogs` + - Added crew context tracking for LLM guardrail events + ## Documentation & Guides - - - Added documentation for `guardrail` attributes and usage examples - - Added integration guide for `neatlogs` - - Updated documentation for Agent repository and `Agent.kickoff` usage - + + - Added documentation for `guardrail` attributes and usage examples + - Added integration guide for `neatlogs` + - Updated documentation for Agent repository and `Agent.kickoff` usage + @@ -392,19 +470,19 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.141.0) - + ## Core Improvements & Fixes - - - Sped up GitHub Actions tests through parallelization - + + - Sped up GitHub Actions tests through parallelization + ## New Features & Enhancements - - - Added crew context tracking for LLM guardrail events - + + - Added crew context tracking for LLM guardrail events + ## Documentation & Guides - - - Added documentation for Agent repository usage - - Added documentation for `Agent.kickoff` method + + - Added documentation for Agent repository usage + - Added documentation for `Agent.kickoff` method @@ -412,32 +490,32 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.140.0) - + ## Core Improvements & Fixes - - - Fixed typo in test prompts - - Fixed project name normalization by stripping trailing slashes during crew creation - - Ensured environment variables are written in uppercase - - Updated LiteLLM dependency - - Refactored collection handling in `RAGStorage` - - Implemented PEP 621 dynamic versioning - + + - Fixed typo in test prompts + - Fixed project name normalization by stripping trailing slashes during crew creation + - Ensured environment variables are written in uppercase + - Updated LiteLLM dependency + - Refactored collection handling in `RAGStorage` + - Implemented PEP 621 dynamic versioning + ## New Features & Enhancements - - - Added capability to track LLM calls by task and agent - - Introduced `MemoryEvents` to monitor memory usage - - Added console logging for memory system and LLM guardrail events - - Improved data training support for models up to 7B parameters + + - Added capability to track LLM calls by task and agent + - Introduced `MemoryEvents` to monitor memory usage + - Added console logging for memory system and LLM guardrail events + - Improved data training support for models up to 7B parameters - Added Scarf and Reo.dev analytics tracking - CLI workos login - + ## Documentation & Guides - - - Updated CLI LLM documentation - - Added Nebius integration to the docs - - Corrected typos in installation and pt-BR documentation - - Added docs about `MemoryEvents` - - Implemented docs redirects and included development tools + + - Updated CLI LLM documentation + - Added Nebius integration to the docs + - Corrected typos in installation and pt-BR documentation + - Added docs about `MemoryEvents` + - Implemented docs redirects and included development tools @@ -445,35 +523,35 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.134.0) - + ## Core Improvements & Fixes - - - Fixed tools parameter syntax - - Fixed type annotation in `Task` - - Fixed SSL error when retrieving LLM data from GitHub - - Ensured compatibility with Pydantic 2.7.x - - Removed `mkdocs` from project dependencies - - Upgraded Langfuse code examples to use Python SDK v3 - - Added sanitize role feature in `mem0` storage - - Improved Crew search during memory reset - - Improved console printer output - + + - Fixed tools parameter syntax + - Fixed type annotation in `Task` + - Fixed SSL error when retrieving LLM data from GitHub + - Ensured compatibility with Pydantic 2.7.x + - Removed `mkdocs` from project dependencies + - Upgraded Langfuse code examples to use Python SDK v3 + - Added sanitize role feature in `mem0` storage + - Improved Crew search during memory reset + - Improved console printer output + ## New Features & Enhancements - - - Added support for initializing a tool from defined `Tool` attributes - - Added official way to use MCP Tools within a `CrewBase` - - Enhanced MCP tools support to allow selecting multiple tools per agent in `CrewBase` - - Added Oxylabs Web Scraping tools - + + - Added support for initializing a tool from defined `Tool` attributes + - Added official way to use MCP Tools within a `CrewBase` + - Enhanced MCP tools support to allow selecting multiple tools per agent in `CrewBase` + - Added Oxylabs Web Scraping tools + ## Documentation & Guides - - - Updated `quickstart.mdx` - - Added docs on `LLMGuardrail` events - - Updated documentation with comprehensive service integration details - - Updated recommendation filters for MCP and Enterprise tools - - Updated docs for Maxim observability - - Added pt-BR documentation translation - - General documentation improvements + + - Updated `quickstart.mdx` + - Added docs on `LLMGuardrail` events + - Updated documentation with comprehensive service integration details + - Updated recommendation filters for MCP and Enterprise tools + - Updated docs for Maxim observability + - Added pt-BR documentation translation + - General documentation improvements @@ -481,30 +559,30 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.130.0) - + ## Core Improvements & Fixes - - - Removed duplicated message related to Tool result output - - Fixed missing `manager_agent` tokens in `usage_metrics` from kickoff - - Fixed telemetry singleton to respect dynamic environment variables - - Fixed issue where Flow status logs could hide human input - - Increased default X-axis spacing for flow plotting - + + - Removed duplicated message related to Tool result output + - Fixed missing `manager_agent` tokens in `usage_metrics` from kickoff + - Fixed telemetry singleton to respect dynamic environment variables + - Fixed issue where Flow status logs could hide human input + - Increased default X-axis spacing for flow plotting + ## New Features & Enhancements - - - Added support for multi-org actions in the CLI - - Enabled async tool executions for more efficient workflows - - Introduced `LiteAgent` with Guardrail integration - - Upgraded `LiteLLM` to support latest OpenAI version - + + - Added support for multi-org actions in the CLI + - Enabled async tool executions for more efficient workflows + - Introduced `LiteAgent` with Guardrail integration + - Upgraded `LiteLLM` to support latest OpenAI version + ## Documentation & Guides - - - Documented minimum `UV` version for Tool repository - - Improved examples for Hallucination Guardrail - - Updated planning docs for LLM usage - - Added documentation for Maxim support in Agent observability - - Expanded integrations documentation with images for enterprise features - - Fixed guide on persistence + + - Documented minimum `UV` version for Tool repository + - Improved examples for Hallucination Guardrail + - Updated planning docs for LLM usage + - Added documentation for Maxim support in Agent observability + - Expanded integrations documentation with images for enterprise features + - Fixed guide on persistence - Updated Python version support to support python 3.13.x @@ -514,25 +592,25 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.126.0) ### What’s Changed - + #### Core Improvements & Fixes - + - Added support for Python 3.13 - Fixed agent knowledge sources issue - Persisted available tools from a Tool repository - Enabled tools to be loaded from Agent repository via their own module - Logged usage of tools when called by an LLM - + #### New Features & Enhancements - + - Added streamable-http transport support in MCP integration - Added support for community analytics - Expanded OpenAI-compatible section with a Gemini example - Introduced transparency features for prompts and memory systems - Minor enhancements for Tool publishing - + #### Documentation & Guides - + - Major restructuring of docs for better navigation - Expanded MCP integration documentation - Updated memory docs and README visuals @@ -556,23 +634,23 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.121.0) # What’s Changed - + ## Core Improvements & Fixes - + - Fixed encoding error when creating tools - Fixed failing llama test - Updated logging configuration for consistency - Enhanced telemetry initialization and event handling - + ## New Features & Enhancements - + - Added markdown attribute to the Task class - Added reasoning attribute to the Agent class - Added inject_date flag to Agent for automatic date injection - Implemented HallucinationGuardrail (no-op with test coverage) - + ## Documentation & Guides - + - Added documentation for StagehandTool and improved MDX structure - Added documentation for MCP integration and updated enterprise docs - Documented knowledge events and updated reasoning docs @@ -587,8 +665,8 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.120.1) ## Whats New - - * Fixes Interpolation with hyphens + + * Fixes Interpolation with hyphens @@ -596,26 +674,26 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.120.0) - + ### Core Improvements & Fixes • Enabled full Ruff rule set by default for stricter linting • Addressed race condition in FilteredStream using context managers • Fixed agent knowledge reset issue • Refactored agent fetching logic into utility module - + ### New Features & Enhancements • Added support for loading an Agent directly from a repository • Enabled setting an empty context for Task • Enhanced Agent repository feedback and fixed Tool auto-import behavior • Introduced direct initialization of knowledge (bypassing knowledge_sources) - + ### Documentation & Guides • Updated security.md for current security practices • Cleaned up Google setup section for clarity • Added link to AI Studio when entering Gemini key • Updated Arize Phoenix observability guide • Refreshed flow documentation - + @@ -624,23 +702,23 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.119.0) What’s Changed - + ## Core Improvements & Fixes - + - Improved test reliability by enhancing pytest handling for flaky tests - Fixed memory reset crash when embedding dimensions mismatch - Enabled parent flow identification for Crew and LiteAgent - Prevented telemetry-related crashes when unavailable - Upgraded LiteLLM version for better compatibility - Fixed llama converter tests by removing skip_external_api - + ## New Features & Enhancements - + - Introduced knowledge retrieval prompt re-writting in Agent for improved tracking and debugging - Made LLM setup and quickstart guides model-agnostic - + ## Documentation & Guides - + - Added advanced configuration docs for the RAG tool - Updated Windows troubleshooting guide - Refined documentation examples for better clarity @@ -652,21 +730,21 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.118.0) - + ### Core Improvements & Fixes - + - Fixed issues with missing prompt or system templates. - Removed global logging configuration to avoid unintended overrides. - Renamed TaskGuardrail to LLMGuardrail for improved clarity. - Downgraded litellm to version 1.167.1 for compatibility. - Added missing __init__.py files to ensure proper module initialization. - + ### New Features & Enhancements - + - Added support for no-code Guardrail creation to simplify AI behavior controls. - + ### Documentation & Guides - + - Removed CrewStructuredTool from public documentation to reflect internal usage. - Updated enterprise documentation and YouTube embed for improved onboarding experience. @@ -677,9 +755,9 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.117.1) * build: upgrade crewai-tools - * upgrade liteLLM to latest version + * upgrade liteLLM to latest version * Fix Mem0 OSS - + @@ -688,17 +766,17 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.117.0) # What's Changed - + ## New Features & Enhancements - + - Added `result_as_answer` parameter support in `@tool` decorator. - Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro. - Enhanced knowledge management capabilities. - Added Huggingface provider option in CLI. - Improved compatibility and CI support for Python 3.10+. - + ## Core Improvements & Fixes - + - Fixed issues with incorrect template parameters and missing inputs. - Improved asynchronous flow handling with coroutine condition checks. - Enhanced memory management with isolated configuration and correct memory object copying. @@ -708,16 +786,16 @@ mode: "wide" - Raised explicit exceptions when flows fail. - Removed unused code and redundant comments from various modules. - Updated GitHub App token action to v2. - + ## Documentation & Guides - + - Enhanced documentation structure, including enterprise deployment instructions. - Automatically create output folders for documentation generation. - Fixed broken link in `WeaviateVectorSearchTool` documentation. - Fixed guardrail documentation usage and import paths for JSON search tools. - Updated documentation for `CodeInterpreterTool`. - Improved SEO, contextual navigation, and error handling for documentation pages. - + @@ -726,28 +804,28 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.114.0) # What's Changed - + ## New Features & Enhancements - + - Agents as an atomic unit. (`Agent(...).kickoff()`) - Support to Custom LLM implementations. - Integrated External Memory and Opik observability. - Enhanced YAML extraction. - Multimodal agent validation. - Added Secure fingerprints for agents and crews. - + ## Core Improvements & Fixes - + - Improved serialization, agent copying, and Python compatibility. - - Added wildcard support to emit() + - Added wildcard support to emit() - Added support for additional router calls and context window adjustments. - Fixed typing issues, validation, and import statements. - Improved method performance. - Enhanced agent task handling, event emissions, and memory management. - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs. - + ## Documentation & Guides - + - Improved documentation structure, theme, and organization. - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix. - Updated tool configuration examples, prompts, and observability docs. @@ -760,21 +838,21 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.108.0) # Features - + - Converted tabs to spaces in crew.py template in PR #2190 - Enhanced LLM Streaming Response Handling and Event System in PR #2266 - Included model_name in PR #2310 - Enhanced Event Listener with rich visualization and improved logging in PR #2321 - Added fingerprints in PR #2332 - + # Bug Fixes - + - Fixed Mistral issues in PR #2308 - Fixed a bug in documentation in PR #2370 - Fixed type check error in fingerprint property in PR #2369 - + # Documentation Updates - + - Improved tool documentation in PR #2259 - Updated installation guide for the uv tool package in PR #2196 - Added instructions for upgrading crewAI with the uv tool in PR #2363 @@ -791,7 +869,7 @@ mode: "wide" - Improved async flow support and addressed agent response formatting. - Enhanced memory reset functionality and fixed CLI memory commands. - Fixed type issues, tool calling properties, and telemetry decoupling. - + **New Features & Enhancements** - Added Flow state export and improved state utilities. - Enhanced agent knowledge setup with optional crew embedder. @@ -799,12 +877,12 @@ mode: "wide" - Added support for Python 3.10 and ChatOllama from langchain_ollama. - Integrated context window size support for the o3-mini model. - Added support for multiple router calls. - + **Documentation & Guides** - Improved documentation layout and hierarchical structure. - Added QdrantVectorSearchTool guide and clarified event listener usage. - Fixed typos in prompts and updated Amazon Bedrock model listings. - + @@ -812,32 +890,32 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.102.0) - + ### Core Improvements & Fixes - + - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models. - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks. - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class. - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types. - + ### New Features & Enhancements - + - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support. - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation. - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files. - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues. - Adding new tool QdrantVectorSearchTool - + ### Documentation & Guides - + - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation. - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation. - Fixed Various Typos & Formatting Issues. - + ### Maintenance & Miscellaneous - + - Refined Google Docs integrations and task handling for the current year. - + @@ -846,13 +924,13 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.100.0) * Feat: Add Composio docs - * Feat: Add SageMaker as a LLM provider + * Feat: Add SageMaker as a LLM provider * Fix: Overall LLM connection issues * Fix: Using safe accessors on training - * Fix: Add version check to crew_chat.py + * Fix: Add version check to crew_chat.py * Docs: New docs for crewai chat * Docs: Improve formatting and clarity in CLI and Composio Tool docs - + @@ -876,9 +954,9 @@ mode: "wide" * Fix: TYPOS * Fix: Nested pydantic model issue * Fix: Docling issues - * Fix: union issue + * Fix: union issue * Docs updates - + @@ -894,7 +972,7 @@ mode: "wide" * Feat: Add Workflow Permissions * Feat: Add support for langfuse with litellm * Feat: Portkey Integration with CrewAI - * Feat: Add interpolate_only method and improve error handling + * Feat: Add interpolate_only method and improve error handling * Feat: Docling Support * Feat: Weviate Support * Fix: output_file not respecting system path @@ -910,7 +988,7 @@ mode: "wide" * Fix: include event emitter in flows * Fix: Docstring, Error Handling, and Type Hints Improvements * Fix: Suppressed userWarnings from litellm pydantic issues - + @@ -918,10 +996,10 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.86.0) - * remove all references to pipeline and pipeline router - * docs: Add Nvidia NIM as provider in Custom LLM - * add knowledge demo + improve knowledge docs - * Brandon/cre 509 hitl multiple rounds of followup + * remove all references to pipeline and pipeline router + * docs: Add Nvidia NIM as provider in Custom LLM + * add knowledge demo + improve knowledge docs + * Brandon/cre 509 hitl multiple rounds of followup * New docs about yaml crew with decorators. Simplify template crew @@ -940,7 +1018,7 @@ mode: "wide" * Update readme for running mypy * Add knowledge to mint.json * Update Github actions - * Docs Update Agents docs to include two approaches for creating an agent + * Docs Update Agents docs to include two approaches for creating an agent * Documentation Improvements: LLM Configuration and Usage @@ -965,7 +1043,7 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.80.0) - * Fixing Tokens callback replacement bug + * Fixing Tokens callback replacement bug * Fixing Step callback issue * Add cached prompt tokens info on usage metrics * Fix crew_train_success test @@ -984,15 +1062,15 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.79.0) - * Add inputs to flows + * Add inputs to flows * Enhance log storage to support more data types * Add support to IBM memory * Add Watson as an option in CLI * Add security.md file - * Replace .netrc with uv environment variables + * Replace .netrc with uv environment variables * Move BaseTool to main package and centralize tool description generation * Raise an error if an LLM doesnt return a response - * Fix flows to support cycles and added in test + * Fix flows to support cycles and added in test * Update how we name crews and fix missing config * Update docs @@ -1025,9 +1103,9 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.76.0) * fix/fixed missing API prompt + CLI docs update - * chore(readme): fixing step for 'running tests' in the contribution + * chore(readme): fixing step for 'running tests' in the contribution * support unsafe code execution. add in docker install and running checks - * Fix memory imports for embedding functions by + * Fix memory imports for embedding functions by @@ -1046,11 +1124,11 @@ mode: "wide" * Fixing test post training * Simplify flows * Adapt `crewai tool install ` - * Ensure original embedding config works + * Ensure original embedding config works * Fix bugs - * Update docs - Including adding Cerebras LLM example configuration to LLM docs + * Update docs - Including adding Cerebras LLM example configuration to LLM docs * Drop unnecessary tests - + @@ -1058,7 +1136,7 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.74.2) - * feat: add poetry.lock to uv migration + * feat: add poetry.lock to uv migration * fix tool calling issue @@ -1087,9 +1165,9 @@ mode: "wide" * Create `crewai create flow` command * Create `crewai tool create ` command * Add Git validations for publishing tools - * fix: JSON encoding date objects + * fix: JSON encoding date objects * New Docs - * Update README + * Update README * Bug fixes @@ -1117,7 +1195,7 @@ mode: "wide" - Adding initial tools API - TYPOS - Updating Docs - + Fixes: #1359 #1355 #1353 #1356 and others @@ -1194,7 +1272,7 @@ mode: "wide" - Adds the ability to not use system prompt use_system_prompt on the Agent - Adds the ability to not use stop words (to support o1 models) use_stop_words on the Agent - Sliding context window gets renamed to respect_context_window, and enable by default - - Delegation is now disabled by default + - Delegation is now disabled by default - Inner prompts were slightly changed as well - Overall reliability and quality of results - New support for: @@ -1223,8 +1301,8 @@ mode: "wide" * Fix Azure support * Add support to Python 3.10 * Moving away from Pydantic v1 - - + + @@ -1252,7 +1330,7 @@ mode: "wide" - JSON truncation issues - Fix logging types - Only import AgentOps if the Env Key is set - - Sanitize agent roles to ensure valid directory names (Windows) + - Sanitize agent roles to ensure valid directory names (Windows) - Tools name shouldn't contain space for OpenAI - A bunch of minor issues @@ -1272,7 +1350,7 @@ mode: "wide" - **[Breaking Change]** Type Safe output - All crews and tasks now return a proper object TaskOuput and CrewOutput - - **[Feature]** New planning feature for crews (plan before act) + - **[Feature]** New planning feature for crews (plan before act) - by adding planning=True to the Crew instance - **[Feature]** Introduced Replay Feature - New CLI that allow you to list the tasks from last run and replay from a specific one @@ -1392,9 +1470,9 @@ mode: "wide" - Making gpt-4o the default model - Adding new docs for LangTrace, Browserbase and Exa Search - Adding timestamp to logging - - - + + + @@ -1429,7 +1507,7 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/v0.30.4) **Docs Update will follow** sorry about that and thank you for bearing with me, we are launching new docs soon! - + ➿ Fixing task callback 🧙 Ability to set a specific agent as manager instead of having crew create your one 📄 Ability to set system, prompt and response templates, so it works more reliable with opensource models (works better with smaller models) @@ -1445,7 +1523,7 @@ mode: "wide" 🐛 Smaller bug fixes (typos and such) 👬 Fixing co-worker / coworker issues 👀 Smaller Readme Updates - + @@ -1462,7 +1540,7 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/0.28.7) - Bug fixes - - Updating crewAI tool version with bug fixes + - Updating crewAI tool version with bug fixes @@ -1517,7 +1595,7 @@ mode: "wide" - 💠 **Inner Prompt Improvements:** A finer conversational flow. - 📝 **Improving tool usage with better parsing** - 🔒 **Security improvements and updating dependencies** - - 📄 **Documentation improved** + - 📄 **Documentation improved** - 🐛 **Bug fixes** @@ -1719,20 +1797,20 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/v0.5.0) This new version bring a lot of new features and improvements to the library. - + ## Features - Adding Task Callbacks. - Adding support for Hierarchical process. - Adding ability to references specific tasks in another task. - Adding ability to parallel task execution. - + ## Improvements - Revamping Max Iterations and Max Requests per Minute. - Developer experience improvements, docstrings and such. - Small improvements and TYPOs. - Fix static typing errors. - Updated README and Docs. - + @@ -1755,7 +1833,7 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/v0.1.23) - - Many Reliability improvements + - Many Reliability improvements - Prompt changes - Initial changes for supporting multiple languages - Fixing bug on task repeated execution @@ -1775,7 +1853,7 @@ mode: "wide" - Removing WIP code. (@joaomdmoura) - A lot of developer quality of life improvements (Special thanks to @greysonlalonde). - Updating to pydantic v2 (Special thanks to @greysonlalonde as well). - + @@ -1792,11 +1870,11 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/v0.1.1) # CrewAI v0.1.1 Release Notes - + ## What's New - + - **Crew Verbose Mode**: Now allowing you to inspect a the tasks are being executed. - + - **README and Docs Updates**: A series of minor updates on the docs @@ -1806,34 +1884,34 @@ mode: "wide" [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/v0.1.0) # CrewAI v0.1.0 Release Notes - + We are thrilled to announce the initial release of CrewAI, version 0.1.0! CrewAI is a framework designed to facilitate the orchestration of autonomous AI agents capable of role-playing and collaboration to accomplish complex tasks more efficiently. - + ## What's New - + - **Initial Launch**: CrewAI is now officially in the wild! This foundational release lays the groundwork for AI agents to work in tandem, each with its own specialized role and objectives. - + - **Role-Based Agent Design**: Define and customize agents with specific roles, goals, and the tools they need to succeed. - + - **Inter-Agent Delegation**: Agents are now equipped to autonomously delegate tasks, enabling dynamic distribution of workload among the team. - + - **Task Management**: Create and assign tasks dynamically with the flexibility to specify the tools needed for each task. - + - **Sequential Processes**: Set up your agents to tackle tasks one after the other, ensuring organized and predictable workflows. - + - **Documentation**: Start exploring CrewAI with our initial documentation that guides you through the setup and use of the framework. - + ## Enhancements - + - Detailed API documentation for the `Agent`, `Task`, `Crew`, and `Process` classes. - Examples and tutorials to help you build your first CrewAI application. - Basic setup for collaborative and delegation mechanisms among agents. - + ## Known Issues - + - As this is the first release, there may be undiscovered bugs and areas for optimization. We encourage the community to report any issues found during use. - + ## Upcoming Features - + - **Advanced Process Management**: In future releases, we will introduce more complex processes for task management including consensual and hierarchical workflows. diff --git a/docs/en/concepts/agents.mdx b/docs/en/concepts/agents.mdx index c5d99983e..2f26a5699 100644 --- a/docs/en/concepts/agents.mdx +++ b/docs/en/concepts/agents.mdx @@ -20,7 +20,7 @@ Think of an agent as a specialized team member with specific skills, expertise, -CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time. +CrewAI AMP includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time. ![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png) diff --git a/docs/en/concepts/cli.mdx b/docs/en/concepts/cli.mdx index c6724fc06..0e50054bc 100644 --- a/docs/en/concepts/cli.mdx +++ b/docs/en/concepts/cli.mdx @@ -5,7 +5,7 @@ icon: terminal mode: "wide" --- -Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library. +Since release 0.140.0, CrewAI AMP started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library. ## Overview @@ -186,9 +186,9 @@ def crew(self) -> Crew: ### 10. Deploy -Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com). +Deploy the crew or flow to [CrewAI AMP](https://app.crewai.com). -- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise. +- **Authentication**: You need to be authenticated to deploy to CrewAI AMP. You can login or create an account with: ```shell Terminal crewai login @@ -203,7 +203,7 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com). ### 11. Organization Management -Manage your CrewAI Enterprise organizations. +Manage your CrewAI AMP organizations. ```shell Terminal crewai org [COMMAND] [OPTIONS] @@ -227,17 +227,17 @@ crewai org switch ``` -You must be authenticated to CrewAI Enterprise to use these organization management commands. +You must be authenticated to CrewAI AMP to use these organization management commands. - **Create a deployment** (continued): - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically). -- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise. +- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI AMP. ```shell Terminal crewai deploy push ``` - - Initiates the deployment process on the CrewAI Enterprise platform. + - Initiates the deployment process on the CrewAI AMP platform. - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID). - **Deployment Status**: You can check the status of your deployment with: @@ -262,7 +262,7 @@ You must be authenticated to CrewAI Enterprise to use these organization managem ```shell Terminal crewai deploy remove ``` - This deletes the deployment from the CrewAI Enterprise platform. + This deletes the deployment from the CrewAI AMP platform. - **Help Command**: You can get help with the CLI with: ```shell Terminal @@ -270,22 +270,20 @@ You must be authenticated to CrewAI Enterprise to use these organization managem ``` This shows the help message for the CrewAI Deploy CLI. -Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI. +Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI. ### 11. Login -Authenticate with CrewAI Enterprise using a secure device code flow (no email entry required). +Authenticate with CrewAI AMP using a secure device code flow (no email entry required). ```shell Terminal crewai login @@ -356,7 +354,7 @@ crewai config reset #### Available Configuration Parameters -- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance +- `enterprise_base_url`: Base URL of the CrewAI AMP instance - `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0) - `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource - `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests @@ -372,7 +370,7 @@ crewai config list Example output: | Setting | Value | Description | | :------------------ | :----------------------- | :---------------------------------------------------------- | -| enterprise_base_url | https://app.crewai.com | Base URL of the CrewAI Enterprise instance | +| enterprise_base_url | https://app.crewai.com | Base URL of the CrewAI AMP instance | | org_name | Not set | Name of the currently active organization | | org_uuid | Not set | UUID of the currently active organization | | oauth2_provider | workos | OAuth2 provider (e.g., workos, okta, auth0) | diff --git a/docs/en/concepts/event-listener.mdx b/docs/en/concepts/event-listener.mdx index 33550139d..69c65c230 100644 --- a/docs/en/concepts/event-listener.mdx +++ b/docs/en/concepts/event-listener.mdx @@ -20,7 +20,7 @@ CrewAI uses an event bus architecture to emit events throughout the execution li When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur. -CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations. +CrewAI AMP provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations. ![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png) diff --git a/docs/en/concepts/flows.mdx b/docs/en/concepts/flows.mdx index f8278375c..92d63a1c0 100644 --- a/docs/en/concepts/flows.mdx +++ b/docs/en/concepts/flows.mdx @@ -875,14 +875,13 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F Also, check out our YouTube video on how to use flows in CrewAI below! ## Running Flows diff --git a/docs/en/concepts/tasks.mdx b/docs/en/concepts/tasks.mdx index ffb292209..3a8334bb1 100644 --- a/docs/en/concepts/tasks.mdx +++ b/docs/en/concepts/tasks.mdx @@ -14,7 +14,7 @@ Tasks provide all necessary details for execution, such as a description, the ag Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency. -CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code. +CrewAI AMP includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code. ![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png) @@ -897,14 +897,13 @@ except RuntimeError as e: Check out the video below to see how to use structured outputs in CrewAI: ## Conclusion diff --git a/docs/en/concepts/tools.mdx b/docs/en/concepts/tools.mdx index e23d2478e..09ab025b3 100644 --- a/docs/en/concepts/tools.mdx +++ b/docs/en/concepts/tools.mdx @@ -17,7 +17,7 @@ This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/cre enabling everything from simple searches to complex interactions and effective teamwork among agents. -CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days. +CrewAI AMP provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days. The Enterprise Tools Repository includes: - Pre-built connectors for popular enterprise systems @@ -208,7 +208,7 @@ from crewai.tools import BaseTool class AsyncCustomTool(BaseTool): name: str = "async_custom_tool" description: str = "An asynchronous custom tool" - + async def _run(self, query: str = "") -> str: """Asynchronously run the tool""" # Your async implementation here diff --git a/docs/en/enterprise/features/agent-repositories.mdx b/docs/en/enterprise/features/agent-repositories.mdx index 66f1ccc44..879d39045 100644 --- a/docs/en/enterprise/features/agent-repositories.mdx +++ b/docs/en/enterprise/features/agent-repositories.mdx @@ -1,12 +1,16 @@ --- title: 'Agent Repositories' description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects' -icon: 'database' +icon: 'people-group' mode: "wide" --- Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort. + + ![Agent Repositories](/images/enterprise/agent-repositories.png) + + ## Benefits of Agent Repositories - **Standardization**: Maintain consistent agent definitions across your organization @@ -14,25 +18,21 @@ Agent Repositories allow enterprise users to store, share, and reuse agent defin - **Governance**: Implement organization-wide policies for agent configurations - **Collaboration**: Enable teams to share and build upon each other's work -## Using Agent Repositories - -### Prerequisites +## Creating and Use Agent Repositories 1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com). -2. You need to be authenticated using the CrewAI CLI. -3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command: +2. Create agents with specific roles and goals for your workflows. +3. Configure tools and capabilities for each specialized assistant. +4. Deploy agents across projects via visual interface or API integration. -```bash -crewai org switch -``` + + ![Agent Repositories](/images/enterprise/create-agent-repository.png) + -### Creating and Managing Agents in Repositories - -To create and manage agents in repositories,Enterprise Dashboard. ### Loading Agents from Repositories -You can load agents from repositories in your code using the `from_repository` parameter: +You can load agents from repositories in your code using the `from_repository` parameter to run locally: ```python from crewai import Agent @@ -42,7 +42,6 @@ from crewai import Agent researcher = Agent( from_repository="market-research-agent" ) - ``` ### Overriding Repository Settings diff --git a/docs/en/enterprise/features/automations.mdx b/docs/en/enterprise/features/automations.mdx new file mode 100644 index 000000000..675977e01 --- /dev/null +++ b/docs/en/enterprise/features/automations.mdx @@ -0,0 +1,104 @@ +--- +title: Automations +description: "Manage, deploy, and monitor your live crews (automations) in one place." +icon: "rocket" +mode: "wide" +--- + +## Overview + +Automations is the live operations hub for your deployed crews. Use it to deploy from GitHub or a ZIP file, manage environment variables, re‑deploy when needed, and monitor the status of each automation. + + + ![Automations Overview](/images/enterprise/automations-overview.png) + + + +## Deployment Methods + +### Deploy from GitHub + +Use this for version‑controlled projects and continuous deployment. + + + + Click Configure GitHub and authorize access. + + + Choose the Repository and Branch you want to deploy from. + + + Turn on Automatically deploy new commits to ship updates on every push. + + + Add secrets individually or use Bulk View for multiple variables. + + + Click Deploy to create your live automation. + + + + + ![GitHub Deployment](/images/enterprise/deploy-from-github.png) + + +### Deploy from ZIP + +Ship quickly without Git—upload a compressed package of your project. + + + + Select the ZIP archive from your computer. + + + Provide any required variables or keys. + + + Click Deploy to create your live automation. + + + + + ![ZIP Deployment](/images/enterprise/deploy-from-zip.png) + + +## Automations Dashboard + +The table lists all live automations with key details: + +- **CREW**: Automation name +- **STATUS**: Online / Failed / In Progress +- **URL**: Endpoint for kickoff/status +- **TOKEN**: Automation token +- **ACTIONS**: Re‑deploy, delete, and more + +Use the top‑right controls to filter and search: + +- Search by name +- Filter by Status +- Filter by Source (GitHub / Studio / ZIP) + +Once deployed, you can view the automation details and have the **Options** dropdown menu to `chat with this crew`, `Export React Component` and `Export as MCP`. + + + ![Automations Table](/images/enterprise/automations-table.png) + + +## Best Practices + +- Prefer GitHub deployments for version control and CI/CD +- Use re‑deploy to roll forward after code or config updates or set it to auto-deploy on every push + +## Related + + + + Deploy a Crew from GitHub or ZIP file. + + + Trigger automations via webhooks or API. + + + Stream real-time events and updates to your systems. + + diff --git a/docs/en/enterprise/features/crew-studio.mdx b/docs/en/enterprise/features/crew-studio.mdx new file mode 100644 index 000000000..b4ea0b238 --- /dev/null +++ b/docs/en/enterprise/features/crew-studio.mdx @@ -0,0 +1,88 @@ +--- +title: Crew Studio +description: "Build new automations with AI assistance, a visual editor, and integrated testing." +icon: "pencil" +mode: "wide" +--- + +## Overview + +Crew Studio is an interactive, AI‑assisted workspace for creating new automations from scratch using natural language and a visual workflow editor. + + + ![Crew Studio Overview](/images/enterprise/crew-studio-overview.png) + + +## Prompt‑based Creation + +- Describe the automation you want; the AI generates agents, tasks, and tools. +- Use voice input via the microphone icon if preferred. +- Start from built‑in prompts for common use cases. + + + ![Prompt Builder](/images/enterprise/crew-studio-prompt.png) + + +## Visual Editor + +The canvas reflects the workflow as nodes and edges with three supporting panels that allow you to configure the workflow easily without writing code; a.k.a. "**vibe coding AI Agents**". + +You can use the drag-and-drop functionality to add agents, tasks, and tools to the canvas or you can use the chat section to build the agents. Both approaches share state and can be used interchangeably. + +- **AI Thoughts (left)**: streaming reasoning as the workflow is designed +- **Canvas (center)**: agents and tasks as connected nodes +- **Resources (right)**: drag‑and‑drop components (agents, tasks, tools) + + + ![Visual Canvas](/images/enterprise/crew-studio-canvas.png) + + +## Execution & Debugging + +Switch to the Execution view to run and observe the workflow: + +- Event timeline +- Detailed logs (Details, Messages, Raw Data) +- Local test runs before publishing + + + ![Execution View](/images/enterprise/crew-studio-execution.png) + + +## Publish & Export + +- Publish to deploy a live automation +- Download source as a ZIP for local development or customization + + + ![Publish & Download](/images/enterprise/crew-studio-publish.png) + + +Once published, you can view the automation details and have the **Options** dropdown menu to `chat with this crew`, `Export React Component` and `Export as MCP`. + + + ![Published Automation](/images/enterprise/crew-studio-published.png) + + +## Best Practices + +- Iterate quickly in Studio; publish only when stable +- Keep tools constrained to minimum permissions needed +- Use Traces to validate behavior and performance + +## Related + + + + Enable Crew Studio. + + + Build a Crew. + + + Deploy a Crew from GitHub or ZIP file. + + + Export a React Component. + + diff --git a/docs/en/enterprise/features/integrations.mdx b/docs/en/enterprise/features/integrations.mdx deleted file mode 100644 index c10e25fad..000000000 --- a/docs/en/enterprise/features/integrations.mdx +++ /dev/null @@ -1,186 +0,0 @@ ---- -title: Integrations -description: "Connected applications for your agents to take actions." -icon: "plug" -mode: "wide" ---- - -## Overview - -Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services. - - - ![Integrations](/images/enterprise/crew_connectors.png) - - -## Supported Integrations - -### **Communication & Collaboration** -- **Gmail** - Manage emails and drafts -- **Slack** - Workspace notifications and alerts -- **Microsoft** - Office 365 and Teams integration - -### **Project Management** -- **Jira** - Issue tracking and project management -- **ClickUp** - Task and productivity management -- **Asana** - Team task and project coordination -- **Notion** - Page and database management -- **Linear** - Software project and bug tracking -- **GitHub** - Repository and issue management - -### **Customer Relationship Management** -- **Salesforce** - CRM account and opportunity management -- **HubSpot** - Sales pipeline and contact management -- **Zendesk** - Customer support ticket management - -### **Business & Finance** -- **Stripe** - Payment processing and customer management -- **Shopify** - E-commerce store and product management - -### **Productivity & Storage** -- **Google Sheets** - Spreadsheet data synchronization -- **Google Calendar** - Event and schedule management -- **Box** - File storage and document management - -and more to come! - -## Prerequisites - -Before using Authentication Integrations, ensure you have: - -- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial. - - -## Setting Up Integrations - -### 1. Connect Your Account - -1. Navigate to [CrewAI Enterprise](https://app.crewai.com) -2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors -3. Click **Connect** on your desired service from the Authentication Integrations section -4. Complete the OAuth authentication flow -5. Grant necessary permissions for your use case -6. All set! Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) in **Integration** tab - - - ![Integrations](/images/enterprise/enterprise_action_auth_token.png) - - -### 2. Install Integration Tools - -All you need is the latest version of `crewai-tools` package. - -```bash -uv add crewai-tools -``` - -## Usage Examples - -### Basic Usage - - All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go. - - -```python -from crewai import Agent, Task, Crew -from crewai_tools import CrewaiEnterpriseTools - -# Get enterprise tools (Gmail tool will be included) -enterprise_tools = CrewaiEnterpriseTools( - enterprise_token="your_enterprise_token" -) -# print the tools -print(enterprise_tools) - -# Create an agent with Gmail capabilities -email_agent = Agent( - role="Email Manager", - goal="Manage and organize email communications", - backstory="An AI assistant specialized in email management and communication.", - tools=enterprise_tools -) - -# Task to send an email -email_task = Task( - description="Draft and send a follow-up email to john@example.com about the project update", - agent=email_agent, - expected_output="Confirmation that email was sent successfully" -) - -# Run the task -crew = Crew( - agents=[email_agent], - tasks=[email_task] -) - -# Run the crew -crew.kickoff() -``` - -### Filtering Tools - -```python -from crewai_tools import CrewaiEnterpriseTools - -enterprise_tools = CrewaiEnterpriseTools( - actions_list=["gmail_find_email"] # only gmail_find_email tool will be available -) -gmail_tool = enterprise_tools["gmail_find_email"] - -gmail_agent = Agent( - role="Gmail Manager", - goal="Manage gmail communications and notifications", - backstory="An AI assistant that helps coordinate gmail communications.", - tools=[gmail_tool] -) - -notification_task = Task( - description="Find the email from john@example.com", - agent=gmail_agent, - expected_output="Email found from john@example.com" -) - -# Run the task -crew = Crew( - agents=[slack_agent], - tasks=[notification_task] -) -``` - -## Best Practices - -### Security -- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks -- **Regular Audits**: Periodically review connected integrations and their permissions -- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow - - -### Filtering Tools -On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to. - - - ![Integrations](/images/enterprise/filtering_enterprise_action_tools.png) - - - -### Scoped Deployments for multi user organizations -You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account. - - - This is useful for multi user organizations where you want to scope the integration to a specific user. - - - -Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew. - - - ![Integrations](/images/enterprise/user_bearer_token.png) - - - - -### Getting Help - - - Contact our support team for assistance with integration setup or troubleshooting. - diff --git a/docs/en/enterprise/features/marketplace.mdx b/docs/en/enterprise/features/marketplace.mdx new file mode 100644 index 000000000..dc7e0d916 --- /dev/null +++ b/docs/en/enterprise/features/marketplace.mdx @@ -0,0 +1,46 @@ +--- +title: Marketplace +description: "Discover, install, and govern reusable assets for your enterprise crews." +icon: "store" +mode: "wide" +--- + +## Overview + +The Marketplace provides a curated surface for discovering integrations, internal tools, and reusable assets that accelerate crew development. + + + ![Marketplace Overview](/images/enterprise/marketplace-overview.png) + + +## Discoverability + +- Browse by category and capability +- Search for assets by name or keyword + +## Install & Enable + +- One‑click install for approved assets +- Enable or disable per crew as needed +- Configure required environment variables and scopes + + + ![Install & Configure](/images/enterprise/marketplace-install.png) + + +You can also download the templates directly from the marketplace by clicking on the `Download` button so +you can use them locally or refine them to your needs. + +## Related + + + + Connect external apps and manage internal tools your agents can use. + + + Publish and install tools to enhance your crews' capabilities. + + + Store, share, and reuse agent definitions across teams and projects. + + diff --git a/docs/en/enterprise/features/rbac.mdx b/docs/en/enterprise/features/rbac.mdx index c6271c4a2..7a121fe05 100644 --- a/docs/en/enterprise/features/rbac.mdx +++ b/docs/en/enterprise/features/rbac.mdx @@ -7,16 +7,16 @@ mode: "wide" ## Overview -RBAC in CrewAI Enterprise enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls. +RBAC in CrewAI AMP enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls. - RBAC overview in CrewAI Enterprise - + RBAC overview in CrewAI AMP + ## Users and Roles -Each member in your CrewAI workspace is assigned a role, which determines their access across various features. +Each member in your CrewAI workspace is assigned a role, which determines their access across various features. You can: @@ -28,7 +28,7 @@ You can configure users and roles in Settings → Roles. - Go to Settings → Roles in CrewAI Enterprise. + Go to Settings → Roles in CrewAI AMP. Use a predefined role (Owner, Member) or click Create role to define a custom one. @@ -93,12 +93,10 @@ The organization owner always has access. In private mode, only whitelisted user - Automation Visibility settings in CrewAI Enterprise - + Automation Visibility settings in CrewAI AMP + Contact our support team for assistance with RBAC questions. - - diff --git a/docs/en/enterprise/features/tools-and-integrations.mdx b/docs/en/enterprise/features/tools-and-integrations.mdx new file mode 100644 index 000000000..4e60021db --- /dev/null +++ b/docs/en/enterprise/features/tools-and-integrations.mdx @@ -0,0 +1,249 @@ +--- +title: Tools & Integrations +description: "Connect external apps and manage internal tools your agents can use." +icon: "wrench" +mode: "wide" +--- + +## Overview + +Tools & Integrations is the central hub for connecting third‑party apps and managing internal tools that your agents can use at runtime. + + + ![Tools & Integrations Overview](/images/enterprise/crew_connectors.png) + + +## Explore + + + + + ## Agent Apps (Integrations) + + Connect enterprise‑grade applications (e.g., Gmail, Google Drive, HubSpot, Slack) via OAuth to enable agent actions. + + + + Click Connect on an app and complete OAuth. + + + Optionally adjust scopes, triggers, and action availability. + + + Connected services become available as tools for your agents. + + + + + ![Integrations Grid](/images/enterprise/agent-apps.png) + + + ### Connect your Account + + 1. Go to Integrations + 2. Click Connect on the desired service + 3. Complete the OAuth flow and grant scopes + 4. Copy your Enterprise Token from the Integration tab + + + ![Enterprise Token](/images/enterprise/enterprise_action_auth_token.png) + + + ### Install Integration Tools + + To use the integrations locally, you need to install the latest `crewai-tools` package. + + ```bash + uv add crewai-tools + ``` + + ### Usage Example + + + All services you have authenticated will be available as tools. Add `CrewaiEnterpriseTools` to your agent and you’re set. + + + ```python + from crewai import Agent, Task, Crew + from crewai_tools import CrewaiEnterpriseTools + + # Get enterprise tools (Gmail tool will be included) + enterprise_tools = CrewaiEnterpriseTools( + enterprise_token="your_enterprise_token" + ) + # print the tools + print(enterprise_tools) + + # Create an agent with Gmail capabilities + email_agent = Agent( + role="Email Manager", + goal="Manage and organize email communications", + backstory="An AI assistant specialized in email management and communication.", + tools=enterprise_tools + ) + + # Task to send an email + email_task = Task( + description="Draft and send a follow-up email to john@example.com about the project update", + agent=email_agent, + expected_output="Confirmation that email was sent successfully" + ) + + # Run the task + crew = Crew( + agents=[email_agent], + tasks=[email_task] + ) + + # Run the crew + crew.kickoff() + ``` + + ### Filtering Tools + + ```python + from crewai_tools import CrewaiEnterpriseTools + + enterprise_tools = CrewaiEnterpriseTools( + actions_list=["gmail_find_email"] # only gmail_find_email tool will be available + ) + + + gmail_tool = enterprise_tools["gmail_find_email"] + + + gmail_agent = Agent( + role="Gmail Manager", + goal="Manage gmail communications and notifications", + backstory="An AI assistant that helps coordinate gmail communications.", + tools=[gmail_tool] + ) + + notification_task = Task( + description="Find the email from john@example.com", + agent=gmail_agent, + expected_output="Email found from john@example.com" + ) + + crew = Crew( + agents=[gmail_agent], + tasks=[notification_task] + ) + ``` + + On a deployed crew, you can specify which actions are available for each integration from the service settings page. + + + ![Filter Actions](/images/enterprise/filtering_enterprise_action_tools.png) + + + ### Scoped Deployments (multi‑user orgs) + + You can scope each integration to a specific user. For example, a crew that connects to Google can use a specific user’s Gmail account. + + + Useful when different teams/users must keep data access separated. + + + Use the `user_bearer_token` to scope authentication to the requesting user. If the user isn’t logged in, the crew won’t use connected integrations. Otherwise it falls back to the default bearer token configured for the deployment. + + + ![User Bearer Token](/images/enterprise/user_bearer_token.png) + + +
+ ### Catalog + + #### Communication & Collaboration + - Gmail — Manage emails and drafts + - Slack — Workspace notifications and alerts + - Microsoft — Office 365 and Teams integration + + #### Project Management + - Jira — Issue tracking and project management + - ClickUp — Task and productivity management + - Asana — Team task and project coordination + - Notion — Page and database management + - Linear — Software project and bug tracking + - GitHub — Repository and issue management + + #### Customer Relationship Management + - Salesforce — CRM account and opportunity management + - HubSpot — Sales pipeline and contact management + - Zendesk — Customer support ticket management + + #### Business & Finance + - Stripe — Payment processing and customer management + - Shopify — E‑commerce store and product management + + #### Productivity & Storage + - Google Sheets — Spreadsheet data synchronization + - Google Calendar — Event and schedule management + - Box — File storage and document management + + …and more to come! + + + + + ## Internal Tools + + Create custom tools locally, publish them on CrewAI AMP Tool Repository and use them in your agents. + + + Before running the commands below, make sure you log in to your CrewAI AMP account by running this command: + ```bash + crewai login + ``` + + + + ![Internal Tool Detail](/images/enterprise/tools-integrations-internal.png) + + + + + Create a new tool locally. + ```bash + crewai tool create your-tool + ``` + + + Publish the tool to the CrewAI AMP Tool Repository. + ```bash + crewai tool publish + ``` + + + Install the tool from the CrewAI AMP Tool Repository. + ```bash + crewai tool install your-tool + ``` + + + + Manage: + + - Name and description + - Visibility (Private / Public) + - Required environment variables + - Version history and downloads + - Team and role access + + + ![Internal Tool Detail](/images/enterprise/tool-configs.png) + + + + + +## Related + + + + Create, publish, and version custom tools for your organization. + + + Automate workflows and integrate with external platforms and services. + + diff --git a/docs/en/enterprise/features/traces.mdx b/docs/en/enterprise/features/traces.mdx index bb281bae3..98d6f7c95 100644 --- a/docs/en/enterprise/features/traces.mdx +++ b/docs/en/enterprise/features/traces.mdx @@ -11,7 +11,7 @@ Traces provide comprehensive visibility into your crew executions, helping you m ## What are Traces? -Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record: +Traces in CrewAI AMP are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record: - Agent thoughts and reasoning - Task execution details @@ -28,9 +28,9 @@ Traces in CrewAI Enterprise are detailed execution records that capture every as - Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records. + Once in your CrewAI AMP dashboard, click on the **Traces** to view all execution records. - + You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace. @@ -112,7 +112,7 @@ Traces are invaluable for troubleshooting issues with your crews: When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for: - + - Failed tasks - Unexpected agent decisions - Tool usage errors @@ -122,19 +122,19 @@ Traces are invaluable for troubleshooting issues with your crews: ![Failure Points](/images/enterprise/failure.png) - + Use execution metrics to identify performance bottlenecks: - + - Tasks that took longer than expected - Excessive token usage - Redundant tool operations - Unnecessary API calls - + Analyze token usage and cost estimates to optimize your crew's efficiency: - + - Consider using smaller models for simpler tasks - Refine prompts to be more concise - Cache frequently accessed information @@ -153,5 +153,5 @@ CrewAI batches trace uploads to reduce overhead on high-volume runs: This yields more stable tracing under load while preserving detailed task/agent telemetry. - Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features. - \ No newline at end of file + Contact our support team for assistance with trace analysis or any other CrewAI AMP features. + diff --git a/docs/en/enterprise/features/webhook-streaming.mdx b/docs/en/enterprise/features/webhook-streaming.mdx index d7058956d..45b13d8c7 100644 --- a/docs/en/enterprise/features/webhook-streaming.mdx +++ b/docs/en/enterprise/features/webhook-streaming.mdx @@ -7,8 +7,8 @@ mode: "wide" ## Overview -Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to -CrewAI Enterprise, such as model calls, tool usage, and flow steps. +Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to +CrewAI AMP, such as model calls, tool usage, and flow steps. ## Usage @@ -65,99 +65,104 @@ CrewAI supports both system events and custom events in Enterprise Event Streami ### Flow Events: - - flow_created - - flow_started - - flow_finished - - flow_plot - - method_execution_started - - method_execution_finished - - method_execution_failed + - `flow_created` + - `flow_started` + - `flow_finished` + - `flow_plot` + - `method_execution_started` + - `method_execution_finished` + - `method_execution_failed` ### Agent Events: - - agent_execution_started - - agent_execution_completed - - agent_execution_error - - lite_agent_execution_started - - lite_agent_execution_completed - - lite_agent_execution_error - - agent_logs_started - - agent_logs_execution - - agent_evaluation_started - - agent_evaluation_completed - - agent_evaluation_failed + - `agent_execution_started` + - `agent_execution_completed` + - `agent_execution_error` + - `lite_agent_execution_started` + - `lite_agent_execution_completed` + - `lite_agent_execution_error` + - `agent_logs_started` + - `agent_logs_execution` + - `agent_evaluation_started` + - `agent_evaluation_completed` + - `agent_evaluation_failed` ### Crew Events: - - crew_kickoff_started - - crew_kickoff_completed - - crew_kickoff_failed - - crew_train_started - - crew_train_completed - - crew_train_failed - - crew_test_started - - crew_test_completed - - crew_test_failed - - crew_test_result + - `crew_kickoff_started` + - `crew_kickoff_completed` + - `crew_kickoff_failed` + - `crew_train_started` + - `crew_train_completed` + - `crew_train_failed` + - `crew_test_started` + - `crew_test_completed` + - `crew_test_failed` + - `crew_test_result` ### Task Events: - - task_started - - task_completed - - task_failed - - task_evaluation + - `task_started` + - `task_completed` + - `task_failed` + - `task_evaluation` ### Tool Usage Events: - - tool_usage_started - - tool_usage_finished - - tool_usage_error - - tool_validate_input_error - - tool_selection_error - - tool_execution_error + - `tool_usage_started` + - `tool_usage_finished` + - `tool_usage_error` + - `tool_validate_input_error` + - `tool_selection_error` + - `tool_execution_error` ### LLM Events: - - llm_call_started - - llm_call_completed - - llm_call_failed - - llm_stream_chunk + - `llm_call_started` + - `llm_call_completed` + - `llm_call_failed` + - `llm_stream_chunk` ### LLM Guardrail Events: - - llm_guardrail_started - - llm_guardrail_completed + - `llm_guardrail_started` + - `llm_guardrail_completed` ### Memory Events: - - memory_query_started - - memory_query_completed - - memory_query_failed - - memory_save_started - - memory_save_completed - - memory_save_failed - - memory_retrieval_started - - memory_retrieval_completed + - `memory_query_started` + - `memory_query_completed` + - `memory_query_failed` + - `memory_save_started` + - `memory_save_completed` + - `memory_save_failed` + - `memory_retrieval_started` + - `memory_retrieval_completed` ### Knowledge Events: - - knowledge_search_query_started - - knowledge_search_query_completed - - knowledge_search_query_failed - - knowledge_query_started - - knowledge_query_completed - - knowledge_query_failed + - `knowledge_search_query_started` + - `knowledge_search_query_completed` + - `knowledge_search_query_failed` + - `knowledge_query_started` + - `knowledge_query_completed` + - `knowledge_query_failed` ### Reasoning Events: - - agent_reasoning_started - - agent_reasoning_completed - - agent_reasoning_failed + - `agent_reasoning_started` + - `agent_reasoning_completed` + - `agent_reasoning_failed` -Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list. +Event names match the internal event bus. See GitHub for the full list of events. You can emit your own custom events, and they will be delivered through the webhook stream alongside system events. + + + Full list of events + Contact our support team for assistance with webhook integration or troubleshooting. - \ No newline at end of file + + diff --git a/docs/en/enterprise/guides/automation-triggers.mdx b/docs/en/enterprise/guides/automation-triggers.mdx index e46b46b4c..61fe4691c 100644 --- a/docs/en/enterprise/guides/automation-triggers.mdx +++ b/docs/en/enterprise/guides/automation-triggers.mdx @@ -1,22 +1,72 @@ --- -title: "Automation Triggers" -description: "Automatically execute your CrewAI workflows when specific events occur in connected integrations" -icon: "bolt" +title: "Triggers Overview" +description: "Understand how CrewAI AMP triggers work, how to manage them, and where to find integration-specific playbooks" +icon: "face-smile" mode: "wide" --- -Automation triggers enable you to automatically run your CrewAI deployments when specific events occur in your connected integrations, creating powerful event-driven workflows that respond to real-time changes in your business systems. +CrewAI AMP triggers connect your automations to real-time events across the tools your teams already use. Instead of polling systems or relying on manual kickoffs, triggers listen for changes—new emails, calendar updates, CRM status changes—and immediately launch the crew or flow you specify. -## Overview + + ![Automation Triggers Overview](/images/enterprise/crew_connectors.png) + -With automation triggers, you can: +### Integration Playbooks + +Deep-dive guides walk through setup and sample workflows for each integration: + + + + Enable crews when emails arrive or threads update. + + + + React to calendar events as they are created, updated, or cancelled. + + + + Handle Drive file uploads, edits, and deletions. + + + + Automate responses to new Outlook messages and calendar updates. + + + + Audit file activity and sharing changes in OneDrive. + + + + Kick off workflows when new Teams chats start. + + + + Launch automations from HubSpot workflows and lifecycle events. + + + + Connect Salesforce processes to CrewAI for CRM automation. + + + + Start crews directly from Slack slash commands. + + + + Bridge CrewAI with thousands of Zapier-supported apps. + + + +## Trigger Capabilities + +With triggers, you can: - **Respond to real-time events** - Automatically execute workflows when specific conditions are met - **Integrate with external systems** - Connect with platforms like Gmail, Outlook, OneDrive, JIRA, Slack, Stripe and more - **Scale your automation** - Handle high-volume events without manual intervention - **Maintain context** - Access trigger data within your crews and flows -## Managing Automation Triggers +## Managing Triggers ### Viewing Available Triggers @@ -25,7 +75,7 @@ To access and manage your automation triggers: 1. Navigate to your deployment in the CrewAI dashboard 2. Click on the **Triggers** tab to view all available trigger integrations - + List of available automation triggers @@ -35,7 +85,7 @@ This view shows all the trigger integrations available for your deployment, alon Each trigger can be easily enabled or disabled using the toggle switch: - + Enable or disable triggers with toggle @@ -48,24 +98,46 @@ Simply click the toggle to change the trigger state. Changes take effect immedia Track the performance and history of your triggered executions: - + List of executions triggered by automation -## Building Automation +## Building Trigger-Driven Automations Before building your automation, it's helpful to understand the structure of trigger payloads that your crews and flows will receive. -### Payload Samples Repository +### Trigger Setup Checklist -We maintain a comprehensive repository with sample payloads from various trigger sources to help you build and test your automations: +Before wiring a trigger into production, make sure you: -**🔗 [CrewAI Enterprise Trigger Payload Samples](https://github.com/crewAIInc/crewai-enterprise-trigger-payload-samples)** +- Connect the integration under **Tools & Integrations** and complete any OAuth or API key steps +- Enable the trigger toggle on the deployment that should respond to events +- Provide any required environment variables (API tokens, tenant IDs, shared secrets) +- Create or update tasks that can parse the incoming payload within the first crew task or flow step +- Decide whether to pass trigger context automatically using `allow_crewai_trigger_context` +- Set up monitoring—webhook logs, CrewAI execution history, and optional external alerting + +### Payload & Crew Examples Repository + +We maintain a comprehensive repository with end-to-end trigger examples to help you build and test your automations: This repository contains: -- **Real payload examples** from different trigger sources (Gmail, Google Drive, etc.) -- **Payload structure documentation** showing the format and available fields +- **Realistic payload samples** for every supported trigger integration +- **Ready-to-run crew implementations** that parse each payload and turn it into a business workflow +- **Multiple scenarios per integration** (e.g., new events, updates, deletions) so you can match the shape of your data + +| Integration | When it fires | Payload Samples | Crew Examples | +| :-- | :-- | :-- | :-- | +| Gmail | New messages, thread updates | [New alerts, thread updates](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | [`new-email-crew.py`, `gmail-alert-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) | +| Google Calendar | Event created / updated / started / ended / cancelled | [Event lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | [`calendar-event-crew.py`, `calendar-meeting-crew.py`, `calendar-working-location-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) | +| Google Drive | File created / updated / deleted | [File lifecycle payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | [`drive-file-crew.py`, `drive-file-deletion-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) | +| Outlook | New email, calendar event removed | [Outlook payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | [`outlook-message-crew.py`, `outlook-event-removal-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) | +| OneDrive | File operations (create, update, share, delete) | [OneDrive payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | [`onedrive-file-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) | +| HubSpot | Record created / updated (contacts, companies, deals) | [HubSpot payloads](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | [`hubspot-company-crew.py`, `hubspot-contact-crew.py`, `hubspot-record-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot) | +| Microsoft Teams | Chat thread created | [Teams chat payload](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | [`teams-chat-created-crew.py`](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) | + +Use these samples to understand payload shape, copy the matching crew, and then replace the test payload with your live trigger data. ### Triggers with Crew @@ -177,3 +249,7 @@ def delegate_to_crew(self, crewai_trigger_payload: dict = None): - If you are developing, make sure the inputs include the `crewai_trigger_payload` parameter with the correct payload Automation triggers transform your CrewAI deployments into responsive, event-driven systems that can seamlessly integrate with your existing business processes and tools. + + + Check them out on GitHub! + diff --git a/docs/en/enterprise/guides/azure-openai-setup.mdx b/docs/en/enterprise/guides/azure-openai-setup.mdx index 6754208f7..408fe8601 100644 --- a/docs/en/enterprise/guides/azure-openai-setup.mdx +++ b/docs/en/enterprise/guides/azure-openai-setup.mdx @@ -19,8 +19,8 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle - - 4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure. + + 4. In another tab, open `CrewAI AMP > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure. 5. On the same page, add environment variables from step 3: - One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview - Another named `AZURE_API_KEY` (using the Key). @@ -28,7 +28,7 @@ This guide walks you through connecting Azure OpenAI with Crew Studio for seamle - 7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults. + 7. In `CrewAI AMP > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults. @@ -49,4 +49,4 @@ If you encounter issues: - Verify the Target URI format matches the expected pattern - Check that the API key is correct and has proper permissions - Ensure network access is configured to allow CrewAI connections -- Confirm the deployment model matches what you've configured in CrewAI +- Confirm the deployment model matches what you've configured in CrewAI diff --git a/docs/en/enterprise/guides/build-crew.mdx b/docs/en/enterprise/guides/build-crew.mdx index f7b9be6d6..38ea95853 100644 --- a/docs/en/enterprise/guides/build-crew.mdx +++ b/docs/en/enterprise/guides/build-crew.mdx @@ -7,19 +7,17 @@ mode: "wide" ## Overview -[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments. +[CrewAI AMP](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments. ## Getting Started ### Installation and Setup diff --git a/docs/en/enterprise/guides/deploy-crew.mdx b/docs/en/enterprise/guides/deploy-crew.mdx index 7d7d7ef1c..c017bd4db 100644 --- a/docs/en/enterprise/guides/deploy-crew.mdx +++ b/docs/en/enterprise/guides/deploy-crew.mdx @@ -1,12 +1,12 @@ --- title: "Deploy Crew" -description: "Deploying a Crew on CrewAI Enterprise" +description: "Deploying a Crew on CrewAI AMP" icon: "rocket" mode: "wide" --- -After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow. +After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI AMP platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow. ## Prerequisites @@ -39,10 +39,10 @@ The CLI provides the fastest way to deploy locally developed crews to the Enterp - First, you need to authenticate your CLI with the CrewAI Enterprise platform: + First, you need to authenticate your CLI with the CrewAI AMP platform: ```bash - # If you already have a CrewAI Enterprise account, or want to create one: + # If you already have a CrewAI AMP account, or want to create one: crewai login ``` @@ -124,7 +124,7 @@ The CrewAI CLI offers several commands to manage your deployments: ## Option 2: Deploy Directly via Web Interface -You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine. +You can also deploy your crews directly through the CrewAI AMP web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine. @@ -134,9 +134,9 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf - + - 1. Log in to [CrewAI Enterprise](https://app.crewai.com) + 1. Log in to [CrewAI AMP](https://app.crewai.com) 2. Click on the button "Connect GitHub" @@ -190,7 +190,7 @@ You can also deploy your crews directly through the CrewAI Enterprise web interf ## ⚠️ Environment Variable Security Requirements -**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed. +**Important**: CrewAI AMP has security restrictions on environment variable names that can cause deployment failures if not followed. ### Blocked Environment Variable Patterns diff --git a/docs/en/enterprise/guides/enable-crew-studio.mdx b/docs/en/enterprise/guides/enable-crew-studio.mdx index 328cad261..8c3e5422d 100644 --- a/docs/en/enterprise/guides/enable-crew-studio.mdx +++ b/docs/en/enterprise/guides/enable-crew-studio.mdx @@ -1,17 +1,17 @@ --- title: "Enable Crew Studio" -description: "Enabling Crew Studio on CrewAI Enterprise" +description: "Enabling Crew Studio on CrewAI AMP" icon: "comments" mode: "wide" --- -Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface. +Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface. ## What is Crew Studio? -Crew Studio is an innovative way to create AI agent crews without writing code. +Crew Studio is an innovative way to create AI agent crews without writing code. ![Crew Studio Interface](/images/enterprise/crew-studio-interface.png) @@ -24,7 +24,7 @@ With Crew Studio, you can: - Select appropriate tools - Configure necessary inputs - Generate downloadable code for customization -- Deploy directly to the CrewAI Enterprise platform +- Deploy directly to the CrewAI AMP platform ## Configuration Steps @@ -32,14 +32,14 @@ Before you can start using Crew Studio, you need to configure your LLM connectio - Go to the **LLM Connections** tab in your CrewAI Enterprise dashboard and create a new LLM connection. + Go to the **LLM Connections** tab in your CrewAI AMP dashboard and create a new LLM connection. Feel free to use any LLM provider you want that is supported by CrewAI. - + Configure your LLM connection: - + - Enter a `Connection Name` (e.g., `OpenAI`) - Select your model provider: `openai` or `azure` - Select models you'd like to use in your Studio-generated Crews @@ -48,28 +48,28 @@ Before you can start using Crew Studio, you need to configure your LLM connectio - For OpenAI: Add `OPENAI_API_KEY` with your API key - For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details - Click `Add Connection` to save your configuration - + ![LLM Connection Configuration](/images/enterprise/llm-connection-config.png) - + Once you complete the setup, you'll see your new connection added to the list of available connections. - + ![Connection Added](/images/enterprise/connection-added.png) - + In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings: - + - Select default models for agents and other components - Set default configurations for Crew Studio - + Click `Save Settings` to apply your changes. - + ![LLM Defaults Configuration](/images/enterprise/llm-defaults.png) @@ -82,38 +82,38 @@ Now that you've configured your LLM connection and default settings, you're read - Navigate to the **Studio** section in your CrewAI Enterprise dashboard. + Navigate to the **Studio** section in your CrewAI AMP dashboard. - + Start a conversation with the Crew Assistant by describing the problem you want to solve: - + ```md I need a crew that can research the latest AI developments and create a summary report. ``` - + The Crew Assistant will ask clarifying questions to better understand your requirements. - + Review the generated crew configuration, including: - + - Agents and their roles - Tasks to be performed - Required inputs - Tools to be used - + This is your opportunity to refine the configuration before proceeding. - + Once you're satisfied with the configuration, you can: - + - Download the generated code for local customization - - Deploy the crew directly to the CrewAI Enterprise platform + - Deploy the crew directly to the CrewAI AMP platform - Modify the configuration and regenerate the crew - + After deployment, test your crew with sample inputs to ensure it performs as expected. @@ -130,38 +130,37 @@ Here's a typical workflow for creating a crew with Crew Studio: Start by describing your problem: - + ```md I need a crew that can analyze financial news and provide investment recommendations ``` - + Respond to clarifying questions from the Crew Assistant to refine your requirements. - + Review the generated crew plan, which might include: - + - A Research Agent to gather financial news - An Analysis Agent to interpret the data - A Recommendations Agent to provide investment advice - + Approve the plan or request changes if necessary. - + Download the code for customization or deploy directly to the platform. - + Test your crew with sample inputs and refine as needed. - Contact our support team for assistance with Crew Studio or any other CrewAI Enterprise features. + Contact our support team for assistance with Crew Studio or any other CrewAI AMP features. - diff --git a/docs/en/enterprise/guides/gmail-trigger.mdx b/docs/en/enterprise/guides/gmail-trigger.mdx new file mode 100644 index 000000000..2caefc045 --- /dev/null +++ b/docs/en/enterprise/guides/gmail-trigger.mdx @@ -0,0 +1,85 @@ +--- +title: "Gmail Trigger" +description: "Trigger automations when Gmail events occur (e.g., new emails, labels)." +icon: "envelope" +mode: "wide" +--- + +## Overview + +Use the Gmail Trigger to kick off your deployed crews when Gmail events happen in connected accounts, such as receiving a new email or messages matching a label/filter. + + + Make sure Gmail is connected in Tools & Integrations and the trigger is enabled for your deployment. + + +## Enabling the Gmail Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **Gmail** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Process new emails + +When a new email arrives, the Gmail Trigger will send the payload to your Crew or Flow. Below is a Crew example that parses and processes the trigger payload. + +```python +@CrewBase +class GmailProcessingCrew: + @agent + def parser(self) -> Agent: + return Agent( + config=self.agents_config['parser'], + ) + + @task + def parse_gmail_payload(self) -> Task: + return Task( + config=self.tasks_config['parse_gmail_payload'], + agent=self.parser(), + ) + + @task + def act_on_email(self) -> Task: + return Task( + config=self.tasks_config['act_on_email'], + agent=self.parser(), + ) +``` + +The Gmail payload will be available via the standard context mechanisms. See the payload samples repository for structure and fields. + +### Sample payloads & crews + +The [CrewAI AMP Trigger Examples repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/gmail) includes: + +- `new-email-payload-1.json` / `new-email-payload-2.json` — production-style new message alerts with matching crews in `new-email-crew.py` +- `thread-updated-sample-1.json` — follow-up messages on an existing thread, processed by `gmail-alert-crew.py` + +Use these samples to validate your parsing logic locally before wiring the trigger to your live Gmail accounts. + +## Monitoring Executions + +Track history and performance of triggered runs: + + + List of executions triggered by automation + + +## Payload Reference + +See the sample payloads and field descriptions: + + + Gmail samples in Trigger Examples Repo + + +## Troubleshooting + +- Ensure Gmail is connected in Tools & Integrations +- Verify the Gmail Trigger is enabled on the Triggers tab +- Check the execution logs and confirm the payload is passed as `crewai_trigger_payload` diff --git a/docs/en/enterprise/guides/google-calendar-trigger.mdx b/docs/en/enterprise/guides/google-calendar-trigger.mdx new file mode 100644 index 000000000..cf2d32471 --- /dev/null +++ b/docs/en/enterprise/guides/google-calendar-trigger.mdx @@ -0,0 +1,65 @@ +--- +title: "Google Calendar Trigger" +description: "Kick off crews when Google Calendar events are created, updated, or cancelled" +icon: "calendar" +mode: "wide" +--- + +## Overview + +Use the Google Calendar trigger to launch automations whenever calendar events change. Common use cases include briefing a team before a meeting, notifying stakeholders when a critical event is cancelled, or summarizing daily schedules. + + + Make sure Google Calendar is connected in **Tools & Integrations** and enabled for the deployment you want to automate. + + +## Enabling the Google Calendar Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **Google Calendar** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Summarize meeting details + +The snippet below mirrors the `calendar-event-crew.py` example in the trigger repository. It parses the payload, analyses the attendees and timing, and produces a meeting brief for downstream tools. + +```python +from calendar_event_crew import GoogleCalendarEventTrigger + +crew = GoogleCalendarEventTrigger().crew() +result = crew.kickoff({ + "crewai_trigger_payload": calendar_payload, +}) +print(result.raw) +``` + +Use `crewai_trigger_payload` exactly as it is delivered by the trigger so the crew can extract the proper fields. + +## Sample payloads & crews + +The [Google Calendar examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_calendar) show how to handle multiple event types: + +- `new-event.json` → standard event creation handled by `calendar-event-crew.py` +- `event-updated.json` / `event-started.json` / `event-ended.json` → in-flight updates processed by `calendar-meeting-crew.py` +- `event-canceled.json` → cancellation workflow that alerts attendees via `calendar-meeting-crew.py` +- Working location events use `calendar-working-location-crew.py` to extract on-site schedules + +Each crew transforms raw event metadata (attendees, rooms, working locations) into the summaries your teams need. + +## Monitoring Executions + +The **Executions** list in the deployment dashboard tracks every triggered run and surfaces payload metadata, output summaries, and errors. + + + List of executions triggered by automation + + +## Troubleshooting + +- Ensure the correct Google account is connected and the trigger is enabled +- Confirm your workflow handles all-day events (payloads use `start.date` and `end.date` instead of timestamps) +- Check execution logs if reminders or attendee arrays are missing—calendar permissions can limit fields in the payload diff --git a/docs/en/enterprise/guides/google-drive-trigger.mdx b/docs/en/enterprise/guides/google-drive-trigger.mdx new file mode 100644 index 000000000..19c10837b --- /dev/null +++ b/docs/en/enterprise/guides/google-drive-trigger.mdx @@ -0,0 +1,61 @@ +--- +title: "Google Drive Trigger" +description: "Respond to Google Drive file events with automated crews" +icon: "folder" +mode: "wide" +--- + +## Overview + +Trigger your automations when files are created, updated, or removed in Google Drive. Typical workflows include summarizing newly uploaded content, enforcing sharing policies, or notifying owners when critical files change. + + + Connect Google Drive in **Tools & Integrations** and confirm the trigger is enabled for the automation you want to monitor. + + +## Enabling the Google Drive Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **Google Drive** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Summarize file activity + +The drive example crews parse the payload to extract file metadata, evaluate permissions, and publish a summary. + +```python +from drive_file_crew import GoogleDriveFileTrigger + +crew = GoogleDriveFileTrigger().crew() +crew.kickoff({ + "crewai_trigger_payload": drive_payload, +}) +``` + +## Sample payloads & crews + +Explore the [Google Drive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/google_drive) to cover different operations: + +- `new-file.json` → new uploads processed by `drive-file-crew.py` +- `updated-file.json` → file edits and metadata changes handled by `drive-file-crew.py` +- `deleted-file.json` → deletion events routed through `drive-file-deletion-crew.py` + +Each crew highlights the file name, operation type, owner, permissions, and security considerations so downstream systems can respond appropriately. + +## Monitoring Executions + +Track history and performance of triggered runs with the **Executions** list in the deployment dashboard. + + + List of executions triggered by automation + + +## Troubleshooting + +- Verify Google Drive is connected and the trigger toggle is enabled +- If a payload is missing permission data, ensure the connected account has access to the file or folder +- The trigger sends file IDs only; use the Drive API if you need to fetch binary content during the crew run diff --git a/docs/en/enterprise/guides/hubspot-trigger.mdx b/docs/en/enterprise/guides/hubspot-trigger.mdx index a5cd8a553..c2a7549f3 100644 --- a/docs/en/enterprise/guides/hubspot-trigger.mdx +++ b/docs/en/enterprise/guides/hubspot-trigger.mdx @@ -5,22 +5,22 @@ icon: "hubspot" mode: "wide" --- -This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows. +This guide provides a step-by-step process to set up HubSpot triggers for CrewAI AMP, enabling you to initiate crews directly from HubSpot Workflows. ## Prerequisites -- A CrewAI Enterprise account +- A CrewAI AMP account - A HubSpot account with the [HubSpot Workflows](https://knowledge.hubspot.com/workflows/create-workflows) feature ## Setup Steps - - - Log in to your `CrewAI Enterprise account > Triggers` + + - Log in to your `CrewAI AMP account > Triggers` - Select `HubSpot` from the list of available triggers - - Choose the HubSpot account you want to connect with CrewAI Enterprise - - Follow the on-screen prompts to authorize CrewAI Enterprise access to your HubSpot account - - A confirmation message will appear once HubSpot is successfully connected with CrewAI Enterprise + - Choose the HubSpot account you want to connect with CrewAI AMP + - Follow the on-screen prompts to authorize CrewAI AMP access to your HubSpot account + - A confirmation message will appear once HubSpot is successfully connected with CrewAI AMP - Log in to your `HubSpot account > Automations > Workflows > New workflow` @@ -51,4 +51,14 @@ This guide provides a step-by-step process to set up HubSpot triggers for CrewAI ## Additional Resources -For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). \ No newline at end of file +### Sample payloads & crews + +You can jump-start development with the [HubSpot examples in the trigger repository](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/hubspot): + +- `record-created-contact.json`, `record-updated-contact.json` → contact lifecycle events handled by `hubspot-contact-crew.py` +- `record-created-company.json`, `record-updated-company.json` → company enrichment flows in `hubspot-company-crew.py` +- `record-created-deals.json`, `record-updated-deals.json` → deal pipeline automation in `hubspot-record-crew.py` + +Each crew demonstrates how to parse HubSpot record fields, enrich context, and return structured insights. + +For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). diff --git a/docs/en/enterprise/guides/kickoff-crew.mdx b/docs/en/enterprise/guides/kickoff-crew.mdx index 243c2375f..80bb47969 100644 --- a/docs/en/enterprise/guides/kickoff-crew.mdx +++ b/docs/en/enterprise/guides/kickoff-crew.mdx @@ -1,19 +1,19 @@ --- title: "Kickoff Crew" -description: "Kickoff a Crew on CrewAI Enterprise" +description: "Kickoff a Crew on CrewAI AMP" icon: "flag-checkered" mode: "wide" --- ## Overview -Once you've deployed your crew to the CrewAI Enterprise platform, you can kickoff executions through the web interface or the API. This guide covers both approaches. +Once you've deployed your crew to the CrewAI AMP platform, you can kickoff executions through the web interface or the API. This guide covers both approaches. ## Method 1: Using the Web Interface ### Step 1: Navigate to Your Deployed Crew -1. Log in to [CrewAI Enterprise](https://app.crewai.com) +1. Log in to [CrewAI AMP](https://app.crewai.com) 2. Click on the crew name from your projects list 3. You'll be taken to the crew's detail page @@ -83,7 +83,7 @@ Once execution is complete: ## Method 2: Using the API -You can also kickoff crews programmatically using the CrewAI Enterprise REST API. +You can also kickoff crews programmatically using the CrewAI AMP REST API. ### Authentication @@ -184,4 +184,3 @@ If an execution fails: Contact our support team for assistance with execution issues or questions about the Enterprise platform. - diff --git a/docs/en/enterprise/guides/microsoft-teams-trigger.mdx b/docs/en/enterprise/guides/microsoft-teams-trigger.mdx new file mode 100644 index 000000000..10878af40 --- /dev/null +++ b/docs/en/enterprise/guides/microsoft-teams-trigger.mdx @@ -0,0 +1,52 @@ +--- +title: "Microsoft Teams Trigger" +description: "Kick off crews from Microsoft Teams chat activity" +icon: "microsoft" +mode: "wide" +--- + +## Overview + +Use the Microsoft Teams trigger to start automations whenever a new chat is created. Common patterns include summarizing inbound requests, routing urgent messages to support teams, or creating follow-up tasks in other systems. + + + Confirm Microsoft Teams is connected under **Tools & Integrations** and enabled in the **Triggers** tab for your deployment. + + +## Enabling the Microsoft Teams Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **Microsoft Teams** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Summarize a new chat thread + +```python +from teams_chat_created_crew import MicrosoftTeamsChatTrigger + +crew = MicrosoftTeamsChatTrigger().crew() +result = crew.kickoff({ + "crewai_trigger_payload": teams_payload, +}) +print(result.raw) +``` + +The crew parses thread metadata (subject, created time, roster) and generates an action plan for the receiving team. + +## Sample payloads & crews + +The [Microsoft Teams examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/microsoft-teams) include: + +- `chat-created.json` → chat creation payload processed by `teams-chat-created-crew.py` + +The crew demonstrates how to extract participants, initial messages, tenant information, and compliance metadata from the Microsoft Graph webhook payload. + +## Troubleshooting + +- Ensure the Teams connection is active; it must be refreshed if the tenant revokes permissions +- Confirm the webhook subscription in Microsoft 365 is still valid if payloads stop arriving +- Review execution logs for payload shape mismatches—Graph notifications may omit fields when a chat is private or restricted diff --git a/docs/en/enterprise/guides/onedrive-trigger.mdx b/docs/en/enterprise/guides/onedrive-trigger.mdx new file mode 100644 index 000000000..51de175db --- /dev/null +++ b/docs/en/enterprise/guides/onedrive-trigger.mdx @@ -0,0 +1,53 @@ +--- +title: "OneDrive Trigger" +description: "Automate responses to OneDrive file activity" +icon: "cloud" +mode: "wide" +--- + +## Overview + +Start automations when files change inside OneDrive. You can generate audit summaries, notify security teams about external sharing, or update downstream line-of-business systems with new document metadata. + + + Connect OneDrive in **Tools & Integrations** and toggle the trigger on for your deployment. + + +## Enabling the OneDrive Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **OneDrive** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Audit file permissions + +```python +from onedrive_file_crew import OneDriveFileTrigger + +crew = OneDriveFileTrigger().crew() +crew.kickoff({ + "crewai_trigger_payload": onedrive_payload, +}) +``` + +The crew inspects file metadata, user activity, and permission changes to produce a compliance-friendly summary. + +## Sample payloads & crews + +The [OneDrive examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/onedrive) showcase how to: + +- Parse file metadata, size, and folder paths +- Track who created and last modified the file +- Highlight permission and external sharing changes + +`onedrive-file-crew.py` bundles the analysis and summarization tasks so you can add remediation steps as needed. + +## Troubleshooting + +- Ensure the connected account has permission to read the file metadata included in the webhook +- If the trigger fires but the payload is missing `permissions`, confirm the site-level sharing settings allow Graph to return this field +- For large tenants, filter notifications upstream so the crew only runs on relevant directories diff --git a/docs/en/enterprise/guides/outlook-trigger.mdx b/docs/en/enterprise/guides/outlook-trigger.mdx new file mode 100644 index 000000000..21bda5407 --- /dev/null +++ b/docs/en/enterprise/guides/outlook-trigger.mdx @@ -0,0 +1,52 @@ +--- +title: "Outlook Trigger" +description: "Launch automations from Outlook emails and calendar updates" +icon: "microsoft" +mode: "wide" +--- + +## Overview + +Automate responses when Outlook delivers a new message or when an event is removed from the calendar. Teams commonly route escalations, file tickets, or alert attendees of cancellations. + + + Connect Outlook in **Tools & Integrations** and ensure the trigger is enabled for your deployment. + + +## Enabling the Outlook Trigger + +1. Open your deployment in CrewAI AMP +2. Go to the **Triggers** tab +3. Locate **Outlook** and switch the toggle to enable + + + Enable or disable triggers with toggle + + +## Example: Summarize a new email + +```python +from outlook_message_crew import OutlookMessageTrigger + +crew = OutlookMessageTrigger().crew() +crew.kickoff({ + "crewai_trigger_payload": outlook_payload, +}) +``` + +The crew extracts sender details, subject, body preview, and attachments before generating a structured response. + +## Sample payloads & crews + +Review the [Outlook examples](https://github.com/crewAIInc/crewai-enterprise-trigger-examples/tree/main/outlook) for two common scenarios: + +- `new-message.json` → new mail notifications parsed by `outlook-message-crew.py` +- `event-removed.json` → calendar cleanup handled by `outlook-event-removal-crew.py` + +Each crew demonstrates how to handle Microsoft Graph payloads, normalize headers, and keep humans in-the-loop with concise summaries. + +## Troubleshooting + +- Verify the Outlook connector is still authorized; the subscription must be renewed periodically +- If attachments are missing, confirm the webhook subscription includes the `includeResourceData` flag +- Review execution logs when events fail to match—cancellation payloads lack attendee lists by design and the crew should account for that diff --git a/docs/en/enterprise/guides/react-component-export.mdx b/docs/en/enterprise/guides/react-component-export.mdx index 8e811b2ee..6081e3c6d 100644 --- a/docs/en/enterprise/guides/react-component-export.mdx +++ b/docs/en/enterprise/guides/react-component-export.mdx @@ -1,11 +1,11 @@ --- title: "React Component Export" -description: "Learn how to export and integrate CrewAI Enterprise React components into your applications" +description: "Learn how to export and integrate CrewAI AMP React components into your applications" icon: "react" mode: "wide" --- -This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications. +This guide explains how to export CrewAI AMP crews as React components and integrate them into your own applications. ## Exporting a React Component @@ -38,7 +38,7 @@ To run this React component locally, you'll need to set up a React development e npx create-react-app my-crew-app ``` - Change into the project directory: - + ```bash cd my-crew-app ``` @@ -77,7 +77,7 @@ To run this React component locally, you'll need to set up a React development e - In your project directory, run: - + ```bash npm start ``` @@ -101,4 +101,4 @@ You can then customise the `CrewLead.jsx` to add color, title etc - Customize the component styling to match your application's design - Add additional props for configuration - Integrate with your application's state management -- Add error handling and loading states \ No newline at end of file +- Add error handling and loading states diff --git a/docs/en/enterprise/guides/salesforce-trigger.mdx b/docs/en/enterprise/guides/salesforce-trigger.mdx index aa3a526e8..4b0010edb 100644 --- a/docs/en/enterprise/guides/salesforce-trigger.mdx +++ b/docs/en/enterprise/guides/salesforce-trigger.mdx @@ -5,7 +5,7 @@ icon: "salesforce" mode: "wide" --- -CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations. +CrewAI AMP can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations. ## Overview @@ -18,15 +18,20 @@ Salesforce is a leading customer relationship management (CRM) platform that hel ## Demo - - - + ## Getting Started To set up Salesforce triggers: -1. **Contact Support**: Reach out to CrewAI Enterprise support for assistance with Salesforce trigger setup +1. **Contact Support**: Reach out to CrewAI AMP support for assistance with Salesforce trigger setup 2. **Review Requirements**: Ensure you have the necessary Salesforce permissions and API access 3. **Configure Connection**: Work with the support team to establish the connection between CrewAI and your Salesforce instance 4. **Test Triggers**: Verify the triggers work correctly with your specific use cases @@ -42,4 +47,4 @@ Common Salesforce + CrewAI trigger scenarios include: ## Next Steps -For detailed setup instructions and advanced configuration options, please contact CrewAI Enterprise support who can provide tailored guidance for your specific Salesforce environment and business needs. \ No newline at end of file +For detailed setup instructions and advanced configuration options, please contact CrewAI AMP support who can provide tailored guidance for your specific Salesforce environment and business needs. diff --git a/docs/en/enterprise/guides/team-management.mdx b/docs/en/enterprise/guides/team-management.mdx index a3cd3fd07..cc51f1824 100644 --- a/docs/en/enterprise/guides/team-management.mdx +++ b/docs/en/enterprise/guides/team-management.mdx @@ -1,27 +1,27 @@ --- title: "Team Management" -description: "Learn how to invite and manage team members in your CrewAI Enterprise organization" +description: "Learn how to invite and manage team members in your CrewAI AMP organization" icon: "users" mode: "wide" --- -As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step. +As an administrator of a CrewAI AMP account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step. ## Inviting Team Members - - Log in to your CrewAI Enterprise account + - Log in to your CrewAI AMP account - Look for the gear icon (⚙️) in the top right corner of the dashboard - Click on the gear icon to access the **Settings** page: - + Settings Page - On the Settings page, you'll see a `Members` tab - Click on the `Members` tab to access the **Members** page: - + Members Tab @@ -43,7 +43,7 @@ You can add roles to your team members to control their access to different part - - Log in to your CrewAI Enterprise account + - Log in to your CrewAI AMP account - Look for the gear icon (⚙️) in the top right corner of the dashboard - Click on the gear icon to access the **Settings** page: @@ -85,4 +85,4 @@ You can add roles to your team members to control their access to different part - **Invitation Acceptance**: Invited members will need to accept the invitation to join your organization - **Email Notifications**: You may want to inform your team members to check their email (including spam folders) for the invitation -By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI Enterprise organization. \ No newline at end of file +By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI AMP organization. diff --git a/docs/en/enterprise/features/tool-repository.mdx b/docs/en/enterprise/guides/tool-repository.mdx similarity index 88% rename from docs/en/enterprise/features/tool-repository.mdx rename to docs/en/enterprise/guides/tool-repository.mdx index 7ec7ff346..5161cdfc7 100644 --- a/docs/en/enterprise/features/tool-repository.mdx +++ b/docs/en/enterprise/guides/tool-repository.mdx @@ -20,11 +20,11 @@ The repository is not a version control system. Use Git to track code changes an Before using the Tool Repository, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account +- A [CrewAI AMP](https://app.crewai.com) account - [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed - uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv) - [Git](https://git-scm.com) installed and configured -- Access permissions to publish or install tools in your CrewAI Enterprise organization +- Access permissions to publish or install tools in your CrewAI AMP organization ## Installing Tools @@ -54,11 +54,11 @@ researcher = Agent( ## Adding other packages after installing a tool -After installing a tool from the CrewAI Enterprise Tool Repository, you need to use the `crewai uv` command to add other packages to your project. +After installing a tool from the CrewAI AMP Tool Repository, you need to use the `crewai uv` command to add other packages to your project. Using pure `uv` commands will fail due to authentication to tool repository being handled by the CLI. By using the `crewai uv` command, you can add other packages to your project without having to worry about authentication. Any `uv` command can be used with the `crewai uv` command, making it a powerful tool for managing your project's dependencies without the hassle of managing authentication through environment variables or other methods. -Say that you have installed a custom tool from the CrewAI Enterprise Tool Repository called "my-tool": +Say that you have installed a custom tool from the CrewAI AMP Tool Repository called "my-tool": ```bash crewai tool install my-tool @@ -131,7 +131,7 @@ crewai tool publish To delete a tool: -1. Go to [CrewAI Enterprise](https://app.crewai.com) +1. Go to [CrewAI AMP](https://app.crewai.com) 2. Navigate to **Tools** 3. Select the tool 4. Click **Delete** @@ -142,12 +142,11 @@ Deletion is permanent. Deleted tools cannot be restored or re-installed. ## Security Checks -Every published version undergoes automated security checks, and are only available to install after they pass. +Every published version undergoes automated security checks, and are only available to install after they pass. You can check the security check status of a tool at: -`CrewAI Enterprise > Tools > Your Tool > Versions` - +`CrewAI AMP > Tools > Your Tool > Versions` Contact our support team for assistance with API integration or troubleshooting. diff --git a/docs/en/enterprise/guides/update-crew.mdx b/docs/en/enterprise/guides/update-crew.mdx index b6aa9f74b..6fa49e0e1 100644 --- a/docs/en/enterprise/guides/update-crew.mdx +++ b/docs/en/enterprise/guides/update-crew.mdx @@ -1,12 +1,12 @@ --- title: "Update Crew" -description: "Updating a Crew on CrewAI Enterprise" +description: "Updating a Crew on CrewAI AMP" icon: "pencil" mode: "wide" --- -After deploying your crew to CrewAI Enterprise, you may need to make updates to the code, security settings, or configuration. +After deploying your crew to CrewAI AMP, you may need to make updates to the code, security settings, or configuration. This guide explains how to perform these common update operations. @@ -23,7 +23,7 @@ There are several reasons you might want to update your crew deployment: When you've pushed new commits to your GitHub repository and want to update your deployment: -1. Navigate to your crew in the CrewAI Enterprise platform +1. Navigate to your crew in the CrewAI AMP platform 2. Click on the `Re-deploy` button on your crew details page @@ -36,7 +36,7 @@ This will trigger an update that you can track using the progress bar. The syste If you need to generate a new bearer token (for example, if you suspect the current token might have been compromised): -1. Navigate to your crew in the CrewAI Enterprise platform +1. Navigate to your crew in the CrewAI AMP platform 2. Find the `Bearer Token` section 3. Click the `Reset` button next to your current token @@ -87,4 +87,3 @@ If you encounter any issues after updating, you can view deployment logs in the Contact our support team for assistance with updating your crew or troubleshooting deployment issues. - diff --git a/docs/en/enterprise/guides/webhook-automation.mdx b/docs/en/enterprise/guides/webhook-automation.mdx index 2228b95ef..4cf5de09c 100644 --- a/docs/en/enterprise/guides/webhook-automation.mdx +++ b/docs/en/enterprise/guides/webhook-automation.mdx @@ -1,17 +1,17 @@ --- title: "Webhook Automation" -description: "Automate CrewAI Enterprise workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com" +description: "Automate CrewAI AMP workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com" icon: "webhook" mode: "wide" --- -CrewAI Enterprise allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com. +CrewAI AMP allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com. ## Setting Up Webhooks - - Navigate to the CrewAI Enterprise dashboard + - Navigate to the CrewAI AMP dashboard - Look for the `/kickoff` section, which is used to start the crew execution Kickoff Interface @@ -44,7 +44,7 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti 3. Add an HTTP action step - Set the action to `Send HTTP request` - Use `POST` as the method - - Set the URL to your CrewAI Enterprise kickoff endpoint + - Set the URL to your CrewAI AMP kickoff endpoint - Add necessary headers (e.g., `Bearer Token`) ActivePieces Headers @@ -59,7 +59,7 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti - 1. Create a new flow in ActivePieces and name it + 1. Create a new flow in ActivePieces and name it ActivePieces Flow @@ -152,4 +152,4 @@ CrewAI Enterprise allows you to automate your workflow using webhooks. This arti } ``` - \ No newline at end of file + diff --git a/docs/en/enterprise/guides/zapier-trigger.mdx b/docs/en/enterprise/guides/zapier-trigger.mdx index d308a63bb..df586a781 100644 --- a/docs/en/enterprise/guides/zapier-trigger.mdx +++ b/docs/en/enterprise/guides/zapier-trigger.mdx @@ -5,11 +5,11 @@ icon: "bolt" mode: "wide" --- -This guide will walk you through the process of setting up Zapier triggers for CrewAI Enterprise, allowing you to automate workflows between CrewAI Enterprise and other applications. +This guide will walk you through the process of setting up Zapier triggers for CrewAI AMP, allowing you to automate workflows between CrewAI AMP and other applications. ## Prerequisites -- A CrewAI Enterprise account +- A CrewAI AMP account - A Zapier account - A Slack account (for this specific example) @@ -32,7 +32,7 @@ This guide will walk you through the process of setting up Zapier triggers for C - Connect your Slack account if you haven't already. - + - Add a new action step to your Zap. - Choose CrewAI+ as your action app and Kickoff as the Action Event @@ -41,8 +41,8 @@ This guide will walk you through the process of setting up Zapier triggers for C - - - Connect your CrewAI Enterprise account. + + - Connect your CrewAI AMP account. - Select the appropriate Crew for your workflow. @@ -51,8 +51,8 @@ This guide will walk you through the process of setting up Zapier triggers for C - Configure the inputs for the Crew using the data from the Slack message. - - - Add another action step to format the text output from CrewAI Enterprise. + + - Add another action step to format the text output from CrewAI AMP. - Use Zapier's formatting tools to convert the Markdown output to HTML. @@ -67,7 +67,7 @@ This guide will walk you through the process of setting up Zapier triggers for C - Add a final action step to send the formatted output via email. - Choose your preferred email service (e.g., Gmail, Outlook). - Configure the email details, including recipient, subject, and body. - - Insert the formatted CrewAI Enterprise output into the email body. + - Insert the formatted CrewAI AMP output into the email body. Zapier 7 @@ -97,8 +97,8 @@ This guide will walk you through the process of setting up Zapier triggers for C ## Tips for Success -- Ensure that your CrewAI Enterprise inputs are correctly mapped from the Slack message. +- Ensure that your CrewAI AMP inputs are correctly mapped from the Slack message. - Test your Zap thoroughly before turning it on to catch any potential issues. - Consider adding error handling steps to manage potential failures in the workflow. -By following these steps, you'll have successfully set up Zapier triggers for CrewAI Enterprise, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI Enterprise output. \ No newline at end of file +By following these steps, you'll have successfully set up Zapier triggers for CrewAI AMP, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI AMP output. diff --git a/docs/en/enterprise/integrations/asana.mdx b/docs/en/enterprise/integrations/asana.mdx index 34d989498..5e5a2ea46 100644 --- a/docs/en/enterprise/integrations/asana.mdx +++ b/docs/en/enterprise/integrations/asana.mdx @@ -13,7 +13,7 @@ Enable your agents to manage tasks, projects, and team coordination through Asan Before using the Asana integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - An Asana account with appropriate permissions - Connected your Asana account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Asana integration, ensure you have: ### 1. Connect Your Asana Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Asana** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for task and project management diff --git a/docs/en/enterprise/integrations/box.mdx b/docs/en/enterprise/integrations/box.mdx index de51cd532..472788505 100644 --- a/docs/en/enterprise/integrations/box.mdx +++ b/docs/en/enterprise/integrations/box.mdx @@ -13,7 +13,7 @@ Enable your agents to manage files, folders, and documents through Box. Upload f Before using the Box integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Box account with appropriate permissions - Connected your Box account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Box integration, ensure you have: ### 1. Connect Your Box Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Box** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for file and folder management diff --git a/docs/en/enterprise/integrations/clickup.mdx b/docs/en/enterprise/integrations/clickup.mdx index 7704fcc3e..a8549f72a 100644 --- a/docs/en/enterprise/integrations/clickup.mdx +++ b/docs/en/enterprise/integrations/clickup.mdx @@ -13,7 +13,7 @@ Enable your agents to manage tasks, projects, and productivity workflows through Before using the ClickUp integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A ClickUp account with appropriate permissions - Connected your ClickUp account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the ClickUp integration, ensure you have: ### 1. Connect Your ClickUp Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **ClickUp** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for task and project management diff --git a/docs/en/enterprise/integrations/github.mdx b/docs/en/enterprise/integrations/github.mdx index 5c30d330f..2e439b96c 100644 --- a/docs/en/enterprise/integrations/github.mdx +++ b/docs/en/enterprise/integrations/github.mdx @@ -13,7 +13,7 @@ Enable your agents to manage repositories, issues, and releases through GitHub. Before using the GitHub integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A GitHub account with appropriate repository permissions - Connected your GitHub account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the GitHub integration, ensure you have: ### 1. Connect Your GitHub Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **GitHub** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for repository and issue management diff --git a/docs/en/enterprise/integrations/gmail.mdx b/docs/en/enterprise/integrations/gmail.mdx index f2e870d7f..594ece22f 100644 --- a/docs/en/enterprise/integrations/gmail.mdx +++ b/docs/en/enterprise/integrations/gmail.mdx @@ -13,7 +13,7 @@ Enable your agents to manage emails, contacts, and drafts through Gmail. Send em Before using the Gmail integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Gmail account with appropriate permissions - Connected your Gmail account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Gmail integration, ensure you have: ### 1. Connect Your Gmail Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Gmail** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for email and contact management diff --git a/docs/en/enterprise/integrations/google_calendar.mdx b/docs/en/enterprise/integrations/google_calendar.mdx index a9bca43e8..01eb8a031 100644 --- a/docs/en/enterprise/integrations/google_calendar.mdx +++ b/docs/en/enterprise/integrations/google_calendar.mdx @@ -13,7 +13,7 @@ Enable your agents to manage calendar events, schedules, and availability throug Before using the Google Calendar integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Google account with Google Calendar access - Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Google Calendar integration, ensure you have: ### 1. Connect Your Google Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Google Calendar** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for calendar and contact access diff --git a/docs/en/enterprise/integrations/google_sheets.mdx b/docs/en/enterprise/integrations/google_sheets.mdx index 7ae78fd27..9ccad0d33 100644 --- a/docs/en/enterprise/integrations/google_sheets.mdx +++ b/docs/en/enterprise/integrations/google_sheets.mdx @@ -13,7 +13,7 @@ Enable your agents to manage spreadsheet data through Google Sheets. Read rows, Before using the Google Sheets integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Google account with Google Sheets access - Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) - Spreadsheets with proper column headers for data operations @@ -22,7 +22,7 @@ Before using the Google Sheets integration, ensure you have: ### 1. Connect Your Google Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Google Sheets** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for spreadsheet access diff --git a/docs/en/enterprise/integrations/hubspot.mdx b/docs/en/enterprise/integrations/hubspot.mdx index a97bd6dc5..e3aeda8ab 100644 --- a/docs/en/enterprise/integrations/hubspot.mdx +++ b/docs/en/enterprise/integrations/hubspot.mdx @@ -13,7 +13,7 @@ Enable your agents to manage companies and contacts within HubSpot. Create new r Before using the HubSpot integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription. +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription. - A HubSpot account with appropriate permissions. - Connected your HubSpot account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors). @@ -21,7 +21,7 @@ Before using the HubSpot integration, ensure you have: ### 1. Connect Your HubSpot Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors). +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors). 2. Find **HubSpot** in the Authentication Integrations section. 3. Click **Connect** and complete the OAuth flow. 4. Grant the necessary permissions for company and contact management. diff --git a/docs/en/enterprise/integrations/jira.mdx b/docs/en/enterprise/integrations/jira.mdx index 53827f300..1eedb8fb9 100644 --- a/docs/en/enterprise/integrations/jira.mdx +++ b/docs/en/enterprise/integrations/jira.mdx @@ -13,7 +13,7 @@ Enable your agents to manage issues, projects, and workflows through Jira. Creat Before using the Jira integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Jira account with appropriate project permissions - Connected your Jira account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Jira integration, ensure you have: ### 1. Connect Your Jira Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Jira** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for issue and project management diff --git a/docs/en/enterprise/integrations/linear.mdx b/docs/en/enterprise/integrations/linear.mdx index 844ca0a88..875c64808 100644 --- a/docs/en/enterprise/integrations/linear.mdx +++ b/docs/en/enterprise/integrations/linear.mdx @@ -13,7 +13,7 @@ Enable your agents to manage issues, projects, and development workflows through Before using the Linear integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Linear account with appropriate workspace permissions - Connected your Linear account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Linear integration, ensure you have: ### 1. Connect Your Linear Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Linear** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for issue and project management diff --git a/docs/en/enterprise/integrations/notion.mdx b/docs/en/enterprise/integrations/notion.mdx index 76a0a6c8b..5fb1afaf0 100644 --- a/docs/en/enterprise/integrations/notion.mdx +++ b/docs/en/enterprise/integrations/notion.mdx @@ -13,7 +13,7 @@ Enable your agents to manage pages, databases, and content through Notion. Creat Before using the Notion integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Notion account with appropriate workspace permissions - Connected your Notion account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors) @@ -21,7 +21,7 @@ Before using the Notion integration, ensure you have: ### 1. Connect Your Notion Account -1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors) +1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors) 2. Find **Notion** in the Authentication Integrations section 3. Click **Connect** and complete the OAuth flow 4. Grant the necessary permissions for page and database management diff --git a/docs/en/enterprise/integrations/salesforce.mdx b/docs/en/enterprise/integrations/salesforce.mdx index 6e3fb3e7b..38fec82d2 100644 --- a/docs/en/enterprise/integrations/salesforce.mdx +++ b/docs/en/enterprise/integrations/salesforce.mdx @@ -13,7 +13,7 @@ Enable your agents to manage customer relationships, sales processes, and data t Before using the Salesforce integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Salesforce account with appropriate permissions - Connected your Salesforce account through the [Integrations page](https://app.crewai.com/integrations) diff --git a/docs/en/enterprise/integrations/shopify.mdx b/docs/en/enterprise/integrations/shopify.mdx index 7d68c99a7..671570bfe 100644 --- a/docs/en/enterprise/integrations/shopify.mdx +++ b/docs/en/enterprise/integrations/shopify.mdx @@ -13,7 +13,7 @@ Enable your agents to manage e-commerce operations through Shopify. Handle custo Before using the Shopify integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Shopify store with appropriate admin permissions - Connected your Shopify store through the [Integrations page](https://app.crewai.com/integrations) diff --git a/docs/en/enterprise/integrations/slack.mdx b/docs/en/enterprise/integrations/slack.mdx index 78960f11d..ee1a17fc2 100644 --- a/docs/en/enterprise/integrations/slack.mdx +++ b/docs/en/enterprise/integrations/slack.mdx @@ -13,7 +13,7 @@ Enable your agents to manage team communication through Slack. Send messages, se Before using the Slack integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Slack workspace with appropriate permissions - Connected your Slack workspace through the [Integrations page](https://app.crewai.com/integrations) diff --git a/docs/en/enterprise/integrations/stripe.mdx b/docs/en/enterprise/integrations/stripe.mdx index d5b75a61a..cd25f1c5c 100644 --- a/docs/en/enterprise/integrations/stripe.mdx +++ b/docs/en/enterprise/integrations/stripe.mdx @@ -13,7 +13,7 @@ Enable your agents to manage payments, subscriptions, and customer billing throu Before using the Stripe integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Stripe account with appropriate API permissions - Connected your Stripe account through the [Integrations page](https://app.crewai.com/integrations) diff --git a/docs/en/enterprise/integrations/zendesk.mdx b/docs/en/enterprise/integrations/zendesk.mdx index eea4edfce..b7b025679 100644 --- a/docs/en/enterprise/integrations/zendesk.mdx +++ b/docs/en/enterprise/integrations/zendesk.mdx @@ -13,7 +13,7 @@ Enable your agents to manage customer support operations through Zendesk. Create Before using the Zendesk integration, ensure you have: -- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription +- A [CrewAI AMP](https://app.crewai.com) account with an active subscription - A Zendesk account with appropriate API permissions - Connected your Zendesk account through the [Integrations page](https://app.crewai.com/integrations) diff --git a/docs/en/enterprise/introduction.mdx b/docs/en/enterprise/introduction.mdx index 7fb894cc1..f01747d66 100644 --- a/docs/en/enterprise/introduction.mdx +++ b/docs/en/enterprise/introduction.mdx @@ -1,5 +1,5 @@ --- -title: "CrewAI Enterprise" +title: "CrewAI AMP" description: "Deploy, monitor, and scale your AI agent workflows" icon: "globe" mode: "wide" @@ -7,13 +7,13 @@ mode: "wide" ## Introduction -CrewAI Enterprise provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment. +CrewAI AMP(Agent Management Platform) provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment. - CrewAI Enterprise Dashboard + CrewAI AMP Dashboard -CrewAI Enterprise extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time. +CrewAI AMP extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time. ## Key Features diff --git a/docs/en/enterprise/resources/frequently-asked-questions.mdx b/docs/en/enterprise/resources/frequently-asked-questions.mdx index 2830b40ed..1253d7eef 100644 --- a/docs/en/enterprise/resources/frequently-asked-questions.mdx +++ b/docs/en/enterprise/resources/frequently-asked-questions.mdx @@ -1,6 +1,6 @@ --- title: FAQs -description: "Frequently asked questions about CrewAI Enterprise" +description: "Frequently asked questions about CrewAI AMP" icon: "circle-question" mode: "wide" --- @@ -129,15 +129,14 @@ mode: "wide" Here's a tutorial on how to consistently get structured outputs from your agents: - - - + diff --git a/docs/en/examples/cookbooks.mdx b/docs/en/examples/cookbooks.mdx index 124b86a2f..1bd7f016d 100644 --- a/docs/en/examples/cookbooks.mdx +++ b/docs/en/examples/cookbooks.mdx @@ -7,17 +7,43 @@ mode: "wide" ## Quickstarts & Demos - - - Interactive notebooks for hands-on exploration. + + + Coordinate multiple agents on shared tasks. Includes notebook with end-to-end collaboration pattern. + + + Teach agents to reason about multi-step plans before execution using the planning toolkit. + + + + Explore self-reflection loops, critique prompts, and structured thinking patterns. + + + + + + Apply task-level guardrails with retries, validation functions, and safe fallbacks. + + + + Connect CrewAI to Gemini with search grounding for factual, citation-rich outputs. + + + + Generate video recaps using Gemini multimodal LLM and CrewAI orchestration. + + + + - Feature demos and small projects showcasing specific CrewAI capabilities. + View all notebooks and feature demos showcasing specific CrewAI capabilities. + + + Missing a pattern? Drop a request in the community forum and we’ll expand the library. Use Cookbooks to learn a pattern quickly, then jump to Full Examples for production‑grade implementations. - - diff --git a/docs/en/index.mdx b/docs/en/index.mdx new file mode 100644 index 000000000..ff6466f27 --- /dev/null +++ b/docs/en/index.mdx @@ -0,0 +1,105 @@ +--- +title: "CrewAI Documentation" +description: "Build collaborative AI agents, crews, and flows — production ready from day one." +icon: "house" +mode: "wide" +--- + +
+ CrewAI +
+

Ship multi‑agent systems with confidence

+

+ Design agents, orchestrate crews, and automate flows with guardrails, memory, knowledge, and observability baked in. +

+
+ + + +
+ +
+ +## Get started + + + + Overview of CrewAI concepts, architecture, and what you can build with agents, crews, and flows. + + + Install via `uv`, configure API keys, and set up the CLI for local development. + + + Spin up your first crew in minutes. Learn the core runtime, project layout, and dev loop. + + + +## Build the basics + + + + Compose agents with tools, memory, knowledge, and structured outputs using Pydantic. Includes templates and best practices. + + + Orchestrate start/listen/router steps, manage state, persist execution, and resume long-running workflows. + + + Define sequential, hierarchical, or hybrid processes with guardrails, callbacks, and human-in-the-loop triggers. + + + +## Enterprise journey + + + + Manage environments, redeploy safely, and monitor live runs directly from the Enterprise console. + + + Connect Gmail, Slack, Salesforce, and more. Pass trigger payloads into crews and flows automatically. + + + Invite teammates, configure RBAC, and control access to production automations. + + + +## What’s new + + + + Unified overview for Gmail, Drive, Outlook, Teams, OneDrive, HubSpot, and more — now with sample payloads and crews. + + + Call existing CrewAI automations or Amazon Bedrock Agents directly from your crews using the updated integration toolkit. + + + + + Browse the examples and cookbooks for end-to-end reference implementations across agents, flows, and enterprise automations. + + +## Stay connected + + + + If CrewAI helps you ship faster, give us a star and share your builds with the community. + + + Ask questions, showcase workflows, and request features alongside other builders. + + diff --git a/docs/en/installation.mdx b/docs/en/installation.mdx index 37fbb8063..96f04d853 100644 --- a/docs/en/installation.mdx +++ b/docs/en/installation.mdx @@ -9,14 +9,12 @@ mode: "wide" Watch this video tutorial for a step-by-step demonstration of the installation process: ## Text Tutorial @@ -169,7 +167,7 @@ We recommend using the `YAML` template scaffolding for a structured approach to For teams and organizations, CrewAI offers enterprise deployment options that eliminate setup complexity: -### CrewAI Enterprise (SaaS) +### CrewAI AMP (SaaS) - Zero installation required - just sign up for free at [app.crewai.com](https://app.crewai.com) - Automatic updates and maintenance - Managed infrastructure and scaling diff --git a/docs/en/learn/human-input-on-execution.mdx b/docs/en/learn/human-input-on-execution.mdx index b8b19f013..c4414bff0 100644 --- a/docs/en/learn/human-input-on-execution.mdx +++ b/docs/en/learn/human-input-on-execution.mdx @@ -1,18 +1,18 @@ --- title: Human Input on Execution description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools. -icon: user-check +icon: user-plus mode: "wide" --- ## Human input in agent execution -Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. +Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively. ## Using human input with CrewAI -To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. +To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output. ### Example: @@ -96,4 +96,4 @@ result = crew.kickoff() print("######################") print(result) -``` \ No newline at end of file +``` diff --git a/docs/en/learn/llm-selection-guide.mdx b/docs/en/learn/llm-selection-guide.mdx index 3250178fc..0c305aec8 100644 --- a/docs/en/learn/llm-selection-guide.mdx +++ b/docs/en/learn/llm-selection-guide.mdx @@ -44,7 +44,7 @@ The most critical step in LLM selection is understanding what your task actually - **Creative Tasks** demand a different type of cognitive capability focused on generating novel, engaging, and contextually appropriate content. This includes storytelling, marketing copy creation, and creative problem-solving. The model needs to understand nuance, tone, and audience while producing content that feels authentic and engaging rather than formulaic. - + - **Structured Data** tasks require precision and consistency in format adherence. When working with JSON, XML, or database formats, the model must reliably produce syntactically correct output that can be programmatically processed. These tasks often have strict validation requirements and little tolerance for format errors, making reliability more important than creativity. @@ -52,7 +52,7 @@ The most critical step in LLM selection is understanding what your task actually - **Technical Content** sits between structured data and creative content, requiring both precision and clarity. Documentation, code generation, and technical analysis need to be accurate and comprehensive while remaining accessible to the intended audience. The model must understand complex technical concepts and communicate them effectively. - + - **Short Context** scenarios involve focused, immediate tasks where the model needs to process limited information quickly. These are often transactional interactions where speed and efficiency matter more than deep understanding. The model doesn't need to maintain extensive conversation history or process large documents. @@ -74,7 +74,7 @@ Understanding model capabilities requires looking beyond marketing claims and be However, reasoning models often come with trade-offs in terms of speed and cost. They may also be less suitable for creative tasks or simple operations where their sophisticated reasoning capabilities aren't needed. Consider these models when your tasks involve genuine complexity that benefits from systematic, step-by-step analysis. - + General purpose models offer the most balanced approach to LLM selection, providing solid performance across a wide range of tasks without extreme specialization in any particular area. These models are trained on diverse datasets and optimized for versatility rather than peak performance in specific domains. @@ -82,7 +82,7 @@ Understanding model capabilities requires looking beyond marketing claims and be While general purpose models may not achieve the peak performance of specialized alternatives in specific domains, they offer operational simplicity and reduced complexity in model management. They're often the best starting point for new projects, allowing teams to understand their specific needs before potentially optimizing with more specialized models. - + Fast and efficient models prioritize speed, cost-effectiveness, and resource efficiency over sophisticated reasoning capabilities. These models are optimized for high-throughput scenarios where quick responses and low operational costs are more important than nuanced understanding or complex reasoning. @@ -90,7 +90,7 @@ Understanding model capabilities requires looking beyond marketing claims and be The key consideration with efficient models is ensuring that their capabilities align with your task requirements. While they can handle many routine operations effectively, they may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation. They're best used for well-defined, routine operations where speed and cost matter more than sophistication. - + Creative models are specifically optimized for content generation, writing quality, and creative thinking tasks. These models typically excel at understanding nuance, tone, and style while producing engaging, contextually appropriate content that feels natural and authentic. @@ -98,7 +98,7 @@ Understanding model capabilities requires looking beyond marketing claims and be When selecting creative models, consider not just their ability to generate text, but their understanding of audience, context, and purpose. The best creative models can adapt their output to match specific brand voices, target different audience segments, and maintain consistency across extended content pieces. - + Open source models offer unique advantages in terms of cost control, customization potential, data privacy, and deployment flexibility. These models can be run locally or on private infrastructure, providing complete control over data handling and model behavior. @@ -151,7 +151,7 @@ content_writer = Agent( ) data_processor = Agent( - role="Data Analysis Specialist", + role="Data Analysis Specialist", goal="Extract and organize key data points from research sources", backstory="Detail-oriented analyst focused on accuracy and efficiency", llm=processing_llm, # Fast, cost-effective model for routine tasks @@ -178,7 +178,7 @@ The key to successful multi-model implementation is understanding how different Cost considerations are particularly important for manager LLMs since they're involved in every operation. The model needs to provide sufficient capability for effective coordination while remaining cost-effective for frequent use. This often means finding models that offer good reasoning capabilities without the premium pricing of the most sophisticated options. - + Function calling LLMs handle tool usage across all agents, making them critical for crews that rely heavily on external tools and APIs. These models need to excel at understanding tool capabilities, extracting parameters accurately, and handling tool responses effectively. @@ -186,7 +186,7 @@ The key to successful multi-model implementation is understanding how different Many teams find that specialized function calling models or general purpose models with strong tool support work better than creative or reasoning-focused models for this role. The key is ensuring that the model can reliably bridge the gap between natural language instructions and structured tool calls. - + Individual agents can override crew-level LLM settings when their specific needs differ significantly from the general crew requirements. This capability allows for fine-tuned optimization while maintaining operational simplicity for most agents. @@ -210,7 +210,7 @@ Effective task definition is often more important than model selection in determ Common mistakes include being too vague about objectives, failing to provide necessary context, setting unclear success criteria, or combining multiple unrelated tasks into a single description. The goal is to provide enough information for the agent to succeed while maintaining focus on a single, clear objective. - + Expected output guidelines serve as a contract between the task definition and the agent, clearly specifying what the deliverable should look like and how it will be evaluated. These guidelines should describe both the format and structure needed, as well as the key elements that must be included for the output to be considered complete. @@ -230,7 +230,7 @@ Effective task definition is often more important than model selection in determ Sequential dependencies work best when there's a clear logical progression from one task to another and when the output of one task genuinely improves the quality or feasibility of subsequent tasks. However, they can create bottlenecks if not managed carefully, so it's important to identify which dependencies are truly necessary versus those that are merely convenient. - + Parallel execution becomes valuable when tasks are independent of each other, time efficiency is important, or different expertise areas are involved that don't require coordination. This approach can significantly reduce overall execution time while allowing specialized agents to work on their areas of strength simultaneously. @@ -286,10 +286,10 @@ domain_expert = Agent( role="B2B SaaS Marketing Strategist", goal="Develop comprehensive go-to-market strategies for enterprise software", backstory=""" - You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO. - You understand the nuances of enterprise sales cycles, the importance of product-market - fit in different verticals, and how to balance growth metrics with unit economics. - You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving + You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO. + You understand the nuances of enterprise sales cycles, the importance of product-market + fit in different verticals, and how to balance growth metrics with unit economics. + You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving you perspective on both established and disruptive go-to-market strategies. """, llm=LLM(model="claude-3-5-sonnet", temperature=0.3) # Balanced creativity with domain knowledge @@ -317,9 +317,9 @@ tech_writer = Agent( role="API Documentation Specialist", # Specific role for clear LLM requirements goal="Create comprehensive, developer-friendly API documentation", backstory=""" - You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints, - and SDK integration guides. You've worked with developer tools companies and - understand what developers need: clear examples, comprehensive error handling, + You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints, + and SDK integration guides. You've worked with developer tools companies and + understand what developers need: clear examples, comprehensive error handling, and practical use cases. You prioritize accuracy and usability over marketing fluff. """, llm=LLM( @@ -327,13 +327,13 @@ tech_writer = Agent( temperature=0.1 # Low temperature for accuracy ), tools=[code_analyzer_tool, api_scanner_tool], - verbose=True + verbose=True ) ``` **Alignment Checklist:** - ✅ **Role Specificity**: Clear domain and responsibilities -- ✅ **LLM Match**: Model strengths align with role requirements +- ✅ **LLM Match**: Model strengths align with role requirements - ✅ **Backstory Depth**: Provides domain context the LLM can leverage - ✅ **Tool Integration**: Tools support the agent's specialized function - ✅ **Parameter Tuning**: Temperature and settings optimize for role needs @@ -351,26 +351,26 @@ Rather than repeating the strategic framework, here's a tactical checklist for i - Which agents handle the most complex reasoning tasks? - Which agents primarily do data processing or formatting? - Are any agents heavily tool-dependent? - + **Action**: Document current agent roles and identify optimization opportunities. - + **Set Your Baseline:** ```python # Start with a reliable default for the crew default_crew_llm = LLM(model="gpt-4o-mini") # Cost-effective baseline - + crew = Crew( agents=[...], tasks=[...], memory=True ) ``` - + **Action**: Establish your crew's default LLM before optimizing individual agents. - + **Identify and Upgrade Key Agents:** ```python @@ -380,25 +380,25 @@ Rather than repeating the strategic framework, here's a tactical checklist for i llm=LLM(model="gemini-2.5-flash-preview-05-20"), # Premium for coordination # ... rest of config ) - - # Creative or customer-facing agents + + # Creative or customer-facing agents content_agent = Agent( role="Content Creator", llm=LLM(model="claude-3-5-sonnet"), # Best for writing # ... rest of config ) ``` - + **Action**: Upgrade 20% of your agents that handle 80% of the complexity. - + **Once you deploy your agents to production:** - - Use [CrewAI Enterprise platform](https://app.crewai.com) to A/B test your model selections + - Use [CrewAI AMP platform](https://app.crewai.com) to A/B test your model selections - Run multiple iterations with real inputs to measure consistency and performance - Compare cost vs. performance across your optimized setup - Share results with your team for collaborative decision-making - + **Action**: Replace guesswork with data-driven validation using the testing platform. @@ -413,7 +413,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i However, reasoning models often come with higher costs and slower response times, so they're best reserved for tasks where their sophisticated capabilities provide genuine value rather than being used for simple operations that don't require complex reasoning. - + Creative models become valuable when content generation is the primary output and the quality, style, and engagement level of that content directly impact success. These models excel when writing quality and style matter significantly, creative ideation or brainstorming is needed, or brand voice and tone are important considerations. @@ -421,7 +421,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i Creative models may be less suitable for technical or analytical tasks where precision and factual accuracy are more important than engagement and style. They're best used when the creative and communicative aspects of the output are primary success factors. - + Efficient models are ideal for high-frequency, routine operations where speed and cost optimization are priorities. These models work best when tasks have clear, well-defined parameters and don't require sophisticated reasoning or creative capabilities. @@ -429,7 +429,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i The key with efficient models is ensuring that their capabilities align with task requirements. They can handle many routine operations effectively but may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation. - + Open source models become attractive when budget constraints are significant, data privacy requirements exist, customization needs are important, or local deployment is required for operational or compliance reasons. @@ -451,12 +451,12 @@ Rather than repeating the strategic framework, here's a tactical checklist for i ```python # Strategic agent gets premium model manager = Agent(role="Strategy Manager", llm=LLM(model="gpt-4o")) - - # Processing agent gets efficient model + + # Processing agent gets efficient model processor = Agent(role="Data Processor", llm=LLM(model="gpt-4o-mini")) ``` - + **The Problem**: Not understanding how CrewAI's LLM hierarchy works - crew LLM, manager LLM, and agent LLM settings can conflict or be poorly coordinated. @@ -470,12 +470,12 @@ Rather than repeating the strategic framework, here's a tactical checklist for i manager_llm=LLM(model="gpt-4o"), # For crew coordination process=Process.hierarchical # When using manager_llm ) - + # Agents inherit crew LLM unless specifically overridden agent1 = Agent(llm=LLM(model="claude-3-5-sonnet")) # Override for specific needs ``` - + **The Problem**: Choosing models based on general capabilities while ignoring function calling performance for tool-heavy CrewAI workflows. @@ -493,7 +493,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i ) ``` - + **The Problem**: Making complex model selection decisions based on theoretical performance without validating with actual CrewAI workflows and tasks. @@ -503,7 +503,7 @@ Rather than repeating the strategic framework, here's a tactical checklist for i ```python # Start with this crew = Crew(agents=[...], tasks=[...], llm=LLM(model="gpt-4o-mini")) - + # Test performance, then optimize specific agents as needed # Use Enterprise platform testing to validate improvements ``` @@ -541,7 +541,7 @@ Focus on understanding your requirements first, then select models that best mat ### Enterprise-Grade Model Validation -For teams serious about optimizing their LLM selection, the **CrewAI Enterprise platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy. +For teams serious about optimizing their LLM selection, the **CrewAI AMP platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy. ![Enterprise Testing Interface](/images/enterprise/enterprise-testing.png) @@ -571,23 +571,23 @@ The Enterprise platform transforms model selection from guesswork into a data-dr Choose models based on what the task actually requires, not theoretical capabilities or general reputation. - + Align model strengths with agent roles and responsibilities for optimal performance. - + Maintain coherent model selection strategy across related components and workflows. - + Validate choices through real-world usage rather than benchmarks alone. - + Start simple and optimize based on actual performance and needs. - + Balance performance requirements with cost and complexity constraints. @@ -614,7 +614,7 @@ These tables/metrics showcase selected leading models in each category and are n **Best for Manager LLMs and Complex Analysis** - + | Model | Intelligence Score | Cost ($/M tokens) | Speed | Best Use in CrewAI | |:------|:------------------|:------------------|:------|:------------------| | **o3** | 70 | $17.50 | Fast | Manager LLM for complex multi-agent coordination | @@ -625,10 +625,10 @@ These tables/metrics showcase selected leading models in each category and are n These models excel at multi-step reasoning and are ideal for agents that need to develop strategies, coordinate other agents, or analyze complex information. - + **Best for Development and Tool-Heavy Workflows** - + | Model | Coding Performance | Tool Use Score | Cost ($/M tokens) | Best Use in CrewAI | |:------|:------------------|:---------------|:------------------|:------------------| | **Claude 4 Sonnet** | Excellent | 72.7% | $6.00 | Primary coding agent, technical documentation | @@ -639,10 +639,10 @@ These tables/metrics showcase selected leading models in each category and are n These models are optimized for code generation, debugging, and technical problem-solving, making them ideal for development-focused crews. - + **Best for High-Throughput and Real-Time Applications** - + | Model | Speed (tokens/s) | Latency (TTFT) | Cost ($/M tokens) | Best Use in CrewAI | |:------|:-----------------|:---------------|:------------------|:------------------| | **Llama 4 Scout** | 2,600 | 0.33s | $0.27 | High-volume processing agents | @@ -653,10 +653,10 @@ These tables/metrics showcase selected leading models in each category and are n These models prioritize speed and efficiency, perfect for agents handling routine operations or requiring quick responses. **Pro tip**: Pairing these models with fast inference providers like Groq can achieve even better performance, especially for open-source models like Llama. - + **Best All-Around Models for General Crews** - + | Model | Overall Score | Versatility | Cost ($/M tokens) | Best Use in CrewAI | |:------|:--------------|:------------|:------------------|:------------------| | **GPT-4.1** | 53 | Excellent | $3.50 | General-purpose crew LLM | @@ -677,19 +677,19 @@ These tables/metrics showcase selected leading models in each category and are n **Strategy**: Implement a multi-model approach where premium models handle strategic thinking while efficient models handle routine operations. - + **When budget is a primary constraint**: Focus on models like **DeepSeek R1**, **Llama 4 Scout**, or **Gemini 2.0 Flash**. These provide strong performance at significantly lower costs. **Strategy**: Use cost-effective models for most agents, reserving premium models only for the most critical decision-making roles. - + **For specific domain expertise**: Choose models optimized for your primary use case. **Claude 4** series for coding, **Gemini 2.5 Pro** for research, **Llama 405B** for function calling. **Strategy**: Select models based on your crew's primary function, ensuring the core capability aligns with model strengths. - + **For data-sensitive operations**: Consider open-source models like **Llama 4** series, **DeepSeek V3**, or **Qwen3** that can be deployed locally while maintaining competitive performance. @@ -715,16 +715,16 @@ These tables/metrics showcase selected leading models in each category and are n Begin with well-established models like **GPT-4.1**, **Claude 3.7 Sonnet**, or **Gemini 2.0 Flash** that offer good performance across multiple dimensions and have extensive real-world validation. - + Determine if your crew has specific requirements (coding, reasoning, speed) that would benefit from specialized models like **Claude 4 Sonnet** for development or **o3** for complex analysis. For speed-critical applications, consider fast inference providers like **Groq** alongside model selection. - + Use different models for different agents based on their roles. High-capability models for managers and complex tasks, efficient models for routine operations. - + Track performance metrics relevant to your use case and be prepared to adjust model selections as new models are released or pricing changes. - \ No newline at end of file + diff --git a/docs/en/learn/using-annotations.mdx b/docs/en/learn/using-annotations.mdx index 25a2e01b1..0cbc6ed76 100644 --- a/docs/en/learn/using-annotations.mdx +++ b/docs/en/learn/using-annotations.mdx @@ -42,6 +42,15 @@ class LinkedinProfileCrew(): The `@CrewBase` annotation is used to decorate the main crew class. This class typically contains configurations and methods for creating agents, tasks, and the crew itself. + +`@CrewBase` does more than register the class: + +- **Configuration bootstrapping:** looks for `agents_config` and `tasks_config` (defaulting to `config/agents.yaml` and `config/tasks.yaml`) beside the class file, loads them at instantiation, and safely falls back to empty dicts if files are missing. +- **Decorator orchestration:** keeps memoized references to every method marked with `@agent`, `@task`, `@before_kickoff`, or `@after_kickoff` so they are instantiated once per crew and executed in declaration order. +- **Hook wiring:** automatically attaches the preserved kickoff hooks to the `Crew` object returned by the `@crew` method, making them run before and after `.kickoff()`. +- **MCP integration:** when the class defines `mcp_server_params`, `get_mcp_tools()` lazily starts an MCP server adapter, hydrates the declared tools, and an internal after-kickoff hook stops the adapter. See [MCP overview](/en/mcp/overview) for adapter configuration details. + + ### 2. Tool Definition ```python @@ -100,7 +109,7 @@ def crew(self) -> Crew: process=Process.sequential, verbose=True ) -``` +``` The `@crew` annotation is used to decorate the method that creates and returns the `Crew` object. This method assembles all the components (agents and tasks) into a functional crew. @@ -139,4 +148,4 @@ Note how the `llm` and `tools` in the YAML file correspond to the methods decora - **Flexibility**: Design your crew to be flexible by allowing easy addition or removal of agents and tasks. - **YAML-Code Correspondence**: Ensure that the names and structures in your YAML files correspond correctly to the decorated methods in your Python code. -By following these guidelines and properly using annotations, you can create well-structured and maintainable crews using the CrewAI framework. \ No newline at end of file +By following these guidelines and properly using annotations, you can create well-structured and maintainable crews using the CrewAI framework. diff --git a/docs/en/mcp/overview.mdx b/docs/en/mcp/overview.mdx index bd61a16f7..14d7ac48c 100644 --- a/docs/en/mcp/overview.mdx +++ b/docs/en/mcp/overview.mdx @@ -21,14 +21,12 @@ We currently support the following transport mechanisms: Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI: ## Installation @@ -175,6 +173,16 @@ class CrewWithMCP: # ... rest of your crew setup ... ``` + +When a crew class is decorated with `@CrewBase`, the adapter lifecycle is managed for you: + +- The first call to `get_mcp_tools()` lazily creates a shared `MCPServerAdapter` that is reused by every agent in the crew. +- The adapter automatically shuts down after `.kickoff()` completes thanks to an implicit after-kickoff hook injected by `@CrewBase`, so no manual cleanup is required. +- If `mcp_server_params` is not defined, `get_mcp_tools()` simply returns an empty list, allowing the same code paths to run with or without MCP configured. + +This makes it safe to call `get_mcp_tools()` from multiple agent methods or selectively enable MCP per environment. + + ### Connection Timeout Configuration You can configure the connection timeout for MCP servers by setting the `mcp_connect_timeout` class attribute. If no timeout is specified, it defaults to 30 seconds. diff --git a/docs/en/observability/neatlogs.mdx b/docs/en/observability/neatlogs.mdx index 04ebfeda3..24b17cfce 100644 --- a/docs/en/observability/neatlogs.mdx +++ b/docs/en/observability/neatlogs.mdx @@ -89,10 +89,9 @@ All with just two lines of code. ### 🔍 Full Demo (4 min) @@ -361,7 +359,7 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre icon="rocket" href="http://app.crewai.com" > - Get started with CrewAI Enterprise and deploy your crew in a production environment with just a few clicks. + Get started with CrewAI AMP and deploy your crew in a production environment with just a few clicks. + + Invoke live CrewAI Platform automations, pass custom inputs, and poll for results directly from your agent. + + + + Call Amazon Bedrock Agents from your crews, reuse AWS guardrails, and stream responses back into the workflow. + + + +## **Common Use Cases** + +- **Chain automations**: Kick off an existing CrewAI deployment from within another crew or flow +- **Enterprise hand-off**: Route tasks to Bedrock Agents that already encapsulate company logic and guardrails +- **Hybrid workflows**: Combine CrewAI reasoning with downstream systems that expose their own agent APIs +- **Long-running jobs**: Poll external automations and merge the final results back into the current run + +## **Quick Start Example** + +```python +from crewai import Agent, Task, Crew +from crewai_tools import InvokeCrewAIAutomationTool +from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool + +# External automation +analysis_automation = InvokeCrewAIAutomationTool( + crew_api_url="https://analysis-crew.acme.crewai.com", + crew_bearer_token="YOUR_BEARER_TOKEN", + crew_name="Analysis Automation", + crew_description="Runs the production-grade analysis pipeline", +) + +# Managed agent on Bedrock +knowledge_router = BedrockInvokeAgentTool( + agent_id="bedrock-agent-id", + agent_alias_id="prod", +) + +automation_strategist = Agent( + role="Automation Strategist", + goal="Orchestrate external automations and summarise their output", + backstory="You coordinate enterprise workflows and know when to delegate tasks to specialised services.", + tools=[analysis_automation, knowledge_router], + verbose=True, +) + +execute_playbook = Task( + description="Run the analysis automation and ask the Bedrock agent for executive talking points.", + agent=automation_strategist, +) + +Crew(agents=[automation_strategist], tasks=[execute_playbook]).kickoff() +``` + +## **Best Practices** + +- **Secure credentials**: Store API keys and bearer tokens in environment variables or a secrets manager +- **Plan for latency**: External automations may take longer—set appropriate polling intervals and timeouts +- **Reuse sessions**: Bedrock Agents support session IDs so you can maintain context across multiple tool calls +- **Validate responses**: Normalise remote output (JSON, text, status codes) before forwarding it to downstream tasks +- **Monitor usage**: Track audit logs in CrewAI Platform or AWS CloudWatch to stay ahead of quota limits and failures diff --git a/docs/enterprise-api.base.yaml b/docs/enterprise-api.base.yaml index 457ea22bb..c4a4c31ab 100644 --- a/docs/enterprise-api.base.yaml +++ b/docs/enterprise-api.base.yaml @@ -1,40 +1,40 @@ openapi: 3.0.3 info: - title: CrewAI Enterprise API + title: CrewAI AMP API description: | - REST API for interacting with your deployed CrewAI crews on CrewAI Enterprise. - + REST API for interacting with your deployed CrewAI crews on CrewAI AMP. + ## Getting Started - - 1. **Find your crew URL**: Get your unique crew URL from the CrewAI Enterprise dashboard + + 1. **Find your crew URL**: Get your unique crew URL from the CrewAI AMP dashboard 2. **Copy examples**: Use the code examples from each endpoint page as templates 3. **Replace placeholders**: Update URLs and tokens with your actual values 4. **Test with your tools**: Use cURL, Postman, or your preferred API client - + ## Authentication - + All API requests require a bearer token for authentication. There are two types of tokens: - + - **Bearer Token**: Organization-level token for full crew operations - **User Bearer Token**: User-scoped token for individual access with limited permissions - - You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard. - + + You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AMP dashboard. + ## Reference Documentation - + This documentation provides comprehensive examples for each endpoint: - + - **Request formats** with all required and optional parameters - **Response examples** for success and error scenarios - **Code samples** in multiple programming languages - **Authentication patterns** with proper Bearer token usage - + Copy the examples and customize them with your actual crew URL and authentication tokens. - + ## Workflow - + 1. **Discover inputs** using `GET /inputs` - 2. **Start execution** using `POST /kickoff` + 2. **Start execution** using `POST /kickoff` 3. **Monitor progress** using `GET /status/{kickoff_id}` version: 1.0.0 contact: @@ -43,7 +43,7 @@ info: url: https://crewai.com servers: - url: https://your-actual-crew-name.crewai.com - description: Replace with your actual deployed crew URL from the CrewAI Enterprise dashboard + description: Replace with your actual deployed crew URL from the CrewAI AMP dashboard - url: https://my-travel-crew.crewai.com description: Example travel planning crew (replace with your URL) - url: https://content-creation-crew.crewai.com @@ -58,7 +58,7 @@ paths: summary: Get Required Inputs description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - + Retrieves the list of all required input parameters that your crew expects for execution. Use this endpoint to discover what inputs you need to provide when starting a crew execution. operationId: getRequiredInputs @@ -82,7 +82,7 @@ paths: value: inputs: ["budget", "interests", "duration", "age"] outreach_crew: - summary: Outreach crew inputs + summary: Outreach crew inputs value: inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"] '401': @@ -97,10 +97,10 @@ paths: summary: Start Crew Execution description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - - Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used + + Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used to track the execution progress and retrieve results. - + Crew executions can take anywhere from seconds to minutes depending on their complexity. Consider using webhooks for real-time notifications or implement polling with the status endpoint. operationId: startCrewExecution @@ -152,7 +152,7 @@ paths: inputs: budget: "1000 USD" interests: "games, tech, ai, relaxing hikes, amazing food" - duration: "7 days" + duration: "7 days" age: "35" meta: requestId: "travel-req-123" @@ -204,9 +204,9 @@ paths: summary: Get Execution Status description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - + Retrieves the current status and results of a crew execution using its kickoff ID. - + The response structure varies depending on the execution state: - **running**: Execution in progress with current task info - **completed**: Execution finished with full results @@ -283,9 +283,9 @@ components: scheme: bearer description: | **📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.* - - Use your actual Bearer Token or User Bearer Token from the CrewAI Enterprise dashboard for real API calls. - + + Use your actual Bearer Token or User Bearer Token from the CrewAI AMP dashboard for real API calls. + **Bearer Token**: Organization-level access for full crew operations **User Bearer Token**: User-scoped access with limited permissions @@ -309,7 +309,7 @@ components: description: Number of completed tasks example: 1 total_tasks: - type: integer + type: integer description: Total number of tasks in the crew example: 3 @@ -430,5 +430,5 @@ components: schema: $ref: '#/components/schemas/Error' example: - error: "Internal Server Error" - message: "An unexpected error occurred" \ No newline at end of file + error: "Internal Server Error" + message: "An unexpected error occurred" diff --git a/docs/enterprise-api.en.yaml b/docs/enterprise-api.en.yaml index 645601eae..c4a4c31ab 100644 --- a/docs/enterprise-api.en.yaml +++ b/docs/enterprise-api.en.yaml @@ -1,40 +1,40 @@ openapi: 3.0.3 info: - title: CrewAI Enterprise API + title: CrewAI AMP API description: | - REST API for interacting with your deployed CrewAI crews on CrewAI Enterprise. - + REST API for interacting with your deployed CrewAI crews on CrewAI AMP. + ## Getting Started - - 1. **Find your crew URL**: Get your unique crew URL from the CrewAI Enterprise dashboard + + 1. **Find your crew URL**: Get your unique crew URL from the CrewAI AMP dashboard 2. **Copy examples**: Use the code examples from each endpoint page as templates 3. **Replace placeholders**: Update URLs and tokens with your actual values 4. **Test with your tools**: Use cURL, Postman, or your preferred API client - + ## Authentication - + All API requests require a bearer token for authentication. There are two types of tokens: - + - **Bearer Token**: Organization-level token for full crew operations - **User Bearer Token**: User-scoped token for individual access with limited permissions - - You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard. - + + You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI AMP dashboard. + ## Reference Documentation - + This documentation provides comprehensive examples for each endpoint: - + - **Request formats** with all required and optional parameters - **Response examples** for success and error scenarios - **Code samples** in multiple programming languages - **Authentication patterns** with proper Bearer token usage - + Copy the examples and customize them with your actual crew URL and authentication tokens. - + ## Workflow - + 1. **Discover inputs** using `GET /inputs` - 2. **Start execution** using `POST /kickoff` + 2. **Start execution** using `POST /kickoff` 3. **Monitor progress** using `GET /status/{kickoff_id}` version: 1.0.0 contact: @@ -43,7 +43,7 @@ info: url: https://crewai.com servers: - url: https://your-actual-crew-name.crewai.com - description: Replace with your actual deployed crew URL from the CrewAI Enterprise dashboard + description: Replace with your actual deployed crew URL from the CrewAI AMP dashboard - url: https://my-travel-crew.crewai.com description: Example travel planning crew (replace with your URL) - url: https://content-creation-crew.crewai.com @@ -58,7 +58,7 @@ paths: summary: Get Required Inputs description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - + Retrieves the list of all required input parameters that your crew expects for execution. Use this endpoint to discover what inputs you need to provide when starting a crew execution. operationId: getRequiredInputs @@ -82,7 +82,7 @@ paths: value: inputs: ["budget", "interests", "duration", "age"] outreach_crew: - summary: Outreach crew inputs + summary: Outreach crew inputs value: inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"] '401': @@ -97,10 +97,10 @@ paths: summary: Start Crew Execution description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - - Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used + + Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used to track the execution progress and retrieve results. - + Crew executions can take anywhere from seconds to minutes depending on their complexity. Consider using webhooks for real-time notifications or implement polling with the status endpoint. operationId: startCrewExecution @@ -152,7 +152,7 @@ paths: inputs: budget: "1000 USD" interests: "games, tech, ai, relaxing hikes, amazing food" - duration: "7 days" + duration: "7 days" age: "35" meta: requestId: "travel-req-123" @@ -204,9 +204,9 @@ paths: summary: Get Execution Status description: | **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.* - + Retrieves the current status and results of a crew execution using its kickoff ID. - + The response structure varies depending on the execution state: - **running**: Execution in progress with current task info - **completed**: Execution finished with full results @@ -283,12 +283,12 @@ components: scheme: bearer description: | **📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.* - - Use your actual Bearer Token or User Bearer Token from the CrewAI Enterprise dashboard for real API calls. - + + Use your actual Bearer Token or User Bearer Token from the CrewAI AMP dashboard for real API calls. + **Bearer Token**: Organization-level access for full crew operations **User Bearer Token**: User-scoped access with limited permissions - + schemas: ExecutionRunning: type: object @@ -309,10 +309,10 @@ components: description: Number of completed tasks example: 1 total_tasks: - type: integer + type: integer description: Total number of tasks in the crew example: 3 - + ExecutionCompleted: type: object properties: @@ -335,7 +335,7 @@ components: type: number description: Total execution time in seconds example: 108.5 - + ExecutionError: type: object properties: @@ -351,7 +351,7 @@ components: type: number description: Time until error occurred in seconds example: 23.1 - + TaskResult: type: object properties: @@ -371,7 +371,7 @@ components: type: number description: Time taken to execute this task in seconds example: 45.2 - + Error: type: object properties: @@ -383,7 +383,7 @@ components: type: string description: Detailed error message example: "Invalid bearer token provided" - + ValidationError: type: object properties: @@ -401,7 +401,7 @@ components: items: type: string example: ["budget", "interests"] - + responses: UnauthorizedError: description: Authentication failed - check your bearer token @@ -412,7 +412,7 @@ components: example: error: "Unauthorized" message: "Invalid or missing bearer token" - + NotFoundError: description: Resource not found content: @@ -422,7 +422,7 @@ components: example: error: "Not Found" message: "The requested resource was not found" - + ServerError: description: Internal server error content: @@ -430,6 +430,5 @@ components: schema: $ref: '#/components/schemas/Error' example: - error: "Internal Server Error" + error: "Internal Server Error" message: "An unexpected error occurred" - diff --git a/docs/enterprise-api.ko.yaml b/docs/enterprise-api.ko.yaml index da0b6c23f..d2509e498 100644 --- a/docs/enterprise-api.ko.yaml +++ b/docs/enterprise-api.ko.yaml @@ -2,8 +2,8 @@ openapi: 3.0.3 info: title: CrewAI 엔터프라이즈 API description: | - CrewAI Enterprise에 배포된 crew와 상호작용하기 위한 REST API입니다. - + CrewAI AMP에 배포된 crew와 상호작용하기 위한 REST API입니다. + ## 시작하기 1. **Crew URL 확인**: 대시보드에서 고유한 crew URL을 확인하세요 2. **예제 복사**: 각 엔드포인트의 예제를 템플릿으로 사용하세요 @@ -25,7 +25,7 @@ paths: summary: 필요 입력값 조회 description: | **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.* - + 실행에 필요한 입력 파라미터 목록을 반환합니다. operationId: getRequiredInputs responses: @@ -52,7 +52,7 @@ paths: summary: Crew 실행 시작 description: | **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.* - + 제공된 입력으로 새로운 실행을 시작하고 kickoff ID를 반환합니다. operationId: startCrewExecution requestBody: @@ -89,7 +89,7 @@ paths: summary: 실행 상태 조회 description: | **📋 참조 예제만 제공** - *요청 형식을 보여줍니다. 실제 호출은 cURL 예제를 복사해 URL과 토큰을 교체하세요.* - + kickoff ID로 실행 상태와 결과를 조회합니다. operationId: getExecutionStatus parameters: @@ -228,4 +228,3 @@ components: application/json: schema: $ref: '#/components/schemas/Error' - diff --git a/docs/enterprise-api.pt-BR.yaml b/docs/enterprise-api.pt-BR.yaml index 77c9e4644..4629e834c 100644 --- a/docs/enterprise-api.pt-BR.yaml +++ b/docs/enterprise-api.pt-BR.yaml @@ -1,40 +1,40 @@ openapi: 3.0.3 info: - title: CrewAI Enterprise API + title: CrewAI AMP API description: | - REST API para interagir com suas crews implantadas no CrewAI Enterprise. - + REST API para interagir com suas crews implantadas no CrewAI AMP. + ## Introdução - - 1. **Encontre a URL da sua crew**: Obtenha sua URL única no painel do CrewAI Enterprise + + 1. **Encontre a URL da sua crew**: Obtenha sua URL única no painel do CrewAI AMP 2. **Copie os exemplos**: Use os exemplos de cada endpoint como modelo 3. **Substitua os placeholders**: Atualize URLs e tokens com seus valores reais 4. **Teste com suas ferramentas**: Use cURL, Postman ou seu cliente preferido - + ## Autenticação - + Todas as requisições exigem um token bearer. Existem dois tipos: - + - **Bearer Token**: Token em nível de organização para operações completas - **User Bearer Token**: Token com escopo de usuário com permissões limitadas - - Você encontra os tokens na aba Status da sua crew no painel do CrewAI Enterprise. - + + Você encontra os tokens na aba Status da sua crew no painel do CrewAI AMP. + ## Documentação de Referência - + Este documento fornece exemplos completos para cada endpoint: - + - **Formatos de requisição** com parâmetros obrigatórios e opcionais - **Exemplos de resposta** para sucesso e erro - **Amostras de código** em várias linguagens - **Padrões de autenticação** com uso correto de Bearer token - + Copie os exemplos e personalize com sua URL e tokens reais. - + ## Fluxo - + 1. **Descubra os inputs** usando `GET /inputs` - 2. **Inicie a execução** usando `POST /kickoff` + 2. **Inicie a execução** usando `POST /kickoff` 3. **Monitore o progresso** usando `GET /status/{kickoff_id}` version: 1.0.0 contact: @@ -43,7 +43,7 @@ info: url: https://crewai.com servers: - url: https://your-actual-crew-name.crewai.com - description: Substitua pela URL real da sua crew no painel do CrewAI Enterprise + description: Substitua pela URL real da sua crew no painel do CrewAI AMP security: - BearerAuth: [] paths: @@ -52,7 +52,7 @@ paths: summary: Obter Inputs Requeridos description: | **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.* - + Retorna a lista de parâmetros de entrada que sua crew espera. operationId: getRequiredInputs responses: @@ -81,7 +81,7 @@ paths: summary: Iniciar Execução da Crew description: | **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.* - + Inicia uma nova execução da crew com os inputs fornecidos e retorna um kickoff ID. operationId: startCrewExecution requestBody: @@ -102,7 +102,7 @@ paths: interests: "games, tech, ai, relaxing hikes, amazing food" duration: "7 days" age: "35" - + responses: '200': description: Execução iniciada com sucesso @@ -125,7 +125,7 @@ paths: summary: Obter Status da Execução description: | **📋 Exemplo de Referência** - *Mostra o formato da requisição. Para testar com sua crew real, copie o cURL e substitua URL + token.* - + Retorna o status atual e os resultados de uma execução usando o kickoff ID. operationId: getExecutionStatus parameters: @@ -163,7 +163,7 @@ components: scheme: bearer description: | **📋 Referência** - *Os tokens mostrados são apenas exemplos.* - Use seus tokens reais do painel do CrewAI Enterprise. + Use seus tokens reais do painel do CrewAI AMP. schemas: ExecutionRunning: @@ -180,7 +180,7 @@ components: completed_tasks: type: integer total_tasks: - type: integer + type: integer ExecutionCompleted: type: object @@ -265,4 +265,3 @@ components: application/json: schema: $ref: '#/components/schemas/Error' - diff --git a/docs/images/enterprise/agent-apps.png b/docs/images/enterprise/agent-apps.png new file mode 100644 index 0000000000000000000000000000000000000000..14d9171c6dba303274b6b2c504566d517f22e91d GIT binary patch literal 1097224 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