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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
160 lines
6.4 KiB
Plaintext
160 lines
6.4 KiB
Plaintext
---
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title: "Overview"
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description: "Learn how to build, customize, and optimize your CrewAI applications with comprehensive guides and tutorials"
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icon: "face-smile"
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mode: "wide"
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---
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## Learn CrewAI
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This section provides comprehensive guides and tutorials to help you master CrewAI, from basic concepts to advanced techniques. Whether you're just getting started or looking to optimize your existing implementations, these resources will guide you through every aspect of building powerful AI agent workflows.
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## Getting Started Guides
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### Core Concepts
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<CardGroup cols={2}>
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<Card title="Sequential Process" icon="list-ol" href="/en/learn/sequential-process">
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Learn how to execute tasks in a sequential order for structured workflows.
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</Card>
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<Card title="Hierarchical Process" icon="sitemap" href="/en/learn/hierarchical-process">
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Implement hierarchical task execution with manager agents overseeing workflows.
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</Card>
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<Card title="Conditional Tasks" icon="code-branch" href="/en/learn/conditional-tasks">
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Create dynamic workflows with conditional task execution based on outcomes.
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</Card>
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<Card title="Async Kickoff" icon="bolt" href="/en/learn/kickoff-async">
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Execute crews asynchronously for improved performance and concurrency.
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</Card>
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</CardGroup>
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### Agent Development
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<CardGroup cols={2}>
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<Card title="Customizing Agents" icon="user-gear" href="/en/learn/customizing-agents">
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Learn how to customize agent behavior, roles, and capabilities.
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</Card>
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<Card title="Coding Agents" icon="code" href="/en/learn/coding-agents">
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Build agents that can write, execute, and debug code automatically.
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</Card>
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<Card title="Multimodal Agents" icon="images" href="/en/learn/multimodal-agents">
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Create agents that can process text, images, and other media types.
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</Card>
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<Card title="Custom Manager Agent" icon="user-tie" href="/en/learn/custom-manager-agent">
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Implement custom manager agents for complex hierarchical workflows.
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</Card>
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</CardGroup>
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## Advanced Features
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### Workflow Control
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<CardGroup cols={2}>
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<Card title="Human in the Loop" icon="user-check" href="/en/learn/human-in-the-loop">
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Integrate human oversight and intervention into agent workflows.
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</Card>
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<Card title="Human Input on Execution" icon="hand-paper" href="/en/learn/human-input-on-execution">
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Allow human input during task execution for dynamic decision making.
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</Card>
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<Card title="Replay Tasks" icon="rotate-left" href="/en/learn/replay-tasks-from-latest-crew-kickoff">
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Replay and resume tasks from previous crew executions.
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</Card>
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<Card title="Kickoff for Each" icon="repeat" href="/en/learn/kickoff-for-each">
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Execute crews multiple times with different inputs efficiently.
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</Card>
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</CardGroup>
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### Customization & Integration
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<CardGroup cols={2}>
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<Card title="Custom LLM" icon="brain" href="/en/learn/custom-llm">
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Integrate custom language models and providers with CrewAI.
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</Card>
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<Card title="LLM Connections" icon="link" href="/en/learn/llm-connections">
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Configure and manage connections to various LLM providers.
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</Card>
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<Card title="Create Custom Tools" icon="wrench" href="/en/learn/create-custom-tools">
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Build custom tools to extend agent capabilities.
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</Card>
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<Card title="Using Annotations" icon="at" href="/en/learn/using-annotations">
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Use Python annotations for cleaner, more maintainable code.
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</Card>
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</CardGroup>
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## Specialized Applications
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### Content & Media
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<CardGroup cols={2}>
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<Card title="DALL-E Image Generation" icon="image" href="/en/learn/dalle-image-generation">
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Generate images using DALL-E integration with your agents.
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</Card>
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<Card title="Bring Your Own Agent" icon="user-plus" href="/en/learn/bring-your-own-agent">
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Integrate existing agents and models into CrewAI workflows.
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</Card>
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</CardGroup>
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### Tool Management
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<CardGroup cols={2}>
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<Card title="Force Tool Output as Result" icon="hammer" href="/en/learn/force-tool-output-as-result">
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Configure tools to return their output directly as task results.
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</Card>
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</CardGroup>
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## Learning Path Recommendations
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### For Beginners
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1. Start with **Sequential Process** to understand basic workflow execution
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2. Learn **Customizing Agents** to create effective agent configurations
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3. Explore **Create Custom Tools** to extend functionality
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4. Try **Human in the Loop** for interactive workflows
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### For Intermediate Users
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1. Master **Hierarchical Process** for complex multi-agent systems
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2. Implement **Conditional Tasks** for dynamic workflows
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3. Use **Async Kickoff** for performance optimization
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4. Integrate **Custom LLM** for specialized models
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### For Advanced Users
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1. Build **Multimodal Agents** for complex media processing
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2. Create **Custom Manager Agents** for sophisticated orchestration
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3. Implement **Bring Your Own Agent** for hybrid systems
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4. Use **Replay Tasks** for robust error recovery
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## Best Practices
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### Development
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- **Start Simple**: Begin with basic sequential workflows before adding complexity
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- **Test Incrementally**: Test each component before integrating into larger systems
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- **Use Annotations**: Leverage Python annotations for cleaner, more maintainable code
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- **Custom Tools**: Build reusable tools that can be shared across different agents
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### Production
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- **Error Handling**: Implement robust error handling and recovery mechanisms
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- **Performance**: Use async execution and optimize LLM calls for better performance
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- **Monitoring**: Integrate observability tools to track agent performance
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- **Human Oversight**: Include human checkpoints for critical decisions
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### Optimization
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- **Resource Management**: Monitor and optimize token usage and API costs
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- **Workflow Design**: Design workflows that minimize unnecessary LLM calls
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- **Tool Efficiency**: Create efficient tools that provide maximum value with minimal overhead
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- **Iterative Improvement**: Use feedback and metrics to continuously improve agent performance
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## Getting Help
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- **Documentation**: Each guide includes detailed examples and explanations
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- **Community**: Join the [CrewAI Forum](https://community.crewai.com) for discussions and support
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- **Examples**: Check the Examples section for complete working implementations
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- **Support**: Contact [support@crewai.com](mailto:support@crewai.com) for technical assistance
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Start with the guides that match your current needs and gradually explore more advanced topics as you become comfortable with the fundamentals.
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