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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>
153 lines
5.8 KiB
Plaintext
153 lines
5.8 KiB
Plaintext
---
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title: Introduction
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description: Build AI agent teams that work together to tackle complex tasks
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icon: handshake
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mode: "wide"
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---
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# What is CrewAI?
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**CrewAI is the leading open-source framework for orchestrating autonomous AI agents and building complex workflows.**
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It empowers developers to build production-ready multi-agent systems by combining the collaborative intelligence of **Crews** with the precise control of **Flows**.
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- **[CrewAI Flows](/en/guides/flows/first-flow)**: The backbone of your AI application. Flows allow you to create structured, event-driven workflows that manage state and control execution. They provide the scaffolding for your AI agents to work within.
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- **[CrewAI Crews](/en/guides/crews/first-crew)**: The units of work within your Flow. Crews are teams of autonomous agents that collaborate to solve specific tasks delegated to them by the Flow.
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With over 100,000 developers certified through our community courses, CrewAI is the standard for enterprise-ready AI automation.
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### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
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Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
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You can install it with `npx skills add crewaiinc/skills`
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<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
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## The CrewAI Architecture
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CrewAI's architecture is designed to balance autonomy with control.
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### 1. Flows: The Backbone
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<Note>
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Think of a Flow as the "manager" or the "process definition" of your application. It defines the steps, the logic, and how data moves through your system.
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</Note>
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<Frame caption="CrewAI Framework Overview">
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<img src="/images/flows.png" alt="CrewAI Framework Overview" />
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</Frame>
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Flows provide:
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- **State Management**: Persist data across steps and executions.
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- **Event-Driven Execution**: Trigger actions based on events or external inputs.
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- **Control Flow**: Use conditional logic, loops, and branching.
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### 2. Crews: The Intelligence
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<Note>
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Crews are the "teams" that do the heavy lifting. Within a Flow, you can trigger a Crew to tackle a complex problem requiring creativity and collaboration.
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</Note>
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<Frame caption="CrewAI Framework Overview">
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<img src="/images/crews.png" alt="CrewAI Framework Overview" />
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</Frame>
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Crews provide:
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- **Role-Playing Agents**: Specialized agents with specific goals and tools.
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- **Autonomous Collaboration**: Agents work together to solve tasks.
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- **Task Delegation**: Tasks are assigned and executed based on agent capabilities.
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## How It All Works Together
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1. **The Flow** triggers an event or starts a process.
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2. **The Flow** manages the state and decides what to do next.
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3. **The Flow** delegates a complex task to a **Crew**.
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4. **The Crew**'s agents collaborate to complete the task.
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5. **The Crew** returns the result to the **Flow**.
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6. **The Flow** continues execution based on the result.
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## Key Features
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<CardGroup cols={2}>
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<Card title="Production-Grade Flows" icon="arrow-progress">
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Build reliable, stateful workflows that can handle long-running processes and complex logic.
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</Card>
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<Card title="Autonomous Crews" icon="users">
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Deploy teams of agents that can plan, execute, and collaborate to achieve high-level goals.
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</Card>
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<Card title="Flexible Tools" icon="screwdriver-wrench">
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Connect your agents to any API, database, or local tool.
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</Card>
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<Card title="Enterprise Security" icon="lock">
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Designed with security and compliance in mind for enterprise deployments.
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</Card>
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</CardGroup>
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## When to Use Crews vs. Flows
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**The short answer: Use both.**
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For any production-ready application, **start with a Flow**.
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- **Use a Flow** to define the overall structure, state, and logic of your application.
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- **Use a Crew** within a Flow step when you need a team of agents to perform a specific, complex task that requires autonomy.
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| Use Case | Architecture |
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| :--- | :--- |
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| **Simple Automation** | Single Flow with Python tasks |
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| **Complex Research** | Flow managing state -> Crew performing research |
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| **Application Backend** | Flow handling API requests -> Crew generating content -> Flow saving to DB |
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## Why Choose CrewAI?
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- 🧠 **Autonomous Operation**: Agents make intelligent decisions based on their roles and available tools
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- 📝 **Natural Interaction**: Agents communicate and collaborate like human team members
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- 🛠️ **Extensible Design**: Easy to add new tools, roles, and capabilities
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- 🚀 **Production Ready**: Built for reliability and scalability in real-world applications
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- 🔒 **Security-Focused**: Designed with enterprise security requirements in mind
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- 💰 **Cost-Efficient**: Optimized to minimize token usage and API calls
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## Ready to Start Building?
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<CardGroup cols={2}>
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<Card
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title="Build Your First Flow"
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icon="diagram-project"
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href="/en/guides/flows/first-flow"
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>
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Learn how to create structured, event-driven workflows with precise control over execution.
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</Card>
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<Card
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title="Build Your First Crew"
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icon="users-gear"
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href="/en/guides/crews/first-crew"
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>
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Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.
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</Card>
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</CardGroup>
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<CardGroup cols={3}>
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<Card
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title="Install CrewAI"
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icon="wrench"
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href="/en/installation"
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>
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Get started with CrewAI in your development environment.
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</Card>
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<Card
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title="Quick Start"
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icon="bolt"
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href="en/quickstart"
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>
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Scaffold a Flow, run a crew with one agent, and generate a report end to end.
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</Card>
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<Card
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title="Join the Community"
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icon="comments"
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href="https://community.crewai.com"
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>
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Connect with other developers, get help, and share your CrewAI experiences.
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</Card>
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</CardGroup>
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