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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>
This commit is contained in:
Lucas Gomide
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docs/edge/en/quickstart.mdx
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docs/edge/en/quickstart.mdx
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---
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title: Quickstart
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description: Build your first CrewAI Flow in minutes — orchestration, state, and an agent crew that produces a real report.
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icon: rocket
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mode: "wide"
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---
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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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In this guide you will **create a Flow** that sets a research topic, runs a **crew with one agent** (a researcher using web search), and ends with a **markdown report** on disk. Flows are the recommended way to structure production apps: they own **state** and **execution order**, while **agents** do the work inside a crew step.
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If you have not installed CrewAI yet, follow the [installation guide](/en/installation) first.
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## Prerequisites
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- Python environment and the CrewAI CLI (see [installation](/en/installation))
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- An LLM configured with the right API keys — see [LLMs](/en/concepts/llms#setting-up-your-llm)
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- A [Serper.dev](https://serper.dev/) API key (`SERPER_API_KEY`) for web search in this tutorial
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## Build your first Flow
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<Steps>
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<Step title="Create a Flow project">
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From your terminal, scaffold a Flow project (the folder name uses underscores, e.g. `latest_ai_flow`):
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<CodeGroup>
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```shell Terminal
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crewai create flow latest-ai-flow
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cd latest_ai_flow
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```
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</CodeGroup>
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This creates a Flow app under `src/latest_ai_flow/`, including a starter crew under `crews/content_crew/` that you will replace with a minimal **single-agent** research crew in the next steps.
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</Step>
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<Step title="Configure one agent in JSONC">
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Create `src/latest_ai_flow/crews/content_crew/agents/researcher.jsonc` (create the `agents/` directory if needed). Variables like `{topic}` are filled from `crew.kickoff(inputs=...)`.
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```jsonc agents/researcher.jsonc
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{
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"role": "{topic} Senior Data Researcher",
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"goal": "Uncover cutting-edge developments in {topic}",
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"backstory": "You're a seasoned researcher who finds relevant information and presents it clearly.",
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"tools": ["SerperDevTool"],
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"settings": {
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"verbose": true
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}
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}
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```
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</Step>
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<Step title="Configure the crew in `crew.jsonc`">
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Create `src/latest_ai_flow/crews/content_crew/crew.jsonc`:
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```jsonc crew.jsonc
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{
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"name": "Research Crew",
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"agents": ["researcher"],
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"tasks": [
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{
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"name": "research_task",
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"description": "Conduct thorough research about {topic}. Use web search to find recent, credible information.",
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"expected_output": "A markdown report with clear sections: key trends, notable tools or companies, and implications. Aim for 800-1200 words. No fenced code blocks around the whole document.",
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"agent": "researcher",
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"output_file": "output/report.md",
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"markdown": true
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}
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],
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"process": "sequential",
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"verbose": true
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}
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```
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</Step>
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<Step title="Load the JSON crew (`content_crew.py`)">
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Replace the generated `content_crew.py` with a small loader that turns `crew.jsonc` into a `Crew`.
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```python content_crew.py
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# src/latest_ai_flow/crews/content_crew/content_crew.py
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from pathlib import Path
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from crewai.project import load_crew
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def kickoff_content_crew(inputs: dict):
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crew, default_inputs = load_crew(Path(__file__).with_name("crew.jsonc"))
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return crew.kickoff(inputs={**default_inputs, **inputs})
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```
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</Step>
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<Step title="Define the Flow in `main.py`">
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Connect the crew to a Flow: a `@start()` step sets the topic in **state**, and a `@listen` step runs the crew. The task’s `output_file` still writes `output/report.md`.
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```python main.py
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# src/latest_ai_flow/main.py
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from pydantic import BaseModel
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from crewai.flow import Flow, listen, start
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from latest_ai_flow.crews.content_crew.content_crew import kickoff_content_crew
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class ResearchFlowState(BaseModel):
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topic: str = ""
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report: str = ""
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class LatestAiFlow(Flow[ResearchFlowState]):
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@start()
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def prepare_topic(self, crewai_trigger_payload: dict | None = None):
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if crewai_trigger_payload:
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self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
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else:
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self.state.topic = "AI Agents"
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print(f"Topic: {self.state.topic}")
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@listen(prepare_topic)
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def run_research(self):
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result = kickoff_content_crew(inputs={"topic": self.state.topic})
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self.state.report = result.raw
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print("Research crew finished.")
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@listen(run_research)
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def summarize(self):
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print("Report path: output/report.md")
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def kickoff():
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LatestAiFlow().kickoff()
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def plot():
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LatestAiFlow().plot()
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if __name__ == "__main__":
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kickoff()
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```
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<Tip>
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If your package name differs from `latest_ai_flow`, change the `kickoff_content_crew` import to match your project’s module path.
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</Tip>
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</Step>
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<Step title="Set environment variables">
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In `.env` at the project root, set:
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- `SERPER_API_KEY` — from [Serper.dev](https://serper.dev/)
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- Your model provider keys as required — see [LLM setup](/en/concepts/llms#setting-up-your-llm)
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</Step>
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<Step title="Install and run">
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<CodeGroup>
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```shell Terminal
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crewai install
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crewai run
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```
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</CodeGroup>
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`crewai run` executes the Flow entrypoint defined in your project (same command as for crews; project type is `"flow"` in `pyproject.toml`).
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</Step>
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<Step title="Check the output">
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You should see logs from the Flow and the crew. Open **`output/report.md`** for the generated report (excerpt):
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<CodeGroup>
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```markdown output/report.md
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# AI Agents: Recent Landscape and Trends
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## Executive summary
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…
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## Key trends
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- **Tool use and orchestration** — …
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- **Enterprise adoption** — …
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## Implications
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…
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```
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</CodeGroup>
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Your actual file will be longer and reflect live search results.
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</Step>
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</Steps>
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## How this run fits together
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1. **Flow** — `LatestAiFlow` runs `prepare_topic` first, then `run_research`, then `summarize`. State (`topic`, `report`) lives on the Flow.
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2. **Crew** — `kickoff_content_crew` loads `crew.jsonc` and runs one task with one agent: the researcher uses **Serper** to search the web, then writes the structured report.
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3. **Artifact** — The task’s `output_file` writes the report under `output/report.md`.
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To go deeper on Flow patterns (routing, persistence, human-in-the-loop), see [Build your first Flow](/en/guides/flows/first-flow) and [Flows](/en/concepts/flows). For crews without a Flow, see [Crews](/en/concepts/crews). For a single `Agent` and `kickoff()` without tasks, see [Agents](/en/concepts/agents#direct-agent-interaction-with-kickoff).
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<Check>
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You now have an end-to-end Flow with an agent crew and a saved report — a solid base to add more steps, crews, or tools.
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</Check>
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### Naming consistency
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The names in `crew.jsonc` must match the files and task references you use:
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- `agents: ["researcher"]` loads `agents/researcher.jsonc`
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- `tasks[].agent: "researcher"` assigns the task to that agent
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## Deploying
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Push your Flow to **[CrewAI AMP](https://app.crewai.com)** once it runs locally and your project is in a **GitHub** repository. From the project root:
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<CodeGroup>
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```bash Authenticate
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crewai login
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```
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```bash Create deployment
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crewai deploy create
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```
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```bash Check status & logs
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crewai deploy status
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crewai deploy logs
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```
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```bash Ship updates after you change code
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crewai deploy push
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```
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```bash List or remove deployments
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crewai deploy list
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crewai deploy remove <deployment_id>
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```
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</CodeGroup>
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<Tip>
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The first deploy usually takes **around 1 minute**. Full prerequisites and the web UI flow are in [Deploy to AMP](/en/enterprise/guides/deploy-to-amp).
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</Tip>
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<CardGroup cols={2}>
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<Card title="Deploy guide" icon="book" href="/en/enterprise/guides/deploy-to-amp">
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Step-by-step AMP deployment (CLI and dashboard).
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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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Discuss ideas, share projects, and connect with other CrewAI developers.
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</Card>
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</CardGroup>
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