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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>
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100 lines
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---
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title: 실행 중 인간 입력
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description: 복잡한 의사결정 과정에서 실행 중 CrewAI와 인간 입력을 통합하고, 에이전트의 속성과 도구의 모든 기능을 활용하는 방법.
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icon: user-plus
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mode: "wide"
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---
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## 에이전트 실행에서의 인간 입력
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인간 입력은 여러 에이전트 실행 시나리오에서 매우 중요하며, 에이전트가 필요할 때 추가 정보나 설명을 요청할 수 있게 해줍니다.
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이 기능은 특히 복잡한 의사결정 과정이나 에이전트가 작업을 효과적으로 완료하기 위해 더 많은 세부 정보가 필요할 때 유용하게 사용됩니다.
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## CrewAI에서 인간 입력 사용하기
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에이전트 실행에 인간 입력을 통합하려면, 태스크 정의에서 `human_input` 플래그를 설정하세요. 이 기능이 활성화되면 에이전트는 최종 답변을 제공하기 전에 사용자에게 입력을 요청합니다.
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이 입력은 추가적인 컨텍스트를 제공하거나, 모호성을 해소하거나, 에이전트의 출력을 검증하는 데 사용할 수 있습니다.
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### 예시:
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```shell
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pip install crewai
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```
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```python Code
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import os
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from crewai import Agent, Task, Crew
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from crewai_tools import SerperDevTool
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os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
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os.environ["OPENAI_API_KEY"] = "Your Key"
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# Loading Tools
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search_tool = SerperDevTool()
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# Define your agents with roles, goals, tools, and additional attributes
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researcher = Agent(
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI and data science',
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backstory=(
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"You are a Senior Research Analyst at a leading tech think tank. "
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"Your expertise lies in identifying emerging trends and technologies in AI and data science. "
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"You have a knack for dissecting complex data and presenting actionable insights."
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),
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verbose=True,
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allow_delegation=False,
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tools=[search_tool]
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)
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writer = Agent(
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role='Tech Content Strategist',
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goal='Craft compelling content on tech advancements',
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backstory=(
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"You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation. "
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"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
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),
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verbose=True,
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allow_delegation=True,
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tools=[search_tool],
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cache=False, # Disable cache for this agent
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)
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# Create tasks for your agents
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task1 = Task(
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description=(
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"Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
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"Identify key trends, breakthrough technologies, and potential industry impacts. "
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"Compile your findings in a detailed report. "
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"Make sure to check with a human if the draft is good before finalizing your answer."
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),
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expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
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agent=researcher,
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human_input=True
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)
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task2 = Task(
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description=(
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"Using the insights from the researcher\'s report, develop an engaging blog post that highlights the most significant AI advancements. "
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"Your post should be informative yet accessible, catering to a tech-savvy audience. "
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"Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
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),
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expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
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agent=writer,
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human_input=True
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)
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# Instantiate your crew with a sequential process
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crew = Crew(
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agents=[researcher, writer],
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tasks=[task1, task2],
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verbose=True,
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memory=True,
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planning=True # Enable planning feature for the crew
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)
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# Get your crew to work!
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result = crew.kickoff()
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print("######################")
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print(result)
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```
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