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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>
96 lines
3.7 KiB
Plaintext
96 lines
3.7 KiB
Plaintext
---
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title: 코딩 에이전트
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description: CrewAI 에이전트가 코드를 작성하고 실행할 수 있도록 하는 방법과, 향상된 기능을 위한 고급 기능을 알아보세요.
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icon: rectangle-code
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mode: "wide"
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---
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## 소개
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CrewAI 에이전트는 이제 코드를 작성하고 실행할 수 있는 강력한 기능을 갖추게 되어 문제 해결 능력이 크게 향상되었습니다. 이 기능은 계산적 또는 프로그래밍적 해결책이 필요한 작업에 특히 유용합니다.
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## 코드 실행 활성화
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에이전트에서 코드 실행을 활성화하려면, 에이전트를 생성할 때 `allow_code_execution` 매개변수를 `True`로 설정하면 됩니다.
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예시는 다음과 같습니다:
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```python Code
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from crewai import Agent
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coding_agent = Agent(
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role="Senior Python Developer",
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goal="Craft well-designed and thought-out code",
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backstory="You are a senior Python developer with extensive experience in software architecture and best practices.",
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allow_code_execution=True
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)
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```
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<Note>
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`allow_code_execution` 매개변수의 기본값은 `False`임을 참고하세요.
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</Note>
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## 중요한 고려 사항
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1. **모델 선택**: 코드 실행을 활성화할 때 Claude 3.5 Sonnet 및 GPT-4와 같은 더 강력한 모델을 사용하는 것이 강력히 권장됩니다.
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이러한 모델은 프로그래밍 개념에 대해 더 잘 이해하고 있으며, 올바르고 효율적인 코드를 생성할 가능성이 높습니다.
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2. **오류 처리**: 코드 실행 기능에는 오류 처리가 포함되어 있습니다. 실행된 코드에서 예외가 발생하면, 에이전트는 오류 메시지를 받아보고 코드를 수정하거나
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대체 솔루션을 제공할 수 있습니다. 기본값이 2인 `max_retry_limit` 파라미터는 작업에 대한 최대 재시도 횟수를 제어합니다.
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3. **종속성**: 코드 실행 기능을 사용하려면 `crewai_tools` 패키지를 설치해야 합니다. 설치되지 않은 경우, 에이전트는 다음과 같은 정보 메시지를 기록합니다:
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"Coding tools not available. Install crewai_tools."
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## 코드 실행 프로세스
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코드 실행이 활성화된 agent가 프로그래밍이 요구되는 작업을 만났을 때:
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<Steps>
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<Step title="작업 분석">
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agent는 작업을 분석하고 코드 실행이 필요하다는 것을 판단합니다.
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</Step>
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<Step title="코드 작성">
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문제를 해결하는 데 필요한 Python 코드를 작성합니다.
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</Step>
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<Step title="코드 실행">
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해당 코드는 내부 코드 실행 도구(`CodeInterpreterTool`)로 전송됩니다.
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</Step>
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<Step title="결과 해석">
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agent는 결과를 해석하여 응답에 반영하거나 추가 문제 해결에 활용합니다.
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</Step>
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</Steps>
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## 예제 사용법
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여기 코드 실행 기능이 있는 agent를 생성하고 이를 task에서 사용하는 자세한 예제가 있습니다:
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```python Code
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from crewai import Agent, Task, Crew
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# Create an agent with code execution enabled
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coding_agent = Agent(
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role="Python Data Analyst",
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goal="Analyze data and provide insights using Python",
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backstory="You are an experienced data analyst with strong Python skills.",
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allow_code_execution=True
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)
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# Create a task that requires code execution
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data_analysis_task = Task(
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description="Analyze the given dataset and calculate the average age of participants.",
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agent=coding_agent
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)
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# Create a crew and add the task
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analysis_crew = Crew(
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agents=[coding_agent],
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tasks=[data_analysis_task]
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)
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# Execute the crew
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result = analysis_crew.kickoff()
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print(result)
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```
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이 예제에서 `coding_agent`는 데이터 분석 작업을 수행하기 위해 Python 코드를 작성하고 실행할 수 있습니다. |