mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-01 13:18:10 +00:00
* 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>
125 lines
5.7 KiB
Plaintext
125 lines
5.7 KiB
Plaintext
---
|
|
title: Weave 통합
|
|
description: Weights & Biases(W&B) Weave를 사용하여 CrewAI 애플리케이션을 추적, 실험, 평가 및 개선하는 방법을 알아보세요.
|
|
icon: radar
|
|
mode: "wide"
|
|
---
|
|
|
|
# Weave 개요
|
|
|
|
[Weights & Biases (W&B) Weave](https://weave-docs.wandb.ai/)는 LLM 기반 애플리케이션을 추적, 실험, 평가, 배포 및 개선하기 위한 프레임워크입니다.
|
|
|
|

|
|
|
|
Weave는 CrewAI 애플리케이션 개발의 모든 단계에서 포괄적인 지원을 제공합니다:
|
|
|
|
- **트레이싱 및 모니터링**: LLM 호출과 애플리케이션 로직을 자동으로 추적하여 프로덕션 시스템을 디버그하고 분석
|
|
- **체계적인 반복**: prompt, 데이터셋, 모델을 개선하고 반복
|
|
- **평가**: 맞춤형 또는 사전 구축된 스코어러를 사용하여 agent 성능을 체계적으로 평가하고 향상
|
|
- **가드레일**: 콘텐츠 모더레이션과 prompt 안전성을 위한 사전 및 사후 보호조치로 agent를 보호
|
|
|
|
Weave는 CrewAI 애플리케이션의 트레이스를 자동으로 캡처하여 agent의 성능, 상호 작용 및 실행 흐름을 모니터링하고 분석할 수 있게 해줍니다. 이를 통해 더 나은 평가 데이터셋을 구축하고 agent 워크플로우를 최적화할 수 있습니다.
|
|
|
|
## 설치 안내
|
|
|
|
<Steps>
|
|
<Step title="필수 패키지 설치">
|
|
```shell
|
|
pip install crewai weave
|
|
```
|
|
</Step>
|
|
<Step title="W&B 계정 설정">
|
|
[Weights & Biases 계정](https://wandb.ai)에 가입하세요. 아직 계정이 없다면 가입이 필요합니다. 트레이스와 메트릭을 확인하려면 계정이 필요합니다.
|
|
</Step>
|
|
<Step title="애플리케이션에서 Weave 초기화">
|
|
다음 코드를 애플리케이션에 추가하세요:
|
|
|
|
```python
|
|
import weave
|
|
|
|
# 프로젝트 이름으로 Weave를 초기화
|
|
weave.init(project_name="crewai_demo")
|
|
```
|
|
|
|
초기화 후, Weave는 트레이스와 메트릭을 확인할 수 있는 URL을 제공합니다.
|
|
</Step>
|
|
<Step title="Crews/Flows 생성">
|
|
```python
|
|
from crewai import Agent, Task, Crew, LLM, Process
|
|
|
|
# 결정론적 출력을 위해 temperature를 0으로 설정하여 LLM 생성
|
|
llm = LLM(model="gpt-4o", temperature=0)
|
|
|
|
# 에이전트 생성
|
|
researcher = Agent(
|
|
role='Research Analyst',
|
|
goal='Find and analyze the best investment opportunities',
|
|
backstory='Expert in financial analysis and market research',
|
|
llm=llm,
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
writer = Agent(
|
|
role='Report Writer',
|
|
goal='Write clear and concise investment reports',
|
|
backstory='Experienced in creating detailed financial reports',
|
|
llm=llm,
|
|
verbose=True,
|
|
allow_delegation=False,
|
|
)
|
|
|
|
# 작업 생성
|
|
research_task = Task(
|
|
description='Deep research on the {topic}',
|
|
expected_output='Comprehensive market data including key players, market size, and growth trends.',
|
|
agent=researcher
|
|
)
|
|
|
|
writing_task = Task(
|
|
description='Write a detailed report based on the research',
|
|
expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
|
|
agent=writer
|
|
)
|
|
|
|
# 크루 생성
|
|
crew = Crew(
|
|
agents=[researcher, writer],
|
|
tasks=[research_task, writing_task],
|
|
verbose=True,
|
|
process=Process.sequential,
|
|
)
|
|
|
|
# 크루 실행
|
|
result = crew.kickoff(inputs={"topic": "AI in material science"})
|
|
print(result)
|
|
```
|
|
</Step>
|
|
<Step title="Weave에서 트레이스 보기">
|
|
CrewAI 애플리케이션 실행 후, 초기화 시 제공된 Weave URL에 방문하여 다음 항목을 확인할 수 있습니다:
|
|
- LLM 호출 및 그 메타데이터
|
|
- 에이전트 상호작용 및 작업 실행 흐름
|
|
- 대기 시간 및 토큰 사용량과 같은 성능 메트릭
|
|
- 실행 중 발생한 오류 또는 이슈
|
|
|
|
<Frame caption="Weave 트레이싱 대시보드">
|
|
<img src="/images/weave-tracing.png" alt="Weave tracing example with CrewAI" />
|
|
</Frame>
|
|
</Step>
|
|
</Steps>
|
|
|
|
## 특징
|
|
|
|
- Weave는 모든 CrewAI 작업을 자동으로 캡처합니다: agent 상호작용 및 태스크 실행; 메타데이터와 토큰 사용량을 포함한 LLM 호출; 도구 사용 및 결과.
|
|
- 이 통합은 모든 CrewAI 실행 메서드를 지원합니다: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, 그리고 `kickoff_for_each_async()`.
|
|
- 모든 [crewAI-tools](https://github.com/crewAIInc/crewAI-tools) 작업의 자동 추적.
|
|
- 데코레이터 패칭(`@start`, `@listen`, `@router`, `@or_`, `@and_`)을 통한 flow 기능 지원.
|
|
- `@weave.op()`과 함께 CrewAI `Task`에 전달된 커스텀 guardrails 추적.
|
|
|
|
지원되는 항목에 대한 자세한 정보는 [Weave CrewAI 문서](https://weave-docs.wandb.ai/guides/integrations/crewai/#getting-started-with-flow)를 참조하세요.
|
|
|
|
## 자료
|
|
|
|
- [📘 Weave 문서](https://weave-docs.wandb.ai)
|
|
- [📊 예시 Weave x CrewAI 대시보드](https://wandb.ai/ayut/crewai_demo/weave/traces?cols=%7B%22wb_run_id%22%3Afalse%2C%22attributes.weave.client_version%22%3Afalse%2C%22attributes.weave.os_name%22%3Afalse%2C%22attributes.weave.os_release%22%3Afalse%2C%22attributes.weave.os_version%22%3Afalse%2C%22attributes.weave.source%22%3Afalse%2C%22attributes.weave.sys_version%22%3Afalse%7D&peekPath=%2Fayut%2Fcrewai_demo%2Fcalls%2F0195c838-38cb-71a2-8a15-651ecddf9d89)
|
|
- [🐦 X](https://x.com/weave_wb) |