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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>
148 lines
5.5 KiB
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148 lines
5.5 KiB
Plaintext
---
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title: Reasoning
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description: "에이전트 reasoning을 활성화하고 사용하는 방법을 배워 작업 실행을 향상하세요."
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icon: brain
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mode: "wide"
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---
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## 개요
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Agent reasoning은 에이전트가 작업을 수행하기 전에 해당 작업을 반성하고 계획을 수립할 수 있도록 해주는 기능입니다. 이를 통해 에이전트는 작업에 더 체계적으로 접근할 수 있으며, 할당된 업무를 수행할 준비가 되었는지 확인할 수 있습니다.
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## 사용 방법
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에이전트에 reasoning을 활성화하려면 에이전트를 생성할 때 `reasoning=True`로 설정하면 됩니다.
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```python
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from crewai import Agent
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agent = Agent(
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role="Data Analyst",
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goal="Analyze complex datasets and provide insights",
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backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
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reasoning=True, # Enable reasoning
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max_reasoning_attempts=3 # Optional: Set a maximum number of reasoning attempts
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)
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```
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## 작동 방식
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reasoning이 활성화되면, 작업을 실행하기 전에 에이전트는 다음을 수행합니다:
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1. 작업을 반영하고 상세한 계획을 수립합니다.
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2. 작업을 실행할 준비가 되었는지 평가합니다.
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3. 준비가 완료되거나 max_reasoning_attempts에 도달할 때까지 필요에 따라 계획을 다듬습니다.
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4. reasoning 계획을 실행 전에 작업 설명에 삽입합니다.
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이 프로세스는 에이전트가 복잡한 작업을 관리하기 쉬운 단계로 분해하고, 시작하기 전에 잠재적인 문제를 식별하는 데 도움을 줍니다.
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## 구성 옵션
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<ParamField body="reasoning" type="bool" default="False">
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reasoning 활성화 또는 비활성화
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</ParamField>
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<ParamField body="max_reasoning_attempts" type="int" default="None">
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실행을 진행하기 전에 계획을 개선할 최대 시도 횟수입니다. None(기본값)인 경우, agent는 준비될 때까지 계속해서 개선을 시도합니다.
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</ParamField>
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## 예제
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다음은 전체 예제입니다:
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```python
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from crewai import Agent, Task, Crew
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# Create an agent with reasoning enabled
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analyst = Agent(
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role="Data Analyst",
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goal="Analyze data and provide insights",
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backstory="You are an expert data analyst.",
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reasoning=True,
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max_reasoning_attempts=3 # Optional: Set a limit on reasoning attempts
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)
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# Create a task
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analysis_task = Task(
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description="Analyze the provided sales data and identify key trends.",
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expected_output="A report highlighting the top 3 sales trends.",
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agent=analyst
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)
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# Create a crew and run the task
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crew = Crew(agents=[analyst], tasks=[analysis_task])
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result = crew.kickoff()
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print(result)
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```
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## 오류 처리
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reasoning 프로세스는 견고하게 설계되어 있으며, 오류 처리가 내장되어 있습니다. reasoning 중에 오류가 발생하면, 에이전트는 reasoning 계획 없이 작업을 계속 실행합니다. 이는 reasoning 프로세스가 실패하더라도 작업이 계속 실행될 수 있도록 보장합니다.
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코드에서 발생할 수 있는 오류를 처리하는 방법은 다음과 같습니다:
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```python
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from crewai import Agent, Task
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import logging
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# reasoning 오류를 캡처하기 위해 로깅을 설정합니다
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logging.basicConfig(level=logging.INFO)
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# reasoning이 활성화된 에이전트를 생성합니다
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agent = Agent(
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role="Data Analyst",
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goal="Analyze data and provide insights",
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reasoning=True,
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max_reasoning_attempts=3
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)
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# 작업을 생성합니다
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task = Task(
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description="Analyze the provided sales data and identify key trends.",
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expected_output="A report highlighting the top 3 sales trends.",
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agent=agent
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)
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# 작업 실행
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# reasoning 중 오류가 발생해도 로그에 기록되며 실행은 계속됩니다
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result = agent.execute_task(task)
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```
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## 예시 Reasoning 출력
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다음은 데이터 분석 작업을 위한 reasoning 계획의 예시입니다:
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```
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Task: Analyze the provided sales data and identify key trends.
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Reasoning Plan:
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I'll analyze the sales data to identify the top 3 trends.
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1. Understanding of the task:
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I need to analyze sales data to identify key trends that would be valuable for business decision-making.
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2. Key steps I'll take:
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- First, I'll examine the data structure to understand what fields are available
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- Then I'll perform exploratory data analysis to identify patterns
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- Next, I'll analyze sales by time periods to identify temporal trends
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- I'll also analyze sales by product categories and customer segments
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- Finally, I'll identify the top 3 most significant trends
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3. Approach to challenges:
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- If the data has missing values, I'll decide whether to fill or filter them
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- If the data has outliers, I'll investigate whether they're valid data points or errors
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- If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
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4. Use of available tools:
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- I'll use data analysis tools to explore and visualize the data
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- I'll use statistical tools to identify significant patterns
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- I'll use knowledge retrieval to access relevant information about sales analysis
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5. Expected outcome:
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A concise report highlighting the top 3 sales trends with supporting evidence from the data.
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READY: I am ready to execute the task.
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```
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이 reasoning 계획은 agent가 작업에 접근하는 방식을 체계적으로 구성하고, 발생할 수 있는 잠재적 문제를 고려하며, 기대되는 결과를 제공하도록 돕습니다. |