Files
crewAI/docs/edge/ko/concepts/reasoning.mdx
Lucas Gomide a237ebabba feat: adopt directory-based docs versioning with Edge channel (#6202)
* 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>
2026-06-17 11:56:59 -04:00

148 lines
5.5 KiB
Plaintext

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