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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
3.4 KiB
Plaintext
96 lines
3.4 KiB
Plaintext
---
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title: Coding Agents
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description: Learn how to enable your CrewAI Agents to write and execute code, and explore advanced features for enhanced functionality.
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icon: rectangle-code
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mode: "wide"
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---
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## Introduction
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CrewAI Agents now have the powerful ability to write and execute code, significantly enhancing their problem-solving capabilities. This feature is particularly useful for tasks that require computational or programmatic solutions.
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## Enabling Code Execution
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To enable code execution for an agent, set the `allow_code_execution` parameter to `True` when creating the agent.
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Here's an example:
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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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Note that `allow_code_execution` parameter defaults to `False`.
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</Note>
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## Important Considerations
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1. **Model Selection**: It is strongly recommended to use more capable models like Claude 3.5 Sonnet and GPT-4 when enabling code execution.
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These models have a better understanding of programming concepts and are more likely to generate correct and efficient code.
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2. **Error Handling**: The code execution feature includes error handling. If executed code raises an exception, the agent will receive the error message and can attempt to correct the code or
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provide alternative solutions. The `max_retry_limit` parameter, which defaults to 2, controls the maximum number of retries for a task.
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3. **Dependencies**: To use the code execution feature, you need to install the `crewai_tools` package. If not installed, the agent will log an info message:
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"Coding tools not available. Install crewai_tools."
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## Code Execution Process
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When an agent with code execution enabled encounters a task requiring programming:
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<Steps>
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<Step title="Task Analysis">
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The agent analyzes the task and determines that code execution is necessary.
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</Step>
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<Step title="Code Formulation">
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It formulates the Python code needed to solve the problem.
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</Step>
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<Step title="Code Execution">
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The code is sent to the internal code execution tool (`CodeInterpreterTool`).
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</Step>
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<Step title="Result Interpretation">
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The agent interprets the result and incorporates it into its response or uses it for further problem-solving.
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</Step>
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</Steps>
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## Example Usage
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Here's a detailed example of creating an agent with code execution capabilities and using it in a task:
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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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In this example, the `coding_agent` can write and execute Python code to perform data analysis tasks. |