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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>
74 lines
2.6 KiB
Plaintext
74 lines
2.6 KiB
Plaintext
---
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title: "Image Generation with DALL-E"
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description: "Learn how to use DALL-E for AI-powered image generation in your CrewAI projects"
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icon: "image"
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mode: "wide"
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---
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CrewAI supports integration with OpenAI's DALL-E, allowing your AI agents to generate images as part of their tasks. This guide will walk you through how to set up and use the DALL-E tool in your CrewAI projects.
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## Prerequisites
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- crewAI installed (latest version)
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- OpenAI API key with access to DALL-E
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## Setting Up the DALL-E Tool
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<Steps>
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<Step title="Import the DALL-E tool">
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```python
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from crewai_tools import DallETool
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```
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</Step>
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<Step title="Add the DALL-E tool to your agent configuration">
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```python
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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tools=[SerperDevTool(), DallETool()], # Add DallETool to the list of tools
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allow_delegation=False,
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verbose=True
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)
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```
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</Step>
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</Steps>
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## Using the DALL-E Tool
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Once you've added the DALL-E tool to your agent, it can generate images based on text prompts. The tool will return a URL to the generated image, which can be used in the agent's output or passed to other agents for further processing.
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### Example Agent Configuration
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```yaml
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role: >
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LinkedIn Profile Senior Data Researcher
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goal: >
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Uncover detailed LinkedIn profiles based on provided name {name} and domain {domain}
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Generate a Dall-e image based on domain {domain}
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backstory: >
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You're a seasoned researcher with a knack for uncovering the most relevant LinkedIn profiles.
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Known for your ability to navigate LinkedIn efficiently, you excel at gathering and presenting
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professional information clearly and concisely.
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```
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### Expected Output
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The agent with the DALL-E tool will be able to generate the image and provide a URL in its response. You can then download the image.
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<Frame>
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<img src="/images/enterprise/dall-e-image.png" alt="DALL-E Image" />
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</Frame>
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## Best Practices
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1. **Be specific in your image generation prompts** to get the best results.
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2. **Consider generation time** - Image generation can take some time, so factor this into your task planning.
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3. **Follow usage policies** - Always comply with OpenAI's usage policies when generating images.
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## Troubleshooting
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1. **Check API access** - Ensure your OpenAI API key has access to DALL-E.
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2. **Version compatibility** - Check that you're using the latest version of crewAI and crewai-tools.
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3. **Tool configuration** - Verify that the DALL-E tool is correctly added to the agent's tool list. |