mirror of
https://github.com/Significant-Gravitas/AutoGPT.git
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c1a1767034
- Add generate_block_docs.py script that introspects block code to
generate markdown
- Support manual content preservation via <!-- MANUAL: --> markers
- Add migrate_block_docs.py to preserve existing manual content from git
HEAD
- Add CI workflow (docs-block-sync.yml) to fail if docs drift from code
- Add Claude PR review workflow (docs-claude-review.yml) for doc changes
- Add manual LLM enhancement workflow (docs-enhance.yml)
- Add GitBook configuration (.gitbook.yaml, SUMMARY.md)
- Fix non-deterministic category ordering (categories is a set)
- Add comprehensive test suite (32 tests)
- Generate docs for 444 blocks with 66 preserved manual sections
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
<!-- Clearly explain the need for these changes: -->
### Changes 🏗️
<!-- Concisely describe all of the changes made in this pull request:
-->
### Checklist 📋
#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
<!-- Put your test plan here: -->
- [x] Extensively test code generation for the docs pages
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> Introduces an automated documentation pipeline for blocks and
integrates it into CI.
>
> - Adds `scripts/generate_block_docs.py` (+ tests) to introspect blocks
and generate `docs/integrations/**`, preserving `<!-- MANUAL: -->`
sections
> - New CI workflows: **docs-block-sync** (fails if docs drift),
**docs-claude-review** (AI review for block/docs PRs), and
**docs-enhance** (optional LLM improvements)
> - Updates existing Claude workflows to use `CLAUDE_CODE_OAUTH_TOKEN`
instead of `ANTHROPIC_API_KEY`
> - Improves numerous block descriptions/typos and links across backend
blocks to standardize docs output
> - Commits initial generated docs including
`docs/integrations/README.md` and many provider/category pages
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
631e53e0f6. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
90 lines
3.1 KiB
Markdown
90 lines
3.1 KiB
Markdown
# System Store Operations
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<!-- MANUAL: file_description -->
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Blocks for browsing and retrieving agent details from the AutoGPT store.
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<!-- END MANUAL -->
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## Get Store Agent Details
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### What it is
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Get detailed information about an agent from the store
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### How it works
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<!-- MANUAL: how_it_works -->
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This block retrieves detailed metadata about a specific agent from the AutoGPT store using the creator's username and agent slug. It returns the agent's name, description, categories, run count, and average rating.
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The store_listing_version_id can be used with other blocks to add the agent to your library or execute it.
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<!-- END MANUAL -->
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### Inputs
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| Input | Description | Type | Required |
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|-------|-------------|------|----------|
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| creator | The username of the agent creator | str | Yes |
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| slug | The name of the agent | str | Yes |
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### Outputs
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| Output | Description | Type |
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|--------|-------------|------|
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| error | Error message if the operation failed | str |
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| found | Whether the agent was found in the store | bool |
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| store_listing_version_id | The store listing version ID | str |
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| agent_name | Name of the agent | str |
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| description | Description of the agent | str |
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| creator | Creator of the agent | str |
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| categories | Categories the agent belongs to | List[str] |
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| runs | Number of times the agent has been run | int |
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| rating | Average rating of the agent | float |
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### Possible use case
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<!-- MANUAL: use_case -->
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**Agent Discovery**: Fetch details about a specific agent before adding it to your library.
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**Agent Validation**: Check an agent's ratings and run count to assess quality and popularity.
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**Dynamic Agent Selection**: Get agent metadata to decide which version or variant to use.
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<!-- END MANUAL -->
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---
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## Search Store Agents
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### What it is
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Search for agents in the store
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### How it works
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<!-- MANUAL: how_it_works -->
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This block searches the AutoGPT agent store using a query string. Filter results by category and sort by rating, runs, or name. Limit controls the maximum number of results returned.
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Results include basic agent information and are output both as a list and individually for workflow iteration.
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<!-- END MANUAL -->
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### Inputs
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| Input | Description | Type | Required |
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|-------|-------------|------|----------|
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| query | Search query to find agents | str | No |
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| category | Filter by category | str | No |
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| sort_by | How to sort the results | "rating" \| "runs" \| "name" \| "updated_at" | No |
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| limit | Maximum number of results to return | int | No |
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### Outputs
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| Output | Description | Type |
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|--------|-------------|------|
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| error | Error message if the operation failed | str |
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| agents | List of agents matching the search criteria | List[StoreAgent] |
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| agent | Basic information of the agent | StoreAgent |
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| total_count | Total number of agents found | int |
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### Possible use case
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<!-- MANUAL: use_case -->
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**Agent Recommendation**: Search for agents that match user needs and recommend the best options.
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**Marketplace Browse**: Allow users to explore available agents by category or keyword.
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**Agent Orchestration**: Find and compose multiple specialized agents for complex workflows.
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<!-- END MANUAL -->
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---
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