iblai-search

name: iblai-search description: Discover agents and learning content in an ibl.ai organization via the platform API — faceted, paginated search over agents and the catalog (courses, programs, pathways, skills), personalized (RAG) recommendations, global/personalized mentor search, and seller-facing sellable-items search; plus admin management of the recommendation prompts. Use to find, browse, or get recommended agents/content (mostly read-only; recommendation-prompt endpoints write).

iblai-search

Discover agents and learning content from the API in three ways: agent search (faceted, full-text search over agents), content search (faceted search over the catalog of courses, programs, pathways, and skills), and recommendations (personalized, RAG-ranked results for the signed-in user) — plus a newer /api/ai-search/... family (global + personalized mentor search, sellable-items search, and admin recommendation-prompt management). Discovery is read-only; only the recommendation-prompt endpoints write. To edit an agent use the /iblai-agent-* skills.

Auth & conventions

• Base URL: https://api.iblai.app/dm — these are Data Manager (DM) endpoints, so the /dm prefix is required; the paths below are appended to it (e.g. https://api.iblai.app/dm/api/search/catalog/).
• Header: Authorization: Api-Token $IBLAI_API_KEY on every request.
• Path vars: {org} = $IBLAI_ORG, {username} = $IBLAI_USERNAME (agent search only; the /api/ai-search/... v2 endpoints and content search are not org-pathed — the org/user come from the token or query params).
• Not connected yet? Run /iblai-login first to populate IBLAI_ORG, IBLAI_USERNAME, and IBLAI_API_KEY.

Reads

Agent search

• GET https://api.iblai.app/dm/api/search/orgs/{org}/users/{username}/mentors/ — search/browse agents. Query params:
  • query — full-text over name/description (e.g. calculus).
  • category — numeric category id (from facets.Category).
  • created_by — author username (from facets["Created By"]).
  • limit, page — pagination.

Envelope: { results[], count, next, previous, current_page, total_pages, facets }. Each result has unique_id, name, description, llm_provider, llm_name, categories, created_by, is_featured, mentor_visibility, slug, profile_image, settings. facets keys: Category, Created By, Featured, LLM Provider, Subject, Audience, Promotion, Recently Accessed.

Content search

• GET https://api.iblai.app/dm/api/search/catalog/ — search/browse the catalog. Query params:
  • query — full-text (e.g. manufacturing).
  • limit, page — pagination.
  • Filter params mirror the response facets: content type (course / program / pathway / skill), language, level, subject, format, price, certificate.

Envelope: { results[], count, next, previous, current_page, total_pages, facets }, where each result is { "type": "course|program|pathway|skill", "data": { } }.

Recommendations

Personalized, RAG-ranked results for the signed-in user. Unlike the two searches above, there is no query: results are computed server-side and are user-dependent (personalized to whoever the token resolves to). The org and user are derived from the token; this endpoint is not org-pathed.

• GET https://api.iblai.app/dm/api/ai-search/recommendations/ — RAG recommendations. Query params:
  • recommendation_type — mentors | courses | programs | resources | pathways.
  • limit — maximum number of recommendations.

Returns a ranked list of the requested type. Call once per type for a mixed set.

A newer /api/ai-search/... family (no org/user in the path — context comes from the token or query params). The recommendations/ endpoint above is part of it.

Global mentor search

• GET https://api.iblai.app/dm/api/ai-search/mentors/ — global agent/mentor search across the platform. Works anonymously; if a token is sent, results are personalized to that user (pass platform_key for RBAC). Query params: query, platform_key, facet filters mirroring the response, and limit/offset pagination. Distinct from the org-pathed agent search above (that one is tenant-scoped; this one is global).

Personalized mentors

• GET https://api.iblai.app/dm/api/ai-search/personalized-mentors/ — personalized, mentor-focused results for the signed-in user (authentication required). Pass platform_key (or tenant) and, where applicable, username.

Sellable items

• GET https://api.iblai.app/dm/api/ai-search/sellable-items/ — seller-facing search over items that can be sold (for paywall/pricing setup). Requires an authenticated user with the CanSellItems RBAC permission. Query params: platform_key, query, item type, and pagination.

Recommendation prompts (admin)

Manage the LLM prompt(s) that drive recommendations. Platform-admin only.

• GET https://api.iblai.app/dm/api/ai-search/recommendation/prompts/ (preferred alias of …/prompts/) — list prompts. Params: platform_key, recommendation_type, active_only, prompt_id.

Writes

Recommendation prompts

Manage the LLM prompt(s) that drive recommendations. Platform-admin only.

• POST https://api.iblai.app/dm/api/ai-search/prompts/ — create a prompt. Confirm with the user first.
• PUT https://api.iblai.app/dm/api/ai-search/prompts/ — update a prompt (by prompt_id). Confirm with the user first.
• DELETE https://api.iblai.app/dm/api/ai-search/prompts/ — delete a prompt (by prompt_id). Confirm with the user first.

Example

Find calculus agents, then manufacturing courses:

curl -s "https://api.iblai.app/dm/api/search/orgs/$IBLAI_ORG/users/$IBLAI_USERNAME/mentors/?query=calculus&limit=10" \
  -H "Authorization: Api-Token $IBLAI_API_KEY"

curl -s "https://api.iblai.app/dm/api/search/catalog/?query=manufacturing&limit=10" \
  -H "Authorization: Api-Token $IBLAI_API_KEY"

Notes

• Discovery (agent/content/recommendations/mentor/sellable-items search) is read-only; the only writes are the admin recommendation-prompt endpoints (POST/PUT/DELETE …/ai-search/prompts/).
• The facets block in each response enumerates the available filter dimensions and counts; use its keys to drive additional filter params.
• An agent result's unique_id is the {mentor} you pass to the /iblai-agent-* skills; a content result's data shape depends on its type.
• Search vs recommendations: search takes an explicit query + filters and is the same for any caller; recommendations take no query and are personalized per user (token-dependent).