Neural web search - find similar content, extract pages, and run deep research
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Read your credentials from ~/.gooseworks/credentials.json:
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")
If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login
All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"
Neural search engine for finding similar content, extracting pages, and deep research.
Retrieve a paginated list of your research tasks. The response follows a cursor-based pagination pattern. Pass the limit parameter to control page size (max 50) and use the cursor token returned in the response to fetch subsequent pages.
Parameters:
11 <= x <= 50curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/research/v1"}'
Get an LLM answer to a question informed by Exa search results. /answer performs an Exa search and uses an LLM to generate either:
A direct answer for specific queries. (i.e.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/answer","body":{"query":"What are the best practices for prompt engineering?"}}'
The search endpoint lets you intelligently search the web and extract contents from the results.By default, it automatically chooses the best search method using Exa’s embeddings-based model and other techniques to find the most relevant results for your query.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/search"}'
"query": "startups building AI coding assistants",
"num_results": 10,
"contents": {"text": true}
}'
Retrieve the status and results of a previously created research task.Use the unique researchId returned from POST /research/v1 to poll until the task is finished.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/research/v1/{researchId}"}'
Find similar links to the link provided and optionally return the contents of the pages.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/findSimilar"}'
"url": "https://example.com/article",
"num_results": 10
}'
Create an asynchronous research task that explores the web, gathers sources, synthesizes findings, and returns results with citations.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/research/v1","body":{"instructions":"Research the current state of AI coding assistants"}}'
Get the full page contents, summaries, and metadata for a list of URLs.Returns instant results from our cache, with automatic live crawling as fallback for uncached pages.
Parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/contents"}'
"ids": ["https://example.com"],
"text": true,
"summary": true
}'
For full endpoint details and parameters:
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/search \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"prompt":"exa API endpoints"}' List all endpoints
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/details \
-H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
-H "Content-Type: application/json" \
-d '{"api":"exa","path":"/research"}' # Get endpoint details
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
日本語の概要は準備中です。原文の説明を表示しています。
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
日本語の概要は準備中です。原文の説明を表示しています。
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
日本語の概要は準備中です。原文の説明を表示しています。
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
日本語の概要は準備中です。原文の説明を表示しています。