# Pythia Search Bundle — Multi-Source OSINT Fan-Out (`apricot_blackberry/pythia-search-bundle`) Actor One subject in, structured intelligence from many public sources out. Tick the sources you want — jobs boards, SEC filings, news, code repos — and pay only per result returned. - **URL**: https://apify.com/apricot\_blackberry/pythia-search-bundle.md - **Developed by:** [Creator Fusion](https://apify.com/apricot_blackberry) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## pythia-actors — Rust workspace for Pythia OSINT Actors on Apify Cargo workspace containing Apify Actors written in Rust. Each Actor is a separate crate under `actors/`. Shared functionality lives in `crates/`. ### Layout ```` pythia-actors/ ├── Cargo.toml ## workspace root, shared deps + release profile ├── crates/ │ ├── apify\_client/ ## minimal Apify API client (input read, dataset write) │ └── source\_common/ ## shared types: Signal, ResultEnvelope, redaction └── actors/ ├── greenhouse/ ## public job-board fetcher │ ├── Cargo.toml │ ├── src/main.rs │ └── .actor/ │ ├── actor.json │ ├── input\_schema.json │ └── Dockerfile └── sec\_edgar/ └── ... (same shape) ```` ### Build ```bash ## Local dev build (fast, debug symbols) cargo build ## Release build (LTO, stripped, optimized — what ships to Apify) cargo build --release ## Run a specific Actor locally APIFY_TOKEN=xxx cargo run -p greenhouse-actor -- --input examples/greenhouse_input.json ```` ### Deployment to Apify Each Actor has its own `.actor/` directory with the Apify deployment scaffold. From inside an Actor's directory: ```bash cd actors/greenhouse apify push ## uploads to Apify, builds Docker image, publishes ``` The `Dockerfile` is multi-stage — `cargo build --release`, then COPY the single binary into a minimal `debian:bookworm-slim` image. Final image is \~50-80 MB depending on the Actor's deps. ### IP protection posture Release profile uses `lto = "fat"` + `codegen-units = 1` + `strip = "symbols"` - `panic = "abort"`. This produces the smallest, most-aggressively-optimized binary, which is also the hardest to reverse-engineer. Combined with Apify's private-Actor model, customers never see source code at any point. For the proprietary scoring/synthesis layer (when built), see the planned `actors/synthesis/` Actor — that's where we'll consider WASM-bound critical paths and additional obfuscation. ### Testing - Per-crate unit tests: `cargo test` - HTTP fixtures via `reqwest` mocks - Integration tests against real public APIs are gated behind `LIVE_NETWORK=1` env var to keep CI deterministic. ### Why Rust on Apify - Compiled binary = strong IP protection vs. minified JS - Single binary, no node\_modules — small Apify Docker image, faster cold start - `tokio` + `reqwest` async fan-out for parallel source fetching - Existing Creator Fusion Rust expertise (ark\_sensor, Ouija eBPF projects) # Actor input Schema ## `subject` (type: `string`): Company slug, domain, query, or keyword. Used as input for every selected source. ## `sources` (type: `array`): Tick one or more sources. Each ticked source is one fan-out call to its private Actor. ## `since_days` (type: `integer`): Default time window for sources that accept one (news, HN, Reddit, EDGAR, etc.). ## `concurrency` (type: `integer`): How many child Actors to trigger at once. Higher = faster but heavier on Apify quota. ## `max_results_per_source` (type: `integer`): Hard cap on signals returned PER child source. Keeps your bill predictable: max bill per run ≈ (start) + (sources × this × per-signal-price). Default 25 = at most 125 signals across 5 sources. ## Actor input object example ```json { "subject": "stripe", "sources": [ "greenhouse", "lever", "sec_edgar", "google_news", "hackernews" ], "since_days": 30, "concurrency": 5, "max_results_per_source": 25 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "subject": "stripe", "sources": [ "greenhouse", "lever", "sec_edgar", "google_news", "hackernews" ], "since_days": 30, "concurrency": 5, "max_results_per_source": 25 }; // Run the Actor and wait for it to finish const run = await client.actor("apricot_blackberry/pythia-search-bundle").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "subject": "stripe", "sources": [ "greenhouse", "lever", "sec_edgar", "google_news", "hackernews", ], "since_days": 30, "concurrency": 5, "max_results_per_source": 25, } # Run the Actor and wait for it to finish run = client.actor("apricot_blackberry/pythia-search-bundle").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "subject": "stripe", "sources": [ "greenhouse", "lever", "sec_edgar", "google_news", "hackernews" ], "since_days": 30, "concurrency": 5, "max_results_per_source": 25 }' | apify call apricot_blackberry/pythia-search-bundle --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=apricot_blackberry/pythia-search-bundle", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pythia Search Bundle — Multi-Source OSINT Fan-Out", "description": "One subject in, structured intelligence from many public sources out. Tick the sources you want — jobs boards, SEC filings, news, code repos — and pay only per result returned.", "version": "0.1", "x-build-id": "n7Lhfd1JcI0W12Jgk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/apricot_blackberry~pythia-search-bundle/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-apricot_blackberry-pythia-search-bundle", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/apricot_blackberry~pythia-search-bundle/runs": { "post": { "operationId": "runs-sync-apricot_blackberry-pythia-search-bundle", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/apricot_blackberry~pythia-search-bundle/run-sync": { "post": { "operationId": "run-sync-apricot_blackberry-pythia-search-bundle", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "subject", "sources" ], "properties": { "subject": { "title": "Subject", "type": "string", "description": "Company slug, domain, query, or keyword. Used as input for every selected source." }, "sources": { "title": "Sources", "uniqueItems": true, "type": "array", "description": "Tick one or more sources. Each ticked source is one fan-out call to its private Actor.", "items": { "type": "string" } }, "since_days": { "title": "Time window (days)", "minimum": 1, "maximum": 730, "type": "integer", "description": "Default time window for sources that accept one (news, HN, Reddit, EDGAR, etc.).", "default": 30 }, "concurrency": { "title": "Concurrent source-Actors", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many child Actors to trigger at once. Higher = faster but heavier on Apify quota.", "default": 5 }, "max_results_per_source": { "title": "Max results per source", "minimum": 1, "maximum": 200, "type": "integer", "description": "Hard cap on signals returned PER child source. Keeps your bill predictable: max bill per run ≈ (start) + (sources × this × per-signal-price). Default 25 = at most 125 signals across 5 sources.", "default": 25 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```