# RFP Response Drafter (`amcllc/rfp-response-drafter`) Actor Draft RFP responses from RFP text + your capabilities: outline, compliance checklist, win themes, and draft sections. - **URL**: https://apify.com/amcllc/rfp-response-drafter.md - **Developed by:** [AMCLLC](https://apify.com/amcllc) (community) - **Categories:** Agents, AI, Other - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## RFP Response Drafter Draft RFP responses from RFP text plus your capability statement. This Actor generates: - win themes (why you should win) - a compliance checklist (what to include, what to confirm) - clarifying questions to ask the buyer - a response outline - draft sections (optional) Use it when you need a strong first draft fast and a checklist to avoid missing requirements. ### Who It's For - consultants and agencies responding to RFPs - contractors bidding on procurement opportunities - teams that want a repeatable response structure ### LLM Configuration (Ollama Cloud / Ollama) Recommended: manage the model + credentials via Actor environment variables (Secrets): ```bash LLM_API_STYLE=ollama LLM_BASE_URL=http://localhost:11434 ## or your Ollama Cloud base URL LLM_MODEL=llama3.1:8b LLM_API_KEY=... ## optional (cloud endpoints often require it) ```` You can also override per-run via input: `llmApiStyle`, `llmBaseUrl`, `llmModel`, `llmApiKey`. ### Notes - Always review and verify compliance before submitting. - This tool drafts text; it does not replace legal/procurement review. # Actor input Schema ## `rfpTitle` (type: `string`): Optional title used in the draft. ## `rfpUrl` (type: `string`): Optional URL to pull RFP text from (HTML pages only). PDFs are not parsed in this version. ## `rfpText` (type: `string`): Paste the RFP text here. This is the best input for high-quality drafts. ## `yourCompany` (type: `string`): Company name to use in the response. ## `capabilityStatement` (type: `string`): Short description of what you do, who you serve, and what makes you different. ## `pastPerformance` (type: `array`): Optional examples of similar projects. Use anonymized descriptions if needed. ## `constraints` (type: `array`): Constraints to respect (e.g., timeline, budget, technology, staffing). ## `tone` (type: `string`): Writing style for the draft. ## `draftMode` (type: `string`): How much to generate. ## `sectionsToDraft` (type: `array`): If draftMode includes sections, these section names are drafted. Leave empty for reasonable defaults. ## `aiEnrichmentEnabled` (type: `boolean`): If disabled, the Actor returns a report without drafting. ## `llmApiStyle` (type: `string`): Which API format to use: 'ollama' (native /api/chat) or 'openai' (OpenAI-compatible /v1/chat/completions). ## `llmBaseUrl` (type: `string`): Base URL for the LLM API. For local Ollama: http://localhost:11434. For Ollama Cloud, set the Cloud base URL here or via LLM\_BASE\_URL env var. ## `llmApiKey` (type: `string`): API key for the LLM provider. Prefer setting LLM\_API\_KEY (or OLLAMA\_API\_KEY) as an Actor secret instead of passing it in input. ## `llmModel` (type: `string`): Model used for drafting. Manage this via LLM\_MODEL/OLLAMA\_MODEL env vars if you prefer. ## `llmChatCompletionsUrl` (type: `string`): Optional full URL override for OpenAI-compatible chat completions (e.g., https://.../v1/chat/completions). Takes precedence over llmBaseUrl when llmApiStyle='openai'. ## `aiMaxInputChars` (type: `integer`): Max characters of RFP text sent to the model (to control cost). ## `aiMaxOutputTokens` (type: `integer`): Maximum output tokens requested. ## `aiTemperature` (type: `number`): Lower values are more deterministic. ## `apifyPpeEnabled` (type: `boolean`): Enable if monetized with Apify pay-per-event. Disable for local development. ## `apifyPpeEventName` (type: `string`): Event to charge per generated draft. ## `apifyPpeChargeOn` (type: `string`): When to charge: 'success' (default) or 'attempt'. ## Actor input object example ```json { "rfpTitle": "RFP Response", "yourCompany": "Your Company", "pastPerformance": [], "constraints": [], "tone": "professional", "draftMode": "outline_plus_sections", "sectionsToDraft": [], "aiEnrichmentEnabled": true, "llmApiStyle": "ollama", "llmBaseUrl": "http://localhost:11434", "llmModel": "llama3.1:8b", "aiMaxInputChars": 30000, "aiMaxOutputTokens": 1800, "aiTemperature": 0.2, "apifyPpeEnabled": true, "apifyPpeEventName": "rfp-draft", "apifyPpeChargeOn": "success" } ``` # Actor output Schema ## `overview` (type: `string`): No description ## `report` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("amcllc/rfp-response-drafter").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 = {} # Run the Actor and wait for it to finish run = client.actor("amcllc/rfp-response-drafter").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 '{}' | apify call amcllc/rfp-response-drafter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=amcllc/rfp-response-drafter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "RFP Response Drafter", "description": "Draft RFP responses from RFP text + your capabilities: outline, compliance checklist, win themes, and draft sections.", "version": "0.1", "x-build-id": "LnYuPa3KNSTG2zc4m" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/amcllc~rfp-response-drafter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-amcllc-rfp-response-drafter", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/amcllc~rfp-response-drafter/runs": { "post": { "operationId": "runs-sync-amcllc-rfp-response-drafter", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/amcllc~rfp-response-drafter/run-sync": { "post": { "operationId": "run-sync-amcllc-rfp-response-drafter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "rfpTitle": { "title": "RFP title", "type": "string", "description": "Optional title used in the draft.", "default": "RFP Response" }, "rfpUrl": { "title": "RFP URL (optional)", "type": "string", "description": "Optional URL to pull RFP text from (HTML pages only). PDFs are not parsed in this version." }, "rfpText": { "title": "RFP text", "type": "string", "description": "Paste the RFP text here. This is the best input for high-quality drafts." }, "yourCompany": { "title": "Your company name", "type": "string", "description": "Company name to use in the response.", "default": "Your Company" }, "capabilityStatement": { "title": "Capability statement", "type": "string", "description": "Short description of what you do, who you serve, and what makes you different." }, "pastPerformance": { "title": "Past performance (optional)", "type": "array", "description": "Optional examples of similar projects. Use anonymized descriptions if needed.", "default": [] }, "constraints": { "title": "Constraints (optional)", "type": "array", "description": "Constraints to respect (e.g., timeline, budget, technology, staffing).", "default": [], "items": { "type": "string" } }, "tone": { "title": "Tone", "enum": [ "professional", "direct", "formal" ], "type": "string", "description": "Writing style for the draft.", "default": "professional" }, "draftMode": { "title": "Draft mode", "enum": [ "outline_only", "outline_plus_sections", "sections_only" ], "type": "string", "description": "How much to generate.", "default": "outline_plus_sections" }, "sectionsToDraft": { "title": "Sections to draft (optional)", "type": "array", "description": "If draftMode includes sections, these section names are drafted. Leave empty for reasonable defaults.", "default": [], "items": { "type": "string" } }, "aiEnrichmentEnabled": { "title": "AI drafting enabled", "type": "boolean", "description": "If disabled, the Actor returns a report without drafting.", "default": true }, "llmApiStyle": { "title": "LLM API style", "enum": [ "ollama", "openai" ], "type": "string", "description": "Which API format to use: 'ollama' (native /api/chat) or 'openai' (OpenAI-compatible /v1/chat/completions).", "default": "ollama" }, "llmBaseUrl": { "title": "LLM base URL", "type": "string", "description": "Base URL for the LLM API. For local Ollama: http://localhost:11434. For Ollama Cloud, set the Cloud base URL here or via LLM_BASE_URL env var.", "default": "http://localhost:11434" }, "llmApiKey": { "title": "LLM API key (optional)", "type": "string", "description": "API key for the LLM provider. Prefer setting LLM_API_KEY (or OLLAMA_API_KEY) as an Actor secret instead of passing it in input." }, "llmModel": { "title": "LLM model", "type": "string", "description": "Model used for drafting. Manage this via LLM_MODEL/OLLAMA_MODEL env vars if you prefer.", "default": "llama3.1:8b" }, "llmChatCompletionsUrl": { "title": "LLM chat completions URL (advanced)", "type": "string", "description": "Optional full URL override for OpenAI-compatible chat completions (e.g., https://.../v1/chat/completions). Takes precedence over llmBaseUrl when llmApiStyle='openai'." }, "aiMaxInputChars": { "title": "AI max input chars", "type": "integer", "description": "Max characters of RFP text sent to the model (to control cost).", "default": 30000 }, "aiMaxOutputTokens": { "title": "AI max output tokens", "type": "integer", "description": "Maximum output tokens requested.", "default": 1800 }, "aiTemperature": { "title": "AI temperature", "type": "number", "description": "Lower values are more deterministic.", "default": 0.2 }, "apifyPpeEnabled": { "title": "Pay per event (PPE) enabled", "type": "boolean", "description": "Enable if monetized with Apify pay-per-event. Disable for local development.", "default": true }, "apifyPpeEventName": { "title": "PPE event name", "type": "string", "description": "Event to charge per generated draft.", "default": "rfp-draft" }, "apifyPpeChargeOn": { "title": "PPE charge timing", "enum": [ "success", "attempt" ], "type": "string", "description": "When to charge: 'success' (default) or 'attempt'.", "default": "success" } } }, "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 } } } } } } } } } } ```