# 💬 LinkedIn Conversation Scraper (`scraply/linkedin-conversation-scraper`) Actor - **URL**: https://apify.com/scraply/linkedin-conversation-scraper.md - **Developed by:** [Scraply](https://apify.com/scraply) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## 💬 LinkedIn Conversation Scraper > **Pull every conversation from your LinkedIn inbox into structured JSON — messages, dates, participants, headlines, distance, pronouns, and more.** No fragile API hacks. Just your own session cookie and a real Chromium tab under the hood. --- ### ✨ Why Choose Us? - 🛡️ **API-free, fingerprint-proof** — drives a real Chromium browser, immune to LinkedIn's JA3 / queryId / CSRF rotations - ⚡ **Live streaming** — every conversation is pushed to the dataset the moment it's parsed (crash mid-run? you still keep what was collected) - 🌐 **Smart proxy escalation** — starts direct, falls back to **datacenter**, then **residential** (3 retries) only when LinkedIn pushes back - 🧩 **Optional profile enrichment** — pulls headline, distance (1st/2nd/3rd), and pronouns from each participant's profile page - 📅 **Date filters** — `today_limit` or `date_limit` to grab only what's recent - 🔗 **Bulk URL mode** — feed it a list of thread or profile URLs to scrape specific conversations - 🎯 **Stylish, modern input UI** with emoji-rich field labels --- ### 🔑 Key Features | Feature | What it does | |--------|-------------| | 📥 **Inbox sweep** | Primary, All, Other, Archive, Spam | | 🔎 **Filters** | InMail, Starred, 1st-degree only, Unread only | | 💬 **Per-thread depth** | Configurable max messages per conversation | | ✨ **Profile enrichment** | Headline + distance + pronouns from each participant | | 🌐 **Tiered proxy fallback** | Direct → Datacenter → Residential (×3 retries) | | 📅 **Date filters** | Today-only or older-than-cutoff | | 💾 **Live saving** | `Actor.push_data` per conversation — no data loss on crash | --- ### 📥 Input | Field | Type | Required | Description | |-------|------|----------|-------------| | 🔐 `li_at` | string | **Yes** | Your `li_at` cookie from www.linkedin.com (treat as a password) | | 🔗 `urls` | array | No | Optional bulk list of thread URLs or profile URLs | | 📥 `list_type` | string | No | `PRIMARY_INBOX` (default), `INBOX`, `SECONDARY_INBOX`, `ARCHIVE`, `SPAM` | | 🔎 `filter` | string | No | `INMAIL`, `STARRED`, `firstDegreeConnections:true`, `read:false` | | 💬 `max_convs` | integer | No | Max conversations to scrape (`0` = unlimited) | | 📝 `max_messages_per_conv` | integer | No | Max messages per conversation (`0` = unlimited) | | 📅 `today_limit` | boolean | No | If true, only today's conversations | | 🗓️ `time_frame` | string | No | `ALL_TIME` (default), `LAST_24_HOURS`, `LAST_3_DAYS`, `LAST_7_DAYS`, `LAST_14_DAYS`, `LAST_30_DAYS`, `LAST_90_DAYS`, `LAST_6_MONTHS`, `LAST_YEAR` | | ✨ `enrich_profiles` | boolean | No | Visit each participant's profile page for headline / distance / pronouns | | ⏱️ `request_delay` | number | No | Float seconds between requests (default `1.0`) | | 🌐 `proxyConfiguration` | object | No | Defaults to **no proxy**; auto-escalates if LinkedIn blocks | #### Example input ```json { "li_at": "AQEDAU...your_cookie...", "list_type": "PRIMARY_INBOX", "filter": "", "max_convs": 10, "max_messages_per_conv": 25, "enrich_profiles": true, "today_limit": false, "proxyConfiguration": { "useApifyProxy": false } } ```` *** ### 📤 Output Each dataset record is one conversation: ```json { "participants": [ { "url": "https://www.linkedin.com/in/ACoAADM...", "distance": "DISTANCE_1", "pronoun": "she/her", "picture": "https://media.licdn.com/.../profile.jpg", "firstname": "Raisul", "lastname": "Islam", "headline": "Founder @ Authentic Digital Marketing", "entityUrn": "ACoAADM..." } ], "status": "ACTIVE", "unread_count": 0, "last_read_at": null, "last_activity_at": "2026-05-03 15:15:00", "is_group_chat": false, "creator": "ACoAAEjB...", "created_at": "2026-05-03 15:15:00", "messages": [ { "content": "test message", "date": "2026-05-03 15:15:00", "who": "me" } ], "total_messages": 1, "last_message_date": "2026-05-03 15:15:00", "last_message_content": "test message", "last_message_who": "me", "days_from_today": 11, "_conversation_id": "2-ZmExYTkx..." } ``` #### Field reference | Field | Description | |-------|-------------| | `participants` | Array of participant objects (name, URL, headline, distance, pronoun, picture, entityUrn) | | `status` | Conversation status (`ACTIVE`) | | `unread_count` | Number of unread messages | | `last_activity_at` | Timestamp of the most recent message (`YYYY-MM-DD HH:MM:SS`) | | `is_group_chat` | True if multi-person thread | | `creator` | URN of whoever sent the first message we collected | | `created_at` | Timestamp of the first collected message | | `messages` | Array of `{ content, date, who }` (oldest → newest) | | `total_messages` | Number of messages collected | | `last_message_*` | Convenience fields for the most recent message | | `days_from_today` | Days since the last activity | | `_conversation_id` | LinkedIn's thread URN | *** ### 🚀 How to Use (Apify Console) 1. 🔓 Log in to [Apify Console](https://console.apify.com) → **Actors**. 2. 🔍 Find **LinkedIn Conversation Scraper** and open it. 3. 🍪 Grab your `li_at` cookie: - Open [linkedin.com](https://www.linkedin.com) in Chrome → press **F12** → **Application** → **Cookies** → `https://www.linkedin.com` → copy the value of `li_at`. 4. 📝 Paste it into the **🔐 LinkedIn Session Cookie (li\_at)** field. 5. ⚙️ Configure inbox type, filter, limits, and proxy if needed. 6. ▶️ Click **Start**. 7. 📺 Watch the live log — each conversation is logged as it's saved. 8. 📂 Open the **Dataset** tab when the run finishes. 9. 📤 Export to **JSON / CSV / XLSX / Excel**. > 💡 **Tip:** Start with `max_convs = 5` to validate everything is working before launching a full sweep. *** ### 🤖 Use via API / MCP #### Run the actor via REST ```bash curl -X POST "https://api.apify.com/v2/acts//runs?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "li_at": "AQEDAU...", "list_type": "PRIMARY_INBOX", "max_convs": 10, "max_messages_per_conv": 25, "enrich_profiles": true, "proxyConfiguration": { "useApifyProxy": false } }' ``` #### Wait for results synchronously ```bash curl -X POST "https://api.apify.com/v2/acts//run-sync-get-dataset-items?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"li_at":"AQEDAU...","max_convs":3}' ``` *** ### 🌐 Proxy Strategy The scraper escalates **only when LinkedIn pushes back**: | Step | Tier | What happens | |------|------|--------------| | 1️⃣ | 🟢 **Direct** | Try without any proxy first | | 2️⃣ | 🟡 **Datacenter** | If LinkedIn blocks → switch to a datacenter proxy | | 3️⃣ | 🔴 **Residential** | If datacenter still gets blocked → residential proxy with **3 retries** | | 🔒 | **Sticky** | Once we escalate, we stay on that tier for the rest of the run | Every escalation step is logged so you can see what's happening: ``` 🌐 Attempting connection without proxy (attempt 1/1)… 🛑 [NONE] LinkedIn redirected to login/checkpoint — treated as a block 🌐 Attempting connection via DATACENTER proxy (proxy.apify.com) — attempt 1/1 ✅ [DATACENTER] Connected successfully to LinkedIn. 🟢 Locked onto DATACENTER proxy for the rest of the run. ``` *** ### 💎 Best Use Cases - 🧑‍💼 **CRM hygiene** — sync LinkedIn DMs into HubSpot / Salesforce - 💼 **Recruitment ops** — track candidate replies across hundreds of outreach threads - 📞 **Sales enablement** — surface stale conversations and follow-ups - 🗂️ **Personal archive** — back up your own messaging history before LinkedIn deletes it - 🔬 **Research** — analyze your own DM patterns for sentiment / topic / response time *** ### 💰 Pricing This actor runs on **pay-per-usage** — you pay only the underlying Apify platform usage (compute units, proxies if used, dataset writes). No additional per-event charges from us. > Run-time guideline: with `enrich_profiles = true`, expect ~5–10 seconds per conversation. With enrichment off, it's typically ~2–3 seconds per conversation. *** ### ❓ Frequently Asked Questions #### Where do I find my `li_at` cookie? Open [linkedin.com](https://www.linkedin.com), press **F12**, go to **Application → Cookies → https://www.linkedin.com**, and copy the **Value** of the `li_at` cookie. It looks like `AQEDA...`. Treat it like a password. #### Will this work without a cookie? No. LinkedIn's messaging endpoints all require an authenticated session. We use *your* cookie so the scraper sees the same inbox you do. #### Why does it sometimes need a proxy? If you run the actor often (or from a flagged IP), LinkedIn may redirect to a login wall. The actor detects this automatically and escalates to a datacenter proxy, then residential. **Most users will never need a proxy.** #### Will LinkedIn ban my account? Use sensible limits. Don't run dozens of large sweeps a day. Increase `request_delay` if you see warnings. Scraping your **own** inbox at human-realistic rates is the lowest-risk pattern. #### Can I scrape group chats? Currently the actor labels every thread as `is_group_chat: false` and only enriches the first participant. Multi-participant threads still work — you just get the first participant's profile detail. #### Can I scrape someone else's inbox? No. The `li_at` cookie ties to whoever generated it. You can only scrape your own inbox. #### How is this different from the LinkedIn API? The official API doesn't expose messaging to third parties. This actor uses a real browser to render the page exactly as you'd see it, bypassing API restrictions while staying within your own account's permissions. *** ### ⚠️ Cautions / Legal - 📚 You may only scrape data your account is **authorized to see** (your own inbox). - 🔐 Never share your `li_at` cookie publicly — it grants full access to your LinkedIn account. - 📜 You are responsible for compliance with LinkedIn's **User Agreement**, GDPR, CCPA, and any other applicable law in your jurisdiction. - 🤝 Don't use this to spam, harass, or unsolicited-message anyone — automated outreach violates LinkedIn's rules. - 🚦 Respect rate limits — protect your account by keeping `request_delay ≥ 1.0` and `max_convs` reasonable. *** ### 🛟 Support and Feedback - 🐛 Found a bug? File an **issue** through the Actor's Issues tab. - 💡 Have a feature request? We listen. - 💬 Need help? Use Apify Console's **Discussion** tab. *** **Built with ❤️ on Apify** • [Documentation](https://docs.apify.com) • [Apify Console](https://console.apify.com) # Actor input Schema ## `list_type` (type: `string`): Which inbox to scrape when no URLs are provided. ## `filter` (type: `string`): Optional filter applied to the inbox list. ## `li_at` (type: `string`): Your `li_at` cookie from www.linkedin.com. Required for authentication. Open LinkedIn in your browser → DevTools → Application → Cookies → www.linkedin.com → copy the `li_at` value. ⚠️ Never share this cookie publicly — treat it like a password. ## `max_convs` (type: `integer`): Maximum number of conversations to scrape. Set `0` for no limit. Higher values = longer runs. ## `max_messages_per_conv` (type: `integer`): Maximum number of messages to extract from each conversation. Set `0` for no limit. ## `today_limit` (type: `boolean`): If enabled, only conversations with activity **today** are included. ## `date_limit` (type: `string`): Skip conversations older than this date. Leave empty for no date limit. ## `enrich_profiles` (type: `boolean`): If enabled, visits each participant's profile page to extract **headline**, **distance** (1st/2nd/3rd), and **pronouns**. Slower but produces richer output. ## `proxyConfiguration` (type: `object`): Configure Apify proxy usage for this run. Leave off for direct connection unless you need a fixed proxy. ## Actor input object example ```json { "list_type": "PRIMARY_INBOX", "filter": "", "max_convs": 10, "max_messages_per_conv": 5, "today_limit": false, "enrich_profiles": true, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "li_at": "", "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("scraply/linkedin-conversation-scraper").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 = { "li_at": "", "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("scraply/linkedin-conversation-scraper").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 '{ "li_at": "", "proxyConfiguration": { "useApifyProxy": false } }' | apify call scraply/linkedin-conversation-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraply/linkedin-conversation-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "💬 LinkedIn Conversation Scraper", "version": "0.1", "x-build-id": "DBSbgaavC7qQLO01b" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraply~linkedin-conversation-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraply-linkedin-conversation-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraply~linkedin-conversation-scraper/runs": { "post": { "operationId": "runs-sync-scraply-linkedin-conversation-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraply~linkedin-conversation-scraper/run-sync": { "post": { "operationId": "run-sync-scraply-linkedin-conversation-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "li_at" ], "properties": { "list_type": { "title": "📥 Inbox Type", "enum": [ "PRIMARY_INBOX", "INBOX", "SECONDARY_INBOX", "ARCHIVE", "SPAM" ], "type": "string", "description": "Which inbox to scrape when no URLs are provided.", "default": "PRIMARY_INBOX" }, "filter": { "title": "🔎 Conversation Filter", "enum": [ "", "INMAIL", "STARRED", "firstDegreeConnections:true", "read:false" ], "type": "string", "description": "Optional filter applied to the inbox list.", "default": "" }, "li_at": { "title": "🔐 LinkedIn Session Cookie (li_at)", "type": "string", "description": "Your `li_at` cookie from www.linkedin.com. Required for authentication. Open LinkedIn in your browser → DevTools → Application → Cookies → www.linkedin.com → copy the `li_at` value. ⚠️ Never share this cookie publicly — treat it like a password." }, "max_convs": { "title": "💬 Maximum Conversations", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Maximum number of conversations to scrape. Set `0` for no limit. Higher values = longer runs.", "default": 10 }, "max_messages_per_conv": { "title": "📝 Maximum Messages per Conversation", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Maximum number of messages to extract from each conversation. Set `0` for no limit.", "default": 5 }, "today_limit": { "title": "📅 Only Today's Conversations", "type": "boolean", "description": "If enabled, only conversations with activity **today** are included.", "default": false }, "date_limit": { "title": "🗓️ Date Limit (YYYY-MM-DD)", "type": "string", "description": "Skip conversations older than this date. Leave empty for no date limit." }, "enrich_profiles": { "title": "✨ Enrich Participant Profiles", "type": "boolean", "description": "If enabled, visits each participant's profile page to extract **headline**, **distance** (1st/2nd/3rd), and **pronouns**. Slower but produces richer output.", "default": true }, "proxyConfiguration": { "title": "🌐 Proxy Configuration", "type": "object", "description": "Configure Apify proxy usage for this run. Leave off for direct connection unless you need a fixed proxy.", "default": { "useApifyProxy": false } } } }, "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 } } } } } } } } } } ```