> ## Documentation Index > Fetch the complete documentation index at: https://docs.agent.ai/llms.txt > Use this file to discover all available pages before exploring further. # Get LinkedIn Activity > Fetch recent LinkedIn posts from specified profiles for social media analysis. ## OpenAPI ````yaml /api-reference/v1/v1.1.0_openapi.json post /action/get_linkedin_activity openapi: 3.0.0 info: version: 1.1.0 title: AI Actions - Get Data description: API specifications for 'Get Data' category AI actions. license: name: MIT servers: - url: https://api-lr.agent.ai/v1 security: - bearerAuth: [] paths: /action/get_linkedin_activity: post: tags: - Social Media & Online Presence summary: Get LinkedIn Activity description: >- Fetch recent LinkedIn posts from specified profiles for social media analysis. operationId: getLinkedinActivity requestBody: required: true content: application/json: schema: type: object properties: profile_urls: type: string format: textarea description: LinkedIn profile URLs, one per line. example: https://linkedin.com/in/dharmesh num_posts: type: integer format: int64 enum: - 1 - 5 - 10 - 25 - 50 - 100 default: 3 description: Number of recent posts to fetch from each profile. required: - profile_urls - num_posts responses: '200': description: LinkedIn activity data content: application/json: schema: $ref: '#/components/schemas/ActionResponse' example: status: 200 response: dharmesh: post_count: 3 posts: - activity_id: urn:li:activity:7295218047521349632 attachments: [] author: background_image: null entity_urn: ACoAAAAKDWUBlFAmXL1HBXFzTLscnoT1eYz66T8 first_name: Dharmesh last_name: Shah object_urn: 658789 profile_id: dharmesh profile_picture: >- https://media.licdn.com/dms/image/v2/C4E03AQGL2VlL9W53ww/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1516232442184?e=1744848000&v=beta&t=UfjG46VfFYolr_n5m5yzgiPO67Yydg6Q39NwmKqsV_E profile_type: personal sub_title: >- Founder and CTO at HubSpot. Helping millions grow better. commentary: >- "I self-identify as an engineer. Even when I was co-CEO of Salesforce, I was still coding on the weekends." ~ Bret Taylor (Google Maps, FriendFeed, Meta, Salesforce, and now Sierra AI) --- Bret is one of the people I most look up to and admire. He doesn't know me, but I feel like I know him. I have watched hours and hours of his online appearances. Some of them multiple times. He's built great products, great teams and a great legacy. I love both his values and his vibe. When I grow up, I want to be a wee bit more like Bret. #goals header_text: null li_url: >- https://www.linkedin.com/posts/dharmesh_goals-activity-7295218047521349632-pcYG?utm_source=combined_share_message&utm_medium=member_ios&rcm=ACoAAFewJzYBzxg3UoUGLhXJD4lGmMAqv_RQdpE num_comments: 22 num_reactions: 362 num_shares: 3 reaction_breakdown: appreciation: 2 empathy: 50 entertainment: 2 interest: 4 like: 280 praise: 24 reshared_activity_details: null time_elapsed: 45 minutes ago - activity_id: urn:li:activity:7295126465598181376 attachments: [] author: background_image: null entity_urn: ACoAAAAKDWUBlFAmXL1HBXFzTLscnoT1eYz66T8 first_name: Dharmesh last_name: Shah object_urn: 658789 profile_id: dharmesh profile_picture: >- https://media.licdn.com/dms/image/v2/C4E03AQGL2VlL9W53ww/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1516232442184?e=1744848000&v=beta&t=UfjG46VfFYolr_n5m5yzgiPO67Yydg6Q39NwmKqsV_E profile_type: personal sub_title: >- Founder and CTO at HubSpot. Helping millions grow better. commentary: >- Many seem to still be hung up on the debate of what is or isn't an agent. Do they need to be autonomous? Do they have to have full "agency"? Here's my simple take: This is not a binary thing, it's a spectrum. Software (which is what agents are) can be less agentic or more agentic. I find it helpful to classify the agents into different types: 1) Conversational/Chat Agents: These are agents that are interacted with in some type of conversational interface (like chat). @HubSpot's Customer Agent is an example. It's an agent that handles queries from a company's customers in a chat UX. 2) Workflow Agents: These execute a set of steps to accomplish a goal. AI is involved at one or more steps (LLMs, image generation, data analysis etc.) For now, the steps are commonly predefined, but they can also be determined by an LLM-powered "orchestrator" (I think of it as a manager that coordinates the work). They can be triggered manually, on a schedule or by a trigger in response to some external event (like a new prospect being added to the CRM). Many of the 700+ agents on Agent.ai are workflow agents. 3) Hybrid Agents: These are app-like agents and use a mix of Chat UX and classic UI (like buttons, text fields and dropdowns). They can run for a while, pause for user input or approval. They can send a notification when work is complete. Most of the 700+ agents on Agent.ai are these Hybrid/app agents. Things the different types of agents share in common: 1) They usually use one or more LLMs for some portion of their work. 2) The LLMs are given access to a library of "tools" in order for them to access data (often via APIs) or take action and do things. 3) They have some notion of "memory" -- at a minimum during the time an agent is running, but increasingly, across multiple agent interactions and ideally across all the agents in a system. In the future, I'm hoping there will be an additional feature: 4) Agents will be able to discover each other and collaborate to accomplish higher-order goals. So, given all of that, I'd define agents today like this: AGENTS: Software that uses AI to accomplish a goal requiring multiple predetermined or AI-generated steps. No matter how you define them or classify them, the result is the same: Agents should be useful to humans and help us work better. Now, back to building agents...and helping others build theirs. header_text: null li_url: >- https://www.linkedin.com/posts/dharmesh_many-seem-to-still-be-hung-up-on-the-debate-activity-7295126465598181376-IZ7i?utm_source=combined_share_message&utm_medium=member_ios&rcm=ACoAAFewJzYBzxg3UoUGLhXJD4lGmMAqv_RQdpE num_comments: 72 num_reactions: 476 num_shares: 27 reaction_breakdown: appreciation: 1 empathy: 19 interest: 53 like: 396 praise: 7 reshared_activity_details: null time_elapsed: 6 hours ago - activity_id: urn:li:activity:7294951434633052160 attachments: [] author: background_image: null entity_urn: ACoAAAAKDWUBlFAmXL1HBXFzTLscnoT1eYz66T8 first_name: Dharmesh last_name: Shah object_urn: 658789 profile_id: dharmesh profile_picture: >- https://media.licdn.com/dms/image/v2/C4E03AQGL2VlL9W53ww/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1516232442184?e=1744848000&v=beta&t=UfjG46VfFYolr_n5m5yzgiPO67Yydg6Q39NwmKqsV_E profile_type: personal sub_title: >- Founder and CTO at HubSpot. Helping millions grow better. commentary: >- A long time ago when starting HubSpot and figuring out SEO (Search Engine Optimization), I learned that the best way to rank in Google was to be rank-worthy. Create the web page that *should* rank compared to the alternatives. Over time what should rank does rank. Today, I came across this old Charlie Munger quote that captured this idea brilliantly: "The best way to get what you want is to deserve what you want." header_text: null li_url: >- https://www.linkedin.com/posts/dharmesh_a-long-time-ago-when-starting-hubspot-and-activity-7294951434633052160-kr0N?utm_source=combined_share_message&utm_medium=member_ios&rcm=ACoAAFewJzYBzxg3UoUGLhXJD4lGmMAqv_RQdpE num_comments: 70 num_reactions: 728 num_shares: 8 reaction_breakdown: appreciation: 3 empathy: 77 entertainment: 1 interest: 14 like: 612 praise: 21 reshared_activity_details: null time_elapsed: 18 hours ago components: schemas: ActionResponse: type: object properties: status: type: integer format: int32 description: HTTP status code of the action response response: type: object description: Response data from the action securitySchemes: bearerAuth: type: http scheme: bearer description: >- Bearer token from your account ([https://agent.ai/user/integrations#api](https://agent.ai/user/integrations#api)) ````