AI Agents Registry

**DISCALIMER** This example uses cookies to authenticate to LinkedIn, and it's meant only as an example or the selenium tool, using this for real-world applicat

Welcome to the Meeting Assistant Flow project, powered by crewAI. This example demonstrates how you can leverage Flows from crewAI to automate the process of ma

Welcome to the Lead Score Flow project, powered by crewAI. This example demonstrates how you can leverage Flows from crewAI to automate the process of scoring l

This project demonstrates how to create a Crew of AI agents and tasks using crewAI. It uses a PDF knowledge source to answer user questions based on the content

Creating a deployment Let's create a deployment of the task_maistro app that we created in module 5. Code structure The following information should be provided

Chatbot with Memory Review Memory is a cognitive function that allows people to store, retrieve, and use information to understand their present and future. The

State Reducers Review We covered a few different ways to define LangGraph state schema, including TypedDict, Pydantic, or Dataclasses. Goals Now, we're going to

Assistants Assistants give developers a quick and easy way to modify and version agents for experimentation. Supplying configuration to the graph Our task_maist

Deployment Review We built up to an agent with memory: * act - let the model call specific tools * observe - pass the tool output back to the model * reason - l

LangChain Academy Welcome to LangChain Academy! Context At LangChain, we aim to make it easy to build LLM applications. One type of LLM application you can buil

Filtering and trimming messages Review Now, we have a deeper understanding of a few things: * How to customize the graph state schema * How to define custom sta

Dynamic breakpoints Review We discussed motivations for human-in-the-loop: (1) Approval - We can interrupt our agent, surface state to a user, and allow the use

Double Texting Seamless handling of double texting is important for handling real-world usage scenarios, especially in chat applications. Users can send multipl

Router Review We built a graph that uses messages as state and a chat model with bound tools. We saw that the graph can: * Return a tool call * Return a natural

Research Assistant Review We've covered a few major LangGraph themes: * Memory * Human-in-the-loop * Controllability Now, we'll bring these ideas together to ta

Chatbot with message summarization Review We've covered how to customize graph state schema and reducer. We've also shown a number of ways to trim or filter mes

State Schema Review In module 1, we laid the foundations! We built up to an agent that can: * act - let the model call specific tools * observe - pass the tool

Chatbot with Profile Schema Review We introduced the LangGraph Memory Store as a way to save and retrieve long-term memories. We built a simple chatbot that use

Memory Agent Review We created a chatbot that saves semantic memories to a single user profile or collection. We introduced Trustcall as a way to update either

Sub-graphs Review We're building up to a multi-agent research assistant that ties together all of the modules from this course. We just covered parallelization,

Connecting to a LangGraph Platform Deployment Deployment Creation We just created a <!--~deployment~ --> deployment for the task_maistro app from module 5. * We

Chatbot with message summarization & external DB memory Review We've covered how to customize graph state schema and reducer. We've also shown a number of trick

Agent Review We built a router. * Our chat model will decide to make a tool call or not based upon the user input * We use a conditional edge to route to a node