Learn how to hack AI agents and applications with this expert guide. Find vulnerabilities, prompt injection risks, and testing strategies for AI security.

Contribute to langchain-ai/langgraph-supervisor development by creating an account on GitHub.

Quickly understand inscrutable LLM frameworks by intercepting API calls.

Foundation models enable many new application interfaces, but one that has especially grown in popularity is the conversational interface, such as with chatbots and assistants. The conversational interface makes it easier for users to give feedback but harder for developers to extract signals. This post will discuss what conversational AI feedback looks like and how to design a system to collect the right feedback without hurting user experience.

A post for developers with advice and workflows for building effective AI agents

Explore the capabilities of AI Agents and their real-world applications. CrewAI showcases the power and versatility of AI technologies across various sectors.

[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025) - chiphuyen/aie-book

See how AppFolio's AI-powered copilot Realm-X has saved property managers over 10 hours per week. Learn how they improved Realm-X's performance 2x using LangSmith and built an agent architecture with LangGraph.

A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team

Counting tokens is a useful task in natural language processing (NLP) that allows us to measure the length and complexity of a text. The two important use cases for counting the tokens are: controlling the length of the prompt - models has limit …

Presents MAG-V, a multi-agent framework that first generates a dataset of questions that mimic customer queries. It then reverse engineer alternate questions from responses to verify agent trajectories. Reports that the… — elvis (@omarsar0)

The whole point is to not let the agent do everything, but to do a… — Jerry Liu (@jerryjliu0)

— Pedro Domingos (@pmddomingos)

Learn important prompt engineering techniques to build use cases with LLMs.

A comprehensive collection of Large Language Model (LLM) resources, tools, and learning materials.

LangGraph is a multi-agent framework. This means not only interacting with other LangGraph agents, but all other types of agents as well, regardless of how they are built. Today we are taking a few steps to to build towards this vision. We are announcing: * Agent Protocol: a common interface for

Rig: Build modular and scalable LLM Applications in Rust. Unified LLM interface, Rust-powered performance, and advanced AI workflow abstractions for efficient development.

I just wanted to validate if it's ok to initialize/compile the graph once and then use it to serve multiple parallel requests in a web application. In other words is the shared state passed fro...

Learn all you need to implement streaming in production using SSE and how to handle streaming errors.

Use Prompt Canvas in LangSmith to collaborate with an AI agent to build and optimize your prompts.

✒️Kiroku is a multi-agent system that helps you organize and write documents Really complex agent (see the diagram below!) that HEAVILY involves a "human in…

Manage multiple AI agents and handle complex conversations