Topic
Langgraph
13 episodes
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3 Years of Graph Engineering with LangGraph
Cooper and Miles unpack LangChain's argument that “graph engineering” is not a new magic category, but a practical way to combine deterministic workflow control with agentic flexibility in LangGraph. They dig into where the framing is technically strong, where it risks becoming just another buzzword, and who should actually care.
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LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls MachineLearningMastery
Pippa and Tyler dig into the article’s real argument: these frameworks are not interchangeable, because each one sits at a different layer of the stack. They test the claims against production reality, especially overhead, debugging, and when abstraction stops paying for itself. The episode lands on a practical view: use the lightest layer that actually earns its keep, and don’t confuse orchestration with magic.
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You Probably Don’t Need an Agent Framework | Towards Data Science
Justy and Cody discuss Shuai Guo's argument that most LLM applications need a clear workflow, not an autonomous agent — and you can build one in plain Python without a framework. They connect it to their past coverage of harness design and loop engineering, agree the core insight is sound, but push on where the 'workflow first' framing breaks down.
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The Protocol That Cleaned Up Our Agent Architecture | Towards Data Science
MCP (Model Context Protocol) is an open standard for how agents discover and call tools. Instead of scattering tool definitions across multiple agent files, you run tools on a separate server that agents connect to at runtime. The protocol provides a clean interoperability boundary—any MCP-compatible client can call any MCP-compatible server without integration work. For teams with multiple overlapping agents, this eliminates schema drift, simplifies approval gates, and decouples the tool layer from the orchestration layer.
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Fault Tolerance in LangGraph: Retries, Timeouts and Error Handlers
Justy is hyped about LangGraph’s first-class fault tolerance primitives (retries, timeouts, error handlers) for production agents, but Cody wants to dig into whether the hype matches reality.
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Implementing Prompt Compression to Reduce Agentic Loop Costs MachineLearningMastery
Justy and Cody kick around whether prompt compression is actually a smart production habit or just another neat demo. Cody starts skeptical about summary drift and hidden complexity, then they get concrete on why long agent loops get expensive fast, what the article's Python example is really proving, and where compressed history plus distilled instructions make sense right now.
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AgentSPEX: An Agent SPecification and EXecution Language
Justy and Cody dig into AgentSPEX, a YAML-based language and runtime for building LLM agents with explicit control flow, typed steps, reusable submodules, parallel execution, and state management. They focus on the gap between loose ReAct prompting and Python-heavy orchestration tools, then unpack how AgentSPEX separates workflow specification from execution while still supporting tools, sandboxing, checkpointing, replay, and visual editing. The conversation lands on who this is for, where it feels shippable, and what a solo builder could try this weekend.
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Google finds that AI agents learn to cooperate when trained against unpredictable opponents
Google's Paradigms of Intelligence team discovered that AI agents naturally develop cooperative behaviors when trained against diverse, unpredictable opponents rather than being programmed with hardcoded coordination rules. This breakthrough offers a scalable alternative to traditional multi-agent frameworks by using standard reinforcement learning techniques to produce adaptive social behaviors through in-context learning.
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LangChain vs LangGraph: Why One's a Drive Through and the Other's a Buffet
In this episode, we explore the differences between LangChain and LangGraph, illustrated through food analogies. We discuss their functionalities, real-world applications, and the importance of choosing the right tool for the task at hand. The episode emphasizes decision-making in AI and how it impacts efficiency and user experience.
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LangGraph Explained from Scratch | Aman Kharwal
This episode dives into LangGraph, a new library that transforms how we build intelligent agents using Large Language Models. We'll explore its unique graph-based approach, practical applications, and why this matters for developers and users alike.
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Deep Agents overview Docs by LangChain
Explore the capabilities of Deep Agents in LangChain, a powerful tool for building specialized agents capable of handling complex tasks with planning and context management.
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LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones
LangChain and LangGraph have released their first major versions, v1.0, focusing on agent flexibility, middleware, and improved model integrations, while ensuring stability and backward compatibility for developers.
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Doubling down on DeepAgents
In this episode, we dive into the exciting updates of LangChain's DeepAgents 0.2 release, exploring its new features, the importance of planning tools, and how it distinguishes itself from LangChain and LangGraph.