Langgraph

LangChain published a framework today for thinking about continual learning in AI agents — and it’s one of the clearest mental models for this problem that’s appeared in the wild. This guide takes that framework and turns it into a practical implementation playbook, with code examples for each layer and decision criteria for choosing between them. The three layers, briefly: agents can learn through context (runtime-injected instructions), storage (external memory), or weights (model fine-tuning). Each has different costs, speeds, and durability characteristics. ...

When developers talk about building AI agents that get smarter over time, they usually mean one of two very different things — and they rarely realize the ambiguity. LangChain’s Harrison Chase published a framework today that finally gives the field a shared vocabulary: continual learning for AI agents happens at three distinct layers, and conflating them leads to systems that are overbuilt for simple problems or structurally incapable of solving hard ones. ...

The AI agent framework landscape has shifted significantly in the last 90 days. The framework you chose for your agentic project in January 2026 might not be the right choice in April — and one major player has effectively retired. This is the freshest comparison available as of this morning, covering 8 SDKs and 3 inter-agent protocols. The 8 Frameworks 1. Claude Agent SDK (Anthropic) Anthropic’s first-party SDK for building agents on Claude. Deep integration with Claude Code’s subagent architecture, native MCP tool support, and CLAUDE.md context injection. Best choice if you’re building Claude-native workflows and want tight model-SDK alignment. Not model-agnostic. ...

The biggest battle in AI right now isn’t about which model is most powerful. It’s about who controls the layer between models and the real world. This is the control layer — the orchestration and management tier of agentic AI that routes tasks, governs agent behavior, manages state, and connects models to tools, data, and each other. And every major tech company is racing to own it. What the Control Layer Actually Is Think of it like this: large language models are powerful engines, but they don’t drive themselves. To do useful work at scale, you need infrastructure that: ...

Every developer who’s shipped an AI agent to production has run into the same wall: the agent remembers nothing across restarts. In-memory state is fine for demos. In production, where agents run for hours across multiple sessions, get killed by infrastructure failures, and need to pick up where they left off, in-memory state is a liability. Your agent’s entire conversational context, decision history, and accumulated knowledge evaporates the moment the process terminates. ...

Your LangGraph agent works perfectly in development. Then it hits production and you discover the problem every agent developer eventually hits: when the process restarts, your agent remembers nothing. In-memory state is fine for demos and local testing. For production agents — especially those handling multi-step workflows that can span hours, serve concurrent users, or need to resume after infrastructure failures — you need persistent state. This guide walks through adding Aerospike Database 8 as a durable memory store for your LangGraph agent. ...

Full autonomy is the goal for many agentic workflows — but full autonomy is also where most production deployments fail their first risk review. The practical path to deploying AI agents in real organizations runs through human-in-the-loop (HITL) patterns: workflows where the agent does the work, humans approve the decisions, and the system handles the handoff cleanly. LangGraph has strong native support for HITL patterns through its interrupt primitives. This guide walks through the core patterns — interrupt points, approval gates, and reversible actions — with working code you can adapt for your own agent workflows. ...

We’ve covered the “big three” AI agent frameworks — LangGraph, CrewAI, and Pydantic AI — in our 2026 framework decision guide. That guide remains the place to go for deep dives on those three. But the landscape has shifted. GitHub repositories for AI agent frameworks grew 535% between 2024 and 2025. Four more frameworks now have production deployments and genuine trade-offs worth understanding: the OpenAI Agents SDK, Claude SDK (Anthropic), Google ADK, and Dify. ...

LangChain has open-sourced Open SWE — a full software engineering agent framework built on LangGraph and their Deep Agents infrastructure — and it’s a meaningful step forward for teams who want to run coding agents that go beyond single-file edits into real, end-to-end software engineering workflows. The official release blog from LangChain dropped this week, following a preview post in January. The GitHub repository is live and active. What Open SWE Is Open SWE is an open-source framework for building and deploying internal coding agents — systems that can take a task description and see it through from understanding to implementation to pull request, running in a cloud sandbox environment along the way. ...

If you’ve been building AI agents with LangChain and wondering when the “enterprise-grade” piece would arrive, March 16 was your answer. LangChain announced a comprehensive partnership with NVIDIA to deliver what both companies are calling an enterprise-grade agentic AI development platform — combining LangChain’s LangSmith, LangGraph, and Deep Agents frameworks with NVIDIA’s full Agent Toolkit stack. At the same time, LangChain joined the Nemotron Coalition, NVIDIA’s global initiative to advance open frontier models. ...