LangGraph

LangGraph sits at the opposite end of the spectrum from plug-and-play agent platforms. It's a Python framework for building stateful agent workflows as directed graphs, where each node is a function and edges define control flow. You write code that explicitly models how your agent thinks, backtracks, and loops. I've used it to build a research assistant that queries APIs, evaluates results, and decides whether to dig deeper or summarise. The autonomy here is structural: you define the decision points, LangGraph handles state persistence and execution. That means you can inspect exactly why an agent chose a path, replay from any checkpoint, and inject human approval gates mid-flow. The learning curve is real. You're not prompting a black box; you're architecting state machines with LLM calls as components. Documentation is thorough but assumes comfort with graphs and async Python. Where LangGraph excels: production systems where you need audit trails, complex branching logic, or multi-agent coordination with explicit handoffs. I built a contract review agent that routes clauses to specialist sub-agents, aggregates findings, and escalates ambiguities. That kind of choreography is painful in higher-level frameworks. Failure modes cluster around complexity. Small projects don't justify the overhead. Debugging cyclic graphs with LLM non-determinism is occasionally maddening. The open-source version is full-featured, but LangChain's hosted platform (LangGraph Cloud) adds deployment, monitoring, and human-in-the-loop APIs at a price. Against competitors: more control than AutoGPT or CrewAI, less opinionated than Semantic Kernel. If you want guardrails and explainability over speed-to-demo, this is the pick. If you just need an agent to answer emails, it's overkill.