We ran the same memory job through four open source memory frameworks, installed all eight tools, and compared default telemetry, prompt size per memory write, install size, advisories, and pricing.
We ran the same two agent job in CrewAI and eight alternatives and compared default telemetry, context handed to the second agent, install size, advisories, and pricing.
We ran the same TypeScript agent job in Mastra, the Vercel AI SDK, and LangGraph.js and compared runaway loop limits, memory, human approval across restarts, install size, and pricing.
An AgentScope vulnerability lets any caller spawn a process via the MCP workspace API. We tested 2.0.11, the open fix PRs, and why pip-audit stays silent.
We ran the same tool calling job in Microsoft Agent Framework, Semantic Kernel, and AutoGen and compared support status, tool loop and memory defaults, install size, security, and how to migrate.
We built the same tool-calling agent in 10 TypeScript AI agent frameworks and compared install size, bundle size, cold start, memory, prompt overhead, and npm adoption to find the best fit for each Node.js team.
We built the same two-agent job in 10 multi-agent frameworks and compared model calls, prompt traffic, context passing, memory, and adoption to find the best fit for each team.
We ran the same agent job offline in 11 open source frameworks and compared memory, install size, outbound telemetry, serving options, and advisories to find the 10 best you can self host.
LangChain, LangGraph, and Deep Agents are three layers of one stack. We ran the same tool calling job in each, captured every model request, and compared request size, install footprint, memory, and tracing defaults.
We tested 9 LangGraph alternatives, from CrewAI and Pydantic AI to Mastra, by running the same tool calling job in each with outbound traffic logged, and compared install size, telemetry, checkpoints, and managed options.