Top AI Agent Frameworks in 2026: A Production-Ready Comparison
We tested 8 AI agent frameworks in production across healthcare, logistics, and fintech. Here’s what actually works — and what breaks when real users show up. Six months ago, picking an AI agent framework meant choosing between LangGraph, CrewAI, and AutoGen. That was the entire conversation. Now every major AI lab ships its own agent SDK. OpenAI, Anthropic, and Google all launched agent development kits in 2026. Microsoft rebuilt AutoGen from scratch. LangGraph hit 126,000 GitHub stars. CrewAI raised funding and shipped enterprise features. The result: 120+ agentic AI tools across 11 categories, and every CTO we talk to asks the same question — which one should we actually build on? Here’s the problem with most framework comparisons: they benchmark toy examples. They test “build a researc
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scan-for-secrets 0.1
Release: scan-for-secrets 0.1 I like publishing transcripts of local Claude Code sessions using my claude-code-transcripts tool but I'm often paranoid that one of my API keys or similar secrets might inadvertently be revealed in the detailed log files. I built this new Python scanning tool to help reassure me. You can feed it secrets and have it scan for them in a specified directory: uvx scan-for-secrets $OPENAI_API_KEY -d logs-to-publish/ If you leave off the -d it defaults to the current directory. It doesn't just scan for the literal secrets - it also scans for common encodings of those secrets e.g. backslash or JSON escaping, as described in the README . If you have a set of secrets you always want to protect you can list commands to echo them in a ~/.scan-for-secrets.conf.sh file. Mi
research-llm-apis 2026-04-04
Release: research-llm-apis 2026-04-04 I'm working on a major change to my LLM Python library and CLI tool. LLM provides an abstraction layer over hundreds of different LLMs from dozens of different vendors thanks to its plugin system, and some of those vendors have grown new features over the past year which LLM's abstraction layer can't handle, such as server-side tool execution. To help design that new abstraction layer I had Claude Code read through the Python client libraries for Anthropic, OpenAI, Gemini and Mistral and use those to help craft curl commands to access the raw JSON for both streaming and non-streaming modes across a range of different scenarios. Both the scripts and the captured outputs now live in this new repo. Tags: llm , apis , json , llms
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You have 50 models. Each trained on different data, different domain, different patient population. You want them to get smarter from each other. So you do the obvious thing — you set up a central aggregator. Round 1: gradients in, averaged weights out. Works fine at N=5. At N=20 you notice the coordinator is sweating. At N=50, round latency has tripled, your smallest sites are timing out, and your bandwidth budget is gone. You tune the hell out of it. Same ceiling. This is not a configuration problem. This is an architecture ceiling. The math underneath it guarantees you hit a wall. A different architecture changes the math. The combinatorics you are not harvesting Start with a fact that has nothing to do with any particular framework: N agents have exactly N(N-1)/2 unique pairwise relati
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