#agents

Another thing like glean that integrates and sucks in data and answers questions

Sounds about right.

I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level.

Fast, script-friendly CLI for Gmail, Calendar, Chat, Classroom, Drive, Docs, Slides, Sheets, Forms, Apps Script, Contacts, Tasks, People, Groups (Workspace), and Keep (Workspace-only). JSON-first output, multiple accounts, and least-privilege auth built in.

I'm a simple boy, so I like simple things. Agents can run Bash and write code well. Bash and code are composable. So what's simpler than having your agent just invoke CLI tools and write code? This is nothing new. We've all been doing this since the beginning. I'd just like to convince you that in many situations, you don't need or even want an MCP server.

An interesting tool that uses playwright to extract structure based on apparently accessibility roles and geometry of “important” elements and use that for an execution agent to process the page results. Important elements are somehow ranked. Then geometry is inferred from those elements.

Also relies on jest-style assertions to explicitly assert whether a step succeeded or failed.

We introduce the Berkeley Function Calling Leaderboard (BFCL), the first comprehensive and executable function call evaluation dedicated to assessing Large Language Models' (LLMs) ability to invoke functions. Unlike previous evaluations, BFCL accounts for various forms of function calls, diverse scenarios, and executability.

In 2024, SWE-bench & SWE-agent helped kickstart the coding agent revolution.

We now ask: What if SWE-agent was 100x smaller, and still worked nearly as well?

mini is for

Researchers who want to benchmark, fine-tune or RL without assumptions, bloat, or surprises
Developers who like their tools like their scripts: short, sharp, and readable
Engineers who want something trivial to sandbox & to deploy anywhere

Here's some details:

Minimal: Just 100 lines of python (+100 total for env, model, script) — no fancy dependencies!
Powerful: Resolves >74% of GitHub issues in the SWE-bench verified benchmark (leaderboard).
Convenient: Comes with UIs that turn this into your daily dev swiss army knife!
Deployable: In addition to local envs, you can use docker, podman, singularity, apptainer, and more
Tested: Codecov
Cutting edge: Built by the Princeton & Stanford team behind SWE-bench and SWE-agent.