Open Source

You know that feeling when you’re deep in a Slack thread debugging a production issue, and you wish Claude could just jump into the conversation? Yeah, me too. And the usual loop is: copy the context, switch tabs, paste into chat, get an answer, switch back, paste the result. Every time. Claude Tag solved this at Anthropic — but it’s closed source, locked to Anthropic’s infra, and has zero audit trail. And I’ve been waiting for an open version since I first tried it. ...

Ever wanted an AI agent to just write a video for you — not a script, not a storyboard, but the actual video file? And I’ve been chasing this for months. Claude can write code. Cursor can debug. But video has always meant opening a timeline, tweaking keyframes, or wrestling with React components. Then HyperFrames dropped. And it’s not just another renderer — it ships 19 built-in AI Agent Skills that let Claude Code, Cursor, or Codex generate and render actual video projects. I spent an afternoon with it. Here’s what I found. ...

Your AI agent just wrote 200 lines of code. But it has no clue Issue #42 exists. Has no idea the last CI run failed. Can’t see the three open PRs you need reviewed. So you Alt-Tab out, open gh CLI, check manually, paste results back into the agent, and continue the slow dance. So that’s the gap GitHub MCP Server fills — and it’s been sitting at 30,924 stars since GitHub open-sourced it. I’ve been running it for a week across Claude Code, Codex, and Cursor. Let me show you what it actually does. ...

Ever found yourself copy-pasting SQL results from pgAdmin back into your chat with Claude Code, hoping it understands the schema? Yeah, me too. The whole “AI agent can’t see your database” problem has been the single biggest productivity gap in the MCP workflow — until now. Google just dropped MCP Toolbox (formerly genai-toolbox), an open-source MCP server that gives AI agents direct access to 15+ databases. Not a wrapper. Not a middleware. Native database connectivity through the MCP protocol, zero boilerplate. ...

Ever started a fresh Claude Code session and spent the first 50 tokens explaining the same project context you laid out yesterday? Me too. And it’s the cold-start tax — on a paid subscription those losses add up fast. So I’ve tried CLAUDE.md (manual upkeep), –continue (full transcript replay, token-heavy), claude-mem (my earlier review), and external memory tools (API calls, data leaves your machine). All work, but none solve the problem cleanly. Then I stumbled on recall — a 3-day-old project with 216 stars. Here is a Claude Code plugin that auto-captures your session and summarizes it locally, zero LLM calls and zero outbound traffic. Worth testing. ...

You built an AI agent that handles complex workflows — then on the next conversation, it’s a clean slate. No memory of what you discussed, no context carryover. Every session starts at zero. That’s the problem Cognee solves. It’s the open-source AI memory platform (18.4k★, actively maintained) that gives agents persistent long-term memory using a self-hosted knowledge graph. Think structured recall, not just vector embeddings. I pip installed it, checked the API, and ran through the basics. Here’s what I found. ...

So you’ve got OpenClaw for TypeScript, Hermes for general-purpose agent work, Goose for CLI-first automation, MiMoCode for MCP-native workflows, OpenCode as a Claude Code clone, and Souveraine for reasoning-heavy tasks. That’s six terminals, six CLIs, six sets of commands, six ways to attach a file. And that’s not even counting OpenHands. Then I found Junction — a VS Code extension that pulls all seven into one sidebar panel. 512 stars in 3 days as of this writing. Here’s why that number already makes sense. ...

Ever noticed how the pace of an open-source project can tell you more about its future than any feature list? Four days ago, Omnigent had 624 stars. Today it’s at 3,842. That’s not normal growth — that’s a signal. I first looked at Omnigent last week when it was fresh out of the gate. The idea was good: one CLI to orchestrate Claude Code, Codex, Pi, and your own agents. But the community has been shipping at a rate that’s hard to ignore — 481 commits in 8 days, 428 forks, a desktop app, Cloudflare Containers support, and a policy engine that actually feels production-grade. ...

Ever since Claude Code launched, the conversation in every dev discord I’m in has been the same: “Can we get this without the Anthropic lock-in?” The answer, as of mid-2025, is Pi Agent Harness — and the community has spoken loudly. 64,000 GitHub stars, 7,800 forks, and 4,600+ commits from a core team that includes mitsuhiko (yes, the creator of Flask and Sentry). But that’s not just traction. No, that’s a movement. ...

Ever tried building an AI agent from scratch? I spent a weekend with LangChain once, and by the end I had a lot of respect for anyone shipping a CrewAI pipeline to production. The code complexity is real. Dify and n8n simplify things with drag-and-drop — but sometimes you don’t want to wire boxes and arrows either. You just want to describe what you need and have it built. So when I found Nexent — a zero-code platform that generates agents from plain English — I had to test it. 5,100 GitHub stars, MIT license, and v2.0 just dropped with a feature most agent builders don’t have: A2A protocol for agent-to-agent collaboration. ...