Build agents that scale with a zero-cost abstraction.
New tools for building agents
GitHub - tilesprivacy/tiles-notebook at dev
A notebook interface that makes working with AI agents easier. - tilesprivacy/tiles-notebook
pguso/ai-agents-from-scratch
Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.
Pi: The Minimal Agent Within OpenClaw
A gentle introduction to the Pi coding agent and why I think it’s a glimpse into the future of software.
New capabilities for building agents on the Anthropic API | Claude
Claude now offers code execution, MCP server connections, file storage, and extended prompt caching through the API—giving developers powerful tools to build agents that analyze data, connect to external systems, and maintain context for longer periods of time.
agentOS - Everything Agents Need to Run and Operate - Rivet
The complete platform for production AI agents. Stateful runtime, universal agent interface, and secure code execution. One SDK, one platform, deploy anywhere.
rivet-dev/agentos
agentOS is a portable open-source operating system for agents. ~6 ms coldstarts, 32x cheaper than sandboxes. Powered by WebAssembly and V8 isolates.
Plans & Pricing | Claude by Anthropic
Choose the Claude plan that fits how you solve problems. Free, Pro, Max, Team, and Enterprise tiers, plus API pricing for developers.
Collaborative AI Engineering: One Dev, Two Dozen Agents, Zero Alignment — Maggie Appleton, GitHub
Agent Plugins
A portable package format for reusable components that extend AI agents.
OpenPoke: Recreating Poke's Architecture
How Poke's orchestrated multi-agent system works, what OpenPoke replicates, and the lessons for builders shipping AI assistants.
Composable, fast, and secure dev environments | Workshop | Ubuntu
Launch agent-ready, sandboxed development environments with a single command. Define them via simple YAML configs, share them to recreate on different machines.
shepherd-agents/shepherd
A runtime substrate that turns an agent's execution into a reversible, Git-like trace, so meta-agents can observe, fork, replay, and revert any run. Couples agent and environments in a copy-on-write fork ~5x faster than docker commit, with ~95% KV-cache reuse on replay. Framework built for meta-agents to supervise, optimize, and train other agents
The Anatomy of an Agent Harness
Learn how agent harnesses transform AI models into autonomous work engines. Explore core components: filesystems, sandboxes, and memory.
