Tag
A guide on building custom agent harnesses using LangChain's create_agent, focusing on middleware for customization.
A developer shares a 20-chapter tutorial rebuilding a Claude Code–style coding agent from scratch, showing the entire agent loop in ~6 lines, with support for local models and multiple LLM APIs.
Describes a technique to reduce LLM costs in browser agent tasks by using a single planning call followed by deterministic execution, achieving 50x cost reduction compared to standard agent loops.
A tutorial repo that teaches the Model Context Protocol (MCP) by building it step by step in Node.js, with a local-first setup using node-llama-cpp, GGUF models, and a custom agent loop.
A developer introduces Lauren, an open-source tool that makes agent loops dynamically editable mid-run, allowing users to add, refine, or merge tasks without stopping the loop.
Teknium shares recent performance improvements for tool calling in AI agents, including deferring imports, cutting 47% of per-conversation function calls, and deferring compression feasibility checks, with links to working code on GitHub.
Sharing experience from the AI loop at the Yang Zhang lab group meeting, including automated theorem proving, multi-machine collaboration, distilling a private experience base, and mentioning examples of Fields medalists using AI to solve mathematical problems.
HASP is a framework that upgrades agent skills into executable program functions acting as guardrails, enabling direct intervention in LLM agent loops and improving performance on complex tasks like web-search, math reasoning, and coding.
The author introduces `coding-review-agent-loop`, an open-source local CLI that orchestrates multiple coding agents (Claude Code, Codex, Gemini) to review each other's GitHub PRs using existing local authentication, avoiding additional API costs.
Kuri is a Zig-based browser-automation toolkit targeting AI agents, offering a 464 KB binary with ~3 ms cold start and 16% lower token usage per workflow cycle versus agent-browser.
OpenAI introduces the Codex App Server, a standardized protocol and architecture enabling developers to integrate Codex's agent capabilities across different products and IDEs. The system evolved from a practical harness reuse solution into a stable platform supporting rich interaction patterns like workspace exploration, streaming progress, and diff emission.
OpenAI publishes the first post in a series detailing how the Codex CLI agent loop works, explaining the core logic that orchestrates interactions between users, LLMs, and tool calls for software development tasks. The post covers inference, tool call execution, and the design decisions behind Codex's agent harness.