harness
OpenAI turns the Codex harness into a product with an Agents API News
OpenAI put an Agents API into public beta on 10 September 2026 that lets developers build on the same managed Codex harness its own products use, with OpenAI handling session orchestration, context compaction and recovery - making the scaffolding around the model, rather than the model, the thing being sold.
A general vision model can drive a robot if you give it the right interface News
Researchers report that off-the-shelf vision-language models can control robots with no robotics training at all, provided they are handed a simple menu of semantic actions - suggesting a large share of robot capability sits in the interface rather than in the model.
Microsoft's AutoSaddler treats the agent harness as code to be patched, and gains about ten points on three benchmarks News
Microsoft researchers built a system that reads an agent's failure traces, writes structured patches to the harness around the model, and keeps only the patches that survive validation, improving three separate long-horizon benchmarks by 9 to 10 points.
OpenAI open-sourced the agent loop, not the model News
OpenAI released the Codex harness under Apache-2.0, opening the execution runtime that powers its app, CLI, and IDE extension, and on August 24 deprecated the older codex mcp-server command in favor of the new app server.
A proxy with 49,000 stars keeps Claude Code and swaps the model News
Free Claude Code, an MIT-licensed local proxy that lets Claude Code talk to roughly 50 other model providers while preserving Anthropic's wire protocol, has passed 49,000 GitHub stars, and its per-tier routing shows exactly which layer users actually value.
A robot system that improves without touching the model News
Researchers at Tsinghua froze the robot's underlying policy entirely and improved performance by evolving the scaffolding around it instead, shipping versioned packages of critics, recovery playbooks and tools rather than new weights.
A runbook, not a model, hit 95 percent on a live agent benchmark for 15 dollars News
StateM reaches 95.3 percent raw accuracy on Terminal-Bench 2.1 across 445 trials without changing any model weights, using a state-machine runtime and a reusable runbook, at about 15 dollars of final-score API spend against 574.68 dollars for the reference run.
The benchmarks say Opus 5 improved; the people using it disagree News
Anthropic reports Opus 5 as state of the art on coding and knowledge work, while developers on Hacker News and Reddit describe a model that overreaches and burns tokens, and the Claude Code system prompt grew by 48,736 tokens in a single release.
A stronger model built a wrapper that nearly doubled a weaker one's score News
Researchers had a strong model design inference-time scaffolding for weaker models, lifting their average score on four reasoning benchmarks from 0.49 to 0.91 without changing a single parameter.
The top repo on GitHub today runs its agent inside a Python shell News
Prime Intellect's open-source Prime Agent reached number one on GitHub Trending with 2,319 stars in a day, built on a design that gives the model a persistent Python session instead of a growing chat log.
The harness, not the model, moved DeepSeek's score by twenty tasks News
Independent benchmarker Harrison 'sentdex' Kinsley re-ran DeepSeek V4 Flash 0731 on the same 89-task terminal benchmark under a different agent harness and watched it go from 44 solved to 64 solved, without changing the model.
The Same Model Scores 52 or 81 Percent Depending on the Code Wrapped Around It News
A new agent harness lifts Qwen 3.7-Plus from 51.8% to 80.7% on a long-horizon coding benchmark without touching the model, by keeping task state outside the conversation and updating it only from facts a read-only auditor verified in the environment.
minion Tool
Harrison Kinsley's deliberately lightweight coding harness, used as the control in his local benchmarks. Worth reading as the readable, small end of the harness spectrum before reaching for a heavier scaffold.
harness-training Tool
A small PyTorch-shaped framework for treating an agent harness as the thing being trained: the harness file is the weights, an improvement agent is the gradient estimator, a deterministic task panel is the loss, and promotion or rejection is the optimizer step. Built around reproducible, deterministic runs.
StateM Tool
An open-source state-machine runtime for long-running CLI agents: durable states, checked transitions, hooks, and shareable runbooks that survive across models. Its published runbook took an unmodified frontier model to 95.3 percent on Terminal-Bench 2.1.
Prime Agent Tool
Open-source self-improving coding agent that gives the model a persistent Python session as its main tool - files, shell, sub-agents and context management all happen as code, and working state survives past a single chat window. MIT licensed; number one on GitHub Trending today.
OpenAI Agents API Tool
Public beta released 10 September. Build agents on OpenAI's managed Codex harness, with OpenAI handling session orchestration, context compaction and recovery; supports durable sessions, streamed progress, custom tools and MCP servers.
Oh My Pi Tool
Full-featured terminal agent harness that an independent benchmarker measured lifting DeepSeek V4 Flash 0731 from 44 to 64 solved tasks on an 89-task terminal benchmark, with no change to the model. It costs several times the tokens per solve.
Codex app-server Tool
OpenAI's now-open-source agent harness, exposed as a bidirectional JSON-RPC server you can embed in your own application: persistent threads, streamed events, mid-turn interruption, client-owned tools, and human approval handoffs. Apache-2.0.
Codex Harness Tool
The Apache 2.0 agent runtime behind OpenAI's Codex, usable directly if you would rather run the loop yourself than buy the managed version. Ships as a CLI for bounded jobs, an SDK for programmatic control, and an app-server for persistent threads with human approvals.
AutoSaddler Tool
Microsoft's released framework for automatically improving an agent harness from its own failure traces. It diagnoses failed runs, generates structured patches to prompts, tool configurations and control logic, and keeps only patches that survive held-out validation. Reported gains of 9 to 10 points on GAIA2, SWE-Bench Pro and Terminal-Bench 2.0 without touching model weights.