Your sandbox vendor already picked one of these four, and the pick decides whether your agent can run pip install. Rank them by cold start and you get the exact reverse of ranking them by how much Linux the agent gets — because the boot time is the kernel. Answer one question, does the code install things, and the field collapses.
GEPA's reported margins — up to 20% over GRPO, 13% over MIPROv2 — were all measured where an automatic checker was free and a failed run could be described in words. Two of these four optimizers run on a bare scalar; two need a sentence. What your eval function returns decides which half of the field you can use, so change the metric before you change the optimizer.
The same product has been reported at 49.0% and at 94.4% on a benchmark with the same name, depending on who ran it and when. Scores cannot arbitrate this category. What actually differs between the four — and what you cannot change after adoption — is who decides what gets remembered, who invalidates it, and whether you can get it back out.
All four can express the policy. Only two of them answer without the caller supplying the facts — and when the caller is an agent reading attacker-controlled text, that is the entire security property. The second question is the check budget: an agent makes dozens of authorization calls per task, and filtering a retrieval set makes thousands.
A chat product needs several hundred concurrent users before a dedicated GPU beats per-token pricing; an agent needs about a dozen workers, because it re-sends its whole context every step. That arithmetic — not the price per million tokens — is what should decide which of these four you build on.
Meta's 30B open-weights agent model scores 76.0 on SWE-Bench Verified and 24% on τ³-Banking. Five of its six headline numbers measure a model alone against a machine-checkable goal; the sixth measures it working with a person against a written policy — and that is the axis an always-on local assistant lives on.
All four speak CDP, so the automation code ports in a day and the SDK comparison decides nothing. The real choice is who holds the logged-in profile, the credentials that recreate it and the exit IP whose reputation you inherit — plus the fact that the latency spread between them is entirely control plane.
Five rival vendors agreed on a directory layout on 6 August, and explicitly declined to agree on install, distribution, permissions, sandboxing or provenance. The format makes one bundle of instructions plus credentialed tool access portable across six clients — which is exactly why the compensating controls are now yours.
The feature grids converged, so the decision is licence and billing meter — and every meter prices the trace archive that becomes your golden set, regression baseline and fine-tuning corpus. Instrument against OpenTelemetry, dual-write the stream somewhere you own, and the platform becomes a swappable backend.
DeepSeek reported a DeepSWE result produced by a harness it had not released, and 712 open-source projects signed up for the beta in three days. Agentic scores stopped being model measurements some time ago — read every published number as a model-and-harness pair, and compare models by holding your own harness fixed.
The top two terminal coding agents are 0.4 points apart on Terminal-Bench 2.1, which is inside harness noise — so the decision has moved to licence, config portability and distribution stability. Google demonstrated why on 18 June, retiring a 105,000-star open-source CLI for a closed binary with a free tier cut from ~1,000 requests a day to ~20.
Framework comparisons argue about graphs versus crews versus handoffs, but the metaphor stops mattering by week three. What you cannot re-pick eighteen months in is where a run lives, what resume means after a crash, and whether a human can pause a half-finished task — so choose on the state model and the rest of the comparison resolves itself.
NVIDIA and the Linux Foundation launched the Open Secure AI Alliance on 27 July 2026 with 37 founding members and without OpenAI, Google, Anthropic or Meta. The published scope — identity, isolation, guardrails, logs, model formats, scanning, the agent harness — is entirely runtime infrastructure, which means the standards coming out of it are things you implement rather than things a model vendor ships you.
A reranker touches no index and holds no state, so swapping one is an afternoon — which finally makes chasing the leaderboard rational, except the leaderboard measures the axis where these four differ least. What differs by more than an order of magnitude is the billing unit and the licence, and both bite hardest at agent scale.
These four are not four alternatives at one layer — TRL is the trainer API, Axolotl and LlamaFactory wrap it, and Unsloth rewrites its source at import time. That single fact predicts the thing you will actually feel: TRL shipped 1.9.2 in July while two of the others still pin the 0.x line. The famous speed table nobody can source is the wrong axis entirely.
Three of these four constrain the sampler so invalid output cannot be produced, and the choice between them collapses to one question: do your schemas repeat? The fourth does something categorically different, and it is the only one that can enforce the rules that actually break agents — because a grammar guarantees the enum is one of five values and says nothing about which.
Every comparison of these four opens with tokens per second on a fixed batch — the one number that transfers worst to agent traffic, where the same prompt comes back twenty times with a few hundred tokens appended. What separates them is what the KV cache is keyed on, whether constrained decoding survives a full batch, and how much of your quarter the build step eats.
All three READMEs describe the same job — run cases through a model, score the output, fail the build. But at the level of their core data structure they disagree about what an evaluation is: an attack, an assertion, or an experiment. Pick the wrong noun and the tool will not let you write the test you actually need.
Every document-parser comparison is published as an accuracy leaderboard, and accuracy is the axis that transfers worst to your documents. Two things do transfer: a layout pipeline can drop a number but cannot invent one, and the cost curves of self-hosted and hosted parsing cross at a volume you can compute in five minutes.
Every AI gateway sells the same headline feature: automatic failover to a second provider. That feature is not an availability win — it is an untested deploy that fires only during an incident, onto a model your evals never covered. Choose instead on who operates the hop, because that is the decision you cannot reverse cheaply.
Moonshot released 2.8 trillion parameters as a free download on 27 July — and priced its own API above the model it replaced, while no single GPU on the market can hold the weights. Open weights buy agent builders exactly one thing that closed APIs cannot, and it is not cost.
Before you pick a local vector store, notice that Claude Code, Cursor and Codex deleted theirs — the leading coding agents retrieve with grep, not embeddings. If your corpus still needs an index, these four are not competing products but four different architectures: a search library with no storage, a SQLite extension, an embedded engine with a write-ahead log, and a columnar format on disk.
A "guardrail" is not one thing. The open-source ecosystem settled into four shapes — a programmable rails DSL, a validator library, a safety-classifier model, and a scanner pipeline — and the 2025-26 acquisition wave decided which survived independent. Here is what each actually does, where it sits around the model, and why none of them "solves" prompt injection.
When there is no API, an agent has to drive the browser itself — and four open-source projects disagree on how it should see the page. browser-use reads the DOM, Skyvern looks at pixels, Stagehand lets you dial between code and AI, and Playwright MCP is not an agent at all but the standard browser-tool layer any model can call. Picking one is really two decisions: Python or TypeScript, and a framework or an MCP server.
Five frontier-tier models shipped inside a two-week window in June 2026. The differences are no longer about who tops MMLU — each lab is now betting on a different axis: agentic computer use, reasoning cost, multimodal latency, or pure price floor. Pick the axis before you pick the model.
Every coding agent leaves a different telemetry trail — JSONL transcripts, a SQLite store, or only a prose log — so the open-source tracker worth installing depends on which trail your agent leaves. Four trackers, four trails, plus the levers that actually cut the bill.
Every few months, four labs ship a similar-sounding open-weights flagship — MoE, long context, reasoning mode, multimodal. The benchmarks keep getting passed back and forth. The thing that actually decides which one you run in production is the axis each lab is betting on next: multimodal ecosystem, inference economics, agentic reasoning, or permissive-license frontier intelligence.
Four RL-for-trading projects, four near-identical feature lists — Gymnasium env, OHLCV ingest, PPO/SAC/A2C/DQN, backtest evaluation. The thing that actually decides which survives a serious research-or-prod loop is invisible there: who controls the simulation contract.
Most agents start cold and you spend days briefing them. OpenHuman loads a compressed model of your work life in one sync pass — here is how to install it, connect your stack, and get a useful answer in about fifteen minutes.
Three of 2026’s fastest-growing open-source agents look almost identical on a feature list — and behave like completely different species the moment you run them. A diagram-by-diagram tour of where the architectures diverge.