SOTA.exe — State of the Art

SOTA / State of the Art

Who sets the standard — and who could take their place?

v1 · Operator curation (Ki) · Last updated

A field guide, not a world championship. Each SOTA pick is an editorial judgment within the stated scope, based on the sources reviewed. We have not rerun the benchmarks or established an overall winner.

Launch research seed — Ki’s individual sign-off is pending. Author-reported results and community examples are labeled. Scores and voting are not connected yet.

A–G reuse Measure rubric v0.2 exactly. H–J are supplemental lenses, not extra Measure points. Ki’s setup is a challenger, not a verified #1; its loadout is a seeded overview, not public benchmark evidence.

A · Measure dimension

Automation depth

SOTA PICK · Recoverable, unattended execution

Temporal · durable agent workflows

@temporalio · as of

Why this pick

  • Persists workflow history and replays it after a worker restart instead of restarting the entire agent task.
  • The agent integration separates model/tool activities from durable coordination, with retries and long-running approval flows.

Limit: A reliability architecture pick, not a measured uptime winner. Retried side effects still need idempotency; it does not repair arbitrary infrastructure.

Evidence

Challengers / runners-up

Judging criterion: Crash recovery, bounded retries, observable failures and no duplicate external actions.
Challenge this area →

B · Measure dimension

Tool connection breadth

SOTA PICK · Authenticated tools across services

Composio

@ComposioHQ · as of

Why this pick

  • Publishes a catalog spanning Gmail, Google Calendar, Slack and financial-service tools such as Stripe and Alpaca.
  • Per-user authentication, token refresh and event triggers are part of the integration layer, not just tool schemas.

Limit: Catalog breadth is not task success. Financial writes need separate approvals; a connector listing is not a security endorsement.

Evidence

Challengers / runners-up

Judging criterion: Distinct working read/write services, auth isolation and successful end-to-end tasks.
Challenge this area →

C · Measure dimension

Agent autonomy

SOTA PICK · Stateful orchestration and worker delegation

LangGraph

@langchain-ai · as of

Why this pick

  • Makes persistent state, resumable execution and human intervention explicit parts of an agent graph.
  • Its Deep Agents ecosystem adds planning, subagents and filesystem tools on top of that durable runtime.

Limit: An orchestration pick, not proof of a 24/7 autonomous operator. Merge/deploy policy and real completion rates remain application responsibilities.

Evidence

Challengers / runners-up

Judging criterion: Delegation success, restart-safe handoffs and policy-gated unattended completion.
Challenge this area →

D · Measure dimension

Memory and long context

SOTA PICK · Long-horizon conversational recall

Hindsight

@vectorize-io · as of

Why this pick

  • The authors report 83.6% LongMemEval accuracy with an open 20B backbone, versus 39% for full context with the same backbone.
  • Retain, recall and reflect are explicit operations; the team publishes benchmark results alongside the implementation.

Limit: Author-reported, not independently rerun here. Different backbones, budgets and dataset settings must not be treated as a universal ranking.

Evidence

Challengers / runners-up

Judging criterion: Same-model recall accuracy, temporal updates, retrieval cost and portable persistent state.
Challenge this area →

E · Measure dimension

Embodiment

SOTA PICK · Open, cross-device smart-glasses I/O

MentraOS

@Mentra-Community · as of

Why this pick

  • Exposes supported glasses’ displays, microphones, cameras and speakers through a common application interface.
  • Publishes its SDK and device support matrix, including Even Realities and Vuzix hardware, so integration claims can be checked.

Limit: A glasses interoperability pick, not a voice-latency or watch-battery winner. Hardware capabilities vary; no comparable all-day glasses + watch trial was found.

Evidence

Challengers / runners-up

  • LiveKit Agents @livekit

    Realtime voice/video agents and mobile clients; not itself a wearable setup.

  • Ki · Earth Online / hightech-wellness @ki

    Operator-reported glasses + Apple Watch + lifelog integration; public latency, battery and reliability evidence is still missing.

Judging criterion: Supported body-worn inputs/outputs, task latency, all-day reliability and consent controls.
Challenge this area →

F · Measure dimension

Self-modification

SOTA PICK · Measured, feedback-driven prompt changes

GEPA · DSPy prompt optimization

@gepa-ai · as of

Why this pick

  • Proposes prompt edits from execution feedback, then evaluates candidates rather than trusting the agent’s own confidence.
  • Across six tasks, the paper reports gains over GRPO with up to 35× fewer rollouts; model weights remain fixed.

Limit: A bounded optimization method, not a self-maintaining production agent. Held-out tests and rollback are still needed before promoting edits.

Evidence

Challengers / runners-up

Judging criterion: Held-out improvement after self-edits, with a fixed budget, regression checks and rollback.
Challenge this area →

G · Measure dimension

Life penetration

SOTA PICK · Reproducible personal-assistant ecosystem

OpenClaw

@openclaw · as of

Why this pick

  • One self-hosted assistant connects to everyday channels including WhatsApp, Telegram and Slack with local state and memory.
  • Its public showcase documents personal workflows beyond coding; the implementation is available to inspect and adapt.

Limit: Examples are community reports, not audited daily usage. Channel count is not life-domain coverage, and health/finance autonomy is not established.

Evidence

Challengers / runners-up

Judging criterion: Distinct life domains actually used weekly, completed tasks and the human correction rate.
Challenge this area →

H · Supplemental lens

Evaluation & benchmarks

SOTA PICK · Reproducible agent evaluation infrastructure

Harbor · Terminal-Bench harness

@harbor-framework · as of

Why this pick

  • The official Terminal-Bench 2.0 harness runs different agents against versioned task environments.
  • Supports local Docker runs and cloud environments, keeping the agent, model and dataset explicit in each experiment.

Limit: This selects an evaluation method, not the top-scoring agent. Terminal tasks do not measure every personal-assistant capability.

Evidence

Challengers / runners-up

  • Inspect AI @UKGovernmentBEIS

    Composable datasets, agents, scorers and sandboxed evaluations.

Judging criterion: Versioned tasks, reproducible environments, outcome checks and disclosed model/budget.
Challenge this area →

I · Supplemental lens

Security & secrets

SOTA PICK · Execution boundaries and scoped credentials

Anthropic · sandbox + credential proxy

@anthropic · as of

Why this pick

  • Combines OS-enforced filesystem and network boundaries; the engineering report measures 84% fewer permission prompts in internal use.
  • The web environment’s Git proxy checks repository and branch scope before attaching credentials outside the sandbox.

Limit: Fewer prompts is not an attack-blocking score. Correct configuration, secret redaction and adversarial testing remain necessary; no immunity is claimed.

Evidence

Challengers / runners-up

Judging criterion: Least privilege, secrets outside model context, restricted egress and auditable approvals.
Challenge this area →

J · Supplemental lens

Cost efficiency

SOTA PICK · Reducing tool-context token overhead

Anthropic · code execution with MCP

@anthropic · as of

Why this pick

  • Loads tool definitions on demand and processes intermediate data in code instead of passing everything through the model.
  • The published example reduces context from 150,000 to 2,000 tokens (98.7%); this is an example result, not a fleet-wide saving.

Limit: Runtime, retries and code errors can erase savings. No claim of the lowest total cost or cheapest model is made.

Evidence

Challengers / runners-up

  • LiteLLM @BerriAI

    Multi-provider routing and cost tracking; no automatic quality-matched savings guarantee.

Judging criterion: Total cost per successful task at matched quality, including retries, latency and compute.
Challenge this area →

Have better evidence? Register a loadout with a reproducible run, its date, model and budget. Onboarding currently saves a local draft; it does not submit a public challenge or cast a vote.