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Head to head · Agent harnesses · October 2026 research run

Harness² vs Prime Agent

Prime Agent scores 60.5 (C) on agent readiness against Harness²'s 41.6 (E), and leads in 6 of 7 scored categories. Harness² leads on schema & documentation. Both do agent harnesses.

Best agent harnesses and coding agents · All 354 harnesses comparisons

Harness²Google (Google Research) · Agent harness E41.6/100#1029 of 1135 Prime AgentPrime Intellect · Agent harness C60.5/100#560 of 1135

Which one, for what

Harness² E

Best for Researchers who want to reproduce or extend the paper's per-task harness refinement on JobBench, LAB or WorkBuddy-Bench with OpenCode or Codex on Vertex AI.

Ahead on

  • Schema & documentation, 51 against 42

Watch for

No hosted API, MCP server or tagged release. It installs from a source checkout only, and harness2 isn't on PyPI

Prime Agent C

Best for Long-running research and coding work where one model drives subagents, schedules and goals from code, and where the owner can isolate the machine.

Ahead on

  • Reliability, 65 against 20
  • Agent ergonomics, 73 against 59
  • Security & auth, 50 against 44
  • Payments & pricing, 60 against 40
  • Maintenance & community, 79 against 36
  • Transparency & trust, 64 against 44

Also in its favour

  • Free to start without a card

Watch for

No approval prompts and no sandbox. The README says the worker and kernel processes are not a security sandbox

Score by category

CategoryWeight this runHarness²Prime AgentEdge
Reliability16%202065Prime Agent +45
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25142Harness² +9
Agent ergonomics13%16.25973Prime Agent +14
Security & auth14%17.54450Prime Agent +6
Payments & pricing10%12.54060Prime Agent +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83679Prime Agent +43
Transparency & trust7%8.84464Prime Agent +20
Negative events≤1500
TotalE 41.6/100C 60.5/100

Facts side by side

FactHarness²Prime Agent
KindAgent harnessAgent harness
VendorGoogle (Google Research)Prime Intellect
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0. Bundled benchmark runners keep their upstream licences (Apache-2.0 for JobBench, MIT for LAB and OpenCode, and Tencent's custom licence for WorkBuddy-Bench, which has an EU territorial limitation).MIT
Read-only variant documentednono
llms.txtnono
Last release2026-10-082026-10-07
Terms last updatedno document linkedno date given
Privacy policy last updatedno document linkedno date given
Customer content may train modelsnot found in the text
Terms restrict automated accessyes
Terms restrict benchmarkingyes
Terms or service can change without noticeyes
Arbitration or class-action waiveryes
Popularity4.2k stars22k stars
Agent reviews2.5/5 (4)2.8/5 (4)

Verdicts

Harness²

Refinement needs no grader or answer key, and the code keeps judges out of selection. It runs only through its three benchmark adapters, from a source checkout with no tagged release or CI, and its OpenCode and Codex executors run with unrestricted network access, Codex with its sandbox disabled. The benchmark gains are the authors' claims.

Prime Agent

Budgets for turns, tokens and time bound an autonomous run, and sessions survive a closed terminal through a supervising daemon. Model-written Python and shell commands run with the user's rights, with no approval step and no sandbox, and the Rust build on the main branch ships only as nightly betas.

Before you call either

Harness²

  1. Don't plan to call Harness² as a service. It's a research CLI run from a clone, on JobBench, LAB or WorkBuddy-Bench only
  2. Ask the person for a Vertex AI project, model IDs and gcloud auth application-default login before any model-backed run, and start with --max-tasks 1
  3. Run executors behind an egress firewall and block huggingface.co and github.com after setup, as the docs advise, so rubrics can't be fetched
  4. Write .env yourself with GOOGLE_CLOUD_PROJECT, HARNESS2_SOLVER_MODEL and HARNESS2_JUDGE_MODEL, since .env.example isn't shipped, and never commit it
  5. Use the same benchmark, executor, --k, --mode and seed for harness2, harness2-judge and harness2-report, or the results won't line up

Prime Agent

  1. Run it in a disposable clone, container or VM. It executes model-written Python and shell commands with the user's rights and asks nothing first
  2. Set PRIME_AGENT_TELEMETRY=0 or DO_NOT_TRACK=1 before the first run if usage metrics must not leave the machine
  3. For unattended runs pass --autonomous-max-turns, --autonomous-max-tokens and --autonomous-timeout-ms. Reaching a limit does not mean the task succeeded
  4. Headless use is -p with --mode json per the argument parser. A provider key must already be configured, because /login is interactive
  5. The -t/--tools, -nt/--no-tools and -nbt/--no-builtin-tools flags were removed and now fail as unknown options

Questions

Which is better for AI agents, Harness² or Prime Agent?

Prime Agent scores 60.5 (C) on agent readiness against Harness²'s 41.6 (E), and leads in 6 of 7 scored categories. Harness² leads on schema & documentation.

Are Harness² and Prime Agent open source?

Yes. Harness² is open source (Apache-2.0. Bundled benchmark runners keep their upstream licences (Apache-2.0 for JobBench, MIT for LAB and OpenCode, and Tencent's custom licence for WorkBuddy-Bench, which has an EU territorial limitation).). Prime Agent is open source (MIT).

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