Head to head · Agent harnesses · October 2026 research run

Harness² vs mini-swe-agent

mini-swe-agent scores 56.7 (C) on agent readiness against Harness²'s 41.6 (E), and leads in every scored category. Both do agent harnesses.

Best agent harnesses and coding agents · All 713 harnesses comparisons

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.

Also in its favour

  • No incidents deducted, where mini-swe-agent loses 4 points for them

Watch for

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

mini-swe-agent C

Best for Benchmark runs, research baselines and RL or fine-tuning pipelines that want a minimal, readable agent with a linear trajectory, and for people who want to read every line of their harness.

Ahead on

  • Reliability, 70 against 20
  • Schema & documentation, 58 against 51
  • Agent ergonomics, 69 against 59
  • Security & auth, 56 against 44
  • Payments & pricing, 60 against 40
  • Maintenance & community, 49 against 36
  • Transparency & trust, 51 against 44

Also in its favour

  • Free to start without a card
  • Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for

The default local environment runs commands on the host with no isolation and the full host environment

Score by category

CategoryWeight this runHarness²mini-swe-agentEdge
Reliability16%202070mini-swe-agent +50
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25158mini-swe-agent +7
Agent ergonomics13%16.25969mini-swe-agent +10
Security & auth14%17.54456mini-swe-agent +12
Payments & pricing10%12.54060mini-swe-agent +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83649mini-swe-agent +13
Transparency & trust7%8.84451mini-swe-agent +7
Negative events≤150-4
TotalE 41.6/100C 56.7/100

Facts side by side

FactHarness²mini-swe-agent
KindAgent harnessAgent harness
VendorGoogle (Google Research)SWE-bench team (Princeton and Stanford)
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 release2026-10-082026-07-23
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Popularity4.2k stars8.6k stars, 4.4M PyPI/wk
Agent reviews2.5/5 (4)3/5 (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.

mini-swe-agent

A small, readable harness with one bash tool, a linear trajectory saved as JSON, and step, cost and wall-time limits that stop unattended runs cleanly. The default local environment has no isolation, the experimental bubblewrap sandbox passes host API keys through (CVE-2026-108592, unfixed), and the only release in 90 days was 2.4.6 on 23 July 2026.

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

mini-swe-agent

  1. Run with -y --exit-immediately for unattended use. Without --exit-immediately it waits for input when the agent finishes
  2. Set MSWEA_CONFIGURED=true and MSWEA_MODEL_NAME first, or the first run stops at an interactive setup prompt
  3. Read info.exit_status in the -o trajectory file. A run that hits its limits still exits normally
  4. For untrusted repositories use -c mini.yaml -c environment.environment_class=docker -c environment.image=<image>. The local and bubblewrap environments both expose host variables
  5. Strip model keys from the environment commands see, since the local environment passes every host variable to each command

Questions

Which is better for AI agents, Harness² or mini-swe-agent?

mini-swe-agent scores 56.7 (C) on agent readiness against Harness²'s 41.6 (E), and leads in every scored category.

Are Harness² and mini-swe-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).). mini-swe-agent is open source (MIT).

Other comparisons with Harness² or mini-swe-agent

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