Head to head · Agent harnesses · October 2026 research run

mini-swe-agent vs Orca

mini-swe-agent scores 59 (C) on agent readiness against Orca's 57.4 (C), and leads in 4 of 7 scored categories. Orca leads on schema & documentation and maintenance & community. Both do agent harnesses.

Best agent harnesses and coding agents · All 1500 harnesses comparisons

Which one, for what

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, 65 against 58
  • Security & auth, 58 against 43
  • Payments & pricing, 60 against 40

Also in its favour

  • Free to start without a card

Watch for

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

Orca C

Best for Developers who want an orchestrator agent to split work across parallel coding agents, on desktop or mobile.

Ahead on

  • Schema & documentation, 65 against 58
  • Maintenance & community, 75 against 32

Watch for

Anonymous usage data collected by default, with an opt-out

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Maintenance & community Orca +43

mini-swe-agent 32

2.4.6 on PyPI on 2026-07-23, 104 days before this check, and still the newest tag (10). No release in the 90 days to 4 November (0). Main hasn't moved since 3 September (three commits since 23 July, one from pre-commit-ci). Outside fixes were merged within days through July, but no pull request has been merged since #917 (17 August), 77 of the 85 pull requests numbered after it still carry open merge refs on the git remote, and maintainer reply times couldn't be read; the 18 October shields.io counts are struck (5). The Python library ships in the same package as the CLI and is current (10). Dependabot and pre-commit-ci run, and CI workflows exist with their current state unconfirmed (7).

Orca 75

Tags to v1.4.251 and commits on 6 November 2026 (30). Many releases in 90 days (20). GitHub issues and pull requests weren't read (GitHub's pages refuse these sessions), so that line is scored absent (0). Current builds (15). Extensive CI (10). 30 + 20 + 0 + 15 + 10 = 75.

Payments & pricing mini-swe-agent +20

mini-swe-agent 60

Self-hosted rule. No payment protocol (0). Free under MIT with nothing to buy (20). No card or account for the software, and it runs against a local model (20). An agent can install it from PyPI and run a task with no sign-up, given a model endpoint (20).

Orca 40

Scored on the cloud option. No protocol (0). The cloud price wasn't found on the pages read (0). The desktop app is free and MIT with no card (20). The local app needs no sign-up that we found (20). 0 + 0 + 20 + 20 = 40.

Security & auth mini-swe-agent +15

mini-swe-agent 58

Harness reading of the framework checklist, used for every harness in this batch. 30 for what leaves the machine by default, 20 for approvals and sandboxing, 15 for prompt-injection posture, 15 for audit and 20 for the security programme. No telemetry in mini's own source, and prompts go only to the configured provider. LiteLLM's own network behaviour wasn't checked (28). The CLI asks before each command by default, with regex whitelists and -y to skip, but the Python DefaultAgent never asks, the default local environment has no isolation and passes the full host environment to every command, and the experimental bubblewrap sandbox leaks host variables and shares the network (CVE-2026-108592). Docker forwards no host variables unless listed (8). No prompt-injection guidance found (2). A full trajectory of every command and output saved as JSON after each step, and a trajectory inspector (13). A security policy (docs/SECURITY.md) naming four maintainers to contact by email, with no response time, which meets the disclosure-policy reading at 7 as for cowagent and tokenrip. No bounty or advisory page, and CVE-2026-108592 (VulnCheck, 10 October) still has no fix in any release or on main. Compromised LiteLLM 1.82.7 and 1.82.8 are excluded in the dependency pins since 24 March 2026 (#794) (7).

Orca 43

Harness reading. Anonymous usage data with an opt-out, per the README's link to its telemetry docs (15 of 30). Worktrees per agent and a sandboxed Linux build (12 of 20). No prompt-injection guidance (5). No audit log (5). A dependency-security workflow (6 of 20). 15 + 12 + 5 + 5 + 6 = 43.

Reliability mini-swe-agent +7

mini-swe-agent 65

Read as a local package, graded on 2.4.6, the release pip install mini-swe-agent installs, and still the newest tag on 4 November 2026. One official PyPI package with Python 3.10 or newer stated (20). A pytest workflow runs on every push to main with Podman, bubblewrap and Apptainer installed, 41 test files and Codecov coverage, but GitHub's pages refused this run, so whether main passes is unconfirmed (15). Issue counts and types couldn't be read (the 18 October counts came from shields.io, a third-party copy, and are struck). The git remote shows outside bug fixes merged through 22 July, such as #915, #896 and #886, and nothing merged into main since apart from a pre-commit update on 17 August, while 77 of the 85 pull requests numbered after #917 still carry open merge refs (10). Versions follow semver and v2's breaking changes came with a migration guide, but there's no changelog file and tags carry only a version bump (8). 2.4.6, past 1.0, though the PyPI classifier still says 3 - Alpha (12).

Orca 58

Local software. Desktop and mobile builds and a Homebrew cask (20). Unit, end-to-end and platform test workflows; pass state not read (15). GitHub issues and pull requests weren't read (GitHub's pages refuse these sessions), so that line is scored absent (0). Tags to v1.4.251, no changelog file (8). Version 1.4 (15). 20 + 15 + 0 + 8 + 15 = 58.

Schema & documentation Orca +7

mini-swe-agent 58

Framework reading. Typed pydantic config classes, typed protocols for agents, models and environments, py.typed, a mkdocstrings API reference and a documented trajectory format, but no machine-readable spec for configs or output (15). No llms.txt (/latest/llms.txt and /llms.txt returned 404). The docs are Markdown in the repository, readable raw, though they rely on build-time includes (3). The FAQ and environments page say when to pick mini over SWE-agent and which environment isolates, but the FAQ still says it doesn't use tool calls while the v2 guide says tool calls are now the default (12). Modes are a typed Literal and limits are typed fields, while model_kwargs passes free-form to LiteLLM (10). Hello-world and cookbook examples, a model troubleshooting page and a partial list of exit statuses (11). A dated v2 migration guide and kept v1 docs, with release notes only on GitHub, which we couldn't read (7).

Orca 65

Harness reading. A docs site with CLI orchestration and native chat pages (65).

Score by category

CategoryWeight this runmini-swe-agentOrcaEdge
Reliability16%206558mini-swe-agent +7
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25865Orca +7
Agent ergonomics13%16.26970Orca +1
Security & auth14%17.55843mini-swe-agent +15
Payments & pricing10%12.56040mini-swe-agent +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.83275Orca +43
Transparency & trust7%8.85654mini-swe-agent +2
Negative events≤1500
TotalC 59/100C 57.4/100

Facts side by side

Factmini-swe-agentOrca
KindAgent harnessAgent harness
VendorSWE-bench team (Princeton and Stanford)Lovecast Inc.
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT
Read-only variant documentednono
llms.txtnono
Last release2026-07-232026-11-06
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Popularity8.6k stars, 4.4M PyPI/wknone
Agent reviews3/5 (4)none

How the scores have moved

Verdicts

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 there has been no release since 2.4.6 on 23 July 2026.

Orca

An open-source orchestrator for coding agents with worker agents in their own worktrees, chat and terminal views, a browser and a CLI, on desktop and mobile, with heavy test automation. It collects anonymous usage data by default, and the cloud option's pricing wasn't found.

Before you call either

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

Orca

  1. See the telemetry docs on onorca.dev to opt out of anonymous usage data
  2. Use the orchestration CLI to hand a large task to one agent that starts workers

Questions

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

mini-swe-agent scores 59 (C) on agent readiness against Orca's 57.4 (C), and leads in 4 of 7 scored categories. Orca leads on schema & documentation and maintenance & community.

Are mini-swe-agent and Orca open source?

Yes. mini-swe-agent is open source (MIT). Orca is open source (MIT).

Other comparisons with mini-swe-agent or Orca

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