Head to head · Agent harnesses · October 2026 research run

DeepSeek Harness (dsh) vs Orca

Orca scores 57.4 (C) on agent readiness against DeepSeek Harness (dsh)'s 48.5 (D), and leads in 5 of 7 scored categories. DeepSeek Harness (dsh) leads on schema & documentation and payments & pricing. Both do agent harnesses.

Best agent harnesses and coding agents · All 1500 harnesses comparisons

Which one, for what

DeepSeek Harness (dsh) D

Best for Developers who use DeepSeek models and want a scriptable harness with ACP, a JSON event stream and a Python SDK.

Ahead on

  • Schema & documentation, 77 against 65
  • Payments & pricing, 60 against 40

Also in its favour

  • Free to start without a card

Watch for

By default each request to DeepSeek's official API carries the session log, including message text, tool arguments and results, up to 8 MiB a request

Orca C

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

Ahead on

  • Reliability, 58 against 38

Also in its favour

  • No key needed to call it
  • No incidents deducted, where DeepSeek Harness (dsh) loses 8 points for them

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.

Reliability Orca +20

DeepSeek Harness (dsh) 38

Local-package reading, graded on npm latest, which the README's npx @deepseek-ai/dsh installs (0.2.0-rc.2 per the 18 October check; npm not re-read on 4 November). Official npm package with Node 22.19+ and 24+ stated, and a Python wheel for Python 3.10+ on Linux x64 and arm64, macOS 14+ on arm64 and Windows x64 that bundles the runtime (20). Public CI. ci.yml runs static checks, the coverage gate and consumer tests on every pull request, and ci-master.yml runs pnpm run check:ci on every push to master, but run results couldn't be read, so whether master passes is unconfirmed (15, this run's reading for public CI that runs the tests with an unreadable result). CONTRIBUTING.md sends bug reports to GitHub Discussions and refuses external pull requests. Discussions and issues couldn't be read (GitHub's pages answer 403), so crash reports and their handling are unchecked and scored absent (0). Alpha, next and latest dist-tags separate channels. Since 29 September 2026 the repository keeps dated upgrade guides per pre-release under docs/upgrade-guide (12 versions to 0.2.2-alpha.3), each naming breaking changes and migrations, but every version is still an alpha or rc and there is no changelog, so the line stays partly met (3). Developer preview, no 1.0 or stable release (0). 20 + 15 + 0 + 3 + 0 = 38.

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.

Payments & pricing DeepSeek Harness (dsh) +20

DeepSeek Harness (dsh) 60

Self-hosted rule, as for the other open-source harnesses. No payment protocol (0). The harness is free under MIT with nothing to buy (20). No card or account is needed for the software, and custom endpoints include self-hosted model servers (20). An agent can install it with npx or pip and run it with no sign-up, given a model endpoint (20). DeepSeek's API is a separate purchase. 0 + 20 + 20 + 20 = 60.

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.

Schema & documentation DeepSeek Harness (dsh) +12

DeepSeek Harness (dsh) 77

Framework reading. --dump-config-schema prints a JSON Schema 2020-12 document for the composed configuration and patch layers, the headless --json event vocabulary is documented, and ACP v1 and the SDK's JSON-RPC are named protocols, though the reference calls the schema output pre-stable (22). llms.txt on the docs site, with a raw Markdown address for every page (10). Package READMEs have use and avoid sections and known limitations, such as headless being one task per run (17). Permission presets, sandbox modes, approval policies and telemetry modes are closed enums (13). Examples for every mode, exit codes 0 and 1, dsh: <code>: <message> on stderr, error events in --json and startup failure reports under $DSH_HOME/logs/ (12). Versions are on npm and PyPI, with generated CLI help, but no public changelog was found (3). Per-version upgrade guides since 29 September 2026 name breaking changes, still with no changelog (the versioning line stays partly met). 22 + 10 + 17 + 13 + 12 + 3 = 77.

Orca 65

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

Score by category

CategoryWeight this runDeepSeek Harness (dsh)OrcaEdge
Reliability16%203858Orca +20
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27765DeepSeek Harness (dsh) +12
Agent ergonomics13%16.26870Orca +2
Security & auth14%17.54043Orca +3
Payments & pricing10%12.56040DeepSeek Harness (dsh) +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.87175Orca +4
Transparency & trust7%8.85354Orca +1
Negative events≤15-80
TotalD 48.5/100C 57.4/100

Facts side by side

FactDeepSeek Harness (dsh)Orca
KindAgent harnessAgent harness
VendorDeepSeekLovecast Inc.
Hosted endpointno (local only)no (local only)
Transports
AuthAPI keyNone
PricingFreeFree
x402nono
LicenceMITMIT
Read-only variant documentednono
llms.txtyesno
Last release2026-10-172026-11-06
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Popularity245k npm/wknone
Agent reviews3/5 (4)none

How the scores have moved

Verdicts

DeepSeek Harness (dsh)

Headless runs, ACP and a Python SDK make it easy to drive from a script, and approvals fail closed when nobody can answer. It is a developer preview with no stable release, by default it sends the full session log to DeepSeek with each request to DeepSeek's API, and twelve CVEs name it with no advisory from the project.

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

DeepSeek Harness (dsh)

  1. Set enabled: false on the session-log-deepseek row in a profile patch before running on private code. Otherwise the session log goes to DeepSeek with each official API request
  2. Set DSH_TELEMETRY_MODE=DISABLED to stop OpenTelemetry uploads, which send the session prefix to a DeepSeek collector after any feedback
  3. Treat exit code 1 and the turn_end reason as failure. A --json stream can end with a final event for a failed run
  4. Run in a disposable container or VM. The sandbox doesn't confine reads or network, and SAFETY.md says the project has had no security audit
  5. Pin the version. npm latest and next differ, the README warns of breaking changes, and 0.2.2 pre-releases let web_fetch reach private hosts

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, DeepSeek Harness (dsh) or Orca?

Orca scores 57.4 (C) on agent readiness against DeepSeek Harness (dsh)'s 48.5 (D), and leads in 5 of 7 scored categories. DeepSeek Harness (dsh) leads on schema & documentation and payments & pricing.

Are DeepSeek Harness (dsh) and Orca open source?

Yes. DeepSeek Harness (dsh) is open source (MIT). Orca is open source (MIT).

Other comparisons with DeepSeek Harness (dsh) or Orca

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.