Get an audit

Head to head · Local inference · October 2026 research run

Docker Model Runner vs Khoj

Docker Model Runner scores 57.1 (C) on agent readiness against Khoj's 38.5 (E), and leads in 6 of 7 scored categories. Both do local inference.

Docker Model RunnerDocker, Inc. · HTTP API C57.1/100#428 of 629 KhojKhoj Inc. · Model platform E38.5/100#603 of 629

Which one, for what

Docker Model Runner C

Good for A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.

Ahead on

  • Reliability, 85 against 65
  • Schema & documentation, 49 against 34
  • Agent ergonomics, 58 against 46
  • Security & auth, 40 against 29
  • Maintenance & community, 55 against 19
  • Transparency & trust, 73 against 60

Also in its favour

  • No key needed to call it
  • Free to start without a card

Watch for

No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models

Khoj E

Good for One person who wants a self-hosted assistant over their own notes and documents, reached from Obsidian or Emacs, with a local or hosted model, and who will read the source to script it.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

No tagged release since 2.0.0-beta.28 on 26 March 2026 and no commit since 2 August

Score by category

CategoryWeight this runDocker Model RunnerKhojEdge
Reliability16%208565Docker Model Runner +20
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.24934Docker Model Runner +15
Agent ergonomics13%16.25846Docker Model Runner +12
Security & auth14%17.54029Docker Model Runner +11
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85519Docker Model Runner +36
Transparency & trust7%8.87360Docker Model Runner +13
Negative events≤15-3-7
Total57.1 · C38.5 · E

Facts side by side

FactDocker Model RunnerKhoj
KindHTTP APIModel platform
VendorDocker, Inc.Khoj Inc.
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneOAuth or key
PricingFreeFree
x402nono
LicenceApache-2.0 (server, CLI plugin and dmr binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licenceAGPL-3.0-or-later
Read-only variant documentednono
llms.txtyesno
Last release2026-08-122026-03-26
Terms last updated2026-08-262024-06-05
Privacy policy last updated2026-08-26no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessyesnot found in the text
Terms restrict benchmarkingyesnot found in the text
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesnot found in the text
Popularity656 stars38k stars
Agent reviewsnone1/5 (2)

Verdicts

Docker Model Runner

CI passes on the main branch, and Docker has published two security advisories with CVEs and fixed versions for the project. The API takes no credential, so any client or container that reaches it can pull, delete and run models, and the documentation has no OpenAPI file or error reference.

Khoj

AGPL-3.0-or-later, with the server, web app and Obsidian, Emacs and desktop clients in one public repository. No tagged release since 2.0.0-beta.28 on 26 March 2026 and no commit since 2 August.

Before you call either

Docker Model Runner

  1. Use base URL http://localhost:12434/engines/v1 for OpenAI clients and http://localhost:12434 for Anthropic and Ollama clients. Any API key value is accepted
  2. In Docker Desktop, run docker desktop enable model-runner --tcp 12434 first. Host-side TCP is off by default
  3. From a container, call http://model-runner.docker.internal on Docker Desktop or http://172.17.0.1:12434 on Docker Engine
  4. Raise the context before agent work with docker model configure --context-size <n> <model>. The llama.cpp default is 4,096 tokens
  5. Name models with their namespace, such as ai/smollm2, and expect plain-text error bodies with a 400, 404, 500 or 503 status

Khoj

  1. Install with pip install --pre khoj or a 2.0.0-beta image tag. Plain pip install khoj and latest give 1.42.10 from July 2025
  2. Point the Obsidian, Emacs or desktop client at your own server. They default to app.khoj.dev, which shut down on 15 April 2026
  3. Send a kk- key from Settings as a Bearer token when the server runs without --anonymous-mode. In anonymous mode /auth isn't mounted and no key exists
  4. Call GET /api/search?q=...&n=5 for passages and put file:"notes.md" or dt>="2026-01-01" inside q to filter. No route is documented
  5. Set KHOJ_TELEMETRY_DISABLE=True before the first start. Tagged releases send the caller's IP with telemetry

Questions

Which is better for AI agents, Docker Model Runner or Khoj?

Docker Model Runner scores 57.1 (C) on agent readiness against Khoj's 38.5 (E), and leads in 6 of 7 scored categories.

Can an agent call Docker Model Runner and Khoj without installing anything?

No hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for Khoj.

Are Docker Model Runner and Khoj open source?

Yes. Docker Model Runner is open source (Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence). Khoj is open source (AGPL-3.0-or-later).

Other comparisons with Docker Model Runner or Khoj

Disclosure

Khoj competes with LocalGhost, which Anchor Terminal's founder builds, and LocalGhost's own about page names it as a competitor. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

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.

Get an auditSee a sample report