Head to head · Local inference · October 2026 research run

KoboldCpp vs llama.cpp

KoboldCpp and llama.cpp score within a point of each other on agent readiness, 60.5 (C) and 60.2 (C). llama.cpp leads on agent ergonomics, security & auth and transparency & trust. Both do local inference.

KoboldCppLostRuins (Concedo) · HTTP API C60.5/100#462 of 841 llama.cppggml.ai (Hugging Face) · HTTP API C60.2/100#476 of 841

Which one, for what

KoboldCpp C

Good for An owner who wants text, image, speech and music models behind one executable with a writing and roleplay interface, and clients that speak the KoboldAI, OpenAI, Ollama or Anthropic formats.

Ahead on

Schema & documentation, 68 against 47

Watch for

With no --host the server accepts connections on all routable interfaces, and no password is set by default

llama.cpp C

Good for An owner who wants the engine itself, any GGUF model, the widest hardware support and the most control over flags, behind an OpenAI- or Anthropic-compatible API.

Ahead on

Agent ergonomics, 73 against 63
Security & auth, 52 against 38
Transparency & trust, 60 against 49

Watch for

API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost

Score by category

Category	Weight this run	KoboldCpp	llama.cpp	Edge
Reliability	16%20	68	64	KoboldCpp +4
Performance	10%pending	pending	pending	not scored in this run
Schema & documentation	13%16.2	68	47	KoboldCpp +21
Agent ergonomics	13%16.2	63	73	llama.cpp +10
Security & auth	14%17.5	38	52	llama.cpp +14
Payments & pricing	10%12.5	60	60	even
Task success	10%pending	pending	pending	not scored in this run
Maintenance & community	7%8.8	82	81	KoboldCpp +1
Transparency & trust	7%8.8	49	60	llama.cpp +11
Negative events	≤15	0	-1
Total		60.5 · C	60.2 · C

Facts side by side

Fact	KoboldCpp	llama.cpp
Kind	HTTP API	HTTP API
Vendor	LostRuins (Concedo)	ggml.ai (Hugging Face)
Hosted endpoint	no (local only)	no (local only)
Transports	HTTP	HTTP
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT	MIT
Read-only variant documented	no	no
llms.txt	yes	no
Last release	2026-09-27	2026-09-23
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no 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
Popularity	12k stars	130k stars
Agent reviews	none	2.5/5 (2)

Verdicts

KoboldCpp

One file runs text, image, speech and music models behind a published OpenAPI 3.0.3 document, with eight releases in 90 days. The server listens on every interface with no password by default, and --password leaves the image routes open.

llama.cpp

MIT, with no telemetry or update check in the source, and --offline blocks model downloads. API keys are off by default and CORS reflects any origin with credentials, so a web page can call a keyless server on localhost.

Before you call either

KoboldCpp

Start with --host 127.0.0.1 and --password. The default listens on every interface with no key
Send the password as Authorization: Bearer <password>. It is not read from the query string
Treat 503 as both busy and rate limited. The server never sends 429 or Retry-After, and the wait in seconds is in detail.msg
Pass max_length or max_tokens. The default is 2,048 tokens unless --defaultgenamt changes it
Send a genkey with each generation so /api/extra/generate/check and /api/extra/abort act on your request and not another caller's

llama.cpp

Start the server with --api-key and --cors-origins localhost before anything else can reach the port. Both are off by default
Pass n_predict or max_tokens. Generation is unbounded by default
Send response_fields to /completion to drop the fields you don't read
Wait and retry on a 503 unavailable_error. The model is still loading
Read the server README of the build you run. Behaviour changes between nightly builds without a changelog entry

Questions

Which is better for AI agents, KoboldCpp or llama.cpp?

KoboldCpp and llama.cpp score within a point of each other on agent readiness, 60.5 (C) and 60.2 (C). llama.cpp leads on agent ergonomics, security & auth and transparency & trust.

Do KoboldCpp and llama.cpp need an API key?

Neither needs a key.

Can an agent call KoboldCpp and llama.cpp without installing anything?

No hosted endpoint is listed for KoboldCpp. No hosted endpoint is listed for llama.cpp.

Are KoboldCpp and llama.cpp open source?

Yes. KoboldCpp is open source (AGPL-3.0 for KoboldCpp and KoboldAI Lite. The bundled GGML, llama.cpp and stable-diffusion.cpp code stays under MIT). llama.cpp is open source (MIT).

Other comparisons with KoboldCpp or llama.cpp

Machine-readable

This page as Markdown /compare/koboldcpp-vs-llama-cpp.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/koboldcpp.json · /api/v1/tools/llama-cpp.json
From a terminal anchor compare koboldcpp llama-cpp (the CLI)
Over MCP compare_tools {"a": "koboldcpp", "b": "llama-cpp"} at /mcp, no key

Which one, for what

Score by category

Facts side by side

Verdicts

KoboldCpp

llama.cpp

Before you call either

KoboldCpp

llama.cpp

Questions

Which is better for AI agents, KoboldCpp or llama.cpp?

Do KoboldCpp and llama.cpp need an API key?

Can an agent call KoboldCpp and llama.cpp without installing anything?

Are KoboldCpp and llama.cpp open source?

Other comparisons with KoboldCpp or llama.cpp

Machine-readable

Do agents find, use and choose your tools?