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A self-evolving agent that runs entirely on your machine. No API keys, no cloud, no telemetry, no network — verified by a test that blocks every outbound socket and runs the whole loop anyway.
ollama run hoodarunner/selfevolve-reviewer
Applications
ollama launch claude --model hoodarunner/selfevolve-reviewer
ollama launch opencode --model hoodarunner/selfevolve-reviewer
ollama launch hermes --model hoodarunner/selfevolve-reviewer
ollama launch openclaw --model hoodarunner/selfevolve-reviewer
Models
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selfevolve-reviewer
A qwen2.5:7b-instruct variant preloaded with the system prompt and decoding
settings from selfevolve — a
code-review agent that learns from an engineer’s corrections and stores the
lessons locally.
ollama run hoodarunner/selfevolve-reviewer
Paste in a function and it reviews it: a small number of high-signal comments, each with a concrete concern and a specific recommendation. It is told not to restate what the code does, not to flag formatting a linter already handles, and to return nothing when the code is fine.
What this model is not
It does not learn. The learning lives in the application, not in these
weights. selfevolve keeps accepted, rejected and edited review comments in a
local SQLite file, distils them into scoped rules, and injects the relevant ones
into the next review. A model on a public registry cannot contain anyone’s
accumulated judgement — and shouldn’t, since those rules are specific to one
person’s codebases.
Pointing selfevolve at this model changes nothing. The agent sends its own
system message on every request, which takes precedence over the SYSTEM baked
into a Modelfile. This exists so the review prompt can be tried in one command,
with no Python and no clone.
Why instruct rather than reasoning
Under a JSON-schema constraint a reasoning model’s thinking phase has nowhere to
go. On a real review, qwen3:8b timed out at 180 seconds where
qwen2.5:7b-instruct finished the same file in 22. Reasoning quality is not the
bottleneck for this task: the model supplies general code knowledge, and the
learned rules supply the judgement.
The full loop
retrieve lessons ─► review ─► human accepts/rejects/edits ─► reflect ─► persist
▲ │
└────────────────── one SQLite file ◄──────────────────────────────┘
Runs entirely offline — one runtime dependency, local Ollama for generation and embeddings, and CI that proves the whole loop inside a network namespace with no interfaces.
MIT.