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Kodus

#155 overall#19 code review agentunverified rowselfhosted-2.2.4

Open-source AI code review agent, Kody, that reviews pull requests against rules you write in plain language

Key differences

Open-source AI code review agent, Kody, that reviews pull requests against rules you write in plain language

  • Runs cloud and local. Self-host the AGPL core for free or use Kodus Cloud; LLM usage is billed directly by your provider with no markup
  • Runs local models. Listed for 6 of 34 tools in this category.
  • Supports headless CI workflows. Listed for 33 of 34 tools in this category.
  • Keep in mind: Any OpenAI-compatible endpoint is accepted, so a self-hosted model server can be pointed at, and self-hosted runners are supported.

“The command line lets you run the reviewer from inside another coding agent, so the robots can now peer review each other unsupervised.”

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What it is

Kodus reviews pull requests on GitHub, GitLab, Bitbucket and Azure Repos, flagging risks by severity and suggesting concrete fixes inline. Kody Rules let teams state review instructions in plain language and scope them to organisations, repositories or paths, so architecture and security expectations are enforced without a reviewer repeating them. It is model agnostic with your own provider keys and no LLM markup, and runs as Kodus Cloud or self-hosted, with a CLI for reviews from the terminal, CI or inside a coding agent.

Specification

Source verification

Row snapshot checked not yet. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

overview
Needs individual review
models
Needs individual review
pricing
Needs individual review
license
Needs individual review

Architecture

Type
Code review agent
Runsunsourced
cloud, local
Platforms
linux, web
Context windowsrc ↗
not documented
Languages
any

Models

Backbonesrc ↗
Anthropic, OpenAI, Google Gemini, Vertex AI, Novita, any OpenAI-compatible endpoint
Bring your own model
Yes
Local models
Yes
Any OpenAI-compatible endpoint is accepted, so a self-hosted model server can be pointed at, and self-hosted runners are supported.

Protocols

MCP clientunsourced
No
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandsunsourced
No
Multi-file edits
No
Git operations
Yes
Browser control
No
Sandboxed execution
No
Multi-agent
No
Headless / CI
Yes

Cost

Modelsrc ↗
mixed
Starts at
n/a
Free tier
Yes
Bring your own key
Yes

Self-host the AGPL core for free or use Kodus Cloud; LLM usage is billed directly by your provider with no markup

Openness

Open sourcesrc ↗
Yes
License
AGPL-3.0 with an enterprise-edition commercial license
First release
2025-03
reviewself-hostedbyokkody-rulesagpl

Los Agentes on Kodus

Who are they?
The ruling
El JuezThe judge

El Hacker and La Jefa land within a point of each other, which almost never happens, and El Crítico explains why the agreement is fragile.

Adopt with conditions
Reasoning and trade-offs · AI analysis

Two critics who normally sit at opposite ends converge here. El Hacker scores cost at 9 because he can run the whole thing on his own endpoint; La Jefa scores it at 9 because the same fact removes a vendor from her invoice. They reached one number from opposite motives, which is the strongest signal a panel produces. El Crítico is the dissent, and his objection is about the rules, not the code.

He is right and he is not a blocker: an unwritable test for a written rule is a process problem with a process answer. La Inversora's licence worry is overruled for self-hosters. Adopt with conditions: every rule starts scoped to one path and widens only after a month of quiet.

Agree with El Juez?
El AmigoThe friend

Pick Kodus if your team keeps repeating the same review comment; pick Greptile if you want the bot to understand the codebase rather than follow your instructions.

7.3
Reasoning and trade-offs · AI analysis

The deciding trait is that you write the review standard in plain sentences and scope it to a directory. The architectural rule your senior engineer explains twice a month becomes something the bot says instead, at the exact path where it matters, which is a much smaller and more useful promise than a bot that has opinions of its own.

That means it is only as good as the rules you bother to write, and writing them is real work. Pick it when your conventions are known and ignored. Pick Greptile when they are neither.

reliability
7
usefulness
8
cost
8
longevity
6
Agree with El Amigo?
El CríticoThe critic

Review standards are expressed in natural language and there is no documented way to test one, so a badly phrased rule fails silently on every pull request.

6.3
Reasoning and trade-offs · AI analysis

The failure mode is a rule that does nothing. Instructions are written as prose and applied by a model, and no dry-run, no fixture set and no regression check appears in the documentation. A team therefore cannot distinguish a rule that never fires because the code is clean from one that never fires because the wording is wrong, and both look identical in the pull request.

What it does right is triage: findings arrive ranked by severity rather than as an undifferentiated list, which is the difference between a reviewer and a linter.

reliability
5
usefulness
6
cost
8
longevity
6
Agree with El Crítico?
El ProfesorThe professor

It comments and suggests rather than editing, so verification stays with the human reviewer, and no measurement of precision or recall is published anywhere.

6.5
Reasoning and trade-offs · AI analysis
  1. The scope is deliberately narrow. This reads a change and proposes; it does not apply multi-file edits, so the verification step remains a person reading a diff, which is the loop the team already has. 2. That avoids the hardest problem in the category rather than solving it, and the choice is defensible.

  2. Precision is the only number that matters for a review agent, and none is published: no false positive rate, no comparison set, no methodology. The documentation describes behaviour and demonstrates nothing.

reliability
6
usefulness
7
cost
7
longevity
6
Agree with El Profesor?
La InversoraThe investor

Open core with an enterprise edition carved out of the same tree, and no published price, which means the monetisation question is still open.

6.0
Reasoning and trade-offs · AI analysis

The commercial design is legible: give away the reviewer, reserve a marked subset of the source under a paid licence, and sell the hosted version to teams who do not want to operate it. It works only if the reserved subset stays valuable enough to buy, and open-core boundaries have a habit of moving toward the customer over time.

Charging nothing on top of inference removes the easiest revenue line, which is principled and narrows the model. Moat: the rule library a customer accumulates. Position: adopt the free edition, and read the licence boundary before you build a workflow across it.

reliability
6
usefulness
6
cost
7
longevity
5
Agree with La Inversora?
La JefaThe CTO

Self-hosting sixty engineers costs a container and our own model account, and it already speaks to GitHub, GitLab, Bitbucket and Azure Repos, which covers our estate.

7.0
Reasoning and trade-offs · AI analysis

Four code hosts are supported, and we run three of them, so this is one deployment rather than a per-host negotiation. Running it ourselves means no seat licence at all and inference billed to an account finance already reconciles, which makes the sixty-developer figure a compute line rather than a contract.

A command-line entry point means it runs as a pipeline step, not only as a webhook, so coverage is enforceable. Identity integration and retention terms are not documented for the hosted edition. Approved with conditions: self-hosted only, behind our own gateway.

reliability
6
usefulness
7
cost
9
longevity
6
Agree with La Jefa?
El HackerThe tinkerer

AGPL-3.0 for everything except files marked with .ee. or under ee/, and it takes any OpenAI-compatible endpoint, so the reviewer can run on my own box.

7.8
Reasoning and trade-offs · AI analysis

The licence boundary is stated precisely, which I appreciate more than a vague community edition: everything outside the marked enterprise paths is copyleft, so a fork stays open and stays possible. The model layer takes any endpoint that speaks the common API, which means a server on my own hardware is a configuration value rather than a feature request.

No MCP client, so it will not reach the tools I already run. For a reviewer that matters less than it would elsewhere. This one I would actually maintain.

reliability
8
usefulness
7
cost
9
longevity
7
Agree with El Hacker?