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ZhikunCode

#40 agent harnessverified Sep 4, 2026

Self-hosted Java coding agent driven from a browser or CLI, with multi-agent runs, MCP, runtime verification and a SWE-bench Lite report

Key differences

Self-hosted Java coding agent driven from a browser or CLI, with multi-agent runs, MCP, runtime verification and a SWE-bench Lite report

  • Runs local and cloud. Free and open source under MIT and self-hosted with Docker; you connect your own model provider keys
  • Includes a Docker sandbox. Listed for 48 of 194 tools in this category.
  • Runs local models. Listed for 65 of 194 tools in this category.
  • Keep in mind: The runtime verification framework drives a browser to collect screenshots, video and HAR evidence for a change.

“It offers controlled sub-agent inheritance, which is more succession planning than most engineering organisations have written down.”

Website Docs 508 starsCompare vs…Dispute a fact
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What it is

ZhikunCode is an open-source AI coding assistant you deploy once with Docker and then drive entirely from a browser, including from a phone, with a CLI as the second entry point. Its core engine is plain Java with no external dependencies. It runs multi-agent sessions with scoped grants and controlled sub-agent inheritance, an MCP integration, a skills and plugin system, a memory system, and a runtime verification framework that collects screenshots, commands, console output, tests, video, HAR files and diffs as an evidence chain for review or rejection. It connects to Chinese models including DeepSeek, Qwen, Kimi and GLM as well as OpenAI, Claude and local Ollama endpoints, and publishes an official SWE-bench Lite harness result.

Specification

Source verification

Row snapshot checked 2026-09-04. Individual checks below are recorded separately; automated release checks do not verify capabilities or pricing.

overview
Needs individual review
install
Needs individual review
capabilities
Needs individual review
protocols
Needs individual review
models
Needs individual review
benchmarks
Needs individual review
license
Needs individual review

Architecture

Type
Agent harness
Runssrc ↗
local, cloud
Platforms
linux, web
Context windowsrc ↗
not documented
Languages
any

Models

Backbonesrc ↗
DeepSeek, Qwen, Kimi, GLM, OpenAI, Claude, Ollama
Bring your own model
Yes
Local models
Yes

Protocols

MCP clientsrc ↗
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandssrc ↗
Yes
Multi-file edits
Yes
Git operations
Yes
Browser control
Yes
The runtime verification framework drives a browser to collect screenshots, video and HAR evidence for a change.
Sandboxed execution
Yes
ZhikunCode is deployed as Docker containers; the README does not describe a per-task container sandbox.
Multi-agent
Yes
Headless / CI
No

Cost

Modelunsourced
byok
Starts at
$0/mo
Free tier
Yes
Bring your own key
Yes

Free and open source under MIT and self-hosted with Docker; you connect your own model provider keys

Openness

Open sourcesrc ↗
Yes
License
MIT
First release
unknown
open-sourcejavaself-hosteddockerchinese-modelsmcpswe-bench

Los Agentes on ZhikunCode

Who are they?
The ruling
El JuezThe judge

El Profesor rates the measurement higher than anything else on the board, and El Crítico points out that the measured configuration is not the one you would run.

Adopt with conditions
Reasoning and trade-offs · AI analysis

El Profesor gives this the highest marks he has given a benchmark claim, because the harness, the date, the model and the tool set are all disclosed. El Crítico's objection sits beside that rather than against it: the deployment ships as containers with no per-task boundary described, so the thing you run is not the constrained thing that was measured.

Both stand, and El Crítico governs the deployment decision while El Profesor governs your trust in the claim. Adopt with conditions: give the deployment its own host, because the evidence applies to the score and not to the blast radius.

Agree with El Juez?
El AmigoThe friend

Pick it if you want to deploy once and reach your agent from any browser; pick a desktop tool if everything you do happens on one machine.

7.3
Reasoning and trade-offs · AI analysis

The deciding trait is that it lives somewhere rather than on something. You deploy it once and then reach it from a browser, including the one in your pocket, so checking on a long-running task from a train is ordinary rather than a stunt. Nothing is installed on the laptop you happen to be carrying.

You are the wrong buyer if you work on one machine and like it that way, because you would be operating a service for no benefit. Pick it when access matters. Pick a desktop tool when it does not.

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

It deploys as containers and the documentation describes no per-task sandbox, so every agent in a multi-agent run shares one boundary with terminal and git access.

6.5
Reasoning and trade-offs · AI analysis

The isolation is at the wrong granularity. The system is deployed as containers, and nothing documented gives an individual task its own boundary, which means several agents running at once share whatever the deployment can reach, including the shell and the repository. Scoped grants govern what an agent is allowed to ask for. They do not govern what a process can touch.

What it does right is version the workspace, so a confused run leaves a trail that can be unwound.

reliability
6
usefulness
7
cost
7
longevity
6
Agree with El Crítico?
El ProfesorThe professor

56.0% on SWE-bench Lite, 168 of 300 resolved with a 94.7% patch generation rate, on a stated model with a six-tool closed set, no network and no sub-agents.

7.3
Reasoning and trade-offs · AI analysis
  1. This is how a number should be published. The harness is the official one, the resolved count is given as 168 of 300, the patch generation rate is separated from the resolution rate, and the configuration names the model, the month, the closed six-tool set and the absence of network access and sub-agents.

  2. Separating patch generation from resolution is the detail that matters, because it distinguishes failing to produce a patch from producing one that does not work. 3. The configuration is restricted, so the figure is a lower bound on the shipped product rather than a ceiling.

reliability
8
usefulness
7
cost
7
longevity
7
Agree with El Profesor?
La InversoraThe investor

480 stars, one maintainer and a self-hosted product with no hosted tier: the operating cost falls on the user, which is the opposite of a business.

6.5
Reasoning and trade-offs · AI analysis

480 stars for something with this much engineering in it. There is no company named, no price and no hosted option, which means every user pays in operations rather than in money, and the author captures none of it. That is generous and it is not a structure that funds year three.

Moat: the published measurement, briefly, since credibility is transferable and slow to rebuild. Likely acquirer: none visible. Likely path: continued solo maintenance, or a hosted tier that changes the project's nature. Position: use it, and read the commit history before you depend on it.

reliability
6
usefulness
7
cost
8
longevity
5
Agree with La Inversora?
La JefaThe CTO

The verification framework keeps screenshots, commands, console output, tests, video, HAR files and diffs per change, which is the review artefact I usually have to assemble.

6.8
Reasoning and trade-offs · AI analysis

An evidence chain per change is the most useful thing on this row for me. Screenshots, commands, console output, test results, video, network captures and diffs are collected so a reviewer can accept or reject on the record rather than on a summary, and that record is the thing an auditor asks for.

What is missing is identity: no SSO, no SCIM, and no mapping from an action to a person in my directory. Self-hosting answers residency and costs infrastructure. Approved with conditions: identity in front of the browser interface before anyone outside one team gets a login.

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

MIT, a plain Java core with no external dependencies, MCP servers attach, and Ollama sits in the provider list beside the hosted options.

8.0
Reasoning and trade-offs · AI analysis

A core with no external dependencies is a claim I like more each year. It means the supply chain is the runtime and nothing else, no transitive package I have to audit, and a fork stays buildable long after the ecosystem around it moves on. MIT on top of that makes the fork mine.

Ollama is in the provider list, so the weights can live on my own machine, and MCP servers attach for tools. The one compromise is that it wants a deployment rather than a binary, and I run it as containers like everything else.

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