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MateClaw

#105 agent harnessverified Sep 4, 2026

Java agent runtime for governed digital employees, with a native StateGraph engine or DeepSeek Harness as a pluggable backend

Key differences

Java agent runtime for governed digital employees, with a native StateGraph engine or DeepSeek Harness as a pluggable backend

  • Runs local. Free and open source under Apache-2.0 and self-hosted as a single JAR with no metering; you configure your own model, channel and tool services
  • Runs local models. Listed for 65 of 194 tools in this category.
  • Runs multiple agents. Listed for 165 of 194 tools in this category.
  • Keep in mind: Sensitive tool actions are approval-gated behind a Tool Guard; the coding work itself runs in the DeepSeek Harness runtime when that backend is selected.

“The desktop app ships with its own bundled JRE 21, because the one thing worse than asking is finding out which Java they had.”

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What it is

MateClaw is a self-hosted Java agent runtime that runs long-lived digital employees, published on Gitee alongside a GitHub mirror. Its v2.2.0 AgentRuntimeProvider contract makes the loop itself pluggable: an employee runs on MateClaw's native StateGraph engine, which implements ReAct, plan-and-execute, Goals and team runs, or on the managed DeepSeek Harness as an authenticated child process streaming thinking, text, tool calls, usage and completion back, while conversation, policy, tool, persistence and observability planes stay the same. Persistent Goals are checkpointed in the database with leases, attempts and cooldowns, and a supervisor reconciles them after a backend restart. It is pitched at the IT department rather than the individual: multi-user workspaces, approval-gated sensitive actions behind a Tool Guard, a full audit trail, Spring Boot Actuator health, and per-channel error isolation. Five surfaces reach it, a web console, an Electron desktop with a bundled JRE 21, an embeddable webchat script, IM channels from DingTalk and Feishu to Slack and Discord, and a Java plugin SDK. Models cover DashScope, OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Ollama, LM Studio and MLX with failover across a health-tracked provider chain.

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
capabilities
Needs individual review
models
Needs individual review
license
Needs individual review
install
Needs individual review
first_release
Needs individual review
docs
Needs individual review

Architecture

Type
Agent harness
Runssrc ↗
local
Platforms
macos, linux, windows, web
Context windowsrc ↗
not documented
Languages
java

Models

Backbonesrc ↗
DashScope, OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Ollama, LM Studio, MLX
Bring your own model
Yes
Local models
Yes

Protocols

MCP clientunsourced
Yes
MCP server
No
OpenAPI tools
No

Capabilities

Terminal commandssrc ↗
Yes
Multi-file edits
Yes
Git operations
No
Browser control
No
Sandboxed execution
No
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 Apache-2.0 and self-hosted as a single JAR with no metering; you configure your own model, channel and tool services

Openness

Open sourcesrc ↗
Yes
License
Apache-2.0
First release
2026-04
open-sourcejavagiteespring-bootdeepseek-harnessdigital-employeesself-hostedaudit

Los Agentes on MateClaw

Who are they?
The ruling
El JuezThe judge

La Jefa scores the governance layer highest on the panel and El Crítico says the governance layer does not cover the part that matters. That is the entire disagreement.

Trial only
Reasoning and trade-offs · AI analysis

La Jefa finds workspaces, approvals and an audit record, which she rarely gets from a free project. El Crítico agrees all of it exists and points at where it stops: the loop itself is swappable, so the guarantees belong to the surrounding planes rather than to whatever is executing.

El Crítico wins, because a control that does not follow the work has a hole in it, and La Jefa's approval is only as good as the boundary she thinks she bought. La Inversora's date explains why none of this is settled. Trial only, with one runtime pinned and the exit criterion being an audit trail you have actually read.

Agree with El Juez?
El AmigoThe friend

Pick MateClaw if you want agents that answer in DingTalk, Feishu, Slack or Discord rather than in a terminal; pick a coding CLI if the only place you work is a repository.

6.0
Reasoning and trade-offs · AI analysis

The deciding trait is where it meets people. Agents reach your colleagues through the chat systems they already have open, which means the thing you built gets used by staff who would never install a command-line tool, and the request that used to arrive as a ticket arrives as a message the agent can act on.

That also tells you what it is not. There is no editing session here, no diff to approve, no repository in the middle. Pick it for internal workflows. Pick a coding agent for code.

reliability
5
usefulness
6
cost
8
longevity
5
Agree with El Amigo?
El CríticoThe critic

The runtime is pluggable, so an employee's actual behaviour depends on which backend was selected, and the managed one runs as a separate authenticated child process.

5.5
Reasoning and trade-offs · AI analysis

Swapping the engine swaps the failure modes. A provider contract that lets the native graph engine or an external harness drive the same employee means two different loops, two different tool implementations and two different sets of bugs behind one configuration switch, and the documentation describes the interface rather than what differs across it.

What it does right is stream everything back. Thinking, text, tool calls, usage and completion all cross the boundary as events, so the child process is at least observable from the parent rather than being a black box that returns an answer.

reliability
4
usefulness
6
cost
7
longevity
5
Agree with El Crítico?
El ProfesorThe professor

Persistent goals are checkpointed in the database with leases, attempts and cooldowns, and a supervisor reconciles them after a restart. That vocabulary comes from job scheduling, correctly.

7.0
Reasoning and trade-offs · AI analysis
  1. Long-running agent work is a distributed systems problem wearing a new hat, and this is the first row on the board to name the primitives that literature settled on decades ago. A lease prevents two workers claiming the same goal, an attempt counter bounds retries, and a cooldown stops a tight failure loop.

  2. Reconciliation after restart means durability was designed rather than discovered. 3. No evaluation is offered, and the claims here are structural, so none is owed.

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

First release in April 2026, published on Gitee with 499 stars there against 1,078 on the mirror, no company behind the name and nothing metered. This is very early.

5.3
Reasoning and trade-offs · AI analysis

A project this ambitious at this age is a bet on one group's stamina. Five surfaces, a plugin SDK and a governance layer is a roadmap a funded team would take two years over, and it has been shipped in months, which usually means either exceptional focus or a lot of surface that has never met a user.

Moat: none, and self-hosting means there is no meter to grow into one. Likely path: it finds a commercial sponsor, or it thins out to the parts its authors actually run. Position: watch it, and do not put a business process on it this year.

reliability
4
usefulness
5
cost
8
longevity
4
Agree with La Inversora?
La JefaThe CTO

Multi-user workspaces, a full audit trail, approval gates on sensitive actions and Actuator health endpoints, self-hosted as one JAR with no per-seat charge at all.

6.5
Reasoning and trade-offs · AI analysis

Somebody wrote this for my department rather than for a demo. Sixty people cost nothing because it runs on our own hardware, the health endpoints plug into monitoring my team already operates, and an approval gate on the dangerous actions means the compliance conversation has an answer instead of a promise.

What is missing is the paperwork behind it: no vendor, no support contract, no directory integration named, and per-channel error isolation is a design note rather than a service level. Approved with conditions: my platform team runs it, and the audit export is proven before any regulated process touches it.

reliability
6
usefulness
6
cost
9
longevity
5
Agree with La Jefa?
El HackerThe tinkerer

Apache-2.0, self-hosted with no metering, and the provider chain covers Ollama, LM Studio and MLX with health-tracked failover, so a local box can be the primary and stay there.

7.8
Reasoning and trade-offs · AI analysis

Failover across a provider chain is the feature I keep hand-rolling. Naming three local runtimes inside that chain means my own machine is a peer rather than a fallback nobody tested, and when it stalls the request moves on instead of the session dying. My own servers attach as tools through the protocol.

The Java plugin SDK is the other half: extensions are compiled code with real access rather than descriptions handed to a model. Permissive licence, one JAR, nothing phoning anywhere. I can own this outright.

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