3.0.0 — Governed execution, recoverable long tasks and team delivery. Java 21, Spring Boot 4, Spring AI 2 and AgentScope 2 power the native runtime. Consistent context budgets, final-answer checks and team synthesis strengthen delivery while preserving approvals and workspace boundaries. Release and upgrade notes. Release date: 2026-10-26.

Other personal AI agents are built for one person. MateClaw is the one your IT department can actually sign off on.

Multi-user workspaces. Approval-gated sensitive actions. Full audit trail. Spring Boot Actuator health monitoring. Per-channel error isolation so one chat platform's outage doesn't take down the rest. One JAR in your environment; you control persisted data, and task content is sent only to model, channel, or tool services you explicitly configure.

And underneath, a real Agent Runtime. An employee is no longer welded to one reasoning loop. Choose the native AgentScope runtime for ReAct, Plan-and-Execute, Goals, and Team Runs, or run DeepSeek Harness as a managed external loop over SDK JSON-RPC. Both paths converge on the same conversations, workspace boundaries, Tool Guard, event projection, and lifecycle controls.

Most AI tools die when their vendor has a bad day. Most forget you the moment the tab closes. Most give you a chatbox and call it a product.

MateClaw is the whole widget. One deployment. Reasoning, knowledge, memory, tools, channels — built together, not bolted on. And when your primary model is unavailable, the next healthy provider retries the current request.

What changes in 3.0.0

3.0.0 includes the unpublished 2.4.0 development cycle. Release date: 2026-10-26. Download published packages from Releases.

• Native execution: AgentScope Java 2.0.4 runs ReAct and the tool loop within each Plan-Execute step. MateClaw retains plan scheduling, tool approvals, workspace permissions, cancellation, Goal acceptance and persistence. This path does not require a beta AgentScope Harness API.
• Consistent context budgets: request preparation, compaction retries and summary calls account for messages, effective tool schemas and one output reserve. Partial request options inherit model defaults; an explicitly empty tool list disables inherited callbacks. Estimates are conservative, rather than provider billing counts.
• Governed decisions and recovery: typed decisions default to SHADOW mode; existing domain constraints and Goal budgets still apply. Durable checkpoints block blind replay when side effects are uncertain.
• Long tasks and team delivery: unfinished work can continue within runtime limits, requests needing user input wait for a reply, and Team Runs publish completion after final synthesis. Worker progress preserves full results and failure causes.
• Everyday improvements: opt-in strict member file isolation, managed DSH SDK upgrades, voice selection/preview, structured MCP results and stricter model probes.

Use Java 21 for source builds. Back up the database, workspace, plugins and configuration before migration; use that backup for rollback. Architecture and upgrade notes · Model budget rules

Three things that make it different

1 · Your AI doesn't die when a model does

Primary key expired. Vendor returns 401. Network blip. Quota drained.

Other tools hand you a red error card. MateClaw tries the next healthy provider in configured order — including built-in and OpenAI-compatible options such as DashScope, OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Ollama, LM Studio, and MLX — and attempts to recover the current request. It returns an error only when the available chain is exhausted. A provider health tracker parks bad vendors in a cooldown window so they don't waste seconds on every turn.

You don't write a retry script. You drag providers into priority order in Settings → Models and watch the health dashboard fill with green dots as requests route around failures in real time.

2 · Knowledge that links itself

Upload a PDF, a batch of markdown, a scraped page — raw material in.

MateClaw's LLM Wiki digests it into structured pages, builds [[links]] between them, and preserves traceable citations for generated content. Open the citation drawer to inspect the corresponding source chunk and verify page or answer references.

This is the difference between a warehouse and a library.

3 · One product, five surfaces

Same brain. Same memory. Same tools. Different doors.

$0 · No tokens metered. No seats billed. Your server. Your data. Your keys.

What's in the box

Digital employees, not chatbots

You hire coworkers, not chat boxes. Each one has a Role, a Goal, a Backstory, a runtime, a pixel-art avatar, and a color of their own — six built-in templates ship ready (General Assistant · Product Assistant · Research Analyst · Customer Support · Data Analyst · Code Reviewer). Employee identity and governance stay stable even when the execution engine changes.

Agent Runtime: native or DSH (2.2.0+)

The AgentRuntimeProvider contract separates an employee from the engine that runs its turn. The native runtime keeps ReAct, Plan-and-Execute, persistent Goals, and Team Runs inside MateClaw. The DSH runtime launches the official dsh --profile sdk CLI and normalizes its thinking, text, tools, usage, completion, and cancellation events. Managed installation pins 0.2.0-rc.1 (alternative: 0.1.7-rc.2) with reviewed npm locks and Node.js 22. An opt-in 0.2.1-rc.2 candidate adds SDK compatibility updates; macOS arm64 installation, handshake and short real-model/tool checks passed, while default selection stays unchanged. Upgrades copy runtime homes, check the candidate, and switch a retained generation; rollback restores the previous home/configuration without merging newer sessions. Package and real-model adapter checks passed on macOS arm64; Linux x64 and persisted V3→V4 history migration remain unverified. Configure DeepSeek Harness →

Durable long tasks: checkpoint, restart, continue (2.2.0+)

Persistent Goals turn work that takes hours into bounded, recoverable segments. The database preserves the goal checklist, continuation state, attempts, cooldowns, leases, and user input accepted while the worker is busy. After a single backend instance restarts, the supervisor reconciles the interrupted attempt, reads persisted checkpoints and artifacts, and schedules the next safe segment instead of asking you to repeat the task.

For file-producing work, ask the employee to keep a progress ledger, append small verifiable units, inspect the existing tail after recovery, and complete the Goal only after reproducible acceptance checks pass. The runtime does not promise exactly-once behavior for arbitrary external side effects; payments, sends, publishes, and destructive calls still need provider idempotency or review. Run and verify durable Goals →

Prompt pattern: “Create a persistent Goal first. Save the plan and progress in the workspace, write in small checkpoints, resume from existing evidence after errors or restart, and call completeGoal only after every criterion has verifiable evidence.”

Team Runs (2.1.0+)

One request, one durable Team Run. A stable runId links the user's objective, task DAG, worker executions, final synthesis, and deliverables. Chat is the outcome surface, Agents Live groups the workers for real-time observation, and Teams owns history and governance — all three consume the same server projection. Worker conversations no longer flood the normal sidebar; summaries and files lead, while tasks, evidence, approvals, and read-only worker records drill down on demand. Undernea