AIWorkHub

AIWorkHub is a repository-native control plane for multi-model software development. It gives every repository an isolated task system, Source Graph, durable project context, worker runtime and evidence-first review loop.

The extension opens as a retained editor tab and runs one repository-scoped MCP stdio runtime on the workspace host. It does not open a browser, bind a port, expose a LAN service or require an AIWorkHub cloud account.

What's new in 0.9.43

• VS Code LM workers and quality-review calls are now exposed under worker-scoped names and dispatched exclusively through the authenticated worker MCP bridge, not through manager authority.
• Read-only reviewer completion is sealed into a durable, verifiable receipt; result and retry paths converge on that receipt instead of diverging.
• Quality-evidence packets above the bounded argv threshold use file transport, avoiding E2BIG on platforms with small argument limits while preserving the packet-size contract.
• The large-packet regression fixture stays within production packet limits and exercises the file-transport path it was designed to validate.

See the packaged Changelog for the complete release summary.

Create a bounded task, launch a model worker, inspect its evidence and accept or rework it.

Highlights

• Plan and inspect dependency-aware AI tasks from one operational dashboard.
• Delegate to supported local model adapters and track real terminal outcomes.
• Replace repeated raw-source discovery with a repository Source Graph covering 34 configurable code, data and documentation families.
• Send focused code fragments through staged semantic edits and let the local bridge assemble the hash-bound final envelope without model-side full-file regeneration.
• Use 31 bounded source-intelligence modes for symbols, calls, tests, impact, complexity, ownership, hotspots, gaps and task-shaped context bundles.
• Preserve continuity through Session Manager, AI Memory and KB.
• Review diffs, tests, logs, artifacts, approval history and deterministic Quality Evidence before acceptance.
• Run a changed-file Known Bug Scanner across C/C++/CUDA, Python, JavaScript/TypeScript, Go, Java/Kotlin and PHP without treating heuristic warnings as proven failures.
• Measure whether workers used Source Graph throughout the task through authenticated tool-use receipts and continuous-use telemetry.
• Keep repositories isolated in separate .aiworkhub/ authorities.
• Run on Linux, macOS, native Windows, WSL and Remote-SSH.

Operational surface

The retained dashboard combines the task DAG, live worker output, Review Inbox, callback health, model readiness, tool-use statistics, storage retention, Source Graph coverage and bounded viewers for logs, sessions, AI Memory and KB. Settings remain repository-local under .aiworkhub/, so a multi-window installation does not share task or context authority between repositories.

Tasks, callback health, source coverage, context stores, preflight and evidence in one retained editor tab.

Get started

1. Install from the Marketplace (or install a release VSIX) and open a Git repository in VS Code.
2. Run AIWorkHub: Open Dashboard.
3. Select the repository when using a multi-root workspace.
4. Choose Initialize AIWorkHub on first use.
5. Open a new Codex, Claude or MCP-capable chat after registration so the new runtime tools are discovered by that chat process.

Initialization is explicit and idempotent. It creates repository-local state only under .aiworkhub/ and starts the first Source Graph index in the background.

For Claude Code, initialization also maintains the repository-local .mcp.json server registration and the bounded AIWorkHub block in CLAUDE.md. Open a new Claude chat after initialization or an AIWorkHub upgrade. That direct chat is instructed to bootstrap as the manager, call manager Source Graph before broad Read/Grep/Glob discovery, and re-query the graph when its implementation or validation boundary changes. AIWorkHub-launched task processes use the separate worker tool surface.

Run your first task

AIWorkHub is designed for a manager chat that delegates bounded work instead of letting several models edit one checkout without coordination.

Start a new chat after initialization or upgrade and paste:

Use AIWorkHub as manager for the currently bound repository. Call
aiworkhub_manager_bootstrap first; verify repository identity, manager route,
callback, Source Graph and preflight. Do not edit or launch yet. Report what is
ready and what is degraded.

Then describe the desired outcome normally. Ask the manager to create bounded cards and launch only independent, dependency-ready, non-colliding cards in parallel. The MCP server also presents this lifecycle as a mandatory contract: creating a task leaves it pending; exact claim plus launch establishes processing; workers stop at review_ready; callbacks wake the current verified manager; and only that manager accepts or rejects verified evidence.

1. Check the dashboard header. Repository, MCP, Source Graph and callback state should be ready; Preflight explains any unavailable optional model adapters.
2. Tell the manager what outcome you want. The manager creates a task card with an exact objective, acceptance criteria, allowed writes, validation commands and dependencies.
3. The manager selects a ready adapter/model and launches the exact card. Workers receive repository-scoped Source Graph, Session, Memory and KB context and work in an isolated task workspace.
4. Follow Live Output or continue other work. Terminal outcomes are durable and the originating manager receives a callback when review is required.
5. Open the task in Review. Inspect the bounded diff, tests, logs, artifacts, tool-use receipts and independent reviewer evidence.
6. Accept promotes the verified change and finalizes the task. Reject records exact feedback and creates a bounded residual rather than silently discarding the previous evidence.

Dependency cards remain pending until their prerequisites finish. Collision checks prevent two active workers from owning overlapping write paths.

See the complete first-run and manager manual for copy/paste planning/review prompts, Remote-SSH behavior and recovery after an interrupted write acknowledgement.

Models and authentication

AIWorkHub does not proxy credentials or require an AIWorkHub account. It uses models already authenticated in the corresponding editor or CLI.

The Preflight and Workforce views report observed availability, adapter/model identity, outcomes, latency, token evidence and known cost. An optional adapter being unavailable does not block otherwise ready models.

Source Graph and context

Source Graph is an incrementally refreshed structural repository index, not a remote Sourcegraph service. Managers and workers start with low-token focus and slice queries, then use calls, trace, impact, test mapping or typed bundles only when the task needs them. Operations telemetry shows which modes were requested and executed, returned evidence, workflow stage, latency, generation and inter-call gaps.

Session Manager stores current state and handoffs; AI Memory stores durable lessons; KB stores curated project facts; the optional Manager Context Graph preserves bounded manager transcript evidence. All are repository-local and have bounded viewers in the dashboard.

Commands

• AIWorkHub: Open Dashboard — open or reveal the retained editor tab.
• AIWorkHub: Select Repository — bind the dashboard in a multi-root window.
• AIWorkHub: Refresh Dashboard — refresh the current repository snapshot.
• AIWorkHub: Restart MCP Connection — replace only AIWorkHub's selected repository MCP child.

Remote development

AIWorkHub is a workspace extension. In Remote-SSH, install it on the remote extension host; its packaged Python runtime, MCP child and repository state run beside the remote checkout. No port forwarding is required.

If the dashboard is not ready

• Connecting: use AIWorkHub: Restart MCP Connection once and inspect the dashboard's last-log row. The extension restarts only its own child.
• A model is unavailable: open Preflight, confirm the provider is installed and grant the one-time VS Code model consent when prompted.
• Source Graph is empty: initialize the repository, enable the required language family in Settings and run a refresh.
• A chat cannot see tools: open a new chat after installation or upgrade so that client performs MCP discovery against the current runtime.
• Windows upgraded from an old build: activation automatically migrates legacy source/version PYTHONPATH registrations to a host-stable packaged runtime; no manual config.toml edit is required.

Trust and privacy

• Local stdio transport; no AIWorkHub network listener.
• Read-only and launch-disabled by default.
• Repository-specific state, route identity and audit trail.
• No AIWorkHub telemetry upload of prompts, source, credentials or memories.
• Explicit manager authority for context writes and task acceptance.

Read the full Getting Started guide, Architecture, Source Graph guide, Manager Context Graph guide, Security Policy and Product Roadmap.

Development build

npm --prefix vscode-extension install
npm --prefix vscode-extension test
npm --prefix vscode-extension run package
code --install-extension vscode-extension/dist/aiworkhub-*.vsix

AIWorkHub is open source under the MIT License.