AIWorkHub is an open-source multi-model AI coding agent control plane for VS Code and MCP. It does not replace Codex, Claude, Copilot, DeepSeek or GLM—it turns them into one repository-scoped workforce with dependency-aware tasks, durable context, source intelligence and evidence-based review.
AIWorkHub managing its own repository
AIWorkHub connects repository authority, durable context, model routing, isolated execution and independent assurance in one control loop. Solid capabilities are live today; amber work is being hardened; dashed violet capabilities are the next planned layer.
NeedFix intake, Roadmap outcomes, Task DAG, collision guard, Source Graph, Session Manager, AI Memory, KB, Context Graph, provider catalog, outcome-aware ranking, preflight, retained worktrees, semantic edits, MCP tool bridge, validation sandbox, independent review, sealed receipts, provider-free replay, checkpoints, atomic promotion and rollback.
Reviewer MCP response delivery, exact liveness without model wall-clock kills, finalizer leases, validation environment isolation, offline-stage recovery, Windows/Linux parity, Source Graph refresh truth, bounded storage retention and automatic NeedFix lifecycle reconciliation.
NeedFix auto-close and TTL purge, provider/model enable-disable controls, CPU-aware parallelism, durable refresh job IDs, cross-platform finalizer completion, Rust semantic coverage, richer JSON evidence visualization and quality-calibrated release automation.
Every repository owns its task state, indexes, memories, callbacks and audit trail. Models work in bounded scopes; the manager accepts changes only after evidence is checked.
AIWorkHub began while managing long-running, multi-model development: decisions disappeared after context compaction, repositories were scanned repeatedly, parallel changes collided, session knowledge was lost and a model's “done” still required manual proof.
I did not build AIWorkHub to demonstrate multi-agent orchestration. I built it because I needed several coding models to work on real repositories without losing decisions, colliding with each other or asking me to trust unverified completion claims.
Task DAGs, Source Graph, Manager Context Graph, durable memory, callbacks, isolated workers and evidence receipts are direct answers to those failures—not a collection of unrelated agent-demo features.
Repository-bound tasks, dependency planning, model routing, bounded execution and callback delivery.
Structural code context through focused, sliced, impact, trace and bundle queries instead of repeated broad scans.
Separate Session Manager, AI Memory, knowledge base and manager-only Context Graph authorities.
Review diffs, tests, logs, artifacts, tool-use receipts and approval history—not model self-reports.
Parallelize independent cards while dependency and write-scope collision checks preserve task truth.
Use the editor models and authenticated CLIs you already have across Windows, macOS, Linux and Remote SSH.
AIWorkHub can reserve premium models for architecture, difficult reasoning and independent review while routing bounded research, tests, focused edits and mechanical throughput to lower-cost capable models. Source Graph and semantic edits reduce avoidable tokens on every route; model routing then reduces how many of the remaining tokens are bought from the most expensive model.
| Layer | System action | Economic effect |
|---|---|---|
| Token efficiency | Bounded graph context, bounded reads and replacement-only edits | Less unnecessary input and code-output payload |
| Model-mix efficiency | Match task difficulty to capability, readiness, observed quality and known cost | Fewer premium-model tokens for work an economical route can complete |
| Attempt efficiency | Preserve exact failure evidence and issue bounded residual rework | Fewer blind retries and less repeated context/work |
| Deterministic offload | Apply hash-bound edits and run validation locally | No model spend for operations that do not require model judgment |
The mechanism is shipped; the public system-wide ROI is not yet claimed. In the measured Claude cohort, Opus used 19% of tokens but 42.9% of known cost. AIWorkHub counts routing as a saving only after the cheaper route preserves validation and manager-accepted quality.
AIWorkHub does not compete with the models whose work it coordinates. Its real alternative is manual multi-chat orchestration or a custom stack of task boards, worktrees, context stores, scripts and review glue.
| Layer | Examples | Role |
|---|---|---|
| Repository control plane | AIWorkHub | Task authority, routing, context, isolation, evidence, callbacks, review and economics |
| Supported workforce | Codex, Claude, Copilot-hosted models, DeepSeek, GLM | Execution routes coordinated by AIWorkHub—not competitors |
| Adjacent context/edit tools | Graphify, Serena and similar toolkits | Complementary graph, retrieval or semantic capabilities |
| Standalone agent clients | Aider, Cline and similar products | Alternative execution experiences, not the same control-plane layer |
| Actual alternative | Manual coordination or custom in-house glue | Copy/paste context, hand-managed worktrees, retries and review state |
AIWorkHub distinguishes structural bytes, provider tokens, task outcomes, manager decisions, callback durability and missing evidence. The full benchmark matrix publishes favorable and negative results with their exact populations.
The historical capped A/B observation recorded 27.5% fewer total tokens, but pair 1 used mismatched 20k/200k token ceilings. It is not eligible for a causal or product-savings claim; an uncapped matched rerun is required. The envelope result is deterministic structural evidence from one same-evidence fixture; the old 0.8.81 fleet expanded 20.0% and v2 still needs an equivalent live remeasurement. Tool-use outcome differences are observational, and system-wide cost savings remain unmeasured.
AIWorkHub