A long-range Autonomous Development Cruiser for Cursor capable of executing 500+ sequential tasks without quality degradation. Built for fault tolerance, continuous learning, and multi-layered agent orchestration using ping-pong execution loop.
Important
Dispatcher Architecture: DreamTeam is designed to offload deep work from the main chat. The Dispatcher coordinates Left and Right Orchestrators to run batches of 15+ tasks with minimal supervision. Each context switch (ping-pong) performs a context reset, ensuring the dispatcher never hits context ceilings. This is the key to executing 500+ tasks with zero performance degradation.
Quick Start:
python -m dreamteam new-project .(in an empty folder)- Open in Cursor →
/start+ your goal - Start or resume the execution loop:
/run
The system uses a recursive dispatching loop. The Dispatcher coordinates specialized orchestrators to handle batches of tasks, keeping the main context clean and stable.
---
config:
layout: fixed
look: handDrawn
---
flowchart BT
subgraph PhaseP["Planning"]
direction TB
P2["Sub-Planner"]
P1["Planner"]
end
subgraph PhaseE["Execution Loop"]
direction TB
E2["Reviewer"]
E1["Developer"]
E3["DevExperience"]
E4["Git-Ops"]
Upd["update-task Done"]
end
subgraph PhaseM["Maintenance"]
direction TB
M2["FixPlanner"]
M1["Learning / Meta"]
M3["Researcher"]
M4["Auditor"]
end
subgraph Ops["Operational Phases"]
direction LR
PhaseP
PhaseE
PhaseM
end
subgraph Context["Isolated Agent Context"]
direction TB
Ops
LR_Agent{"Left and Right Orchestrator"}
Term[["Terminal Subagent"]]
end
subgraph Engine["DreamTeam Cruiser Engine"]
direction LR
DP["Dispatcher"]
Context
DAG[("Task DAG")]
RAG[("Memory DB")]
Counter[("Counter")]
end
User(["User Goal"]) --> DP
DP -- Switch batch --> LR_Agent
LR_Agent --> Ops
P1 --> P2
E1 --> E2
E2 --> E3
E3 --> E4
E4 --> Upd
M1 --> M2 & E1
Ops -.-> Term
P2 --- DAG
M2 --- DAG
M3 --- RAG
Upd -. "Batch Limit =15 or
Context Overflow" .-> DP
P2 -. Planning Done .-> DP
M4 --> RAG
Counter --> M4 & M3 & M1
DAG --> M4 & M3
P2:::clsWorker
P1:::clsWorker
E2:::clsWorker
E1:::clsWorker
E3:::clsWorker
E4:::clsWorker
Upd:::clsWorker
M2:::clsMaintain
M1:::clsMaintain
M3:::maintain
LR_Agent:::clsOrch
Term:::clsInfra
DP:::clsMain
DAG:::clsDB
RAG:::clsDB
classDef clsMain fill:#4f46e5,color:#fff,stroke:#3730a3,stroke-width:2px,rx:10
classDef clsOrch fill:#f8fafc,stroke:#94a3b8,stroke-width:2px,stroke-dasharray: 4,rx:10
classDef clsWorker fill:#ffffff,stroke:#e2e8f0,stroke-width:1px,rx:4
classDef clsMaintain fill:#ecfdf5,stroke:#10b981,color:#064e3b,rx:4
classDef clsInfra fill:#0f172a,color:#fff,stroke:#1e293b,rx:2
classDef clsDB fill:#fff,stroke:#64748b,stroke-width:2px,shape:cylinder
style LR_Agent stroke:#00C853,fill:#FFD600
style Context fill:#f8fafc,stroke:#e2e8f0,stroke-width:1px
style DAG stroke:#000000,fill:#FF6D00
style RAG fill:#FF6D00
style Counter fill:#FF6D00
style User fill:#00C853
style Engine fill:#fdfaff,stroke:#c4b5fd,stroke-width:2px
Important
Model Inheritance: By default, all sub-agents inherit the model name chosen in the main chat (where /start or /run was invoked).
To optimize project economy:
- Heavy Reasoning (Planner, Auditor, Researcher, Learning): We recommend using frontier models (e.g., Claude 4.6 Sonnet) for high architectural compliance.
- Routine Tasks (Developer, Reviewer, Git-Ops): These can often be shifted to more economical models to maintain sustainability over 500+ sequential tasks.
Thoughtful model selection ensures the Project Budget lasts for the entire Cruiser journey.
The system is built to minimize "Main Chat" context overflow. Using a Dual Orchestrator system (Left/Right), DreamTeam offloads execution to sub-agents, leaving the main chat lean and responsive. This architectural split allows massive task sequences to run even on non-frontier models.
DreamTeam uses a multi-layered intelligence system to ensure stability over long durations:
- Level 1: Fleet Control (Dispatcher): The entry point. It doesn't perform tasks but manages the switching between "Left" and "Right" Orchestrators. This ensures that even for 1000-task journeys, the main chat context remains lean and responsive.
- Level 2: Task Orchestration (Orchestrators): Specialized agents that run inside a fresh context. They decide whether to launch the Planning Phase or the Execution Loop and handle all self-correction triggers.
- Level 3: Specialized Workers:
- Planner & Sub-Planner: Decompose high-level goals into a detailed task DAG.
- Developer: Implements features and runs tests.
- Reviewer: Verifies code quality and architectural compliance.
- Git-Ops: Handles commits and repository maintenance.
- Maintenance Agents: (Researcher, Learning, Meta-Planner, Auditor) Keep the context compressed and the pipeline optimized.
The system is designed to recover from crashes, mismatches, and stuck tasks without manual intervention:
- run-next: Verifies DB↔Files consistency, auto-syncs if needed, and resets stuck tasks.
- recover: Full system reset, integrity verification, and memory health check.
- State-in-DB: All state lives in SQLite. The Cruiser can resume after a break without losing a single bit of context.
DreamTeam improves from production feedback instead of degrading:
- DevExperiencer: Records every outcome, attempt count, and time spent.
- Learning Agent: Analyzes the Experience DB to detect patterns of failure or high friction.
- FixPlanner: Automatically adjusts upcoming tasks (library choices, dependency updates) to avoid recurring roadblocks.
- Developer Updates: The system may augment
.cursor/agents/developer-addendum.mdwith additional instructions to permanently adopt successful patterns.
Launch a minimalistic web dashboard to track your Cruiser's performance:
- KPIs: Total tasks, estimated tokens, and Friction Score (Avg Attempts).
- Visualization: Identify hallucination spikes and time-heavy tasks.
- Task Lineage: Track original plans vs. tasks added during self-correction.
Command:
python -m dreamteam dashboard
- guide/ — Full setup, commands, and best practices.
- INSTRUCTIONS.md — System overview.
- COMMANDS.md — CLI reference.
MIT license
Crafted for Cursor adepts with love from BuLab