iQueue
Intent-stabilized multi-queue reasoning for agentic systems.
iQueue is an open-source control plane for agentic AI: bidirectional expandable highway lanes, rotator multi-queues, pivot-anchored embeddings, KNN + VAE ranking, lightweight + large-model steering, and a γ discount for veering off-highway. Scored queues serialize to markdown contexts for future runs.
Docs
| Documentation | Explanation |
| Tutorial | API |
| Config | MCP |
| Lane Engineering | Orchestration |
| Multimodal | Cloud |
| Integrations | Ablations H1–H7 |
| Roadmap | Source / Issues |
PyPI: iqueue · Repo: ehallford11714/iQueue
Session SDK (v0.3)
import iqueue
with iqueue.Session() as s:
s.configure(skip_hf_download=True)
s.admit("Plan fever differential", system_id="uc_diagnosis")
s.service(ticks=1)
s.pack_contexts()
s.export_audit("outputs/audit") # JSON + Markdown
Multi-model support
| Provider | Mode | Notes |
|---|---|---|
| OpenAI | frontier API | sol / terra / luna → GPT-5.6 (gpt-5.6-sol, …) |
| Anthropic | frontier API | opus5 / fable / sonnet5 → Claude 5 family |
| frontier API | gemini-3.6-flash |
|
| Qwen | DashScope / local | cloud qwen-plus or HF/Ollama |
| OpenRouter | gateway | one key → Sol/Opus/etc. |
| mock (offline) | local | auto-downloads HF Qwen, else deterministic text |
import iqueue
iqueue.configure(
openai_api_key="sk-...",
qwen_api_key="sk-...",
ollama_base_url="http://127.0.0.1:11434",
)
client = iqueue.get_client("qwen")
See docs/MODELS.md. Copy config/models.example.json → config/models.local.json for local keys.
Quick start
git clone https://github.com/ehallford11714/iQueue.git
cd iQueue
python -m venv .venv
# Windows: .venv\Scripts\activate
pip install -r requirements.txt
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.tutorial_demo # progressive tutorial demo (or: iqueue-tutorial)
python -m src.demo
python -m src.highway.demo
python -m src.highway.stack_demo # stacked levels + trainable velocities
python -m src.models.demo # dedicated multi-provider + LM steering
python -m src.experiments.train_eval # proper train/eval loop + metrics
python -m src.experiments.run_gamma_ablation
python -m src.experiments.run_h1_h7 # full H1–H7 ablation battery (or: iqueue-ablation)
python -m src.v03_demo # Session + kernel admit + audit + integrations smoke
Tutorial: docs/TUTORIAL.md — install, core APIs, config, learn-the-road, and copy-paste use cases (diagnosis, incident, research, planning, policy, stack tiers).
Lane Engineering: docs/LANE_ENGINEERING.md — designing and operating expandable LaneQueues (agents, tasks, fill/drain, kinds, stack, policy).
Orchestration + browser: docs/ORCHESTRATION.md — harness pipeline (iqueue-harness), soft /v1/orchestration + MCP iqueue_orchestrate, and web/ IndexedDB / WebGPU / Wasm demo.
Streamlit demo (variable config + learn the road)
pip install -e ".[ui,local]" # streamlit + torch/transformers/peft
# optional CUDA 4-bit: pip install -e ".[qlora]"
streamlit run app.py
All knobs (highway.*, train.*, qlora.*, policy.*, stack.*, models.*, trace.*) are editable in the sidebar or via JSON (config/iqueue.example.json).
# CLI trace with variables
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.trace_runner --config config/iqueue.example.json
# light QLoRA + SFT while learning the road:
python -m src.trace_runner --config config/iqueue.example.json --qlora
HTTP API suite (FastAPI)
pip install -e ".[api]"
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.api --port 8765
# docs: http://127.0.0.1:8765/docs
Route groups: /health, /config, /hardware, /highway, /stack, /policy,
/rotator, /ranking, /pivotgraph, /models, /train, /trace, /cases,
/integrations.
MCP server (Cursor / agent hosts)
pip install -e ".[mcp]"
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.mcp_server # or: iqueue-mcp
Register in Cursor MCP config (cwd = this repo):
{
"mcpServers": {
"iqueue": {
"command": "python",
"args": ["-m", "src.mcp_server"],
"cwd": "<path-to-iQueue>",
"env": { "IQUEUE_SKIP_HF_DOWNLOAD": "1" }
}
}
}
Full tool/resource list: docs/MCP.md. Project agent skills live under .cursor/skills/ (iqueue-native, iqueue-lane-engineering, iqueue-integrations).
External agent frameworks (LangGraph / CrewAI / SmolAgents / DSPy)
pip install -e ".[integrations]" # optional; demos work without it via mock/sim
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.integrations.demo --framework mock
python -m src.integrations.demo --framework langgraph --use-case uc_incident
Lanes can attach a framework runner (attach_framework / run_on_lane). See docs/INTEGRATIONS.md.
Demo test cases (CLI / Streamlit / API)
set IQUEUE_SKIP_HF_DOWNLOAD=1
python -m src.cases_cli --list
python -m src.cases_cli
See docs/CONFIG.md and docs/API.md.
Core ideas
| Piece | Role |
|---|---|
| Pivots | Invariant latent anchors per system / use-case |
| Lane Engineering | Design/operate LaneQueues: kinds, agents, tasks, expand, policy |
| Expandable lanes | Own requests, spawn/assign agents, track tasks, expand under pressure |
| Stacked highways | Multiple levels with different velocities; router + velocities are trainable |
| Highway | Bidirectional FORWARD / REVERSE with lane control & changes |
| Rotator | KNN + VAE specificity selection |
| LM steering | Frontier/local models judge adherence → fit steering vector |
| γ veer penalty | tuple_score = base × γ^{veer_steps} × lane_bonus |
| Context packs | Markdown queue serialization for future context |
Docs & schema
- Tutorial
- Lane Engineering
- Explanation
- External integrations (LangGraph, CrewAI, SmolAgents, DSPy)
- H1–H7 ablations
- HTTP API suite
- MCP server (Cursor tools/resources + skills)
- Config variables
- Models & API keys
- Schema guide
- Open schema
- Roadmap (v0.3.0 items #6–10 shipped)
- Pivot alignment
License
Apache-2.0 — see LICENSE.
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