A.T.L.A.S

Adaptive Test-time Learning and Autonomous Specialization

📚 Docs • ⚙️ Config • 🔧 Setup

Self-hosted AI coding agent infrastructure running entirely on consumer hardware. Demonstrates that sophisticated AI systems—RAG, test-time compute scaling, and continuous learning—can run on a single 16GB consumer GPU.

• 99.5% Success Rate — Ralph Loop retry algorithm with temperature escalation
• Full RAG Pipeline — 100GB vector storage, semantic code search
• Continuous Learning — Nightly LoRA fine-tuning from successful completions
• Consumer Hardware — Single RTX 5060 Ti (16GB VRAM)

Host: 4 vCPU (AMD RYZEN 5 2600) • 12GB DDR4 RAM • 150GB SSD • RHEL 9

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Architecture

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flowchart TB
    subgraph external[" "]
        client(["Client<br/>OpenCode / API"])
    end
    subgraph gateway["Gateway"]
        proxy["LLM Proxy :8000<br/>Auth • Rate Limit"]
        portal["API Portal :3000<br/>Users • Keys"]
    end
    subgraph core["Core Services"]
        rag["RAG API :8001<br/>Orchestration"]
        embed["Embeddings :8080<br/>MiniLM-L6-v2"]
    end

    %% Central inference engine - outside subgraphs for central positioning
    llama["llama-server :8000<br/>Qwen3-14B • GPU"]

    subgraph data["Storage"]
        qdrant[("Qdrant<br/>100GB Vectors")]
        redis[("Redis<br/>Queues • Metrics")]
    end
    subgraph atlas["Task Processing"]
        worker["Task Worker<br/>Ralph Loop<br/>99.5% Success"]
        sandbox["Sandbox :8020<br/>pytest • pylint"]
        dash["Dashboard :3001<br/>Monitoring"]
    end
    subgraph learn["Learning"]
        trainer["Nightly Trainer<br/>LoRA Fine-tune"]
        lora[("Adapters<br/>Hot-swap")]
    end
    %% Gateway flow
    client -->|"request"| proxy
    proxy -.->|"validate key"| portal
    proxy -->|"chat/completions"| rag

    %% RAG API calls llama-server for inference
    rag -->|"inference"| llama
    rag -->|"embed query"| embed
    embed -->|"search vectors"| qdrant

    %% Task submission to Redis
    rag -->|"submit task"| redis
    redis -->|"poll result"| rag

    %% Ralph Loop: Task Worker flow
    redis -->|"pull task"| worker
    worker -->|"generate code"| llama
    worker -->|"test code"| sandbox
    worker -->|"result + training"| redis

    %% Monitoring
    redis -->|"metrics"| dash

    %% Learning pipeline
    redis -.->|"training data"| trainer
    trainer -->|"fine-tune"| lora
    lora -.->|"load LoRA"| llama
    classDef client fill:#37474F,stroke:#263238,color:#fff
    classDef gateway fill:#607D8B,stroke:#455A64,color:#fff
    classDef core fill:#2196F3,stroke:#1565C0,color:#fff
    classDef gpu fill:#4CAF50,stroke:#2E7D32,color:#fff
    classDef storage fill:#00BCD4,stroke:#00838F,color:#fff
    classDef process fill:#FF9800,stroke:#E65100,color:#fff
    classDef learn fill:#9C27B0,stroke:#6A1B9A,color:#fff
    class client client
    class proxy,portal gateway
    class rag core
    class llama,embed gpu
    class qdrant,redis storage
    class worker,sandbox,dash process
    class trainer,lora learn

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Component Details

Layer	Service	Port	Purpose
Gateway	LLM Proxy	8000	Auth, rate limiting
	API Portal	3000	Users, API keys, usage
Core	RAG API	8001	Orchestration, chunking
	llama-server	8000	GPU inference (Qwen3-14B)
	Embeddings	8080	Vectorization (384 dims)
Storage	Qdrant	6333	Vector DB (HNSW)
	Redis	6379	Queues, metrics, cache
Processing	Task Worker	—	Ralph Loop engine
	Sandbox	8020	Isolated execution
	Dashboard	3001	Monitoring UI
Learning	Trainer	—	Nightly LoRA (2am)

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Quick Start

git clone https://github.com/itigges22/atlas.git && cd atlas
cp atlas.conf.example atlas.conf && ./scripts/install.sh
kubectl get pods  # Verify all services running

Requirements: K3s, NVIDIA GPU (8GB+ VRAM), 4+ vCPU, 12GB+ RAM, 50GB+ SSD

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Recommended Client

ATLAS exposes an OpenAI-compatible API, so it works with any client that supports the OpenAI protocol.

Recommended: OpenCode Fork — A terminal-based AI coding agent based on Opencode, forked and optimized for ATLAS.

git clone https://github.com/itigges22/opencode.git && cd opencode
bun install
bun run dev

Alternatives: Cursor, Continue, aider, or any OpenAI-compatible client.

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Key Algorithms

Ralph Loop — 99.5% Success via Test-Time Compute

P(success) = 1 - (1 - p)^k    →    p=0.65, k=5: 99.5%

Attempt	Temp	Strategy
1	0.3	Conservative
2	0.4	Minor variation
3	0.5	Moderate creativity
4	0.6	Explore alternatives
5	0.7	Maximum creativity

Each retry accumulates error context, guiding away from previous failures.

Continuous Learning — Nightly LoRA Fine-tuning

1. Export — Successful completions (rating ≥4) from Redis
2. Train — LoRA (r=8, α=16) on CPU
3. Validate — 66% pass rate required
4. Deploy — Hot-swap via symlink

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Benchmarks

Coming soon — Consumer vs enterprise hardware comparisons.

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Documentation

Architecture	System design, data flows, algorithms
Configuration	All options explained
Setup	Installation guide
Troubleshooting	Common issues

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Contributing

See CONTRIBUTING.md for guidelines.

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Apache 2.0 — LICENSE — Copyright 2025 Isaac Tigges