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Hybrid LLM Routing Agent

AMD Developer Hackathon: ACT II Track 1 Python 3.10+

Token-efficient AI routing for the AMD Developer Cloud. An intelligent agent that routes queries between a cheap local model (Ollama on AMD ROCm) and a powerful remote model (Fireworks AI), minimizing cost while maximizing quality.

🏆 AMD Developer Hackathon: ACT II — Track 1 Submission Built for the AMD Developer Cloud with ROCm-accelerated Ollama.

Table of Contents

Overview

The Problem

Large Language Models (LLMs) are expensive to run at scale. Calling a remote API like hosted Llama 3.1 70B for every query burns through tokens — and budget — fast. But local models (e.g., Llama 3.2 3B running on an AMD GPU) are free to run and can handle most queries well.

The Solution

This agent implements intelligent hybrid routing — try the cheap local model first, validate its output, and only fall back to the expensive remote model when quality suffers. The result: token cost savings of 40–80% with negligible quality loss.

Key Features

  • 4 Routing Strategies: always_local, always_remote, fallback, predictive
  • LLM-Free Response Validation: Echo detection, repetition loops, placeholder markers, format-specific checking (JSON schema, Python syntax)
  • Self-Correction Loop: When local validation fails, the model gets targeted repair instructions (not a blind retry)
  • Local Token Counting: Uses tiktoken to estimate local token usage for accurate cost tracking
  • Response Caching: File-backed JSON cache avoids redundant queries
  • ROCm-Ready: Runs on AMD GPUs via Ollama with ROCm support

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────┐
│   main.py   │────▶│  HybridRouter   │────▶│  LLMClient   │
│  (CLI/REPL) │     │  (routing logic)│     │  (Ollama +    │
└─────────────┘     │  + RouterCache) │     │   Fireworks) │
                    └────────┬────────┘     └──────────────┘
                             │                          ▲
                             ▼                          │
                    ┌─────────────────┐                │
                    │ResponseEvaluator│────────────────┘
                    │(quality checks) │
                    └─────────────────┘

Data Flow (Fallback Strategy)

  1. Receive query via CLI or REPL
  2. Try local — send to Ollama (running on AMD GPU via ROCm)
  3. Validate — run LLM-free quality checks (echo, repetition, format)
  4. Repair loop — if validation fails, feed error back to local model with targeted correction instructions (up to max_retries times)
  5. Fallback — if local still fails, route to Fireworks AI remote model
  6. Report — output execution metrics (latency, tokens, cost, route chosen)

Quick Start

Prerequisites

  • Python 3.10+
  • Ollama (with ROCm support for AMD GPUs)
  • AMD GPU (recommended) or CPU
  • Fireworks AI account (for remote fallback)

1. Clone & Install

git clone https://github.com/yourusername/llm-routing-agent.git
cd llm-routing-agent
pip install -r requirements.txt

2. Setup Ollama (AMD ROCm)

# Install Ollama (use ROCm-enabled build for AMD GPUs)
curl -fsSL https://ollama.com/install.sh | sh

# Pull a local model
ollama pull llama3.2:3b

# Start Ollama
ollama serve

AMD GPU users: Ollama automatically uses ROCm when available. Verify with ollama ps after starting.

3. Configure Environment

cp .env.example .env

Edit .env with your settings:

OLLAMA_HOST=http://localhost:11434
LOCAL_MODEL=llama3.2:3b
FIREWORKS_API_KEY=your_key_here
REMOTE_MODEL=accounts/fireworks/models/llama-v3p1-70b-instruct
REMOTE_PRICE_PER_1M_TOKENS=0.90

No Fireworks key? Leave FIREWORKS_API_KEY blank. The system will auto-mock remote calls so you can still test local routing logic.

4. Run Your First Query

# Simple local query
python main.py --query "What is the capital of Japan?" --strategy always_local

# Fallback strategy (try local, fall back to remote if needed)
python main.py --query "Write a Python function for factorial" --strategy fallback --format python

# Predictive strategy (analyzes complexity, routes accordingly)
python main.py --query "Explain quantum computing" --strategy predictive

Strategies

Strategy Behavior Best For Cost
always_local Always runs on local Ollama Quick Q&A, simple facts $0
always_remote Always runs on Fireworks AI Critical accuracy needed Full remote cost
fallback Try local → validate → repair → fallback Default — best balance 40–80% savings
predictive Analyze complexity → route directly Optimizing latency 50–90% savings

Fallback (Recommended)

The default strategy. It tries the local model, validates the output, and only calls the remote API if the local response fails quality checks. This is the best balance of cost and quality for most use cases.

Predictive

Uses rule-based heuristics (keyword matching, query length, format detection) to classify query complexity upfront. Simple queries go to local; complex ones go directly to remote — saving the latency of a failed local attempt.

Usage

CLI Mode

# All options
python main.py \
  --query "Your query here" \
  --strategy fallback \
  --format text \
  --schema "key1,key2" \
  --no-cache

# JSON output with schema validation
python main.py \
  --query "Generate a JSON object about a banana" \
  --strategy fallback \
  --format json \
  --schema "fruit_name,calories"

# Python code with syntax validation
python main.py \
  --query "Write a recursive factorial function" \
  --strategy predictive \
  --format python

Interactive REPL Mode

python main.py --strategy fallback

Then type queries interactively. Type exit or quit to stop.

Cache Management

# Disable caching for a single query
python main.py --query "..." --no-cache

# Clear all cached results
python main.py --clear-cache

Metrics & Cost Tracking

After each query, the agent displays detailed execution metrics:

==================================================
EXECUTION METRICS
==================================================
Route Chosen:       local
Local Attempts:     1
Remote Attempts:    0
Fallback Triggered: False
Cached:             False
Latency:            1.234s
Local Tokens:       156 (Prompt: 42, Completion: 114)
Remote Tokens:      0 (Prompt: 0, Completion: 0)
Total Tokens:       156
Remote Cost:        $0.000000
Token Cost Saved:   100.0%
Estimated Savings:  $0.000140 (vs. routing all tokens to remote)
--------------------------------------------------
RESPONSE:
...
==================================================

What the Metrics Mean

Metric Description
Route Chosen Which path was taken (local, remote, remote_fallback)
Local Tokens Estimated tokens used by local model (via tiktoken)
Remote Tokens Actual tokens reported by Fireworks API
Token Cost Saved % of total tokens handled locally
Estimated Savings Dollar amount saved by routing locally instead of remote
Remote Cost Actual $ spent on Fireworks API calls

Why local token counting matters: The remote API returns exact token counts, but local Ollama calls don't. We use tiktoken (OpenAI's tokenizer) with cl100k_base encoding to estimate local tokens accurately, giving you a complete picture of your savings.

AMD ROCm Integration

This project is designed to run on AMD Instinct GPUs via the AMD Developer Cloud. At startup, the agent automatically detects AMD GPU availability and reports the hardware in use.

One-Click Setup Script

For AMD Developer Cloud instances, use the automated setup script:

chmod +x scripts/setup-amd-cloud.sh
./scripts/setup-amd-cloud.sh

This script handles everything: GPU detection, ROCm configuration, Ollama setup, model pulling, Python venv, environment config, and a smoke test — all in one command.

💡 Run ./scripts/setup-amd-cloud.sh --dry-run to preview steps without executing.

Automatic GPU Detection

When the agent starts, it detects the GPU environment and reports it in the startup banner:

On an AMD ROCm system:

==================================================
  Hybrid Token-Efficient Routing Agent CLI
==================================================
System:   AMD ROCm detected
GPU:      AMD Instinct MI300X (64 GB VRAM)
Backend:  ROCM
Driver:   ROCm (HIP) 6.2.0
Model:    llama3.2:3b (local) / accounts/fireworks/models/llama-v3p1-70b-instruct (remote)
Strategy: fallback | Expected Format: text
...

On a non-AMD system (Mac, Intel, etc.):

==================================================
  Hybrid Token-Efficient Routing Agent CLI
==================================================
System:   AMD GPU not detected (running on CPU)
Model:    llama3.2:3b (local) / accounts/fireworks/models/llama-v3p1-70b-instruct (remote)
Strategy: fallback | Expected Format: text
...

Detection uses multiple methods (in order):

  1. rocminfo — ROCm hardware inspection utility
  2. torch.cuda.is_available() + torch.version.hip — PyTorch HIP detection
  3. ollama ps — GPU process check

All methods are wrapped in try/except — zero impact on non-AMD systems.

Manual Setup on AMD Developer Cloud

# 1. Launch an AMD Developer Cloud instance with GPU
# 2. Verify GPU detection
rocminfo | grep -i "Marketing Name:"

# 3. Install ROCm-compatible Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 4. (MI300X only) Configure Ollama for gfx942
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null << 'EOF'
[Service]
Environment="HSA_OVERRIDE_GFX_VERSION=9.4.2"
Environment="OLLAMA_HOST=0.0.0.0"
EOF
sudo systemctl daemon-reload
sudo systemctl restart ollama

# 5. Pull a model and start
ollama pull llama3.2:3b
ollama serve &

# 6. Clone and run the agent
git clone https://github.com/yourusername/llm-routing-agent.git
cd llm-routing-agent
pip install -r requirements.txt
python main.py --query "Your query"

ROCm-Optimized Ollama

Ollama natively supports AMD ROCm, including:

AMD GPU Architecture Status
Instinct MI300X gfx942 ✅ Recommended for hackathon
Instinct MI250 gfx90a ✅ Supported
Radeon RX 7900 XTX gfx1100 ✅ Local dev
Radeon PRO W7900 gfx1100 ✅ Local dev

Performance Tips for AMD GPUs

  • Small models (1B–3B): Ideal for high-throughput, low-latency routing on AMD GPUs
  • Larger models (7B+): Use the predictive strategy to avoid local bottlenecks
  • Self-critique is auto-disabled for 1B/3B models to prevent resource-heavy hallucinations
  • For MI300X: set HSA_OVERRIDE_GFX_VERSION=9.4.2 in Ollama config (handled by setup script)

AMD Developer Cloud Quick-Start

Launch an Instance

  1. Go to AMD Developer Cloud
  2. Launch an instance with AMD Instinct MI300X GPU
  3. SSH in using the provided credentials

Run the One-Click Setup

git clone https://github.com/yourusername/llm-routing-agent.git
cd llm-routing-agent
./scripts/setup-amd-cloud.sh

Watch the output — you'll see GPU detection, Ollama installation, model pulling, and a smoke test all happen automatically.

Benchmarking on AMD Hardware

Run the test suite for a quick benchmark:

python test_router.py

Then run strategy comparisons:

python main.py --query "Write a function for merge sort" --strategy always_local --format python
python main.py --query "Write a function for merge sort" --strategy fallback --format python
python main.py --query "Write a function for merge sort" --strategy predictive --format python
python main.py --query "Write a function for merge sort" --strategy always_remote --format python

Add your benchmark results to the AMD GPU Benchmark Table and include them in your pitch deck.

Project Structure

llm-routing-agent/
├── main.py                 # CLI entry point + REPL + metrics display
├── src/
│   ├── client.py           # LLMClient (Ollama local + Fireworks remote + GPU detection)
│   ├── router.py           # HybridRouter (routing logic) + RouterCache
│   └── evaluator.py        # ResponseEvaluator (quality validation)
├── scripts/
│   └── setup-amd-cloud.sh  # One-click AMD Developer Cloud setup
├── test_router.py          # Manual integration test suite
├── requirements.txt        # Python dependencies
├── .env.example            # Environment variable template
├── .router_cache.json      # File-backed cache (auto-generated)
└── README.md               # This file

Testing

Run the Full Test Suite

python test_router.py

This tests all 4 strategies across text, JSON, and Python formats, verifies caching, and auto-mocks remote calls if no Fireworks key is configured.

What the Test Suite Covers

  • 6 test cases from simple greetings to complex reasoning
  • All 4 routing strategies
  • Format validation: text, JSON (with schema), Python syntax
  • Cache verification: ensures duplicate queries hit the cache
  • Fallback logic: validates local→remote fallback behavior

Sample Test Run

Running test cases using 'fallback' strategy...
--------------------------------------------------
[1/6] Testing: Simple Greeting
-> Route Chosen: local
-> Latency: 1.234s | Remote Cost: $0.000000
-> Result: Local model response passed validation!

Benchmarking

Running Your Own Benchmarks

# Compare strategies on the same query
python main.py --query "What is the capital of Japan?" --strategy always_local
python main.py --query "What is the capital of Japan?" --strategy always_remote
python main.py --query "What is the capital of Japan?" --strategy fallback
python main.py --query "What is the capital of Japan?" --strategy predictive

# Batch test with the integrated test suite
python test_router.py

Key Metrics to Track

Metric How to Measure
Cost per query Read Remote Cost (actual) + Estimated Savings (foregone)
Savings rate Token Cost Saved % — proportion of tokens handled locally
Latency Latency in seconds — compare strategies
Quality Manual inspection + local evaluator pass/fail rate

Submitting for the Hackathon

This project is designed for AMD Developer Hackathon: ACT II — Track 1 (Token-Efficient Routing Agent).

Submission Checklist

  • Working prototype: The CLI tool runs on AMD Developer Cloud
  • GitHub repository: Public repo with this README
  • Pitch video (≤5 min, MP4): Demo showing routing in action
  • Slide deck (PDF): Explain architecture, strategies, and benchmarks

What Judges Will Look For

  1. Creativity & Originality — Novel approach to token-efficient routing
  2. Completeness — Robust, working prototype with multiple strategies
  3. AMD Technology — Runs on AMD ROCm via Ollama
  4. Quality/Utility — Real-world usability for cost-aware AI deployment

License

MIT — use it, hack it, ship it.

Built with ❤️ for the AMD Developer Hackathon: ACT II

About

A token-efficient hybrid routing agent that intelligently routes queries between local LLMs (Ollama) and remote APIs (Fireworks AI) with automated self-correction, schema validation, and complexity prediction.

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