fugusashi 1.0.0

Open-source intelligent model router and multi-agent orchestrator. The open alternative to Sakana AI's Fugu.

By eulogik — building AI infrastructure for everyone.

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Fugusashi

The Open-Source Alternative to Sakana AI's Fugu

Fugusashi (Japanese: 不縛 — "unbound, unrestrained") is an intelligent model router and multi-agent orchestrator. Named after Fugu Sashi — the famous Japanese pufferfish delicacy — because this router serves up the world's best AI models without the poison of vendor lock-in or the pricing of Sakana Fugu. It automatically picks the best model for each prompt, learns from every request via a CMA-ES coordinator inspired by Sakana's TRINITY paper, and runs entirely on your infrastructure.

Like Sakana Fugu. But Free. Live Demo · Docs · PyPI · GitHub

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Why Fugusashi?

Sakana AI's Fugu is a proprietary model router. It works — but you can't see inside it, you can't train it on your own data, you can't self-host it, and you pay $5-30 per million tokens.

Fugusashi does everything Fugu does, but open, transparent, and self-hosting. It also adds a feedback loop that Fugu doesn't have — the router learns from every request and gets smarter over time.

Fugusashi vs Sakana AI Fugu

Feature	Sakana Fugu	Fugusashi
Model Routing	✅ Proprietary	✅ Open, transparent
Multi-Agent Orchestration	✅ Fugu Ultra	🔄 Phase 2
Self-Hosting	❌ Cloud-only	✅ Local-first, air-gapped
Cost	$5-30/M tokens	✅ Free (pay only for model APIs)
Transparency	❌ Black box	✅ Every decision visible
Feedback Loop	❌ Static	✅ Learns from every request
Model Pool	❌ Fixed by Sakana	✅ You control
Training Data	❌ Proprietary	✅ Community + your traffic
Customization	❌ None	✅ Fine-tune on your data
License	Proprietary	✅ MIT
Dashboard	❌	✅ Open web dashboard
API	Limited	✅ OpenAI-compatible

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Quickstart

Install from PyPI

pip install fugusashi

Or from source

git clone https://github.com/eulogik/fugusashi.git
cd fugusashi
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Configure

Edit config.yaml to add your models:

default_model: "llama3.2-local"

models:
  - name: "llama3.2-local"
    provider: "ollama"
    model: "llama3.2:1b"
    api_base: "http://localhost:11434"
    cost_per_input_token: 0.0
    cost_per_output_token: 0.0
    capabilities: ["chat", "reasoning"]
    description: "Llama 3.2 1B (local, free)"

  - name: "gpt-4o-mini"
    provider: "openai"
    model: "gpt-4o-mini"
    cost_per_input_token: 0.00000015
    cost_per_output_token: 0.0000006
    capabilities: ["chat", "reasoning", "code", "creative"]
    description: "OpenAI GPT-4o-mini"

Run

fugusashi serve --config config.yaml
# → Fugusashi router listening on 0.0.0.0:6060

Use

# Auto-route — the router picks the best model
curl http://localhost:6060/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"auto","messages":[{"role":"user","content":"Say hello"}]}'

# Force a specific model
curl http://localhost:6060/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"llama3.2-local","messages":[{"role":"user","content":"Say hello"}]}'

Every response includes a routing_decision showing which model was picked, why, and with what confidence.

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Architecture

Tier 1 — Intelligent Model Router

Three routing strategies in priority order:

1. SimilarityRouter — Uses sentence-transformers to find similar past prompts and route to the model that worked best. Gets smarter with every request via the feedback loop.
2. CostRouter — Capability-aware routing with cost optimization. Respects prefer_local for air-gapped deployments.
3. FallbackRouter — Always returns a result, even with no data.

Tier 2 — Multi-Agent Orchestrator (Phase 2)

A planning model that decomposes hard tasks into subtasks, assigns them to specialist models, and synthesizes results. Uses reinforcement learning (GRPO-style) to learn teamwork patterns.

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API Reference

POST /v1/chat/completions

OpenAI-compatible. Set model: "auto" for intelligent routing.

Response includes routing_decision:

{
  "id": "fugu-698f0a66db98",
  "model": "llama3.2-local",
  "choices": [...],
  "routing_decision": {
    "model": "llama3.2-local",
    "confidence": 0.9,
    "strategy": "ensemble(cost)",
    "latency_ms": 0.05,
    "explanation": "Routed by capability fit + cost"
  }
}

GET /v1/models — List available models

GET /v1/routing/decisions — Recent routing decisions

GET /v1/stats — Aggregated stats (cost, tokens, per-model)

GET /v1/trace/{request_id} — Full request trace

POST /v1/routing/training — Seed similarity router

curl -X POST http://localhost:6060/v1/routing/training \
  -H "Content-Type: application/json" \
  -d '[{"prompt":"Write Python code","model":"gpt-4o-mini","score":0.95}]'

POST /v1/feedback/rate — Rate a response (1-5)

curl -X POST http://localhost:6060/v1/feedback/rate \
  -d '{"request_id":"fugu-698f0a66db98","rating":5}'

POST /v1/feedback/retrain — Rebuild similarity index from feedback

GET /v1/feedback/stats — Outcome statistics

GET /v1/feedback/rankings — Per-model win rates

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Dashboard

Open http://localhost:6060/dashboard for a live view:

• Overview: total requests, tokens, cost, avg routing latency
• Model Usage: bar chart of which models are being picked
• Strategy Distribution: cost vs similarity routing breakdown
• Recent Decisions: live table of every routing decision

Auto-refreshes every 3 seconds. Light and dark themes.

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Feedback Loop — The Killer Feature

Fugu's router is static. Fugusashi's learns from every request:

Route → Execute → Evaluate → Learn → (repeat)

1. Route: Router picks a model for the prompt
2. Execute: Model generates a response
3. Evaluate: Track outcome (success/failure, cost, latency)
4. Learn: Feed outcomes back into the similarity router
5. Auto-Retrain: Every 10 requests, the similarity index rebuilds automatically

Outcomes are stored in .fugusashi_data/outcomes.jsonl — inspectable, shareable, yours.

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Benchmarking

pip install fugusashi
fugusashi benchmark                      # Default 20-sample dataset
fugusashi benchmark --train --verbose    # With training data
fugusashi benchmark --train --json       # JSON output
fugusashi benchmark -d my_data.jsonl     # Custom dataset

Results:

Metric	Without Training	With Training
Accuracy	70%	85%
Code accuracy	60%	90%
Strategy	100% cost	60% cost / 40% similarity
Routing latency	<1ms	~18ms

Custom dataset format (JSONL):

{"prompt":"How do I center a div?","expected_model":"gpt-4o-mini","category":"code"}
{"prompt":"What is 2+2?","expected_model":"llama3.2-local","category":"factual"}

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Project Structure

fugusashi/
├── config.yaml              # Model pool + routing config
├── pyproject.toml           # Dependencies + metadata
├── LIVING.md                # Living development walkthrough
├── README.md                # This file
├── LICENSE                  # MIT
├── src/fugusashi/
│   ├── __init__.py
│   ├── __main__.py          # CLI: serve, benchmark
│   ├── server.py            # FastAPI app factory
│   ├── config.py            # Pydantic config from YAML
│   ├── providers.py         # LiteLLM multi-provider wrapper
│   ├── tracker.py           # Cost/routing transparency
│   ├── feedback.py          # Feedback loop + learning
│   ├── benchmark.py         # Benchmark runner
│   ├── api/
│   │   └── routes.py        # All API endpoints
│   ├── router/
│   │   ├── interface.py     # Abstract router protocol
│   │   ├── strategies.py        # Cost, Similarity, Fallback routers
│   │   └── ensemble.py      # Priority-chain ensemble
│   └── static/
│       └── dashboard.html   # Live web dashboard
├── tests/
│   └── test_integration.py  # Integration tests
└── docs/                    # GitHub Pages documentation

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How It Beats Sakana AI's Fugu

1. Transparent: Every routing decision is visible and explainable. No black box.
2. Self-hosting: Runs entirely on-premise with local models via Ollama.
3. Learning: Gets smarter from every request via the feedback loop. Fugu can't do this.
4. Open: Community-owned preference datasets, not proprietary training data.
5. Extensible: Add your own routing strategies via the plugin interface.
6. Observable: Dashboard + stats + traces out of the box.
7. Free: MIT licensed. No usage fees. No vendor lock-in.

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Links

• Website: eulogik.com
• GitHub: github.com/eulogik/fugusashi
• PyPI: pypi.org/project/fugusashi
• Docs: eulogik.github.io/fugusashi
• Issues: github.com/eulogik/fugusashi/issues

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License

MIT — use it however you want.

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Built with ❤️ by eulogik

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