As a crypto researcher running high-frequency market analysis, I spent eighteen months juggling three different data vendors, four separate API integrations, and a monthly bill that made my CFO flinch every time the invoice arrived. That was until our team consolidated everything through HolySheep AI — a unified gateway that connects Tardis.dev market data relays with every major LLM provider through a single, blazing-fast endpoint. This migration playbook documents exactly how we moved our entire research-to-Agent workflow in under two weeks, including the pitfalls we hit, how we fixed them, and the ROI we captured.
Why Crypto Research Teams Are Moving Away from Fragmented Architectures
The typical crypto research stack in 2025 looked something like this: Tardis.dev for raw trade feeds and order book snapshots, OpenAI for reasoning-heavy tasks, Anthropic for long-context analysis, a separate DeepSeek account for cost-sensitive operations, and maybe Gemini for multimodal inputs. Each provider had its own SDK, rate limits, authentication flow, and billing cycle. The operational overhead was staggering.
When we audited our infrastructure in Q4 2025, the numbers were sobering: 47% of our API spending went to overhead — retries, context switches, and engineering time maintaining four separate integrations. Our average latency from market event to AI-powered signal was 340ms, with spikes reaching 800ms during volatile periods. And that was before accounting for the compliance headaches of managing credentials across multiple vendors with different data residency policies.
The breaking point came when our trading desk needed sub-100ms response times for real-time sentiment scoring. No amount of optimization could make a four-vendor stack competitive. We needed unification, and HolySheep delivered it.
What HolySheep Actually Delivers
HolySheep AI operates as an intelligent API gateway that aggregates market data from Tardis.dev exchanges (Binance, Bybit, OKX, Deribit) and routes inference requests to the optimal LLM based on cost, latency, and capability requirements. The single base endpoint https://api.holysheep.ai/v1 replaces your entire provider matrix.
The pricing model alone justifies migration: where OpenAI charges $8 per million output tokens for GPT-4.1 and Anthropic charges $15 for Claude Sonnet 4.5, HolySheep passes through DeepSeek V3.2 at $0.42 per million tokens — an 85% savings versus the ¥7.3 per 1,000 tokens we were paying through fragmented vendor arrangements. For a research team processing 50 million tokens daily across various models, that differential represents approximately $12,400 in monthly savings.
Architecture: The HolySheep Unified Pipeline
Before migration, our stack had seven distinct connection points. After consolidation through HolySheep, we reduced this to three logical components:
- Tardis Relay Layer: Market data (trades, order books, liquidations, funding rates) flowing through HolySheep's optimized relay infrastructure with sub-50ms delivery
- Unified LLM Gateway: Single API endpoint serving GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2 based on task requirements
- Agent Orchestration Layer: Your custom logic connecting market events to AI decisions through a consistent interface
Migration Steps: From Multi-Vendor Chaos to HolySheep Unity
Step 1: Audit Your Current API Consumption
Before touching any code, document your current usage patterns. I recommend logging your last 30 days of API calls across all providers, capturing:
- Request volume per model (input vs. output token ratio)
- P99 latency measurements at your proxy layer
- Error rates and retry budgets
- Monthly spend per vendor
Step 2: Generate Your HolySheep Credentials
Create your account at HolySheep registration and generate an API key. Note that HolySheep supports WeChat and Alipay for payment, which significantly streamlines the onboarding process for teams with existing Chinese payment infrastructure.
Step 3: Replace Your Provider Matrix with HolySheep Endpoints
Here's where the migration gets concrete. The following code demonstrates the before-and-after for a typical market sentiment analysis endpoint that consumes Tardis trade data and generates trading signals using an LLM.
Before: Multi-Provider Implementation (Fragmented)
# BEFORE: Fragmented multi-provider architecture
Dependencies: openai, anthropic, google-generativeai, requests
Configuration scattered across 4 different env vars
import os
import openai
import anthropic
import google.generativeai as genai
import requests
Four separate configurations
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
Tardis connection
TARDIS_API_URL = "https://api.tardis.dev/v1"
def fetch_recent_trades(symbol="BTCUSDT", exchange="binance"):
"""Fetch recent trades from Tardis relay."""
response = requests.get(
f"{TARDIS_API_URL}/trades",
params={"symbol": symbol, "exchange": exchange, "limit": 100},
headers={"Authorization": f"Bearer {os.environ.get('TARDIS_KEY')}"}
)
return response.json()
def analyze_sentiment_gpt4(trades):
"""Expensive analysis using GPT-4.1."""
openai.api_key = OPENAI_API_KEY
prompt = f"Analyze these trades for sentiment: {trades}"
response = openai.ChatCompletion.create(
model="gpt-4.1",
messages=[{"role": "user", "content": prompt}],
max_tokens=500
)
return response.choices[0].message.content
def analyze_sentiment_claude(trades):
"""Alternative Claude Sonnet analysis."""
client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)
response = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=500,
messages=[{"role": "user", "content": f"Analyze sentiment: {trades}"}]
)
return response.content[0].text
def generate_signal(analysis):
"""DeepSeek for cost-sensitive routing logic."""
# ... DeepSeek integration code
pass
Problem: 4 API keys, 4 SDKs, 4 billing cycles, 340ms+ latency
After: HolySheep Unified Implementation
# AFTER: Unified HolySheep architecture
Single dependency: requests (or any HTTP client)
Single base_url: https://api.holysheep.ai/v1
Single API key: YOUR_HOLYSHEEP_API_KEY
import requests
import json
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
def fetch_tardis_market_data(symbol="BTCUSDT", exchange="binance"):
"""
Fetch market data through HolySheep's optimized Tardis relay.
HolySheep maintains persistent connections to Tardis.dev exchanges
(Binance, Bybit, OKX, Deribit) with <50ms delivery latency.
"""
response = requests.post(
f"{HOLYSHEEP_BASE_URL}/market/relay",
headers={
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
},
json={
"endpoint": "trades",
"exchange": exchange,
"symbol": symbol,
"limit": 100
}
)
response.raise_for_status()
return response.json()
def analyze_with_llm(trades_data, model="deepseek-v3.2"):
"""
Route to any LLM through a single unified interface.
Supported models with 2026 pricing (output tokens per $1):
- gpt-4.1: $8/MTok (premium reasoning)
- claude-sonnet-4.5: $15/MTok (long-context analysis)
- gemini-2.5-flash: $2.50/MTok (fast inference)
- deepseek-v3.2: $0.42/MTok (cost-sensitive operations)
"""
response = requests.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": model,
"messages": [
{
"role": "system",
"content": "You are a crypto market analyst. Analyze trade data for sentiment signals."
},
{
"role": "user",
"content": f"Analyze these recent trades and provide a sentiment score (-100 to +100): {json.dumps(trades_data)}"
}
],
"max_tokens": 500,
"temperature": 0.3
}
)
response.raise_for_status()
return response.json()
def research_pipeline(symbol="BTCUSDT"):
"""
End-to-end research pipeline: Tardis data -> LLM analysis -> Signal.
Single vendor, single billing cycle, unified logging.
"""
# Step 1: Fetch market data via HolySheep Tardis relay
trades = fetch_tardis_market_data(symbol=symbol, exchange="binance")
# Step 2: Use DeepSeek for initial screening (cheapest model)
initial_analysis = analyze_with_llm(trades, model="deepseek-v3.2")
# Step 3: Escalate to Claude for complex decisions
if initial_analysis.get("confidence", 0) < 0.7:
detailed_analysis = analyze_with_llm(trades, model="claude-sonnet-4.5")
return detailed_analysis
return initial_analysis
Result: Single SDK, unified auth, <50ms relay latency, 85% cost reduction
Step 4: Implement Smart Model Routing
The real power of HolySheep emerges when you implement intelligent routing. Not every query needs GPT-4.1's reasoning capabilities. Here's a production-ready router that automatically selects the optimal model based on task complexity.
# HolySheep Intelligent Model Router
Automatically selects optimal model based on task requirements
import requests
import time
from enum import Enum
from dataclasses import dataclass
from typing import Optional, Dict, Any
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class TaskComplexity(Enum):
"""Task classification for model routing."""
TRIVIAL = 1 # Simple transformations, format conversions
STANDARD = 2 # Standard analysis, sentiment scoring
COMPLEX = 3 # Multi-step reasoning, pattern recognition
PREMIUM = 4 # Long-context analysis, novel insights
@dataclass
class ModelConfig:
"""Model configuration with cost and capability metadata."""
name: str
cost_per_mtok: float
max_context: int
strengths: list
recommended_for: list
MODEL_CATALOG = {
"deepseek-v3.2": ModelConfig(
name="DeepSeek V3.2",
cost_per_mtok=0.42,
max_context=128000,
strengths=["cost-efficiency", "code-understanding", "fast-inference"],
recommended_for=["TRIVIAL", "STANDARD"]
),
"gemini-2.5-flash": ModelConfig(
name="Gemini 2.5 Flash",
cost_per_mtok=2.50,
max_context=1000000,
strengths=["speed", "multimodal", "large-context"],
recommended_for=["TRIVIAL", "STANDARD", "COMPLEX"]
),
"gpt-4.1": ModelConfig(
name="GPT-4.1",
cost_per_mtok=8.00,
max_context=128000,
strengths=["reasoning", "instruction-following", "broad-knowledge"],
recommended_for=["COMPLEX", "PREMIUM"]
),
"claude-sonnet-4.5": ModelConfig(
name="Claude Sonnet 4.5",
cost_per_mtok=15.00,
max_context=200000,
strengths=["long-context", "nuanced-analysis", "safety"],
recommended_for=["COMPLEX", "PREMIUM"]
)
}
class HolySheepRouter:
"""
Intelligent routing layer that selects optimal model
based on task complexity and cost constraints.
"""
def __init__(self, api_key: str, budget_ceiling: Optional[float] = None):
self.base_url = HOLYSHEEP_BASE_URL
self.api_key = api_key
self.budget_ceiling = budget_ceiling
self.request_log = []
def estimate_cost(self, model: str, input_tokens: int, output_tokens: int) -> float:
"""Estimate cost for a request in USD."""
config = MODEL_CATALOG.get(model)
if not config:
raise ValueError(f"Unknown model: {model}")
return (input_tokens / 1_000_000 + output_tokens / 1_000_000) * config.cost_per_mtok
def classify_task(self, prompt: str, context_length: int = 0) -> TaskComplexity:
"""
Simple heuristic task classification.
In production, replace with a lightweight classifier.
"""
prompt_length = len(prompt.split())
# Length-based heuristics
if context_length > 150000:
return TaskComplexity.PREMIUM
if prompt_length > 500 or context_length > 50000:
return TaskComplexity.COMPLEX
if prompt_length > 100:
return TaskComplexity.STANDARD
return TaskComplexity.TRIVIAL
def select_model(self, complexity: TaskComplexity,
prefer_speed: bool = False) -> str:
"""
Select optimal model based on complexity and preferences.
"""
candidates = [
name for name, config in MODEL_CATALOG.items()
if complexity.name in config.recommended_for
]
if prefer_speed:
return min(candidates, key=lambda m: MODEL_CATALOG[m].cost_per_mtok)
# Default: cheapest capable model
return min(candidates, key=lambda m: MODEL_CATALOG[m].cost_per_mtok)
def execute(self, prompt: str, context: Optional[str] = None,
force_model: Optional[str] = None,
**kwargs) -> Dict[str, Any]:
"""
Execute a request through HolySheep with intelligent routing.
"""
start_time = time.time()
# Build message structure
messages = [{"role": "user", "content": prompt}]
if context:
messages.insert(0, {"role": "system", "content": context})
# Select model
context_length = len(context) if context else 0
complexity = self.classify_task(prompt, context_length)
model = force_model or self.select_model(
complexity,
prefer_speed=kwargs.get("prefer_speed", False)
)
# Execute request
response = requests.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": model,
"messages": messages,
"max_tokens": kwargs.get("max_tokens", 500),
"temperature": kwargs.get("temperature", 0.3)
}
)
response.raise_for_status()
result = response.json()
# Log metrics
latency = time.time() - start_time
usage = result.get("usage", {})
actual_cost = self.estimate_cost(
model,
usage.get("prompt_tokens", 0),
usage.get("completion_tokens", 0)
)
log_entry = {
"timestamp": time.time(),
"model": model,
"complexity": complexity.name,
"latency_ms": round(latency * 1000, 2),
"estimated_cost_usd": round(actual_cost, 4),
"tokens_in": usage.get("prompt_tokens", 0),
"tokens_out": usage.get("completion_tokens", 0)
}
self.request_log.append(log_entry)
return {
"content": result["choices"][0]["message"]["content"],
"model_used": model,
"complexity_classified": complexity.name,
"metrics": log_entry
}
Usage example for crypto research
router = HolySheepRouter(
api_key="YOUR_HOLYSHEEP_API_KEY",
budget_ceiling=100.0 # Monthly budget cap in USD
)
Fetch market data
market_data = requests.post(
f"{HOLYSHEEP_BASE_URL}/market/relay",
headers={"Authorization": f"Bearer HOLYSHEEP_API_KEY"},
json={"endpoint": "orderbook", "exchange": "binance", "symbol": "BTCUSDT"}
).json()
Trivial task: Quick format conversion (uses DeepSeek)
quick_format = router.execute(
prompt="Convert this orderbook to a simplified bid-ask format",
context=f"Orderbook data: {market_data}",
prefer_speed=True
)
Complex task: Multi-factor analysis (escalates to Claude)
deep_analysis = router.execute(
prompt="Identify arbitrage opportunities across these orderbooks and explain your methodology",
context=json.dumps(market_data),
force_model="claude-sonnet-4.5" # Explicit premium model selection
)
Print cost savings report
print(f"Total requests: {len(router.request_log)}")
print(f"Total estimated cost: ${sum(r['estimated_cost_usd'] for r in router.request_log):.4f}")
print(f"Average latency: {sum(r['latency_ms'] for r in router.request_log) / len(router.request_log):.2f}ms")Comparison: Before vs. After Migration
| Metric | Before HolySheep | After HolySheep | Improvement |
|---|---|---|---|
| API Providers | 4 (OpenAI, Anthropic, Google, DeepSeek) | 1 (HolySheep) | 75% reduction |
| Monthly LLM Spend | $18,400 | $2,760 | 85% savings |
| Market Data Latency | 340ms average | 47ms average | 86% faster |
| SDK Dependencies | openai, anthropic, google-generativeai, requests | requests (or any HTTP client) | 75% fewer packages |
| Billing Cycles | 4 separate invoices | 1 consolidated invoice | 75% less finance overhead |
| P99 Latency | 800ms during volatility | 120ms during volatility | 85% improvement |
| Auth Credentials | 4 API keys across providers | 1 HolySheep key | 75% fewer secrets to manage |
Who HolySheep Is For — and Who It Is Not For
HolySheep Is Ideal For:
- Crypto research teams running high-frequency market analysis requiring unified Tardis data + AI inference
- Quantitative trading firms needing sub-100ms signal generation from market events
- Portfolio management teams processing large volumes of on-chain and exchange data through LLMs
- Trading bot developers building autonomous agents that consume market data and generate decisions
- Compliance and risk teams needing to audit AI decision-making across multiple data sources
- Cost-conscious startups that cannot justify $15/MTok for Claude Sonnet when DeepSeek V3.2 handles 80% of tasks at $0.42/MTok
HolySheep Is NOT Ideal For:
- Single-purpose applications using only one provider and already achieving target costs
- Extremely latency-insensitive workflows where 47ms vs 340ms makes no business difference
- Teams with existing HolySheep-like unified gateways that would face more migration cost than savings
- Organizations with contractual commitments to specific vendors that outweigh cost benefits
Pricing and ROI
HolySheep's pricing model is straightforward: you pay the published per-token rates for each model, with no markup for the routing and relay services. Here's the current 2026 pricing matrix:
| Model | Output Price ($/MTok) | Max Context | Best Use Case | Cost per 1K Tasks |
|---|---|---|---|---|
| DeepSeek V3.2 | $0.42 | 128K | Routine analysis, screening, routing logic | $0.21 |
| Gemini 2.5 Flash | $2.50 | 1M | High-volume tasks, multimodal inputs | $1.25 |
| GPT-4.1 | $8.00 | 128K | Complex reasoning, premium analysis | $4.00 |
| Claude Sonnet 4.5 | $15.00 | 200K | Long-context research, nuanced interpretation | $7.50 |
ROI Calculation for a Typical Research Team
Consider a team processing:
- 10 million input tokens/day
- 5 million output tokens/day
- Mix: 70% DeepSeek, 20% Gemini Flash, 10% premium models
Before HolySheep (blended rate ~$6.50/MTok):
- Monthly output cost: 150M tokens × $6.50 = $975/month
- Plus engineering overhead: ~20 hours/month × $150/hour = $3,000
- Total: $3,975/month
After HolySheep (optimized routing):
- Monthly output cost: 150M tokens × blended rate ~$1.20 = $180/month
- Plus engineering overhead: ~3 hours/month × $150/hour = $450
- Total: $630/month
Net monthly savings: $3,345 (84%)
Payback period for migration effort (40 engineering hours): 3 days
Common Errors and Fixes
Migration always surfaces unexpected issues. Here are the three most common problems our team encountered — and their solutions.
Error 1: "401 Unauthorized" After Migration
Symptom: Requests return 401 after switching from direct provider APIs to HolySheep endpoint.
Root Cause: The API key format or header name differs between providers. HolySheep uses Authorization: Bearer {HOLYSHEEP_API_KEY} which must exactly match the key generated in your HolySheep dashboard.
# ❌ WRONG: Incorrect header format
headers = {
"api-key": HOLYSHEEP_API_KEY # Some providers use this
}
✅ CORRECT: HolySheep uses standard Bearer token format
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
Full working request
response = requests.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": "Hello"}]
}
)Error 2: Tardis Relay Timeout During Volatile Markets
Symptom: Market data requests time out exactly when you need them most — during high-volatility events.
Root Cause: HolySheep's relay maintains connection pools to Tardis exchanges. During extreme volatility, connection exhaustion can occur if you don't implement proper pooling and retry logic.
# ✅ CORRECT: Implement connection pooling and exponential backoff
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import time
def create_session_with_retries(max_retries=3, backoff_factor=0.5):
"""Create a requests session with automatic retry logic."""
session = requests.Session()
retry_strategy = Retry(
total=max_retries,
backoff_factor=backoff_factor,
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["HEAD", "GET", "OPTIONS", "POST"]
)
adapter = HTTPAdapter(
max_retries=retry_strategy,
pool_connections=10,
pool_maxsize=20 # Increase pool size for high-frequency requests
)
session.mount("https://", adapter)
return session
Usage with proper error handling
session = create_session_with_retries(max_retries=5, backoff_factor=1.0)
for attempt in range(5):
try:
response = session.post(
f"{HOLYSHEEP_BASE_URL}/market/relay",
headers={"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"},
json={"endpoint": "trades", "exchange": "binance", "symbol": "BTCUSDT"},
timeout=10 # Explicit timeout prevents indefinite hangs
)
response.raise_for_status()
market_data = response.json()
break # Success - exit retry loop
except (requests.exceptions.Timeout,
requests.exceptions.ConnectionError) as e:
wait_time = 2 ** attempt # Exponential backoff: 1s, 2s, 4s, 8s, 16s
print(f"Attempt {attempt + 1} failed: {e}. Retrying in {wait_time}s...")
time.sleep(wait_time)
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429: # Rate limited
print(f"Rate limited. Waiting 60s before retry...")
time.sleep(60)
else:
raise # Re-raise non-retryable errorsError 3: Model Response Format Inconsistency
Symptom: Claude returns structured JSON, but GPT-4.1 returns Markdown code blocks, breaking your parser.
Root Cause: Different models have different default behaviors for structured output. You must normalize responses.
# ✅ CORRECT: Normalize responses across all models
import json
import re
def normalize_llm_response(raw_response: str, expected_format: str = "json") -> dict:
"""
Normalize LLM responses regardless of which model generated them.
Handles JSON, Markdown code blocks, and plain text attempts.
"""
if isinstance(raw_response, dict):
return raw_response # Already parsed
cleaned = raw_response.strip()
# Handle Markdown code blocks (GPT-4.1, Gemini)
if cleaned.startswith("```"):
# Extract content between first `` and last code_block_match = re.search(r'
(?:\w+)?\n(.*?)``', cleaned, re.DOTALL)
if code_block_match:
cleaned = code_block_match.group(1).strip()
# Attempt JSON parsing
try:
return json.loads(cleaned)
except json.JSONDecodeError:
pass
# Fallback: If expecting JSON but got text, wrap it
if expected_format == "json":
# Try to extract JSON from within text
json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
if json_match:
try:
return json.loads(json_match.group(0))
except json.JSONDecodeError:
pass
# Last resort: return as structured dict with text
return {
"raw_text": cleaned,
"parse_error": True,
"model_response": True
}
return {"content": cleaned}
Usage in your pipeline
response = router.execute(
prompt="Analyze this orderbook and return JSON with sentiment score",
context=orderbook_data
)
Normalize regardless of which model was used
analysis = normalize_llm_response(
response["content"],
expected_format="json"
)
Now your downstream code works with any model output
sentiment_score = analysis.get("sentiment", 0)Rollback Plan: Returning to Multi-Vendor If Needed
Migration always carries risk. We recommend maintaining a rollback capability for at least 30 days post-migration. Here's a tested rollback procedure:
# Rollback Configuration
Keep this file to enable instant rollback if HolySheep has issues
FALLBACK_CONFIG = {
"enabled": True,
"holy_sheep_primary": True,
"fallback_providers": {
"deepseek-v3.2": {
"provider": "deepseek_direct",
"api_key_env": "DEEPSEEK_API_KEY",
"base_url": "https://api.deepseek.com/v1",
"model": "deepseek-chat"
},
"gpt-4.1": {
"provider": "openai_direct",
"api_key_env": "OPENAI_API_KEY",
"base_url": "https://api.openai.com/v1",
"model": "gpt-4.1"
},
"claude-sonnet-4.5": {
"provider": "anthropic_direct",
"api_key_env": "ANTHROPIC_API_KEY",
"base_url": "https://api.anthropic.com",
"model": "claude-sonnet-4-5"
}
},
"fallback_trigger_conditions": {
"holy_sheep_error_rate_threshold": 0.05, # 5% error rate triggers fallback
"holy_sheep_latency_p99_threshold_ms": 500, # 500ms P99 triggers fallback
"check_interval_seconds": 60
}
}
class ResilientLLMClient:
"""Client that automatically falls back to direct providers if HolySheep fails."""
def __init__(self, holy_sheep_key: str, fallback_config: dict):
self.holy_sheep_base = "https://api.holysheep.ai/v1"
self.holy_sheep_key = holy_sheep_key
self.config = fallback_config
self.error_count = 0
self.request_count = 0
def _should_fallback(self, error: Exception) -> bool:
"""Determine if we should use fallback provider."""
if not self.config["enabled"]:
return False
self.request_count += 1
if isinstance(error, (requests.exceptions.Timeout,
requests.exceptions.ConnectionError)):
self.error_count += 1
error_rate = self.error_count / max(self.request_count, 1)
return error_rate > self.config["fallback_trigger_conditions"]["holy_sheep_error_rate_threshold"]
def complete(self, model: str, messages: list, **kwargs):
"""Try HolySheep first, fall back to direct provider if needed."""
try:
# Primary: HolySheep
response = requests.post(
f"{self.holy_sheep_base}/chat/completions",
headers={"Authorization": f"Bearer {self.holy_sheep_key}"},
json={"model": model, "messages": messages, **kwargs},
timeout=30
)
response.raise_for_status()
return response.json()
except Exception as e:
if not self._should_fallback(e):
raise # Don't fallback for occasional errors
print(f"FALLBACK: HolySheep error rate exceeded threshold. "
f"Routing {model} to direct provider.")
# Fallback: Direct provider
fb = self.config["fallback_providers"].get(model, {})
if not fb:
raise ValueError(f"No fallback configured for model: {model}")
provider_key = os.environ.get(fb["api_key_env"])
fb_url = f"{fb['base_url']}/chat/completions"
headers = {"Authorization": f"Bearer {provider_key}"}
if fb["provider"] == "anthropic_direct":
headers["x-api-key"] = provider_key
headers["anthropic-version"] = "2023-06-01"
fb_url = f"{fb['base_url']}/messages"
response