Input Memory → LLM Router → Fast/Verify Path → Extracted Facts → Verified Facts → Compressed Memory

from hystersis import Hystersis

client = Hystersis(api_key="your-api-key")

# Set compression mode
client.set_compression_mode("extract")  # or "balanced", "aggressive"

Input: "John works at Acme Corp as a senior engineer and prefers remote work"

Fast extraction (GPT-4o-mini):
  - John works at Acme Corp
  - John is a senior engineer
  - John prefers remote work

Verification (Claude):
  ✓ All facts verified against original
  ✓ No gaps detected
  → Confidence: 0.97

Original: 24 tokens → Compressed: 5 tokens → 79% reduction

# Environment variables
COMPRESSION_ENABLED=true
COMPRESSION_MODE=extract
COMPRESSION_LLM_FAST_PROVIDER=openai
COMPRESSION_LLM_FAST_MODEL=gpt-4o-mini
COMPRESSION_LLM_VERIFY_PROVIDER=anthropic
COMPRESSION_LLM_VERIFY_MODEL=claude-3-5-sonnet
COMPRESSION_COMPLEXITY_THRESHOLD=0.6

# Create memory with compression
memory = client.create_memory(
    content="Alice is a software developer at TechCorp. She works from home three days a week and visits the office on Tuesdays and Thursdays. She prefers async communication over meetings.",
    compression_mode="extract"
)

# Check compression stats
stats = client.get_compression_stats()
print(f"Token reduction: {stats.token_reduction}%")
print(f"Accuracy: {stats.accuracy_retention}%")
print(f"Total tokens saved: {stats.total_tokens_saved}")

# Test compression in playground
result = client.playground_compress(
    content="Long text to compress...",
    mode="extract"
)

curl -X POST "$API_URL/compression/benchmarks/run" \
  -H "X-API-Key: $ADMIN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "corpus": "agent_memory",
    "algorithms": ["radix", "smart_radix", "smart_hybrid", "real_best", "gzip"],
    "iterations": 3,
    "min_retention": 0.9
  }'