hystersis 0.1.0

Persistent memory infrastructure for AI agents

Hystersis Python SDK

Persistent memory infrastructure for AI agents. Give your agents memory that adapts and compounds over time.

Why Use Hystersis SDK?

• Simple - One-line installation, intuitive API
• Powerful - Combine conversation history with knowledge graphs
• Production-ready - Type hints, error handling, timeouts, retries
• Async + Sync - Full async support with AsyncHystersis, sync wrapper with Hystersis
• ProMem Extraction - 97%+ accuracy memory compression (PROPRIETARY)
• Spreading Activation - +23% better multi-hop reasoning (PROPRIETARY)

Installation

pip install hystersis

With integrations:

pip install hystersis[integrations]

Quick Start

Sync Client

from hystersis import Hystersis

client = Hystersis(
    base_url="https://api.hystersis.ai",
    api_key="your-api-key"
)

# Check health
print(client.health())

# Create a session
session = client.create_session(agent_id="my-assistant")

# Add messages
client.add_message(session["id"], "user", "I love machine learning!")
client.add_message(session["id"], "assistant", "That's great! What type?")

# Search semantically
results = client.search("deep learning")

client.close()

Async Client

import asyncio
from hystersis import AsyncHystersis

async def main():
    async with AsyncHystersis(
        base_url="https://api.hystersis.ai",
        api_key="your-api-key"
    ) as client:
        session = await client.create_session(agent_id="my-assistant")
        memory = await client.create_memory(
            content="User likes ML",
            user_id="user-123"
        )
        results = await client.memories_search("deep learning")

asyncio.run(main())

---

Compression Engine

Hystersis includes a proprietary compression engine:

Metric	Hystersis	Mem0
Accuracy Retention	97%+	91%
Token Reduction	80-85%	80%
Multi-hop Reasoning	+23%	baseline

Compression Control

from hystersis import Hystersis, CompressionMode

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

# Set compression mode
client.compression_set_mode(CompressionMode.EXTRACT)
# Options: EXTRACT, BALANCED, AGGRESSIVE

# Get compression statistics
stats = client.compression_get_stats()
print(stats)

Compression Modes

Mode	Accuracy	Reduction	Use Case
EXTRACT	97%+	80-85%	Maximum accuracy (default)
BALANCED	95%+	85-90%	General use
AGGRESSIVE	92%+	90-93%	Cost optimization

Tiered Memory

from hystersis import TierPolicy

client.tier_set_policy(TierPolicy.CONSERVATIVE)
# Options: AGGRESSIVE (1 day), BALANCED (7 days), CONSERVATIVE (30 days)

Enhanced Search

from hystersis import SearchMode

# Spreading Activation for complex queries
results = client.search_enhanced(
    "complex multi-hop query",
    mode=SearchMode.SPREADING
)

# Options: SPREADING (graph), VECTOR (fast), HYBRID (both)

---

API Reference

Initialization

from hystersis import Hystersis, AsyncHystersis

# Sync
client = Hystersis(
    base_url="https://api.hystersis.ai",
    api_key="your-key",
)

# Async
async with AsyncHystersis(base_url="...", api_key="...") as client:
    ...

Or use environment variables:

export HYSTERSIS_API_KEY="your-key"

client = Hystersis()  # Uses env var automatically

Sessions

session = client.create_session(agent_id="support-bot", metadata={"user": "123"})
client.add_message(session["id"], "user", "Hello!")
messages = client.get_messages(session["id"])

Memories

memory = client.create_memory(content="User likes Python", user_id="user-123")
memories = client.memories_list(user_id="user-123", limit=50)
results = client.memories_search("python programming", limit=10)

Entities & Knowledge Graph

entity = client.entities_create(name="Python", entity_type="Language")
client.relations_create(from_id=entity_a["id"], to_id=entity_b["id"], relation_type="RELATED_TO")
relations = client.entities_get_relations(entity["id"])

Skills

skill = client.skills_create(name="debugger", trigger="code error", action="analyze stack trace")
suggestions = client.skills_suggest(trigger="code error", context="python traceback")

Error Handling

from hystersis import (
    HystersisError,
    AuthenticationError,
    NotFoundError,
    ValidationError,
    RateLimitError,
)

try:
    client.create_session(agent_id="my-agent")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Too many requests")

Integrations

pip install hystersis[integrations]

# LangChain
from hystersis.integrations.langchain import HystersisMemory

# LlamaIndex
from hystersis.integrations.llamaindex import HystersisReader

# CrewAI
from hystersis.integrations.crewai import CrewMemory

# LangGraph
from hystersis.integrations.langgraph import HystersisChecker

# AutoGen
from hystersis.integrations.autogen import AutoGenMemory

Full Example

from hystersis import Hystersis, CompressionMode

with Hystersis(base_url="https://api.hystersis.ai", api_key="your-key") as client:
    # Create session
    session = client.create_session(agent_id="support-bot")

    # Store conversation
    client.add_message(session["id"], "user", "I can't access my dashboard")
    client.add_message(session["id"], "assistant", "I'll help you troubleshoot")

    # Create knowledge graph entity
    issue = client.entities_create(
        name="dashboard-access-issue",
        entity_type="Issue",
        properties={"status": "open"}
    )

    # Semantic search
    results = client.memories_search("dashboard access problems")

    # Enhanced search with spreading activation
    similar = client.search_enhanced(
        "permission denied dashboards",
        mode="spreading"
    )

    # Compression stats
    stats = client.compression_get_stats()
    print(f"Token reduction: {stats['token_reduction']*100}%")

Environment Variables

Variable	Description
HYSTERSIS_API_KEY	Default API key
AGENT_MEMORY_API_KEY	Alias for API key (backward compat)

License

MIT