pip install hystersis anthropic

import anthropic
from hystersis import Hystersis

client = Hystersis(api_key="your-hystersis-key")
claude = anthropic.Anthropic(api_key="your-anthropic-key")

def chat_with_memory(user_id: str, message: str) -> str:
    # 1. Retrieve relevant memories
    memories = client.search(
        query=message,
        user_id=user_id,
        limit=10
    )

    # 2. Build context from memories
    memory_context = "\n".join([
        f"- {m['content']}" for m in memories
    ])

    # 3. Send to Claude with memory context
    response = claude.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        system=f"You are a helpful assistant with memory. Relevant context:\n{memory_context}",
        messages=[{"role": "user", "content": message}]
    )

    # 4. Store the conversation as memory
    client.create_memory(
        content=f"User said: {message}. Assistant replied: {response.content[0].text}",
        user_id=user_id
    )

    return response.content[0].text

# Usage
result = chat_with_memory("user_123", "I prefer dark mode for my IDE")
print(result)

def stream_with_memory(user_id: str, message: str):
    memories = client.search(query=message, user_id=user_id, limit=5)
    memory_context = "\n".join([f"- {m['content']}" for m in memories])

    with claude.messages.stream(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        system=f"You have memory. Context:\n{memory_context}",
        messages=[{"role": "user", "content": message}]
    ) as stream:
        full_response = ""
        for text in stream.text_stream:
            print(text, end="", flush=True)
            full_response += text

    client.create_memory(
        content=f"Conversation: User: {message} | Assistant: {full_response}",
        user_id=user_id
    )

    return full_response

def multi_turn_chat(user_id: str):
    session = client.create_session(user_id=user_id)

    while True:
        user_input = input("You: ")
        if user_input.lower() in ["exit", "quit"]:
            break

        # Get session context with memory
        context = client.get_session_context(session_id=session["id"])

        memories = client.search(
            query=user_input,
            user_id=user_id,
            limit=5
        )

        memory_context = "\n".join([f"- {m['content']}" for m in memories])

        response = claude.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            system=f"You have persistent memory. Context:\n{memory_context}",
            messages=context.get("messages", []) + [
                {"role": "user", "content": user_input}
            ]
        )

        # Store in session
        client.add_session_message(
            session_id=session["id"],
            role="user",
            content=user_input
        )
        client.add_session_message(
            session_id=session["id"],
            role="assistant",
            content=response.content[0].text
        )

        # Store as memory with compression
        client.create_memory(
            content=f"User: {user_input}\nAssistant: {response.content[0].text}",
            user_id=user_id,
            compression_mode="extract"
        )

        print(f"Assistant: {response.content[0].text}")

multi_turn_chat("user_123")

def chat_with_tools(user_id: str, message: str):
    memories = client.search(query=message, user_id=user_id, limit=5)
    memory_context = "\n".join([f"- {m['content']}" for m in memories])

    response = claude.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        system=f"You have memory tools. Context:\n{memory_context}",
        messages=[{"role": "user", "content": message}],
        tools=[
            {
                "name": "save_memory",
                "description": "Save important information to long-term memory",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "content": {"type": "string", "description": "The information to remember"},
                        "importance": {"type": "string", "enum": ["high", "medium", "low"]}
                    },
                    "required": ["content"]
                }
            },
            {
                "name": "search_memory",
                "description": "Search long-term memory for relevant information",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "query": {"type": "string", "description": "Search query"},
                        "limit": {"type": "integer", "description": "Max results", "default": 5}
                    },
                    "required": ["query"]
                }
            }
        ]
    )

    # Process tool calls
    for block in response.content:
        if block.type == "tool_use":
            if block.name == "save_memory":
                result = client.create_memory(
                    content=block.input["content"],
                    user_id=user_id
                )
            elif block.name == "search_memory":
                result = client.search(
                    query=block.input["query"],
                    user_id=user_id,
                    limit=block.input.get("limit", 5)
                )

    return response