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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://docs.respan.ai/mcp"
    }
  }
}
Hyperspell is a memory management platform for AI applications, providing persistent memory and context management for LLM-powered products. Hyperspell + Respan gives you full observability over your AI application’s memory operations, letting you trace how context is stored, retrieved, and used across conversations.

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Setup

1

Install dependencies

pip install hyperspell respan-tracing
2

Configure environment variables

.env
HYPERSPELL_API_KEY="YOUR_HYPERSPELL_API_KEY"
RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
RESPAN_BASE_URL="https://api.respan.ai/api"
3

Use Hyperspell with Respan tracing

import os
from hyperspell import Hyperspell
from respan_tracing.decorators import workflow, task
from respan_tracing.main import RespanTelemetry

# Initialize Respan Telemetry
os.environ["RESPAN_API_KEY"] = "YOUR_RESPAN_API_KEY"
k_tl = RespanTelemetry()

# Initialize Hyperspell client
client = Hyperspell(api_key=os.environ["HYPERSPELL_API_KEY"])

@task(name="store_memory")
def store_memory(user_id: str, content: str):
    """Store a memory for a user."""
    return client.memories.create(
        user_id=user_id,
        content=content
    )

@task(name="retrieve_memory")
def retrieve_memory(user_id: str, query: str):
    """Retrieve relevant memories for a user."""
    return client.memories.search(
        user_id=user_id,
        query=query
    )

@workflow(name="memory_workflow")
def memory_workflow():
    store_memory("user_123", "I prefer Python over JavaScript.")
    memories = retrieve_memory("user_123", "programming language preference")
    return memories

if __name__ == "__main__":
    result = memory_workflow()
    print(result)

​
View your traces

After running your workflow, you can see the memory operations traced in the Traces page on Respan.