Hyperspell

A guide to integrating Hyperspell with Respan.
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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.

1{
2 "mcpServers": {
3 "respan-docs": {
4 "url": "https://mcp.respan.ai/mcp/docs"
5 }
6 }
7}

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.

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

1import os
2from hyperspell import Hyperspell
3from respan_tracing.decorators import workflow, task
4from respan_tracing.main import RespanTelemetry
5
6# Initialize Respan Telemetry
7os.environ["RESPAN_API_KEY"] = "YOUR_RESPAN_API_KEY"
8k_tl = RespanTelemetry()
9
10# Initialize Hyperspell client
11client = Hyperspell(api_key=os.environ["HYPERSPELL_API_KEY"])
12
13@task(name="store_memory")
14def store_memory(user_id: str, content: str):
15 """Store a memory for a user."""
16 return client.memories.create(
17 user_id=user_id,
18 content=content
19 )
20
21@task(name="retrieve_memory")
22def retrieve_memory(user_id: str, query: str):
23 """Retrieve relevant memories for a user."""
24 return client.memories.search(
25 user_id=user_id,
26 query=query
27 )
28
29@workflow(name="memory_workflow")
30def memory_workflow():
31 store_memory("user_123", "I prefer Python over JavaScript.")
32 memories = retrieve_memory("user_123", "programming language preference")
33 return memories
34
35if __name__ == "__main__":
36 result = memory_workflow()
37 print(result)

View your traces

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