hyperstack-langgraph 1.0.0

Knowledge graph memory for LangGraph agents. Developer-controlled, zero LLM cost, time-travel debugging.

hyperstack-langgraph

Knowledge graph memory for LangGraph agents. Developer-controlled, zero LLM cost, time-travel debugging.

Install

pip install hyperstack-langgraph langchain-core langgraph

Quick Start (3 lines)

from hyperstack_langgraph import create_memory_agent
from langchain_openai import ChatOpenAI

agent = create_memory_agent(ChatOpenAI(model="gpt-4o"))

That's it. Your agent now has persistent knowledge graph memory. It will:

• Search memory at the start of every conversation
• Store important facts when decisions are made (with user confirmation)
• Traverse the graph to answer "what depends on X?" or "who decided Y?"

Environment Variables

export HYPERSTACK_API_KEY=hs_your_key    # Get free at cascadeai.dev/hyperstack
export HYPERSTACK_WORKSPACE=default
export OPENAI_API_KEY=sk-...             # For your LLM

Usage: Add Memory Tools to Existing Agent

from hyperstack_langgraph import create_hyperstack_tools
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# Create memory tools
memory_tools = create_hyperstack_tools()

# Add to your existing tools
my_tools = [my_calculator, my_web_search] + memory_tools

# Create agent with memory
agent = create_react_agent(ChatOpenAI(model="gpt-4o"), my_tools)

# Use it
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What do we know about our auth setup?"}]},
    config={"configurable": {"thread_id": "session-1"}}
)

Usage: Full Agent with Session Memory

from hyperstack_langgraph import create_memory_agent
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver

agent = create_memory_agent(
    ChatOpenAI(model="gpt-4o"),
    checkpointer=MemorySaver(),  # Session memory (optional)
)

config = {"configurable": {"thread_id": "project-alpha"}}

# First message — agent searches HyperStack for context
agent.invoke({"messages": [{"role": "user", "content": "Let's work on the auth system"}]}, config)

# Agent remembers within session (MemorySaver) AND across sessions (HyperStack)
agent.invoke({"messages": [{"role": "user", "content": "We decided to use Clerk"}]}, config)

Usage: Direct API Client

from hyperstack_langgraph import HyperStackClient

client = HyperStackClient()

# Store
client.store("use-clerk", "Use Clerk for Auth", "Chose Clerk over Auth0",
             card_type="decision", keywords=["clerk", "auth"],
             links=[{"target": "alice", "relation": "decided"}])

# Search
client.search("authentication")

# Graph traversal
client.graph("use-clerk", depth=2)

# Time-travel (Pro+)
client.graph("use-clerk", depth=2, at="2026-02-01T00:00:00Z")

Tools Provided

Tool	Description
hyperstack_search	Search memory for relevant context
hyperstack_store	Save a fact, decision, preference, or person
hyperstack_graph	Traverse knowledge graph (impact analysis, decision trails)
hyperstack_list	List all stored memories
hyperstack_delete	Remove outdated memories

Why HyperStack?

• You control the graph. No LLM auto-extraction. No phantom relationships. Your agent explicitly defines cards and links.
• Zero LLM cost per memory op. Mem0/Zep charge ~$0.002 per operation. HyperStack: $0.
• Time-travel debugging. See the graph as it existed at any point in time. "Git blame for agent memory."
• 30-second setup. No Neo4j, no Docker, no OpenSearch. One API key, done.

Pricing

Plan	Cards	Graph	Price
Free	10	❌	$0
Pro	100	✅ + time-travel	$29/mo
Team	500	✅	$59/mo
Business	2,000	✅	$149/mo

Get a free API key at cascadeai.dev/hyperstack

License

MIT