kronvex 0.5.0

Persistent memory for AI agents — Kronvex Python SDK

Kronvex Python SDK

Persistent memory for AI agents. Three endpoints, one API key, production-ready.

Installation

pip install kronvex                        # core SDK
pip install "kronvex[langchain]"           # + LangChain integration
pip install "kronvex[crewai]"              # + CrewAI integration
pip install "kronvex[langgraph]"           # + LangGraph integration
pip install "kronvex[all-integrations]"    # all three at once

Quick start

from kronvex import Kronvex

kx = Kronvex("kx_your_api_key")
agent = kx.agent("your-agent-id")

# Store a memory
agent.remember("User prefers concise answers", memory_type="preference")

# Recall relevant memories
memories = agent.recall("what does the user prefer?", top_k=5)
for m in memories:
    print(f"[{m['score']:.2f}] {m['content']}")

# Inject context into your prompt
context = agent.inject_context("How should I respond?")
# → "Relevant memories:\n- User prefers concise answers\n..."

Async support

import asyncio
from kronvex import AsyncKronvex

async def main():
    async with AsyncKronvex("kx_your_api_key") as kx:
        agent = kx.agent("your-agent-id")
        await agent.remember("User is based in Paris", memory_type="semantic")
        memories = await agent.recall("where is the user?")

asyncio.run(main())

Framework integrations

LangChain

pip install "kronvex[langchain]"

from kronvex.integrations.langchain import KronvexMemory
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain

memory = KronvexMemory(api_key="kv-your-key", agent_id="your-agent-id")
chain = ConversationChain(llm=ChatOpenAI(), memory=memory)
chain.predict(input="I prefer concise answers.")

CrewAI

pip install "kronvex[crewai]"

import os
os.environ["KRONVEX_API_KEY"] = "kv-your-key"
os.environ["KRONVEX_AGENT_ID"] = "your-agent-id"

from kronvex.integrations.crewai import recall_memory, store_memory, get_context
from crewai import Agent

researcher = Agent(role="Researcher", goal="...", tools=[recall_memory, store_memory])

LangGraph

pip install "kronvex[langgraph]"

from kronvex.integrations.langgraph import make_recall_node, make_store_node

recall_node = make_recall_node("kv-your-key", "your-agent-id")
store_node  = make_store_node("kv-your-key", "your-agent-id")

builder.add_node("recall", recall_node)
builder.add_node("store",  store_node)

API reference

Kronvex(api_key, *, base_url, timeout)

Method	Description
.agent(agent_id)	Get an Agent handle
.create_agent(name)	Create a new agent
.list_agents()	List all agents

Agent

Method	Description
.remember(content, *, memory_type, session_id, ttl_days, pinned, metadata)	Store a memory
.recall(query, *, top_k, memory_type, session_id, threshold)	Semantic search
.inject_context(message, *, top_k, session_id)	Get prompt-ready context block
.sessions()	List session IDs
.memories(*, session_id, memory_type, limit, offset)	List stored memories
.delete_memory(memory_id)	Delete one memory
.clear()	Delete all memories

Links

• Website
• Documentation
• Dashboard
• API Docs
• Node.js SDK