Stop losing 30% of your agent productivity to memory issues. Deploy enterprise-grade agent operations in minutes.
Framework agnostic • No vendor lock-in • Production tested
Real feedback from developers building production agent systems
"The agent memory is nothing more than a list of messages... You're absolutely correct that passing the entire list saturates the context window, leading to reduced accuracy."
— Reddit r/AI_Agents
"LangChain turns into spaghetti fast once you start chaining multiple agents... Hidden state issues if you're not super careful."
— Production Developer
"AutoGPT: Fun to play with. Terrible for production. CrewAI: Random breaks show up quickly in anything long-running or stateful."
— Framework Comparison
Studies show agents lose 30-40% productivity due to memory and state issues
Three pillars that make agent operations reliable and scalable
Agents remember across sessions, conversations, and deployments. No more starting from scratch every time.
Automatic failure handling and state reconstruction. Your agents recover gracefully from any interruption.
Shared state management for agent squads. Coordinate complex workflows without state conflicts.
From complex memory management to simple SDK calls
import json
from typing import List, Dict
class AgentMemory:
def __init__(self):
self.memory = []
self.context_limit = 4000
def add_memory(self, content: str):
self.memory.append(content)
if self._estimate_tokens() > self.context_limit:
self._compress_memory()
def _estimate_tokens(self) -> int:
# Complex token estimation logic
return sum(len(m.split()) * 1.3 for m in self.memory)
def _compress_memory(self):
# Manual memory compression
# Risk of losing important context
self.memory = self.memory[-10:]
def get_relevant_context(self, query: str) -> str:
# Custom relevance scoring
# Prone to errors and context loss
return "\n".join(self.memory[-5:])from agentops import AgentMemory
# Initialize with automatic optimization
memory = AgentMemory(
agent_id="customer_service_bot",
optimization="smart_compress"
)
# Add memories with automatic relevance scoring
memory.remember("User prefers email over phone contact")
memory.remember("Previous issue: billing question resolved")
memory.remember("Customer tier: premium, 3 years")
# Get relevant context automatically
context = memory.recall(
query="customer wants to change contact method",
max_tokens=2000 # Automatic context fitting
)
# Memories persist across sessions automatically
# No manual compression or state management requiredWorks with any agent framework or custom implementation
Enterprise-grade infrastructure with developer-friendly experience
Production-grade reliability with automatic failover and redundancy
Enterprise security standards with end-to-end encryption
Debug agent behavior with detailed logs and performance metrics
Global infrastructure with data residency compliance
Dedicated support team with agent architecture expertise
Seamless integration with existing enterprise systems
Simple setup, immediate value
pip install agentopsimport agentops
agent = agentops.Agent(
name="my_assistant",
memory_strategy="smart_compress"
)agent.remember("User prefers concise responses")
response = agent.process("What's the weather?")
# Memory persists automaticallyFree 14-day trial • No credit card required • Full feature access