How to Add Memory To Your Agent with vLLM
We’re building an agent that adds memory through vLLM — getting your AI to remember things, it’s about time, right?
Prerequisites
- Python 3.11+
- vLLM installed:
pip install vllm - torch installed:
pip install torch - access to a GPU (optional but recommended for performance)
Step 1: Setting Up vLLM
First off, you need to get your environment ready. vLLM has gained traction with a whopping 76,610 stars on GitHub. That’s no joke. Here’s how to install it:
pip install vllm
This command installs vLLM and all its dependencies. If you encounter errors, check your Python version and ensure you’re using Python 3.11 or higher. I once tried to install vLLM on Python 3.8 — big mistake. It’s like trying to run a Tesla on a bicycle battery.
Step 2: Initialize Your vLLM Agent
To add memory to your agent, start by initializing it with vLLM like so:
from vllm import VLLM
agent = VLLM()
This creates a basic VLLM agent. Simple, huh? But here’s the kicker: by default, the agent won’t remember anything. It’s like that friend who only remembers the last pizza you ordered, not the toppings. To make your agent smarter, we need to add memory.
Step 3: Adding Memory Functionality
Now comes the fun part. Memory isn’t just about storing data; it’s about making your agent contextually aware. Here’s how to implement memory:
class Memory:
def __init__(self):
self.memory_store = []
def add_memory(self, item):
self.memory_store.append(item)
def retrieve_memory(self):
return self.memory_store
memory = Memory()
agent.memory = memory
In this code, we created a simple memory class that allows the agent to store and retrieve data. You might hit some snags with data types; ensuring everything is in the right format is critical. Trust me, I once tried to store a list as a string and ended up with a mess that looked like a failed math exam.
Step 4: Interacting with the Agent
Let’s make your agent actually use its memory:
def interact_with_agent(user_input):
agent.memory.add_memory(user_input)
response = f"I remember you said: {user_input}"
return response
print(interact_with_agent("I love pizza!"))
print(agent.memory.retrieve_memory())
When you interact with the agent, it adds your input to its memory and gives you a reminder. If you run into issues here, like the agent not responding with the correct memory, ensure that the memory is being accessed properly. I once forgot to call the memory retrieval function, and my agent just stared at me like it was a deer in headlights.
Step 5: Testing Your Agent
Finally, you need to test everything to ensure it works as expected. Run through various inputs:
print(interact_with_agent("Dogs are the best!"))
print(interact_with_agent("Cats are cool too!"))
print(agent.memory.retrieve_memory())
After running this, check the output. It should show all the memories you added. If it doesn’t, check your add_memory function — it’s gotta be working properly. I once had a typo in my function name that turned it into an unintentional slapstick routine.
The Gotchas
Just because you’ve got memory working doesn’t mean you’re out of the woods. Here are some pitfalls:
- Data Limitations: Make sure you have limits on how much memory the agent can store. If it gets too big, it’ll slow down, much like that one website that takes forever to load.
- Memory Management: You can’t just keep adding memories endlessly. Create a strategy to forget things, otherwise, it’ll become a hoarder.
- Context Awareness: If your agent doesn’t understand the context of memories, it could make awkward references. “Remember when I said pizza? Oh wait, that was your friend.” Oops!
Full Code Example
from vllm import VLLM
class Memory:
def __init__(self):
self.memory_store = []
def add_memory(self, item):
self.memory_store.append(item)
def retrieve_memory(self):
return self.memory_store
agent = VLLM()
memory = Memory()
agent.memory = memory
def interact_with_agent(user_input):
agent.memory.add_memory(user_input)
response = f"I remember you said: {user_input}"
return response
# Test the agent
print(interact_with_agent("I love pizza!"))
print(interact_with_agent("Dogs are the best!"))
print(agent.memory.retrieve_memory())
What’s Next
Now that your agent can remember things, take it a step further. Implement a forget feature — that way, you can manage the memory better. It adds a layer of sophistication that your users will appreciate.
FAQ
Q1: Can I use vLLM with other languages?
A: Not directly; vLLM is tailored for Python. However, you can create wrappers or APIs to interact with other languages.
Q2: How does memory affect performance?
A: More memory can slow down response times if not managed. Keep an eye on the memory size and implement limits.
Q3: Is vLLM open-source?
A: Yes, it’s open-source with an Apache-2.0 license. You can find it on GitHub, and it’s had 15,582 forks and 4,250 open issues as of now.
Data Sources
For additional information, check out the official vLLM documentation here. For memory implementations, you might want to look at other resources like LangChain Blog.
Last updated April 15, 2026. Data sourced from official docs and community benchmarks.
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