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Building a Robust Memory-Driven AI Agent: A Journey to Self-Learning
Hey there, tech enthusiasts! Today, we’re diving into the fascinating world of memory-driven AI agents. Our goal is to create a robust memory layer that enables our agents to learn from their experiences and recall relevant data to tackle complex tasks.
**What is a Memory Engine?**
A memory engine is the backbone of any AI system, responsible for storing and retrieving memories, tracking usage, and salience (importance) of each memory item. Our memory engine will handle three primary types of memory: short-term, long-term, and episodic.
**Short-Term Memory (STM)**
Our short-term memory is a high-speed, high-accessibility memory that stores recent interactions and conversations. We’ll use a basic implementation to store the current chat context.
“`python
mem = MemoryEngine()
agent = MemoryAgent(mem)
“`
**Long-Term Memory (LTM)**
The long-term memory is a larger, more persistent memory that stores data, preferences, and procedures that our agent can use to make informed decisions. We’ll add some data to the LTM, such as preferences and procedures, to support our agent’s decision-making.
“`python
mem.ltm_add(variety=”choice”, textual content=”Desire concise, structured solutions with steps and bullet points when useful.”, tags=[“style”], pinned=True)
mem.ltm_add(variety=”choice”, textual content=”Desire options that run on Google Colab without additional setup.”, tags=[“environment”], pinned=True)
mem.ltm_add(variety=”process”, textual content=”When building agent memory: embed objects, store with salience/novelty coverage, retrieve with hybrid semantic+episodic, and decay overuse to avoid repetition.”, tags=[“agent-memory”])
“`
**Episodic Memory**
An episodic memory stores specific events and lessons learned from the agent’s experiences. We’ll create an episodic memory for a specific task, such as building an agent memory layer for troubleshooting Python errors in Colab.
“`python
mem.episode_add(
job=”Build an agent memory layer for troubleshooting Python errors in Colab”,
constraints={“offline_ok”: True, “single_notebook”: True},
plan=[
“Capture short-term chat context”,
“Store durable constraints/preferences in long-term vector memory”,
“After solving, extract lessons into episodic lines”,
“On new tasks, retrieve top episodic lessons + semantic facts”
],
actions=[
{“type”: “analysis”, “detail”: “Identified recurring failure: missing installs and version mismatches.”},
{“type”: “action”, “detail”: “Added pip install block + minimal fallbacks.”},
{“type”: “action”, “detail”: “Added memory policy: pin constraints, drop low-salience items.”}
],
consequence=”Notebook grew to become sturdy: runs with or without external keys; troubleshooting quality improved with episodic lessons.”,
outcome_score=0.90,
classes=[
“Always include a pip install cell for non-standard deps.”,
“Pin hard constraints (e.g., offline fallback) into long-term memory.”,
“Store a post-task ‘lesson list’ as an episodic trace for reuse.”
],
failure_modes=[
“Assuming an API key exists and crashing when absent.”,
“Storing too much noise into long-term memory causing irrelevant recall context.”
],
tags=[“colab”, “robustness”, “memory”]
)
“`
**Building the Memory-Augmented Agent**
Now that we have our memory in place, let’s create a memory-augmented agent that can respond to questions using the stored memories. We’ll use the `openai_chat` function to interact with the OpenAI API, but for offline use, we’ll fall back to a heuristic responder that uses the stored memories.
“`python
def heuristic_responder(context: str, query: str) -> str:
#…
“`
We’ll create a `MemoryAugmentedAgent` class that uses the `openai_chat` function or the heuristic responder depending on the `USE_OPENAI` variable.
“`python
class MemoryAugmentedAgent:
def __init__(self, mem: MemoryEngine):
self.mem = mem
def reply(self, query: str) -> Dict[str, Any]:
#…
“`
**Testing the Memory-Augmented Agent**
Let’s test the memory-augmented agent with two questions:
“`python
q1 = “I need to build memory for an agent in Colab. What should I store and how do I retrieve it?”
out1 = agent.reply(q1)
print(out1[“reply”])
q2 = “How do I avoid my agent repeating the same memory again and again?”
out2 = agent.reply(q2)
print(out2[“reply”])
“`
**Consolidating Memories**
After answering questions, we’ll consolidate the memories by tracking usage and salience.
“`python
cons = mem.consolidate()
print(“CONSOLIDATION RESULT:”, cons)
“`
**Visualizing Memories**
Finally, let’s show the top rows of the long-term memory (LTM) and episodic memory.
“`python
print(“LTM (top rows):”)
show(mem.ltm_df().head(12))
print(“EPISODES (top rows):”)
show(mem.episodes_df().head(12))
“`
**Conclusion**
In this article, we successfully built a memory-driven AI agent with short-term, long-term, and episodic memory. We added data to the long-term memory, created an episodic memory for a specific task, and implemented a memory-augmented agent that answers questions using the stored memories. We’ve also consolidated memories and visualized the top rows of the LTM and episodic memory. This is a powerful framework for building robust memory-driven AI agents that can learn from their experiences and recall relevant data to tackle complex tasks.
