The Ultimate Guide to LLM Memory: From Context Windows to Advanced Agent Memory Systems

Have you ever had a conversation with a chatbot where you mention your name, and two questions later, it asks you what your name is? This frustrating experience, a form of "digital amnesia," highlights one of the most significant challenges in building truly intelligent AI agents. The core of the issue is that Large Language Models (LLMs) are fundamentally stateless. They don't inherently "remember" past interactions. Each time you send a message, the model processes it as a brand-new event, devoid of history.

The illusion of memory in applications like ChatGPT is just that—an illusion. The application layer cleverly works around this limitation by reminding the model of the conversation history with every single turn. This constant "reminding" is at the heart of context management, a critical skill for any AI engineer. But what happens when the conversation gets too long? What if you need the AI to remember a key fact from an hour ago, or a preference you stated last week?

This is where the real engineering begins. We can't just stuff an infinite amount of history into the model. We need to build sophisticated memory systems. This article is your definitive guide to solving this problem. We'll embark on a journey from the foundational constraints of the context window to the cutting-edge frontiers of agentic memory. We will explore the theory, dive deep into practical code, and showcase a complete, intelligent chat memory system you can build and experiment with yourself. By the end, you'll have the knowledge to transform your AI applications from forgetful tools into truly context-aware, intelligent partners.

Part 1: The Foundation – Deconstructing the LLM's Context Window

Before we can build a memory system, we must first understand the battlefield. In the world of LLMs, the battlefield is the context window. It is the single most important constraint that dictates every decision we make about memory management.

What is a Context Window?

Think of an LLM's context window as its "working memory". It's the finite amount of text the model can "see" and process at any given moment to generate a response. Everything you send to the model—the system prompt that defines its persona, your current query, the chat history, and any documents you want it to analyze—must fit within this window. If the total content exceeds this limit, the model simply cannot process it, leading to errors or, worse, silent truncation where crucial information is lost.

This limitation is analogous to human working memory. You can hold a few thoughts in your head at once to solve a problem, but you can't hold an entire library. Similarly, an LLM has a powerful but limited capacity for immediate context.

The Currency of Context: Tokens Explained

The size of a context window isn't measured in words or characters, but in tokens. A token is the fundamental unit of text that a model processes. It can be a whole word, like "apple," or just a piece of a word, like "ing" or "pre." For example, the phrase "LLM memory is complex" might be broken down into tokens like

["LLM", " memory", " is", " complex"].

Understanding and counting tokens is non-negotiable for memory management. It allows us to know precisely how much "space" our conversation history is consuming and when we are about to exceed the model's limit. For models from providers like OpenAI, the tiktoken library is the industry standard for accurately calculating the token count of a given text.

Here's how you can use tiktoken to count tokens in a simple text and for a more complex chat message structure :

import tiktoken

# Get encoding for your model
encoding = tiktoken.encoding_for_model("gpt-4")

# Count tokens in a simple text
text = "Hello, how are you today?"
token_count = len(encoding.encode(text))
print(f"Token count: {token_count}")

# For chat messages, you must account for metadata
def count_chat_tokens(messages, model="gpt-4"):
    """
    Counts the number of tokens in a list of chat messages.
    """
    encoding = tiktoken.encoding_for_model(model)
    tokens = 0
    for message in messages:
        tokens += 4  # Every message follows <|start|>{role/name}\n{content}<|end|>\n
        for key, value in message.items():
            tokens += len(encoding.encode(value))
    tokens += 2  # Every reply is primed with <|start|>assistant
    return tokens

# Example usage for chat messages
chat_messages = [{"role": "user", "content": "Hello, how are you today?"}]
chat_token_count = count_chat_tokens(chat_messages)
print(f"Chat token count: {chat_token_count}")

The Context Arms Race and Its Trade-offs

In recent years, we've seen a dramatic "context arms race," with model providers releasing versions with ever-expanding context windows. What started at a few thousand tokens has exploded into the hundreds of thousands, and now even millions. This expansion opens up new possibilities, but it doesn't eliminate the need for memory management.

A larger window simply delays the problem and introduces significant trade-offs :

• Cost: API pricing is typically based on the number of tokens processed. Sending a massive context with every call can become prohibitively expensive.

• Latency: The more tokens a model has to process, the longer it takes to generate a response.

• Performance Degradation: Research has shown that some models suffer from a "lost in the middle" problem, where they pay less attention to information buried in the center of a very long context. The quality of recall is not always perfect across the entire window.

  

The table below provides a snapshot of the context windows for some of today's leading models.

Provider	Model Name	Context Window Size (Tokens)
OpenAI	GPT-4o / GPT-4 Turbo	128K
Google	Gemini 1.5 Pro	1M (up to 2M for developers)
Anthropic	Claude 3 Family	200K
OpenAI	GPT-4	8K / 32K

Ultimately, the core challenge is shifting from fitting information into the context to managing it intelligently to maintain a high signal-to-noise ratio, control costs, and ensure high-fidelity responses.

Part 2: Memory Hierarchies – Learning from Human Cognition

To build effective AI memory, we can draw inspiration from human cognitive architecture, which uses a multi-tier memory hierarchy.

• Short-Term Memory (STM): This is the immediate workspace for the current conversation. It has a limited capacity (like the last 5-9 interactions) and lasts only for seconds or minutes. In LLMs, this is implemented directly using the context window.

• Long-Term Memory (LTM): This provides persistent storage across different sessions. Its capacity is virtually unlimited, and it's implemented using external databases or vector stores.

• Working Memory: This is where information is actively manipulated. For an LLM, it's the combination of the current context window and any memories retrieved from long-term storage.

Understanding this hierarchy helps us design more sophisticated systems that don't rely on a single, monolithic memory store.

Part 3: The "Memory Ladder" – From Basic to Advanced Strategies

We will now climb the "memory ladder," starting with simple buffering techniques, advancing to intelligent summarization, and then exploring robust, hybrid memory architectures.

Step 1: Simple Buffering and Trimming

Our journey begins with the most straightforward approach: buffering. This method is the "Hello, World!" of chatbot memory and teaches the fundamental mechanics of state management.

Manual Message Passing

The simplest way to give an LLM memory is to manually collect the conversation history and pass it back to the model with each new query. In frameworks like LangChain, a MessagesPlaceholder in the prompt template acts as a variable that will be populated with the list of previous messages.

Here's a conceptual code example demonstrating this basic principle :

from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages()

chain = prompt | llm

# Manual history management
chat_history = [
    HumanMessage(content="Hi, I'm Alice."),
    AIMessage(content="Hello Alice! How can I help you?"),
]

# The new query is appended to the history for the invocation
response = chain.invoke({
    "messages": chat_history +
})

print(response.content) # The model correctly answers "Your name is Alice."

This works perfectly for short conversations, but as the chat_history list grows, we will inevitably hit the context window limit.

The Sliding Window Technique

To prevent overflow, the most common solution is the sliding window technique. We simply keep only the last k messages in the history. While simple, its major drawback is "context cliffing," where an important piece of information is pushed out of the window and forgotten forever.

LangChain provides a trim_messages helper for this :

from langchain_core.messages import trim_messages, HumanMessage, AIMessage

# This trimmer will keep the most recent messages up to a max count of 2.
trimmer = trim_messages(
    strategy="last",
    max_tokens=2,
    token_counter=len # A simple counter where each message is 1 token
)

messages = [
    HumanMessage(content="Hi!"),
    AIMessage(content="Hello!"),
    HumanMessage(content="How are you?"),
    AIMessage(content="I'm good, thanks!")
]

trimmed = trimmer.invoke(messages)
# Result: [HumanMessage(content='How are you?'), AIMessage(content="I'm good, thanks!")]
print(trimmed)

LangChain in Action: ConversationBufferMemory

Frameworks like LangChain provide abstractions for this pattern. The ConversationBufferMemory component automates storing and providing the conversation history. This is the approach demonstrated in the

Intelligent_Chat_Memory (Basic) project.

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import ChatOpenAI

# Initialize the model
llm = ChatOpenAI(model="gpt-4o")

# Initialize the memory buffer
# This will store messages and inject them into the {history} or {chat_history} variable
memory = ConversationBufferMemory(memory_key="history", return_messages=True)

# The ConversationChain automatically uses the memory object
conversation = ConversationChain(
    llm=llm,
    memory=memory,
    verbose=True # Set to True to see the full prompt being sent to the LLM
)

# First interaction
conversation.predict(input="Hi, I'm Bob.")
# Second interaction
conversation.predict(input="I live in New York.")
# Third interaction
# The history of the first two turns is automatically included in the prompt
conversation.predict(input="What is my name?")

While ConversationBufferMemory simplifies the code, it's not a scalable solution for long conversations.

Step 2: Intelligent Compression with Summarization

Simply forgetting old messages is a brute-force solution. A more sophisticated strategy is summarization: using an LLM to compress older messages into a running summary. This represents a critical paradigm shift from managing context size to managing context fidelity.

LangChain's ConversationSummaryBufferMemory

This is the core technique in the Advance_Intelligent_Chat_Memory (Advanced) project. LangChain's ConversationSummaryBufferMemory provides a hybrid solution that combines a raw buffer of recent messages with a progressively updated summary of older ones.

The magic lies in the max_token_limit trigger. The system monitors the token count of the raw message buffer. Once the limit is exceeded, the oldest messages are sent to an LLM to be summarized, and the summary replaces them in the context.

Here’s how this logic is implemented :

from langchain.chains import ConversationChain
from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import ChatOpenAI

# We need an LLM to power the summarization
llm = ChatOpenAI(model="gpt-4o", temperature=0)

# Initialize the summary buffer memory
# It will start summarizing once the buffer exceeds 1000 tokens.
memory = ConversationSummaryBufferMemory(
    llm=llm,
    max_token_limit=1000,
    return_messages=True
)

# Create the conversation chain with this advanced memory
conversation = ConversationChain(
    llm=llm,
    memory=memory,
    verbose=True
)

# Run a long conversation...
conversation.predict(input="Hi, I'm Carol, a data scientist from London.")
conversation.predict(input="I'm working on a project about LLM memory systems.")
#... many more interactions...

# After the 1000-token limit is crossed, the memory object will contain
# a summary of the early conversation and a buffer of the recent messages.
print(memory.chat_memory.messages)

Pros and Cons of Summarization

• Pros: Effectively preserves context from very long conversations, preventing "context cliffing".

• Cons: Incurs additional cost and latency for summarization calls, and nuances can be lost in the compression process (fidelity loss).

Step 3: Building an External Brain – Hybrid Memory and Retrieval

Summarization maintains the "gist" of a conversation but struggles with high-fidelity facts. To solve this, we need a hybrid architecture that combines our conversational buffer with an external, searchable long-term memory store, much like human memory.

Introducing Long-Term Memory: Vector Stores

The most common way to implement long-term memory is with a vector store. This process, the foundation of Retrieval-Augmented Generation (RAG), involves embedding text into numerical vectors and storing them in a database for semantic search. This allows the AI to find relevant information based on meaning, not just keywords.

The Dual-Phase Memory Architecture

The most robust systems combine these approaches into a dual-phase or hybrid memory architecture.

1. Short-Term / Working Memory: Managed by a ConversationSummaryBufferMemory to maintain the fluid, turn-by-turn context.

2. Long-Term / Episodic Memory: A vector store that acts as a permanent, searchable archive of important facts or key decisions.

Here is a conceptual implementation of a semantic memory retriever and a hybrid system :

import numpy as np
from sentence_transformers import SentenceTransformer
from datetime import datetime

class SemanticMemoryRetriever:
    def init(self):
        self.encoder = SentenceTransformer('all-MiniLM-L6-v2')
        self.memory_store =
        self.embeddings =

    def store_memory(self, message, context):
        """Store message with its semantic embedding"""
        embedding = self.encoder.encode(message)
        self.memory_store.append({
            'message': message,
            'context': context,
            'timestamp': datetime.now()
        })
        self.embeddings.append(embedding)

    def retrieve_relevant_memories(self, query, top_k=5):
        """Retrieve the most relevant memories for a given query"""
        query_embedding = self.encoder.encode(query)

        # Calculate cosine similarity
        similarities = [np.dot(query_embedding, emb) / (np.linalg.norm(query_embedding) * np.linalg.norm(emb)) for emb in self.embeddings]

        top_indices = np.argsort(similarities)[-top_k:]
        return [self.memory_store[i] for i in top_indices]

class HybridMemorySystem:
    def init(self):
        # Note: These would be fully initialized in a real application
        self.short_term = ConversationBufferMemory(max_length=10) 
        self.long_term = SemanticMemoryRetriever()
        self.summary_buffer = ConversationSummaryBufferMemory(max_token_limit=2000)

    def get_context(self, query):
        """Assemble context from multiple memory sources"""
        # Get recent conversation from short-term buffer
        recent_context = self.short_term.get_recent_messages()

        # Get relevant long-term memories via semantic search
        relevant_memories = self.long_term.retrieve_relevant_memories(query)

        # Get the running conversation summary
        summary = self.summary_buffer.get_summary()

        # Combine and prioritize these contexts to feed to the LLM
        return self.combine_contexts(recent_context, relevant_memories, summary)

    def combine_contexts(self, recent, relevant, summary):
        # This method would contain the logic to format the final context string
        # For example: f"Summary: {summary}\nRelevant Info: {relevant}\nRecent Chat: {recent}"
        pass

This hierarchical design is far more powerful, recognizing that conversational flow and factual recall demand different mechanisms.

Part 4: The Frontier of AI Memory – Agentic and Structured Approaches

We now arrive at the frontier of AI memory, moving beyond simple context injection to treat memory as a structured, queryable component of the agent's world model.

ReAct: Memory as an Active Tool

The ReAct (Reason + Act) framework changes the relationship between an agent and its memory. Accessing memory becomes an explicit action the agent decides to take as part of a reasoning loop.

The flow is: Observation -> Thought -> Action -> Observation -> Thought -> Final Answer. This makes memory access a deliberate, auditable step, allowing the agent to decide if, when, and how to use its memory.

Condensing History into Knowledge Graphs

While vector stores find semantically similar text, they don't understand the relationships between entities. Graph-based memory solves this by parsing information into a structured format of nodes (entities) and edges (relationships).

Instead of storing "Alice's friend is Bob," a graph would have:

• Nodes: Alice, Bob

• Edge: Alice —(IS_FRIEND_OF)—> Bob

This unlocks complex, multi-hop reasoning. Frameworks like Graphiti and Mem0 are pioneering this approach, allowing agents to build and query a dynamic knowledge graph in real-time.

Here is a conceptual example of a contextual memory graph using networkx :

import networkx as nx

class ContextualMemoryGraph:
    def init(self):
        self.memory_graph = nx.DiGraph()
        self.embeddings = {} # Assume we have a way to get embeddings

    def calculate_similarity(self, content1, content2):
        # Placeholder for a real similarity function (e.g., cosine similarity of embeddings)
        return 0.8 

    def add_memory_node(self, memory_id, content, context):
        """Add a memory node and connect it to contextually similar nodes"""
        self.memory_graph.add_node(memory_id, content=content)

        # Create edges based on contextual similarity
        for existing_node_id, existing_node_attrs in self.memory_graph.nodes(data=True):
            if memory_id!= existing_node_id:
                similarity = self.calculate_similarity(content, existing_node_attrs['content'])
                if similarity > 0.7:
                    self.memory_graph.add_edge(memory_id, existing_node_id, weight=similarity)

    def retrieve_connected_memories(self, query_memory_id, depth=2):
        """Retrieve memories within a specified graph distance"""
        if query_memory_id not in self.memory_graph:
            return
        return list(nx.single_source_shortest_path_length(
            self.memory_graph, query_memory_id, cutoff=depth
        ).keys())

LangGraph: Stateful Memory Management

LangGraph enables sophisticated, stateful memory management with automatic persistence. By providing a checkpointer when compiling the graph, it saves the conversation state after each step, eliminating the need to pass history manually.

Here is a complete graph that saves conversation history automatically :

from langgraph.graph import StateGraph, START
from langgraph.graph.message import MessagesState
from langgraph.checkpoint.memory import MemorySaver
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI

# Assume llm is already initialized: llm = ChatOpenAI(model="gpt-4o")

# 1. Define the function that calls the model
def call_model(state: MessagesState):
    """A node in the graph that calls the LLM."""
    response = llm.invoke(state["messages"])
    return {"messages": [response]} # Add the response to the state

# 2. Define the state graph
workflow = StateGraph(MessagesState)
workflow.add_node("model", call_model)
workflow.add_edge(START, "model")

# 3. Add an in-memory checkpointer
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

# --- Example Invocation ---
# Each call is linked to a 'thread_id'. LangGraph uses this ID to retrieve
# the correct history from the checkpointer.
thread_config = {"configurable": {"thread_id": "user_123"}}

# First turn
app.invoke(
    {"messages":},
    config=thread_config
)

# Second turn (no need to pass history manually)
response = app.invoke(
    {"messages":},
    config=thread_config
)

# The final AIMessage in the response will contain the answer
print(response['messages'][-1].content)

The Importance of Chunking for Retrieval

The quality of any retrieval-based memory system depends heavily on chunking—how information is broken down and stored. Naive chunking can destroy context. Advanced strategies are critical :

• Sentence-Based Chunking: Splits text at sentence boundaries, preserving complete thoughts.

• Recursive Chunking: Breaks down documents hierarchically using separators like paragraphs and headings to maintain structure.

• Semantic Chunking: Uses embedding models to find natural semantic breaks in the text, grouping related sentences.

Part 5: Best Practices for Production Systems

When implementing memory systems in production, several best practices ensure robustness and efficiency.

Memory Lifecycle Management

Implement proper memory lifecycle management to handle updates and clean up stale memories, preventing indefinite growth and ensuring relevance.

from datetime import datetime, timedelta

class MemoryLifecycleManager:
    def init(self):
        self.memory_store = {}
        self.access_counts = {}
        self.last_access = {}

    def update_memory(self, memory_id, content):
        """Update memory and track its access"""
        self.memory_store[memory_id] = content
        self.access_counts[memory_id] = self.access_counts.get(memory_id, 0) + 1
        self.last_access[memory_id] = datetime.now()

    def cleanup_stale_memories(self, retention_days=30):
        """Remove memories that haven't been accessed recently"""
        cutoff_date = datetime.now() - timedelta(days=retention_days)
        stale_memories = [
            mid for mid, last_access in self.last_access.items()
            if last_access < cutoff_date
        ]

        for memory_id in stale_memories:
            del self.memory_store[memory_id]
            del self.access_counts[memory_id]
            del self.last_access[memory_id]

Error Handling and Fallbacks

Robust error handling for memory operations is crucial. Implement fallbacks in case a primary memory store fails.

import logging

# Assume PrimaryMemoryStore and FallbackMemoryStore are defined classes
# logger = logging.getLogger(__name__)

class RobustMemorySystem:
    def init(self):
        self.primary_memory = PrimaryMemoryStore()
        self.fallback_memory = FallbackMemoryStore()

    def store_memory(self, memory_item):
        """Store memory with fallback handling"""
        try:
            self.primary_memory.store(memory_item)
        except Exception as e:
            logging.warning(f"Primary memory store failed: {e}")
            try:
                self.fallback_memory.store(memory_item)
            except Exception as fallback_error:
                logging.error(f"Fallback memory store failed: {fallback_error}")
                # Implement emergency storage or graceful degradation

    def retrieve_memory(self, query):
        """Retrieve memory with fallback"""
        try:
            return self.primary_memory.retrieve(query)
        except Exception as e:
            logging.warning(f"Primary memory retrieval failed: {e}")
            return self.fallback_memory.retrieve(query)

Memory Evaluation Criteria

When implementing memory systems, evaluate performance across these key dimensions :

1. Signal-to-Noise Ratio: The relevance of retrieved memories to the current query.

2. Recency Bias: The balance between recent and historical information.

3. Memory Efficiency: Storage and retrieval performance, including latency and usage.

4. Context Coherence: The logical flow and consistency of the assembled context.

   

Part 6: The Grand Tour – Exploring the Intelligent Chat Memory Project

The concepts we've discussed are the building blocks of the Intelligent Chat Memory projects, which provide a clear, hands-on demonstration of the evolution from basic to advanced memory management.

Architecture Deep-Dive

The advanced project provides a complete, end-to-end system for a context-aware chatbot with a layered architecture:

1. User Interface (Streamlit): The user interacts with the agent through a clean, web-based UI.

2. Model Selection: A dropdown menu allows selection of the backend LLM (e.g., gpt-4o, gemini-1.5-pro).

3. Conversation Chain: A LangChain ConversationChain links the UI, LLM, and memory component.

4. Intelligent Memory (ConversationSummaryBufferMemory): The heart of the system, which intelligently decides when to trigger summarization to compress older parts of the conversation.

5. Response Generation: The final prompt, containing the summary, recent messages, and new query, is sent to the LLM.

From Basic to Advanced: A Project Comparison

The existence of two separate projects illustrates the trade-offs as you move up the memory ladder.

Feature	Intelligent_Chat_Memory (Basic)	Advance_Intelligent_Chat_Memory (Advanced)
Core Memory Strategy	Simple Buffering (ConversationBufferMemory)	Summarization + Buffering (ConversationSummaryBufferMemory)
How it Works	Stores all messages in a buffer until the context limit is hit.	Keeps recent messages in a buffer and creates a running summary of older messages once a token limit is met.
Strengths	Simple to implement, low latency, and low cost for short chats.	Handles infinitely long conversations, preserves long-term context.
Weaknesses	Suffers from "context cliffing"—abruptly forgets the entire past.	Higher latency and cost due to summarization calls; summary can be lossy.
Ideal Use Case	Quick demos, simple task bots, conversations where history isn't critical.	Sophisticated chatbots, creative co-writing assistants, long-term personal agents.

Best Practices & Decision-Making

Choosing the right memory strategy depends entirely on your specific use case, budget, and performance requirements. The following decision matrix synthesizes the entire article into a practical guide.

Strategy	Best For...	Pros	Cons	Complexity / Cost
Simple Buffer	Short-term customer support bots, simple Q&A tasks.	Low latency, simple to implement, very low cost.	Loses all context beyond the window, fails in long conversations.	Low
Summary Buffer	Long-form creative co-writing, therapy/coaching bots.	Excellent conversational flow, maintains the "gist" of long dialogues.	Summaries can lose specific facts, incurs extra cost/latency.	Medium
Vector Retrieval (RAG)	Factual Q&A over a knowledge base, querying documents.	High factual recall, can access vast amounts of external data.	Can struggle with conversational nuance, retrieval quality is key.	Medium to High
Hybrid (Summary + Vector)	Personalized long-term assistants, complex agentic systems.	Best of both worlds: great flow and factual recall.	Most complex to implement, requires managing two systems.	High
Knowledge Graph	Systems requiring deep, multi-hop reasoning and understanding.	Enables complex queries and structured knowledge representation.	Very high implementation complexity, cutting-edge technology.	Very High

Part 7: Conclusion – Your Journey to Building Agents That Remember

We have traveled the full spectrum of AI memory, climbing the "memory ladder" from the fundamental constraints of the stateless LLM to the sophisticated architecture of a reasoning agent. We've seen that the journey to building agents that remember is an evolution:

• It begins with acknowledging the problem of statelessness and the hard limits of the context window.

• It progresses to simple but fragile solutions like buffering and trimming.

• It matures with intelligent summarization, where we trade cost for context fidelity.

• It becomes robust with hybrid retrieval systems, mimicking human memory.

• And it reaches the frontier with structured knowledge graphs, where an agent doesn't just remember text—it understands the world.

You now possess the theoretical framework and have seen the practical code to build your own stateful, intelligent AI applications. The trade-offs between cost, latency, complexity, and fidelity are no longer abstract concepts but concrete design decisions you are empowered to make.

You've seen the theory and the code. Now it's time to build. The complete, production-ready code for the advanced intelligent chat memory system, complete with a Streamlit UI and multi-LLM support, is available for you to explore, fork, and contribute to on GitHub. Dive in, experiment with the different memory strategies, and see for yourself how to give your AI agents the gift of memory.

🚀 Check out the Advanced Intelligent Chat Memory project here: https://github.com/bitphonix/Advance_Intelligent_Chat_Memory

For a simpler starting point that demonstrates the foundational concepts, see the basic implementation here: https://github.com/bitphonix/Intelligent_Chat_Memory

Star the repositories, open issues with your ideas, and become part of the journey to build agents that truly remember.