Pengelolaan Memori dan Status untuk Agen
Terapkan teknik memori persisten dan pengelolaan status bagi agen LLM untuk mendukung percakapan jangka panjang serta rangkaian tugas kompleks.
Pengelolaan Memori dan Status untuk Agen adalah pelajaran Prompt Engineering & LLM Optimization for Developers gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Prompt Engineering & LLM Optimization for Developers, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Prompt Engineering & LLM Optimization for Developers mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
Agents Need Memory
LLM agents are designed to perform complex tasks. But by default, LLMs are stateless! This means they don't "remember" past interactions.
For an agent to have a meaningful conversation or complete multi-step tasks, it needs a way to recall previous information. This is where memory comes in.
Short & Long-Term Memory
Agent memory can be categorized into two main types:
- Short-Term Memory: This is like a human's working memory. It holds recent, relevant information for immediate use, often the raw conversation history.
- Long-Term Memory: Stores information over extended periods, like past conversations or learned facts. It helps agents retain knowledge beyond the current interaction.
Conversational Buffer Memory
The simplest form of short-term memory for an agent is a conversational buffer. It stores the raw exchange of messages between the user and the agent.
When generating a new response, the agent is prompted with the entire buffer, allowing it to maintain context for the current turn.
Basic Chat Buffer Demo
Here's a simple Python example simulating a conversational buffer. Each new message is added to a list that represents the agent's memory.
class AgentMemory:
def __init__(self):
self.history = []
def add_message(self, role, content):
self.history.append(f"{role}: {content}")
def get_history(self):
return "\n".join(self.history)
# Simulate agent interaction
memory = AgentMemory()
memory.add_message("User", "Hi, what's the weather like?")
memory.add_message("Agent", "It's sunny today.")
memory.add_message("User", "What about tomorrow?")
print("Current conversation history:")
print(memory.get_history())Summarizing for Long-Term
Raw conversation buffers grow quickly, hitting LLM context window limits and increasing costs. Summarization is a key technique for long-term memory.
Instead of sending the full history, we periodically summarize older parts of the conversation, keeping the key points while discarding verbose details.
Summarized Memory Concept
This example shows how a summary might be generated and used. In a real application, an LLM would create the summary, but here we simulate it.
class AgentMemory:
def __init__(self):
self.buffer = []
self.summary = "No previous conversation."
def add_message(self, role, content):
self.buffer.append(f"{role}: {content}")
# In a real app, trigger LLM summarization here
if len(self.buffer) > 4: # Example threshold
self.summarize_buffer()
def summarize_buffer(self):
# Simulate LLM summarizing the buffer
old_messages = "\n".join(self.buffer[:-2])
new_summary = f"Summary of past: {old_messages[:30]}..."
self.summary = new_summary
self.buffer = self.buffer[-2:] # Keep recent messages
def get_full_context(self):
return f"Previous summary: {self.summary}\n" + \
"\n".join(self.buffer)
memory = AgentMemory()
memory.add_message("User", "Tell me about your capabilities.")
memory.add_message("Agent", "I can answer questions.")
memory.add_message("User", "Can you write code?")
memory.add_message("Agent", "Yes, in Python.")
memory.add_message("User", "What about Java?") # This triggers summary
memory.add_message("Agent", "I can try some Java.")
print("Agent's current context:")
print(memory.get_full_context())Agent Task State
Beyond just remembering conversation, agents need to manage state to complete multi-step tasks. This means tracking progress, decisions made, and information collected.
Think of it like a checklist or a finite state machine for the agent's goal. For example, booking a flight requires knowing source, destination, and dates.
Flight Booking State Example
Consider an agent helping book a flight. Its internal state might include:
- Status:
"collecting_info","searching_flights","booking_confirmed" - Departure City:
"London" - Destination City:
"Paris" - Departure Date:
"2024-12-25" - Number of Passengers:
"1"
The agent updates this state as it gathers information from the user.
Memory & State Challenges
Managing agent memory and state introduces several key challenges:
- Context Window Limits: How much information can the LLM process at once?
- Cost: Longer contexts mean more tokens and higher API costs.
- Retrieval: For very long-term memory (e.g., a knowledge base), how does the agent efficiently find the most relevant past information?
- Consistency: Ensuring the agent's state accurately reflects the task's progress and collected data.
Memory Check
Let's check your understanding of agent memory and state management techniques.
Recap: Memory & State
In this lesson, we explored how memory and state are essential for LLM agents to engage in long-term conversations and complete complex tasks.
- We covered short-term conversational buffers and long-term summarization techniques.
- We also looked at how state management helps agents track progress through multi-step processes.
These techniques are fundamental for building truly capable and persistent AI agents.
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Pertanyaan yang Sering Diajukan
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Semua pelajaran dalam kursus ini
- Merancang Sistem Multi-Agen
- Pengelolaan Memori dan Status untuk Agen
- Otomatisasi Alur Kerja Otonom
- Refleksi Agen dan Putaran Koreksi Mandiri