What is an agent's long-term memory and how to build it (summarization, vector memory)?

The agent's long-term memory is a mechanism for storing and utilizing information accumulated by the agent over an extended period to improve interaction quality and decision-making.

Summarization — the process of compressing large volumes of information into a brief but meaningful representation. This helps the agent quickly recall key points without needing to store all details.

Vector memory — storing information as vectors (e.g., embeddings), which allows efficient searching and comparison of similar data using nearest neighbor methods.

How to build long-term memory:

1. Data collection: the agent saves important events, dialogues, facts.
2. Processing and compression: summarization algorithms are applied to reduce data volume.
3. Vectorization: converting texts or other data into vectors using embedding models (e.g., BERT, OpenAI embeddings).
4. Storage: using specialized databases (e.g., Pinecone, FAISS) for efficient search over vector representations.
5. Retrieval: when needed, the agent searches for relevant records in memory using similarity search and uses them for response or decision-making.

Example in Python using an embedding library and FAISS:

from sentence_transformers import SentenceTransformer
import faiss

# Initialize embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Example data
texts = ["Today is sunny", "It will rain tomorrow", "The weather is good"]

# Vectorization
vectors = model.encode(texts)

# Create FAISS index
index = faiss.IndexFlatL2(vectors.shape[1])
index.add(vectors)

# Search in memory
query = "What is the weather tomorrow?"
query_vec = model.encode([query])
D, I = index.search(query_vec, k=1)
print(f"Most similar text: {texts[I[0][0]]}")

Thus, the agent's long-term memory combines summarization for compactness and vector memory for quick retrieval of relevant information.