Memory

Aesoperator uses a sophisticated memory system powered by pgvector and Neon database to maintain context. Unlike other systems, Aesoperator uses a smaller judge model to continuously evaluate stored memories.

Architecture

• Database: Neon PostgreSQL with pgvector extension for vector embeddings

• Storage: Key-value pairs + vector embeddings of content

• Retrieval: Semantic search using cosine similarity

• Management: Dynamic pruning and retention based on relevance

How It Works

Storage

# Store key-value pairs
aesop.memory.set("user_preferences", {
    "theme": "dark",
    "language": "python"
})

# Store with vector embedding
aesop.memory.store(
    "Important context about user workflow",
    embedding_model="text-embedding-3-small"
)

Retrieval

# Get exact key matches
prefs = aesop.memory.get("user_preferences")

# Semantic search
relevant_context = aesop.memory.search(
    query="What was the user working on?",
    limit=5
)

Dynamic Management

Aesoperator uses "judge" models like o3-mini to continuously evaluate stored memories:

1. Relevance Scoring: Judges rate memory importance based on:

   • Recency of access

   • Semantic relevance to current task

   • Historical utility

2. Pruning Decisions: Low-scoring memories are archived or deleted

   • Configurable retention thresholds

   • Task-specific memory lifetimes

   • Automatic cleanup of stale data

3. Context Window Management:

   • Dynamically selects most relevant memories

   • Maintains optimal context size

   • Prevents context overflow

Memory Types

1. Short-term: Task-specific, cleared after completion

2. Working: Persists across related tasks

3. Long-term: Permanent storage for critical information

Example Usage

# Store task context
aesop.memory.store("User is debugging a Python web app")

# Later retrieve relevant context
context = aesop.memory.search("What is the user's current task?")

# Judge model evaluates and prunes
aesop.memory.optimize(
    judge_model="context-evaluator-v1",
    retention_threshold=0.7
)

Configuration

aesop.memory.configure({
    "max_tokens": 100000,
    "pruning_threshold": 0.6,
    "judge_model": "gpt-4",
    "embedding_model": "text-embedding-3-small",
    "retention_days": 30
})

Technical Details

• Uses pgvector's HNSW indexing

• Embeddings cached in Redis for fast retrieval

• Async pruning jobs run on schedule

• Automatic backups to object storage

• Monitoring via Prometheus metrics

Note: Dynamic memory management with judge models is currently in progress. The feature will be generally available in Q2 2025.