Aletheia Blog
Writing for high-intent searches around temporal memory, hybrid retrieval, and infrastructure for agents that need continuity over time.
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Cornerstone posts
OpenAI-Compatible Memory Proxy: Drop-In Persistent Memory for Existing Agents
Adding memory to an existing OpenAI agent usually means rewriting the retrieval layer. Aletheia's proxy adds persistent memory by intercepting the API call—no code changes required.
Knowledge Graph Memory for AI Agents: Why Relationships Matter as Much as Facts
A vector index can retrieve similar passages. A knowledge graph can answer who the user knows, what they prefer, and how their world is connected.
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OpenAI-Compatible Memory Proxy: Drop-In Persistent Memory for Existing Agents
How Aletheia's OpenAI-compatible proxy adds persistent, time-aware memory to any existing OpenAI agent without changing a single line of application code.
Adding memory to an existing OpenAI agent usually means rewriting the retrieval layer. Aletheia's proxy adds persistent memory by intercepting the API call—no code changes required.
Knowledge Graph Memory for AI Agents: Why Relationships Matter as Much as Facts
Why a knowledge graph layer transforms agent memory from a flat text store into a structured understanding of user relationships and preferences.
A vector index can retrieve similar passages. A knowledge graph can answer who the user knows, what they prefer, and how their world is connected.
AI Agent Memory at Scale: From Prototype to Production
What changes when agent memory moves from a single-user demo to a multi-tenant production system serving thousands of users.
A working memory prototype is achievable in an afternoon. A production memory system that stays correct at scale requires a fundamentally different architecture.
Beyond Vector Similarity: Neural-Symbolic Extraction for Agentic Memory
How Aletheia combines BERT-based Neural Extraction with Deterministic Logic to build a more reliable memory engine for AI agents.
Vector similarity is only half the battle. To truly understand a user, an agent needs to extract structured entities, relationships, and numeric metrics. Discover Aletheia's new Neural-Symbolic pipeline.
The Predict-Calibrate Pattern: Keeping User Profiles Compact and Context Windows Lean
Discover how Aletheia's Predict-Calibrate pattern manages evolving user profiles without blowing up your LLM context window.
As user interactions evolve, static profiles become bloated and contradictory. Aletheia uses a Predict-Calibrate pattern to maintain distilled, compact state.
Building AI Agents with Deterministic Aggregation: Why Vector Databases Fail at Math
Learn how Aletheia's Deterministic Aggregation Layer solves the critical failure of vector databases in counting and numeric queries for AI agents.
Vector databases fail at math because embeddings represent meaning, not aggregates. Aletheia fixes this with a built-in Deterministic Aggregation Layer.
Temporal Memory vs Vector Databases
Why temporal memory infrastructure behaves differently from a flat vector store when agents need continuity over time.
A vector database can retrieve similar text. It cannot decide that a newer fact should replace an old one unless you build temporal reasoning on top.
Fact Supersession for Agent Memory
How fact supersession prevents stale claims from competing with newer truth inside an agent memory system.
If your memory layer stores every fact forever at the same priority, your agent will eventually argue with itself. Supersession is how you stop that.
Hybrid Retrieval for Exact and Semantic Recall
Why production memory systems need both semantic search and lexical retrieval instead of treating them as substitutes.
Pure semantic retrieval misses exact strings at the worst possible moments. Pure lexical retrieval misses intent. Hybrid retrieval exists because production queries require both.
Local-First Agent Memory Development
Why teams building memory-heavy AI systems move faster when the same retrieval engine can run locally before cloud deployment.
Memory systems are hard to debug when every experiment depends on a hosted environment. Local-first development shortens that loop dramatically.
Evaluating Agent Memory Beyond Context Length
Why serious memory evaluation should focus on recall quality, temporal correctness, and contradiction handling instead of context window size alone.
A long context window does not prove an agent remembers well. Memory quality is about retrieving the right evidence at the right time.