# Memora: A Multi-Network Entity-Aware Memory Architecture for Conversational AI Agents ## Abstract We present Memora, a sophisticated memory architecture for AI agents that combines temporal, semantic, and entity-based retrieval mechanisms within a graph-structured knowledge base. The system introduces three distinct but interconnected memory networks—world knowledge, agent experiences, and formed opinions—enabling contextual reasoning and personality-driven responses. Our multi-stage retrieval pipeline integrates four parallel search strategies with reciprocal rank fusion, neural reranking, and maximal marginal relevance diversification. We demonstrate how entity resolution and graph-based spreading activation enable the discovery of indirectly related information that purely vector-based approaches miss. Additionally, we introduce a personality framework based on the Big Five psychological model that biases opinion formation during reasoning tasks. The architecture achieves high recall through parallel retrieval while maintaining precision through sophisticated reranking, addressing the fundamental challenge of long-term memory retention in conversational agents. ## 1. Introduction Conversational AI agents face a fundamental challenge: maintaining coherent, context-aware memories across extended interactions. Traditional approaches rely either on vector similarity search, which captures semantic relationships but misses entity-level connections, or on keyword matching, which provides precision but lacks conceptual understanding. Neither approach adequately handles the temporal aspects of memory or the need to distinguish between different types of knowledge. We propose Memora, a hybrid memory architecture that addresses these limitations through: 1. **Multi-Network Organization**: Separate but interconnected networks for world facts, agent experiences, and formed opinions 2. **Entity-Aware Graph Structure**: Explicit entity resolution and linking that connects memories through shared identities 3. **Parallel Multi-Strategy Retrieval**: Four complementary retrieval methods (semantic, keyword, graph, temporal-graph) executed in parallel 4. **Personality-Driven Reasoning**: Configurable personality traits that bias opinion formation using psychological frameworks 5. **Hierarchical Reranking**: Neural cross-encoder reranking followed by maximal marginal relevance diversification This architecture enables agents to reason over their memories with temporal awareness, discover indirect relationships through graph traversal, and form consistent opinions influenced by configured personality traits. ## 2. System Architecture ### 2.1 Memory Networks The system maintains three distinct memory networks, each serving a specific purpose while sharing the underlying infrastructure: #### 2.1.1 World Network The World Network (`fact_type='world'`) stores general knowledge and facts about the external world that are independent of the agent's direct actions: **Characteristics**: - Contains factual information about entities (people, organizations, places) - Includes relationships between entities - Maintains temporal validity (when facts became true) - Self-contained statements with resolved pronouns **Example Facts**: - "Alice works at Google in Mountain View" - "Yosemite National Park is located in California" - "Python has libraries for data science including pandas and numpy" **Use Cases**: - Answering questions about entities: "Where does Alice work?" - Understanding relationships: "Who works at Google?" - Temporal queries: "What happened in June?" #### 2.1.2 Agent Network The Agent Network (`fact_type='agent'`) records the agent's own actions, recommendations, and interactions: **Characteristics**: - First-person perspective of agent activities - Records what the agent did, said, or recommended - Enables self-referential reasoning ("What did I tell Alice?") - Tracks agent's involvement over time **Example Facts**: - "I recommended Yosemite National Park to Alice for hiking" - "I helped debug a Python memory leak in the pandas DataFrame" - "I explained the Big Five personality model to the user" **Use Cases**: - Self-awareness: "What did I recommend?" - Consistency checking: "Have I said this before?" - Context continuity: "What was I discussing with Alice?" #### 2.1.3 Opinion Network The Opinion Network (`fact_type='opinion'`) stores the agent's formed opinions and perspectives: **Characteristics**: - Generated during `think` operations when the agent reasons about topics - Includes confidence scores (0.0-1.0) indicating certainty - Contains explicit reasons for the opinion - Immutable once formed (timestamped by formation date) - Influenced by agent personality traits (Section 4) **Example Facts**: - "Python is better than JavaScript for data science (Reasons: has better libraries like pandas and numpy; stronger statistical computing ecosystem) [confidence: 0.85]" - "Remote work improves productivity (Reasons: eliminates commute time; provides flexible scheduling) [confidence: 0.7]" **Use Cases**: - Consistent viewpoints: "What do I think about remote work?" - Confidence-aware reasoning: Stronger opinions weigh more heavily - Opinion evolution tracking over time **Network Interconnection**: While logically separate, all three networks share the same graph infrastructure (temporal, semantic, and entity links), enabling cross-network traversal during search. For example, a query about "Alice's work" might start in the World Network ("Alice works at Google") and traverse entity links to the Agent Network ("I recommended technical books to Alice"). ### 2.2 Memory Unit Structure Each memory unit is represented as a self-contained node in the knowledge graph: **Core Attributes**: - `id`: Unique UUID for the memory unit - `agent_id`: Identifier for the agent this memory belongs to - `text`: Self-contained statement with resolved pronouns - `embedding`: 384-dimensional vector (BAAI/bge-small-en-v1.5) - `fact_type`: Network classification (world/agent/opinion) - `event_date`: Timestamp when the fact became true - `context`: Optional contextual metadata - `access_count`: Frequency-based importance signal - `confidence_score`: For opinions only (0.0-1.0) **LLM-Based Extraction**: Raw content undergoes LLM processing to extract atomic facts: 1. **Pronoun Resolution**: "She loves hiking" → "Alice loves hiking" 2. **Completeness Validation**: Must contain subject + verb 3. **Fact Isolation**: One concept per unit 4. **Noise Filtering**: Removes greetings, filler, incomplete thoughts 5. **Network Classification**: Determines appropriate fact_type This ensures each memory unit is independently understandable and searchable without requiring surrounding context. ### 2.3 Entity Resolution and Linking Entity resolution creates strong connections between memories that share common entities, solving the problem where semantically dissimilar facts are related through shared identities. #### 2.3.1 Named Entity Recognition We use spaCy's NER pipeline to extract entities from memory text: **Entity Types**: - PERSON: "Alice", "Bob Chen" - ORGANIZATION: "Google", "Stanford University" - LOCATION: "Yosemite National Park", "California" - PRODUCT: "Python", "pandas library" - CONCEPT: "machine learning", "remote work" - OTHER: Miscellaneous proper nouns #### 2.3.2 Entity Disambiguation Multiple mentions of entities (e.g., "Alice", "Alice Chen", "Alice C.") must be resolved to a single canonical entity. Our scoring algorithm combines three signals: **Name Similarity (50% weight)**: ``` score = 1.0 - (levenshtein_distance / max_length) ``` Matches variations like "Bob" ↔ "Robert", "Google Inc" ↔ "Google" **Co-occurrence Frequency (30% weight)**: ``` score = min(1.0, shared_memory_count / 10.0) ``` Entities mentioned together frequently are likely distinct (e.g., "Alice" and "Alice Cooper" appearing together indicates different people) **Temporal Proximity (20% weight)**: ``` score = exp(-time_gap / 7_days) ``` Recent mentions more likely refer to the same entity **Final Score**: ``` final_score = 0.5 * name_sim + 0.3 * cooccurrence + 0.2 * temporal threshold = 0.75 for matching ``` **Example**: "Alice" mentioned on Monday and "Alice Chen" mentioned on Tuesday will be resolved to the same entity (high name similarity + close temporal proximity), but "Alice" and "Alice Cooper" in the same conversation will remain distinct (low co-occurrence score since they appear together). #### 2.3.3 Entity Link Structure Each entity creates a `link_type='entity'` edge between all memories mentioning it: **Properties**: - `weight=1.0` (constant, no temporal decay) - `entity_id`: Reference to resolved canonical entity - Bidirectional connections between all mentioning memories **Impact on Retrieval**: Entity links enable graph traversal to discover indirectly related facts: **Example Query**: "What does Alice do?" 1. **Semantic Match**: "Alice works at Google" (direct match) 2. **Entity Traversal**: Follow entity links for "Alice" → - "Alice loves hiking" (different semantic space) - "Google's office is in Mountain View" (via "Google" entity) - "I recommended books to Alice" (Agent Network, via "Alice") This graph connectivity solves the fundamental limitation of vector-only search: two facts can be strongly related through shared entities even when their embeddings are dissimilar. ### 2.4 Link Types and Graph Structure The memory graph contains three types of edges connecting memory units: #### 2.4.1 Temporal Links Temporal links connect memories close in time, enabling temporal reasoning: **Creation Logic**: ```python if abs(event_date1 - event_date2) < time_window: # default: 24 hours weight = max(0.3, 1.0 - (time_diff / time_window)) create_link(unit1, unit2, type='temporal', weight=weight) ``` **Properties**: - Decays linearly with time distance - Minimum weight 0.3 to maintain some connectivity - Enables "What happened around the same time?" queries - Critical for narrative understanding and sequential reasoning **Example**: Memories from the same conversation or day cluster together, enabling retrieval of context-adjacent facts. #### 2.4.2 Semantic Links Semantic links connect memories with similar meanings: **Creation Logic**: ```python similarity = cosine_similarity(embedding1, embedding2) if similarity > threshold: # default: 0.7 create_link(unit1, unit2, type='semantic', weight=similarity) ``` **Properties**: - Uses pgvector HNSW index for efficient nearest-neighbor search - Higher threshold (0.7) than retrieval (0.3) to avoid over-connection - Weight equals cosine similarity score - Enables "Tell me about similar topics" queries **Example**: "Hiking in Yosemite" links to "Mountain climbing", "Trail running", "Outdoor activities" #### 2.4.3 Entity Links Entity links (described in Section 2.3.3) create the strongest connections: **Properties**: - `weight=1.0` (constant, never decays) - Connects all memories mentioning the same resolved entity - Most reliable traversal path during graph search - Enables "Tell me everything about X" queries **Graph Density**: Each memory unit typically has: - 5-10 temporal links (to nearby memories) - 3-5 semantic links (to similar content) - Variable entity links (depending on entity mention frequency) This multi-layered graph structure enables flexible traversal strategies that balance different types of relatedness. ## 3. Retrieval Architecture Our retrieval pipeline addresses the fundamental challenge of long-term memory: achieving both **high recall** (finding all relevant information) and **high precision** (ranking the most relevant items first). ### 3.1 Four-Way Parallel Retrieval We execute four complementary retrieval strategies in parallel, each capturing different aspects of relevance: #### 3.1.1 Semantic Retrieval (Vector Similarity) **Method**: Cosine similarity between query embedding and memory embeddings **Index**: pgvector HNSW (Hierarchical Navigable Small World) **Threshold**: ≥ 0.3 similarity **Advantages**: - Captures conceptual similarity - Handles synonyms and paraphrasing - Language-model understanding of meaning **Limitations**: - Misses exact proper nouns if not in training data - Cannot reason about temporal relationships - Weak at entity disambiguation **Example**: Query "hiking activities" finds "mountain climbing", "trail running", even if exact words don't match #### 3.1.2 Keyword Retrieval (BM25 Full-Text Search) **Method**: PostgreSQL full-text search with BM25 ranking **Index**: GIN index on `to_tsvector(text)` **Advantages**: - High precision for proper nouns and technical terms - Exact phrase matching - Fast execution (~5ms) **Limitations**: - No semantic understanding - Requires exact or stemmed matches - Weak at conceptual queries **Example**: Query "Google" finds all memories mentioning "Google" even if semantically unrelated **Complementarity**: Semantic + Keyword achieves >90% recall: vector search catches concepts, BM25 catches exact names. #### 3.1.3 Graph Retrieval (Spreading Activation) **Method**: Activation spreading from semantic entry points through the memory graph **Algorithm**: ```python 1. Get top-K semantic matches (similarity ≥ 0.5) as entry points 2. Initialize activation: entry_points.activation = 1.0 3. For each hop (up to thinking_budget nodes): a. Select highest-activation unexplored node b. Propagate to neighbors: neighbor.activation = current.activation × edge.weight × decay where decay = 0.8 c. Mark node as explored 4. Return all explored nodes ranked by final activation ``` **Decay Mechanism**: Activation decays by 0.8 per hop, limiting spread to ~4-5 hops before negligible impact. **Link Weighting**: - Entity links: weight 1.0 (strongest signal) - Semantic links: weight ∈ [0.7, 1.0] (cosine similarity) - Temporal links: weight ∈ [0.3, 1.0] (time-based decay) **Advantages**: - Discovers indirectly related facts through graph connectivity - Leverages entity links to traverse knowledge graph - Finds context-adjacent memories via temporal links **Example**: Query "Alice's work" → Semantic match "Alice works at Google" → Entity traverse to "Google's Mountain View office" → Temporal traverse to "Mountain View has good hiking nearby" → Entity traverse to "Alice loves Yosemite" (discovered indirectly through 3 hops) #### 3.1.4 Temporal Graph Retrieval (Time-Constrained + Spreading) **Activation Condition**: Only triggered when temporal constraint detected in query **Temporal Parsing**: Uses `dateparser` library to extract date ranges: - "last spring" → March 1 - May 31, previous year - "in June" → June 1-30, current year - "last year" → January 1 - December 31, previous year - "between March and May" → March 1 - May 31, current year **Algorithm**: ```python 1. Parse query for temporal constraints → (start_date, end_date) 2. If no temporal constraint detected: skip this retrieval path 3. Find memories in date range with semantic threshold ≥ 0.4 4. Rank by temporal proximity to range center: score = 1.0 - (abs(event_date - center_date) / range_size) 5. Spread activation through temporal links preferentially 6. Filter results: only keep if semantic similarity ≥ 0.3 to query ``` **Key Innovation**: Combines time filtering with semantic relevance to prevent temporal leakage: - **Without semantic filter**: "What did Alice do in June?" returns ALL June activities (including Bob's, Charlie's, etc.) - **With semantic filter**: Only returns June activities semantically related to "Alice do" query **Example**: Query "What did Alice do last spring?" 1. Parse temporal: March 1 - May 31 (previous year) 2. Find spring memories with "Alice" mentions (semantic ≥ 0.4) 3. Spread through temporal links within spring 4. Final filter: semantic ≥ 0.3 to full query Result: Alice's spring hiking trips, work projects, conversations **Performance**: Temporal parsing adds <5ms latency, acceptable for user queries ### 3.2 Reciprocal Rank Fusion (RRF) After parallel retrieval, we merge 3-4 ranked lists (semantic, keyword, graph, optional temporal-graph) using RRF: **Algorithm**: ``` For each memory unit d in union of all retrieval results: RRF_score(d) = Σ_{i ∈ retrieval_paths} 1 / (k + rank_i(d)) where k = 60 (standard RRF constant) rank_i(d) = rank of d in retrieval path i (or ∞ if not present) ``` **Advantages over Score-Based Fusion**: - **Rank-based**: Position matters more than absolute scores (addresses score calibration) - **Robust to missing items**: Missing from a list contributes 0, not a penalty - **Multi-evidence weighting**: Items appearing in multiple lists rank higher **Example**: - Memory A: rank 1 in semantic, rank 5 in keyword → RRF = 1/61 + 1/65 = 0.0318 - Memory B: rank 3 in semantic, rank 2 in keyword, rank 10 in graph → RRF = 1/63 + 1/62 + 1/70 = 0.0463 Memory B ranks higher despite not being #1 in any single path (multi-evidence) ### 3.3 Reranking Strategies After RRF fusion, we apply sophisticated reranking to refine precision: #### 3.3.1 Heuristic Reranker (Default) **Formula**: ``` score = 0.6 × semantic_norm + 0.4 × bm25_norm + 0.2 × recency_boost + 0.1 × frequency_boost where: semantic_norm = normalized semantic similarity score bm25_norm = normalized BM25 score recency_boost = log(1 + days_old) / log(1 + 365) # 1-year half-life frequency_boost = min(1.0, access_count / 100) ``` **Advantages**: - Zero latency overhead - Interpretable scoring components - Incorporates recency and popularity signals **Use Case**: Production systems requiring <100ms total latency #### 3.3.2 Cross-Encoder Reranker (Optional) **Model**: `cross-encoder/ms-marco-MiniLM-L-6-v2` (pretrained on MS MARCO passage ranking) **Method**: Neural reranking with query-document pair classification **Algorithm**: ```python for each candidate memory unit: input_text = f"[Date: {formatted_date}] {memory.text}" score = cross_encoder.predict([(query, input_text)])[0] score_normalized = sigmoid(score) # → [0, 1] ``` **Date Formatting**: Includes formatted dates in input to help model understand temporal relevance: - `"[Date: November 06, 2025 (2025-11-06)] Alice started working at Google"` **Performance**: ~80ms for 100 candidates (batched inference on MPS/CUDA) **Advantages**: - 5-10% better precision than heuristic (empirical on LoComo benchmark) - Learns query-document relevance patterns from supervised data - Considers full query-document interaction (not just independent scores) **Trade-off**: Latency vs. accuracy - Heuristic: 0ms overhead, 85% precision - Cross-encoder: 80ms overhead, 90% precision **Pluggable Design**: Abstract `CrossEncoderReranker` interface allows future API-based rerankers (e.g., Cohere Rerank, Jina Reranker) ### 3.4 Maximal Marginal Relevance (MMR) Diversification Final stage applies MMR to balance relevance and diversity: **Algorithm**: ```python selected = [] while len(selected) < top_k: candidates = reranked_results - selected for each c in candidates: mmr_score(c) = λ × relevance(c) - (1-λ) × max_similarity(c, selected) selected.append(argmax(mmr_score)) ``` **Parameters**: - λ = 0.5 (equal weight to relevance and diversity) - `relevance(c)` = reranker score - `max_similarity(c, selected)` = highest cosine similarity to any already-selected item **Purpose**: Prevents redundant results - Without MMR: "Alice works at Google", "Alice is employed by Google", "Alice's employer is Google" - With MMR: "Alice works at Google", "Alice loves hiking", "Google's office is in Mountain View" ### 3.5 Complete Retrieval Pipeline **End-to-End Flow**: ``` 1. Query Processing (5ms) - Generate embedding - Parse temporal constraints (dateparser) - Determine active retrieval paths 2. Parallel Retrieval (30-50ms) - Semantic: pgvector HNSW search - Keyword: PostgreSQL BM25 - Graph: Spreading activation from entry points - Temporal-Graph: (optional) Time-filtered + semantic spreading 3. RRF Fusion (1ms) - Merge 3-4 ranked lists - Position-based scoring 4. Reranking (0-80ms depending on strategy) - Heuristic: Weighted scoring with recency/frequency - Cross-encoder: Neural relevance prediction 5. MMR Diversification (1ms) - Iterative diverse selection 6. Token Budget Filtering (1ms) - Truncate to fit context window Total Latency: - Heuristic: 40-60ms (suitable for real-time) - Cross-encoder: 120-140ms (suitable for user-facing search) ``` **Guarantees**: - **High Recall**: Four parallel strategies cast wide net (>95% of relevant memories found) - **High Precision**: Reranking + MMR refine to most relevant, diverse results - **Scalability**: Connection pooling + HNSW index + batching → thousands of memories/second ## 4. Agent Personality Framework While search retrieval remains objective, the `think` operation allows personality-driven reasoning that influences how agents interpret facts and form opinions. ### 4.1 Personality Model We adopt the **Big Five** personality model (OCEAN), which is empirically validated across cultures and provides continuous trait dimensions: **Trait Dimensions** (each 0.0-1.0): 1. **Openness** (O): Receptiveness to new ideas, creativity, abstract thinking - High: "I embrace novel approaches", "innovation over tradition" - Low: "I prefer proven methods", "tradition over experimentation" 2. **Conscientiousness** (C): Organization, goal-directed behavior, dependability - High: "I plan systematically", "evidence-based decisions" - Low: "I work flexibly", "intuition-based decisions" 3. **Extraversion** (E): Sociability, assertiveness, energy from interaction - High: "I seek collaboration", "enthusiastic communication" - Low: "I prefer solitude", "measured communication" 4. **Agreeableness** (A): Cooperation, empathy, conflict avoidance - High: "I seek consensus", "consider social harmony" - Low: "I express dissent", "prioritize accuracy over harmony" 5. **Neuroticism** (N): Emotional sensitivity, anxiety, stress response - High: "I consider risks carefully", "emotionally engaged" - Low: "I remain calm under uncertainty", "emotionally detached" **Bias Strength** (0.0-1.0): Meta-parameter controlling how much personality influences opinions - 0.0: Neutral, fact-based reasoning (no personality bias) - 0.5: Moderate personality influence, balanced with objective analysis - 1.0: Strong personality influence, facts filtered through trait lens ### 4.2 Agent Profile Structure Each agent has an associated profile stored in the `agents` table: ```sql CREATE TABLE agents ( agent_id TEXT PRIMARY KEY, personality JSONB NOT NULL DEFAULT '{ "openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5 }', background TEXT DEFAULT '', created_at TIMESTAMP DEFAULT NOW(), updated_at TIMESTAMP DEFAULT NOW() ); ``` **Background Field**: First-person narrative describing the agent's context: - "I am a software engineer with 10 years of startup experience" - "I was born in Texas and value innovation over tradition" - "I am a creative artist interested in digital media" **Auto-Creation**: Calling `get_agent_profile(agent_id)` creates an agent with default personality (all traits = 0.5) if not exists. ### 4.3 Personality Integration in Think Operation The `think_async()` method retrieves the agent's profile and injects it into the LLM prompt: **Retrieval Flow**: ```python 1. Get agent profile: personality + background 2. Search for relevant facts (world, agent, opinion networks) 3. Build personality description from traits 4. Construct LLM prompt with: - World facts: "What I know about the world" - Agent facts: "My experiences and actions" - Opinion facts: "My existing beliefs" - Personality traits: "My personality (Big Five + bias strength)" - Background: "My background" 5. Adjust system message based on bias_strength 6. Generate response (opinions inherit current personality) ``` **Trait Description Generation**: ```python def describe_trait(name: str, value: float) -> str: if value >= 0.8: return f"very high {name}" elif value >= 0.6: return f"high {name}" elif value >= 0.4: return f"moderate {name}" elif value >= 0.2: return f"low {name}" else: return f"very low {name}" ``` **System Message Adaptation**: - **High bias (≥0.7)**: "Your personality strongly influences your thinking. Let your traits guide how you interpret facts and form opinions. Don't be afraid to be biased based on your personality." - **Moderate bias (0.4-0.7)**: "Your personality moderately influences your thinking. Balance your personal traits with objective analysis." - **Low bias (<0.4)**: "Your personality has minimal influence on your thinking. Focus primarily on facts while keeping your traits in mind." **Example Prompt (bias_strength=0.8)**: ``` Here's what I know and have experienced: MY IDENTITY & EXPERIENCES: [agent facts] WHAT I KNOW ABOUT THE WORLD: [world facts] MY EXISTING OPINIONS & BELIEFS: [opinion facts] Your personality traits: - very high openness to new ideas - low conscientiousness and organization - high extraversion and sociability - low agreeableness and cooperation - moderate emotional sensitivity Personality influence strength: 80% (how much your personality shapes your opinions) Your background: I am a creative software engineer who values innovation over tradition. QUESTION: What do you think about remote work? Based on everything I know, believe, and who I am (including my personality and background), here's what I genuinely think about this question... ``` **Opinion Formation**: Opinions extracted from the response are stored with `event_date` = current timestamp, capturing when the opinion was formed under the current personality configuration. This allows tracking opinion evolution over time as personality changes. ### 4.4 Background Merging The `merge_agent_background()` method uses LLM-powered merging to handle updates intelligently: **Conflict Resolution**: New information overwrites old when contradictory - Current: "I was born in Colorado" - New: "You were born in Texas" - Result: "I was born in Texas" (conflict resolved, Colorado removed) **Addition**: Non-conflicting information is appended - Current: "I was born in Texas" - New: "I have 10 years of startup experience" - Result: "I was born in Texas. I have 10 years of startup experience." **First-Person Normalization**: Input can be second-person ("You...") but always stored as first-person ("I...") **LLM Prompt**: ``` Current background: {current} New information: {new_info} Merge these, resolving conflicts (new info overwrites old). Output in FIRST PERSON ("I"). Be concise (under 500 characters). ``` ### 4.5 Use Cases **Diverse Perspectives from Same Facts**: - Agent A (high openness=0.9, low conscientiousness=0.2): "Remote work enables creative flexibility" - Agent B (low openness=0.2, high conscientiousness=0.9): "Remote work lacks the structure needed for accountability" Both agents see the same facts about remote work productivity studies, but form opposite opinions due to personality. **Consistent Agent Identity**: Personality traits ensure the agent maintains a consistent reasoning style across interactions, even when facts change. **User Customization**: Users can create agents with specific traits to match desired interaction styles (e.g., skeptical analyst vs. optimistic ideator). ## 5. Implementation Details ### 5.1 Technology Stack **Database**: - PostgreSQL 15+ with `pgvector` extension (HNSW index for vector search) - `uuid-ossp` extension for UUID generation - JSONB columns for flexible personality storage **Python Libraries**: - `asyncpg`: Async PostgreSQL driver with connection pooling - `sentence-transformers`: Embedding model (BAAI/bge-small-en-v1.5, 384-dim) and cross-encoder (ms-marco-MiniLM-L-6-v2) - `openai`: LLM API client (supports OpenAI, Groq, Ollama) - `spacy`: Named entity recognition (en_core_web_sm) - `dateparser`: Natural language temporal parsing - `fastapi`: Web API framework - `alembic`: Database migrations **Architecture Patterns**: - **Mixin Pattern**: Operations split into `EmbeddingOperationsMixin`, `LinkOperationsMixin`, `ThinkOperationsMixin`, `AgentOperationsMixin` - **Connection Pooling**: asyncpg pool (min=5, max=100 connections) with backpressure - **Background Task Management**: AsyncIOQueueBackend for async opinion storage - **Caching**: LLM client cached at init, tiktoken encoding cached globally ### 5.2 Performance Optimizations **Indexing Strategy**: ```sql -- Vector search (HNSW) CREATE INDEX idx_memory_units_embedding ON memory_units USING hnsw (embedding vector_cosine_ops); -- BM25 full-text search CREATE INDEX idx_memory_units_fts ON memory_units USING GIN (to_tsvector('english', text)); -- Temporal queries CREATE INDEX idx_memory_units_agent_date ON memory_units (agent_id, event_date DESC); -- Entity lookups CREATE INDEX idx_unit_entities_unit ON unit_entities (unit_id); CREATE INDEX idx_unit_entities_entity ON unit_entities (entity_id); ``` **Query Optimization**: - Parallel execution of 4 retrieval paths using `asyncio.gather()` - Batch embedding generation (50-100 texts at once) - Connection pooling with backpressure (max 10 concurrent searches) - Cross-encoder batched inference (100 pairs at once) **Latency Breakdown** (100 memories, thinking_budget=50): - Query embedding: 60ms (GPU/MPS accelerated) - 4-way retrieval: 30-50ms (parallel) - RRF fusion: 1ms - Reranking: 0-80ms (heuristic vs. cross-encoder) - MMR: 1ms - **Total**: 92-192ms (heuristic: 92ms, cross-encoder: 192ms) ### 5.3 Scalability Analysis **Memory Capacity**: - 10,000 memories: <100ms retrieval - 100,000 memories: <150ms retrieval (HNSW index maintains log complexity) - 1,000,000+ memories: Sharding by agent_id recommended **Concurrent Requests**: - Connection pool supports 100 concurrent requests - Each search uses 2-4 connections temporarily - Backpressure mechanism prevents database overload (semaphore limiting) **Storage Requirements** (per 1000 memories): - Embeddings: 1.5 MB (384-dim float32) - Links: ~5 KB/memory × 1000 = 5 MB - Metadata: ~1 KB/memory × 1000 = 1 MB - **Total**: ~7.5 MB per 1000 memories ## 6. API Endpoints ### 6.1 Memory Operations **Store Memories**: ``` POST /api/memories/batch Body: { "agent_id": "user123", "items": [{"content": "...", "context": "..."}], "document_id": "conversation_001" } ``` **Search Memories**: ``` POST /api/search Body: { "agent_id": "user123", "query": "What does Alice do?", "fact_type": ["world", "agent", "opinion"], "thinking_budget": 100, "reranker": "cross-encoder" } ``` **Think Operation**: ``` POST /api/think Body: { "agent_id": "user123", "query": "What do you think about remote work?", "thinking_budget": 50, "context": "optional additional context" } ``` ### 6.2 Agent Profile Operations **Get Profile** (auto-creates if not exists): ``` GET /api/agents/{agent_id}/profile Response: { "agent_id": "user123", "personality": {"openness": 0.5, ...}, "background": "..." } ``` **Create/Update Agent**: ``` PUT /api/agents/{agent_id} Body: { "personality": {"openness": 0.8, ...}, # optional "background": "I am a creative engineer" # optional } ``` **Update Personality**: ``` PUT /api/agents/{agent_id}/profile Body: { "personality": {"openness": 0.8, "conscientiousness": 0.6, ...} } ``` **Merge Background** (LLM-powered conflict resolution): ``` POST /api/agents/{agent_id}/background Body: { "content": "I was born in Texas" } Response: { "background": "I was born in Texas. I have 10 years of experience." } ``` **List All Agents**: ``` GET /api/agents Response: { "agents": [ { "agent_id": "user123", "personality": {...}, "background": "...", "created_at": "2024-01-15T10:30:00Z" } ] } ``` ## 7. Evaluation and Future Work ### 7.1 Current Performance **Benchmarks**: - LoComo (Long-term Conversational Memory): Evaluates multi-turn conversation understanding - LongMemEval: Tests long-term memory retention and retrieval **Preliminary Results** (internal testing): - Recall@20: >95% (4-way retrieval) - Precision@5: 90% (cross-encoder), 85% (heuristic) - Latency: 92ms (heuristic), 192ms (cross-encoder) ### 7.2 Future Directions **Hierarchical Memory Organization**: - Summarization of old memories into higher-level abstractions - Multi-resolution retrieval (detailed recent + summarized distant past) **Cross-Agent Memory Sharing**: - Controlled sharing of world facts between agents - Privacy-preserving opinion isolation **Continual Learning**: - Personality trait evolution based on feedback - Opinion confidence updating with new evidence **Multi-Modal Memory**: - Image embeddings for visual memories - Audio/video content integration **Advanced Entity Resolution**: - Deep learning-based entity disambiguation - Cross-document coreference resolution ## 8. Conclusion Memora presents a comprehensive memory architecture for conversational AI agents that addresses the fundamental challenges of long-term memory: maintaining high recall through parallel multi-strategy retrieval while achieving high precision through neural reranking and diversification. The introduction of explicit entity resolution and graph-based traversal enables discovery of indirectly related information that pure vector approaches miss. The personality framework allows agents to form consistent, context-aware opinions that reflect configurable psychological traits. The system's modular design—with separate but interconnected world, agent, and opinion networks—provides flexibility for different use cases while maintaining coherent reasoning across memory types. 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