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Overview
Why Hindsight?
AI assistants forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the memory bank has learned. This isn't just inconvenient; it fundamentally limits what AI memory banks can do.
The problem is harder than it looks:
- Simple vector search isn't enough — "What did Alice do last spring?" requires temporal reasoning, not just semantic similarity
- Facts get disconnected — Knowing "Alice works at Google" and "Google is in Mountain View" should let you answer "Where does Alice work?" even if you never stored that directly
- Memory banks need opinions — A coding assistant that remembers "the user prefers functional programming" should weigh that when making recommendations
- Context matters — The same information means different things to different memory banks with different personalities
Hindsight solves these problems with a memory system designed specifically for AI memory banks.
What Hindsight Does
graph TB
subgraph Your Application
Agent[AI Agent]
end
subgraph Hindsight
API[Hindsight API]
subgraph Memory Bank
Documents[Documents]
Memories[Memories]
Entities[Entities]
end
end
Agent -->|retain| API
Agent -->|recall| API
Agent -->|reflect| API
API --> Documents
API --> Memories
API --> Entities
Your AI agent stores information via retain(), searches with recall(), and reasons with reflect() — all interactions with its dedicated memory bank
Key Components
Three Memory Types
Hindsight separates memories by type for epistemic clarity:
| Type | What it stores | Example |
|---|---|---|
| World | Objective facts received | "Alice works at Google" |
| Bank | Bank's own actions | "I recommended Python to Bob" |
| Opinion | Formed beliefs + confidence | "Python is best for ML" (0.85) |
Multi-Strategy Retrieval (TEMPR)
Four search strategies run in parallel:
graph LR
Q[Query] --> S[Semantic]
Q --> K[Keyword]
Q --> G[Graph]
Q --> T[Temporal]
S --> RRF[RRF Fusion]
K --> RRF
G --> RRF
T --> RRF
RRF --> CE[Cross-Encoder]
CE --> R[Results]
| Strategy | Best for |
|---|---|
| Semantic | Conceptual similarity, paraphrasing |
| Keyword (BM25) | Names, technical terms, exact matches |
| Graph | Related entities, indirect connections |
| Temporal | "last spring", "in June", time ranges |
Personality Framework (CARA)
Memory banks have Big Five personality traits that influence opinion formation:
| Trait | Low | High |
|---|---|---|
| Openness | Prefers proven methods | Embraces new ideas |
| Conscientiousness | Flexible, spontaneous | Systematic, organized |
| Extraversion | Independent | Collaborative |
| Agreeableness | Direct, analytical | Diplomatic, harmonious |
| Neuroticism | Calm, optimistic | Risk-aware, cautious |
The bias_strength parameter (0-1) controls how much personality influences opinions.
Next Steps
Getting Started
- Installation — Install Hindsight for Python, Node.js, or CLI
- Quick Start — Get up and running in 60 seconds
Core Concepts
- Retain — How memories are stored with multi-dimensional facts
- Recall — How TEMPR's 4-way search retrieves memories
- Reflect — How personality influences reasoning and opinion formation
API Methods
- Retain — Store information in memory banks
- Recall — Search and retrieve memories
- Reflect — Reason with personality
- Memory Banks — Configure personality and background
- Entities — Track people, places, and concepts
- Documents — Manage document sources
- Operations — Monitor async tasks
Deployment
- Server Setup — Deploy with Docker Compose, Helm, or pip