fleet-memory/hindsight-docs/versioned_docs/version-0.3/developer/index.md

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Overview

Why Hindsight?

AI agents forget everything between sessions. Every conversation starts from zero—no context about who you are, what you've discussed, or what the assistant has learned. This isn't just an implementation detail; it fundamentally limits what AI Agents 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
• AI Agents needs to form 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 agents.

What Hindsight Does

graph TB
    subgraph app["<b>Your Application</b>"]
        Agent[AI Agent]
    end

    subgraph hindsight["<b>Hindsight</b>"]
        API[API Server]

        subgraph bank["<b>Memory Bank</b>"]
            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

Disposition Traits

Memory banks have disposition traits that influence how opinions are formed during Reflect:

Trait	Scale	Low (1)	High (5)
Skepticism	1-5	Trusting	Skeptical
Literalism	1-5	Flexible interpretation	Literal interpretation
Empathy	1-5	Detached	Empathetic

These traits only affect the reflect operation, not recall.

Next Steps

Getting Started

• Quick Start — Install and get up and running in 60 seconds
• RAG vs Hindsight — See how Hindsight differs from traditional RAG with real examples

Core Concepts

• Retain — How memories are stored with multi-dimensional facts
• Recall — How TEMPR's 4-way search retrieves memories
• Reflect — How disposition influences reasoning and opinion formation

API Methods

• Retain — Store information in memory banks
• Recall — Search and retrieve memories
• Reflect — Reason with disposition
• Memory Banks — Configure disposition and background
• Documents — Manage document sources
• Operations — Monitor async tasks

Deployment

• Server Setup — Deploy with Docker Compose, Helm, or pip