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 need to consolidate knowledge — A coding assistant that remembers "the user prefers functional programming" should consolidate this into an observation and weigh it 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
Your AI agent stores information via retain(), searches with recall(), and reasons with reflect() — all interactions with its dedicated memory bank
Key Components
Memory Types
Hindsight organizes knowledge into a hierarchy of facts and consolidated knowledge:
| Type | What it stores | Example |
|---|---|---|
| Mental Model | User-curated summaries for common queries | "Team communication best practices" |
| Observation | Automatically consolidated knowledge from facts | "User was a React enthusiast but has now switched to Vue" (captures history) |
| World Fact | Objective facts received | "Alice works at Google" |
| Experience Fact | Bank's own actions and interactions | "I recommended Python to Bob" |
During reflect, the agent checks sources in priority order: Mental Models → Observations → Raw Facts.
Multi-Strategy Retrieval (TEMPR)
Four search strategies run in parallel:
| 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 |
Observation Consolidation
After memories are retained, Hindsight automatically consolidates related facts into observations — deduplicated, evidence-grounded beliefs that the bank has built up across many memories:
- Deduplication: Overlapping facts are merged into a single durable observation instead of piling up as repeats
- Evidence tracking: Each observation references the source memories (with exact quotes) that support it, plus a proof count
- Continuous refinement: Observations are updated — not overwritten — when new evidence supports, contradicts, or extends them; history is preserved
- Freshness awareness: when newer memories have been retained but not yet consolidated,
reflecttreats the affected observations as stale and verifies them against raw facts before relying on them
Mission, Directives & Disposition
Memory banks can be configured to shape how the agent reasons during reflect:
| Configuration | Purpose | Example |
|---|---|---|
| Mission | Natural language identity for the bank | "I am a research assistant specializing in ML. I prefer simplicity over cutting-edge." |
| Directives | Hard rules the agent must follow | "Never recommend specific stocks", "Always cite sources" |
| Disposition | Soft traits that influence reasoning style | Skepticism, literalism, empathy (1-5 scale) |
The mission tells Hindsight what knowledge to prioritize and provides context for reasoning. Directives are guardrails and compliance rules that must never be violated. Disposition traits subtly influence interpretation style.
These settings only affect the reflect operation, not recall.
Clients & Languages
Integrations
Browse all supported integrations in the Integrations Hub.
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 mission, directives, and disposition shape reasoning
API Methods
- Retain — Store information in memory banks
- Recall — Search and retrieve memories
- Reflect — Agentic reasoning with memory
- Mental Models — User-curated summaries for common queries
- Memory Banks — Configure mission, directives, and disposition
- Documents — Manage document sources
- Operations — Monitor async tasks
Deployment
- Server Setup — Deploy with Docker Compose, Helm, or pip