Retain: How Hindsight Stores Memories
When you call retain(), Hindsight transforms conversations and documents into structured, searchable memories that preserve meaning and context.
What Retain Does
Rich Fact Extraction
Hindsight doesn't just store what was said — it captures why, how, and what it means.
What Gets Captured
When you retain "Alice joined Google last spring and was thrilled about the research opportunities", Hindsight extracts:
The core facts:
- Alice joined Google
- This happened last spring
The emotions and meaning:
- She was thrilled
- It represented an important opportunity
The reasoning:
- She chose it for the research opportunities
This rich extraction means you can later ask "Why did Alice join Google?" and get a meaningful answer, not just "she joined Google."
Preserving Context
Traditional systems fragment information:
- "Bob suggested Summer Vibes"
- "Alice wanted something unique"
- "They chose Beach Beats"
Hindsight preserves the full narrative:
- "Alice and Bob discussed naming their summer party playlist. Bob suggested 'Summer Vibes' because it's catchy, but Alice wanted something unique. They ultimately decided on 'Beach Beats' for its playful tone."
This means search results include the full context, not disconnected fragments.
Two Types of Facts
Hindsight distinguishes between world facts (about others) and experience (conversations and events):
| Type | Description | Example |
|---|---|---|
| world | Facts about people, places, things | "Alice works at Google" |
| experience | Conversations and events | "I recommended Python to Alice" |
Note: Observations are consolidated automatically in the background after retain() operations complete. This consolidation process synthesizes patterns from new facts into the bank's knowledge base.
Entity Recognition
Hindsight automatically identifies and tracks entities — the people, organizations, and concepts that matter.
What Gets Recognized
- People: "Alice", "Dr. Smith", "Bob Chen"
- Organizations: "Google", "MIT", "OpenAI"
- Places: "Paris", "Central Park", "California"
- Products & Concepts: "Python", "TensorFlow", "machine learning"
Entity Resolution
The same entity mentioned different ways gets unified:
- "Alice" + "Alice Chen" + "Alice C." → one person
- "Bob" + "Robert Chen" → one person (nickname resolution)
Why it matters: You can ask "What do I know about Alice?" and get everything, even if she was mentioned as "Alice Chen" in some conversations.
Context-Aware Disambiguation
If "Alice" appears with "Google" and "Stanford" multiple times, a new "Alice" mentioning those is likely the same person. Hindsight uses co-occurrence patterns to disambiguate common names.
Entity Labels
You can define a controlled vocabulary of key:value classification labels (e.g. pedagogy:scaffolding, engagement:active) that are extracted at retain time and stored as entities. Because labels become entities, they automatically link related memories in the knowledge graph and improve both semantic and keyword retrieval. Labels can optionally also write to the memory unit's tags, enabling standard tag-based filtering during recall and reflect.
See entity_labels in the bank config for full configuration details.
Building Connections
Memories aren't isolated — Hindsight creates a knowledge graph with four types of connections:
Entity Connections
All facts mentioning the same entity are linked together.
Enables: "Tell me everything about Alice" → retrieves all Alice-related facts
Time-Based Connections
Facts close in time are connected, with stronger links for closer dates.
Enables: "What else happened around then?" → finds contextually related events
Meaning-Based Connections
Semantically similar facts are linked, even if they use different words.
Enables: "Tell me about similar topics" → finds thematically related information
Causal Connections
Cause-effect relationships are explicitly tracked.
Enables: "Why did this happen?" → trace reasoning chains Example: "Alice felt burned out" ← caused by ← "She worked 80-hour weeks"
Understanding Time
Hindsight tracks two temporal dimensions:
When It Happened
For events (meetings, trips, milestones), Hindsight records when they occurred.
- "Alice got married in June 2024" → occurred in June 2024
For general facts (preferences, characteristics), there's no specific occurrence time.
- "Alice prefers Python" → ongoing preference
When You Learned It
Hindsight also tracks when you told it each fact.
Why both?
Imagine in January 2025, someone tells you "Alice got married in June 2024":
- Historical queries work: "What did Alice do in 2024?" → finds the marriage
- Recency ranking works: Recent mentions get priority in search
- Temporal reasoning works: "What happened before her marriage?" → finds earlier events
Without this distinction, old information would either be unsearchable by date or treated as irrelevant.
Tagging Memories
Tags enable visibility scoping—useful when one memory bank serves multiple users but each should only see relevant memories.
- Item tags: Tag individual memories with specific scopes
- Document tags: Apply tags to all items in a batch
- Tag filtering: Filter during recall/reflect by tags
See Retain API for code examples and Recall API for filtering options.
What You Get
After retain() completes:
- Structured facts that preserve meaning, emotions, and reasoning
- Unified entities that resolve different name variations
- Knowledge graph with entity, temporal, semantic, and causal links
- Temporal grounding for both historical and recency-based queries
- Optional tags for filtering during recall
All stored in your isolated memory bank, ready for recall() and reflect().
Steering Extraction with a Mission
By default, retain() extracts all significant facts from the content. You can narrow this focus with a retain mission (retain_mission) — a plain-language description of what this bank should pay attention to.
e.g. Always include technical decisions, API design choices, and architectural trade-offs.
Ignore meeting logistics, greetings, and social exchanges.
The mission is injected into the extraction prompt alongside the built-in rules — it steers the LLM without replacing the extraction logic. It works with any extraction mode (concise, verbose, custom).
For finer control, you can also change the extraction mode:
| Mode | When to use |
|---|---|
concise (default) | General-purpose — selective, fast |
verbose | When you need richer facts with full context and relationships |
custom | When you want to write your own extraction rules entirely |
Set retain_mission and retain_extraction_mode via the bank config API or the HINDSIGHT_API_RETAIN_MISSION environment variable.
Observation Consolidation
After retain() completes, Hindsight automatically triggers observation consolidation in the background. This process:
- Analyzes new facts against existing observations
- Creates new observations when patterns emerge
- Refines existing observations with new evidence
- Tracks which facts support each observation
This happens asynchronously — your retain() call returns immediately while consolidation runs in the background.
See Observations for details on how consolidation works.
Next Steps
- Observations — How knowledge is consolidated after retain
- Recall — How multi-strategy search retrieves relevant memories
- Reflect — How the agentic loop uses observations
- Retain API — Code examples and parameters