fleet-memory/hindsight-docs/static/llms.txt
Nicolò Boschi 0000c54509 add llms.txt
2025-12-10 13:52:38 +01:00

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# Hindsight
> Agent Memory that Works Like Human Memory
Hindsight is an agent memory system that gives AI agents persistent, structured memory across sessions. It extracts facts, entities, and relationships from conversations and enables temporal reasoning, opinion formation, and multi-strategy retrieval.
For complete documentation, see: https://vectorize-io.github.io/hindsight/llms-full.txt
## Core Operations
- **Retain**: Store memories (extracts facts, entities, relationships automatically)
- **Recall**: Retrieve memories (semantic, keyword, graph, temporal search)
- **Reflect**: Deep analysis to form opinions and insights
## Quick Start
```python
from hindsight import HindsightClient
client = HindsightClient(base_url="http://localhost:8888")
# Store
client.retain(bank_id="my-agent", content="Alice works at Google as a software engineer")
# Query
results = client.recall(bank_id="my-agent", query="What does Alice do?")
# Reflect
response = client.reflect(bank_id="my-agent", query="Tell me about Alice")
```
## Key Concepts
### Memory Banks
Each bank is an isolated memory store. One bank per user/agent. Banks contain facts, entities, documents, and their relationships.
### Memory Types
- World facts: General knowledge
- Experience facts: Personal experiences
- Opinion facts: Beliefs with confidence scores
### Document ID for Evolving Conversations
Use `document_id` to group messages in a conversation. Retaining with the same `document_id` replaces the previous version (upsert), keeping memory consistent as conversations evolve.
```python
client.retain(
bank_id="user-123",
content=messages,
document_id="session_abc" # Same ID = replace old version
)
```
## Documentation
- Docs: https://vectorize-io.github.io/hindsight
- GitHub: https://github.com/vectorize-io/hindsight
- Python client: pip install hindsight-client
- TypeScript client: npm install @vectorize-io/hindsight-client
## API Reference
Base URL: http://localhost:8888 (default)
### POST /v1/default/banks/{bank_id}/retain
Store memories in a bank.
### POST /v1/default/banks/{bank_id}/recall
Retrieve memories matching a query.
### POST /v1/default/banks/{bank_id}/reflect
Analyze memories and form opinions/insights.
### GET /v1/default/banks/{bank_id}/profile
Get bank profile (disposition, background).
### PUT /v1/default/banks/{bank_id}/profile
Update bank disposition and background.
## Architecture Patterns
### Per-User Memory
One bank per user. Simplest pattern for chatbots and assistants.
### Support Agent + Shared Knowledge
User bank + shared docs bank. Client orchestrates queries to both banks and merges results.
### With Curated Learnings
User bank + shared docs + learnings bank. Promote verified solutions to shared learnings.