hindsight-docs
vectorize-io/hindsight
Complete technical documentation for Hindsight biomimetic memory system for AI agents.
What is hindsight-docs?
This skill provides comprehensive reference documentation for Hindsight, a memory architecture for AI agents. Use it to understand core concepts (retain/recall/reflect), configure memory banks, deploy the API server, integrate SDKs, and review best practices and cookbook examples.
- Access complete API documentation for retain, recall, reflect, and memory bank operations
- Learn Hindsight architecture, core concepts, and retrieval strategies (semantic, BM25, graph, temporal)
- Review configuration options and environment variables for API server setup
- Find SDK integration guides for Python, Node.js, Rust, and CLI
- Explore deployment instructions for Docker, Kubernetes, and pip installations
- Browse cookbook recipes and full application examples
How to install hindsight-docs
npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docsHow to use hindsight-docs
- 1.Start by reading references/best-practices.md for practical integration rules and anti-patterns
- 2.Use the Glob tool to find documentation by pattern (e.g., references/developer/api/*.md for core operations)
- 3.Use the Grep tool to search for specific concepts (e.g., 'disposition', 'graph retrieval', 'HINDSIGHT_API_')
- 4.Read full documentation files using the Read tool (e.g., references/sdks/python.md for SDK integration)
- 5.Consult references/openapi.json for complete endpoint schemas and request/response models
- 6.Browse references/cookbook/recipes/ and references/cookbook/applications/ for usage patterns and examples
Use cases
- Integrating Hindsight memory into a Python or Node.js agent application
- Configuring memory banks and dispositions for multi-user or multi-agent scenarios
- Deploying Hindsight API server in Kubernetes or Docker environments
- Understanding how to use document_id for conversation evolution and upserts
- Debugging memory retrieval issues or optimizing recall performance
- AI agent developers integrating biomimetic memory systems
- Backend engineers deploying Hindsight infrastructure
- AI/ML engineers optimizing memory retrieval and reasoning
- Teams building multi-user or multi-agent applications with persistent memory
hindsight-docs FAQ
Read references/best-practices.md first. It covers practical rules for missions, tags, content format, and anti-patterns—the fastest way to integrate correctly.
Use the Glob tool to find files by pattern (e.g., references/**/*python*.md), the Grep tool to search content (e.g., pattern: 'disposition'), or the Read tool to access full files directly.
The four core operations are: Retain (store memories with auto-extracted facts/entities/relationships), Recall (retrieve memories using 4 parallel strategies), Reflect (disposition-aware reasoning), and Memory Banks (isolated stores per user/agent).
Use document_id to group messages in a conversation. Sending the same document_id performs an upsert, allowing conversations to evolve. Multi-bank queries require client-side orchestration.
Hindsight API can be deployed via Docker, Kubernetes (with Helm), or pip installation. Configuration uses HINDSIGHT_API_* environment variables, and database migrations run automatically on startup.
Full instructions (SKILL.md)
Source of truth, from vectorize-io/hindsight.
name: hindsight-docs description: Complete Hindsight documentation for AI agents. Use this to learn about Hindsight architecture, APIs, configuration, and best practices.
Hindsight Documentation Skill
Complete technical documentation for Hindsight - a biomimetic memory system for AI agents.
When to Use This Skill
Use this skill when you need to:
- Understand Hindsight architecture and core concepts
- Learn about retain/recall/reflect operations
- Configure memory banks and dispositions
- Set up the Hindsight API server (Docker, Kubernetes, pip)
- Integrate with Python/Node.js/Rust SDKs
- Understand retrieval strategies (semantic, BM25, graph, temporal)
- Debug issues or optimize performance
- Review API endpoints and parameters
- Find cookbook examples and recipes
Documentation Structure
All documentation is in references/ organized by category:
references/
├── best-practices.md # START HERE — missions, tags, formats, anti-patterns
├── faq.md # Common questions and decisions
├── changelog/ # Release history and version changes (index.md + integrations/)
├── openapi.json # Full OpenAPI spec — endpoint schemas, request/response models
├── developer/
│ ├── api/ # Core operations: retain, recall, reflect, memory banks
│ └── *.md # Architecture, configuration, deployment, performance
├── sdks/
│ ├── *.md # Python, Node.js, CLI, embedded
│ └── integrations/ # LiteLLM, AI SDK, OpenClaw, MCP, skills
└── cookbook/
├── recipes/ # Usage patterns and examples
└── applications/ # Full application demos
How to Find Documentation
1. Find Files by Pattern (use Glob tool)
# Core API operations
references/developer/api/*.md
# SDK documentation
references/sdks/*.md
references/sdks/integrations/*.md
# Cookbook examples
references/cookbook/recipes/*.md
references/cookbook/applications/*.md
# Find specific topics
references/**/configuration.md
references/**/*python*.md
references/**/*deployment*.md
2. Search Content (use Grep tool)
# Search for concepts
pattern: "disposition" # Memory bank configuration
pattern: "graph retrieval" # Graph-based search
pattern: "helm install" # Kubernetes deployment
pattern: "document_id" # Document management
pattern: "HINDSIGHT_API_" # Environment variables
# Search in specific areas
path: references/developer/api/
pattern: "POST /v1" # Find API endpoints
path: references/cookbook/
pattern: "def |async def " # Find Python examples
3. Read Full Documentation (use Read tool)
references/developer/api/retain.md
references/sdks/python.md
references/cookbook/recipes/per-user-memory.md
Start Here: Best Practices
Before reading API docs, read the best practices guide. It covers practical rules for missions, tags, content format, observation scopes, and anti-patterns — the fastest way to integrate correctly.
references/best-practices.md
Key Concepts
- Memory Banks: Isolated memory stores (one per user/agent)
- Retain: Store memories (auto-extracts facts/entities/relationships)
- Recall: Retrieve memories (4 parallel strategies: semantic, BM25, graph, temporal)
- Reflect: Disposition-aware reasoning using memories
- document_id: Groups messages in a conversation (upsert on same ID)
- Dispositions: Skepticism, literalism, empathy traits (1-5) affecting reflect
- Mental Models: Consolidated knowledge synthesized from facts
Notes
- Code examples are inlined from working examples
- Configuration uses
HINDSIGHT_API_*environment variables - Database migrations run automatically on startup
- Multi-bank queries require client-side orchestration
- Use
document_idfor conversation evolution (same ID = upsert)
Auto-generated from hindsight-docs/docs/. Run ./scripts/generate-docs-skill.sh to update.
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