
Hindsight Documentation
FreeComprehensive documentation for Hindsight AI memory systems.
Free · Opens the source repo
What Hindsight Documentation does
The Hindsight Documentation skill provides complete technical documentation for the Hindsight memory system designed for AI agents. This skill is essential for developers and designers who want to understand the architecture, APIs, and configuration of Hindsight. It covers key concepts such as memory banks, retain/recall/reflect operations, and retrieval strategies, enabling users to effectively implement and utilize the system in their projects.
Users can leverage this skill to set up the Hindsight API server using Docker, Kubernetes, or pip, and learn how to integrate with various SDKs including Python, Node.js, and Rust. The documentation is structured for easy navigation, with sections dedicated to best practices, common FAQs, and detailed API references. This organization allows users to quickly find the information they need, whether they are debugging issues, optimizing performance, or reviewing API endpoints.
The skill also includes practical examples and code snippets to facilitate understanding and implementation. By following the best practices guide, users can avoid common pitfalls and ensure they are integrating the Hindsight memory system effectively. This documentation is particularly useful for teams working on AI projects that require sophisticated memory management and retrieval capabilities.
When to use it
Use this skill when developing AI applications that require memory management and retrieval capabilities using the Hindsight system.
When not to use it
This skill may not be suitable for projects that do not involve the Hindsight memory system or for users who are not working with AI agents.
What you can build with it
Setting Up Hindsight API
A developer needs to configure the Hindsight API server for their AI project using Docker.
Integrating with SDKs
A team is integrating Hindsight with their existing Python application and needs guidance on SDK usage.
Optimizing Memory Retrieval
An engineer is looking to improve the performance of memory retrieval strategies in their AI agent.
How to install Hindsight Documentation
View source1. Install with the skills CLI
npx skills add vectorize-io/hindsight/hindsight-docs --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by vectorize-ioHindsight 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
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/ # Framework and tool integrations
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
# 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/sdks/
pattern: "def |async def " # Find Python examples
3. Read Full Documentation (use Read tool)
references/developer/api/retain.md
references/sdks/python.md
references/sdks/integrations/litellm.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/ and hindsight-docs/docs-integrations/. Run ./scripts/generate-docs-skill.sh to update.
Frequently asked questions about Hindsight Documentation
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