improve llms.txt
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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.
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For complete documentation, see: https://vectorize-io.github.io/hindsight/llms-full.txt
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## Links
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- [Full Documentation (llms-full.txt)](https://vectorize-io.github.io/hindsight/llms-full.txt): Complete documentation for LLM consumption
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- [Quick Start](https://vectorize-io.github.io/hindsight/developer/api/quickstart): Get started in 60 seconds
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- [Python SDK](https://vectorize-io.github.io/hindsight/sdks/python): Python client library
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- [TypeScript SDK](https://vectorize-io.github.io/hindsight/sdks/nodejs): Node.js/TypeScript client
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- [API Reference](https://vectorize-io.github.io/hindsight/api-reference): REST API documentation
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- [OpenAPI Spec](https://vectorize-io.github.io/hindsight/openapi.json): Machine-readable API specification
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- [MCP Server](https://vectorize-io.github.io/hindsight/sdks/mcp): Model Context Protocol integration
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- [GitHub](https://github.com/vectorize-io/hindsight): Source code and issues
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## Core Operations
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@ -14,6 +23,10 @@ For complete documentation, see: https://vectorize-io.github.io/hindsight/llms-f
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## Quick Start
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```bash
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pip install hindsight-client
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```
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```python
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from hindsight import HindsightClient
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@ -50,39 +63,55 @@ client.retain(
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)
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```
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## Documentation
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## API Endpoints
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- Docs: https://vectorize-io.github.io/hindsight
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- GitHub: https://github.com/vectorize-io/hindsight
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- Python client: pip install hindsight-client
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- TypeScript client: npm install @vectorize-io/hindsight-client
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Base URL: `http://localhost:8888`
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## API Reference
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Base URL: http://localhost:8888 (default)
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### POST /v1/default/banks/{bank_id}/retain
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Store memories in a bank.
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### POST /v1/default/banks/{bank_id}/recall
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Retrieve memories matching a query.
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### POST /v1/default/banks/{bank_id}/reflect
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Analyze memories and form opinions/insights.
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### GET /v1/default/banks/{bank_id}/profile
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Get bank profile (disposition, background).
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### PUT /v1/default/banks/{bank_id}/profile
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Update bank disposition and background.
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| POST | `/v1/default/banks/{bank_id}/retain` | Store memories |
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| POST | `/v1/default/banks/{bank_id}/recall` | Retrieve memories |
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| POST | `/v1/default/banks/{bank_id}/reflect` | Analyze and form opinions |
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| GET | `/v1/default/banks/{bank_id}/profile` | Get bank profile |
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| PUT | `/v1/default/banks/{bank_id}/profile` | Update bank profile |
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| GET | `/v1/default/banks` | List all banks |
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| POST | `/v1/default/banks` | Create a bank |
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## Architecture Patterns
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### Per-User Memory
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One bank per user. Simplest pattern for chatbots and assistants.
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[Guide](https://vectorize-io.github.io/hindsight/cookbook/per-user-memory)
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### Support Agent + Shared Knowledge
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User bank + shared docs bank. Client orchestrates queries to both banks and merges results.
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[Guide](https://vectorize-io.github.io/hindsight/cookbook/support-agent-with-shared-knowledge)
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### With Curated Learnings
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User bank + shared docs + learnings bank. Promote verified solutions to shared learnings.
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## Installation
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### Docker (recommended)
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```bash
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docker run -p 8888:8888 -e HINDSIGHT_API_LLM_PROVIDER=openai -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY ghcr.io/vectorize-io/hindsight
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```
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### Python (embedded)
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```bash
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pip install hindsight-all
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```
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### Clients
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```bash
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pip install hindsight-client # Python
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npm install @vectorize-io/hindsight-client # TypeScript
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```
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## MCP Integration
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Hindsight provides a Model Context Protocol server for direct AI agent integration:
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```bash
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pip install hindsight-mcp-server
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hindsight-mcp-server --api-url http://localhost:8888
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```
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Tools exposed: `retain`, `recall`, `reflect`, `list_banks`, `create_bank`
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