Installation

Hindsight can be deployed in several ways depending on your infrastructure and requirements.

Don't want to manage infrastructure?

Hindsight Cloud is a fully managed service that handles all infrastructure, scaling, and maintenance — sign up here.

Supported Platforms

Hindsight runs on Linux, macOS, and Windows:

Platform	Docker	Bare Metal (pip)	Embedded DB (pg0)	Notes
Linux (x86_64, ARM64)	✅	✅	✅	Fully supported, recommended for production
macOS (Apple Silicon, Intel)	✅	✅	✅	Fully supported
Windows (x86_64)	✅	✅	✅	Fully supported — see Windows setup for external PostgreSQL option

All platforms support the embedded database (pg0) for development. On Windows, you can also use an external PostgreSQL installation — see the Windows section for a step-by-step guide.

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Prerequisites

PostgreSQL

Hindsight requires PostgreSQL 14+ with a vector extension for similarity search. The supported extensions are:

• pgvector (default)
• pgvectorscale
• vchord

Configure which one to use with HINDSIGHT_API_VECTOR_EXTENSION. See Configuration for details.

By default, Hindsight uses pg0 — an embedded PostgreSQL that runs locally on your machine. This is convenient for development but not recommended for production.

For production, use an external PostgreSQL with one of the supported vector extensions:

• Supabase — Managed PostgreSQL with pgvector built-in
• Neon — Serverless PostgreSQL with pgvector
• Azure Database for PostgreSQL — With pgvector and pgvectorscale support
• AWS RDS / Cloud SQL — With pgvector extension enabled
• Self-hosted — PostgreSQL 14+ with your preferred vector extension

LLM Provider

You need an LLM API key for fact extraction, entity resolution, and answer generation. See Models for supported providers, model recommendations, and configuration.

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Docker

Best for: Quick start, development, small deployments

Run everything in one container with embedded PostgreSQL:

export OPENAI_API_KEY=sk-xxx

docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
  ghcr.io/vectorize-io/hindsight:latest

• API Server: http://localhost:8888
• Control Plane (Web UI): http://localhost:9999

Docker Image Variants

Variant	Size (AMD64)	Size (ARM64)	When to use
Full (latest)	~9 GB	~3.7 GB	Default. Works out of the box with no external services except the LLM.
Slim (slim)	~500 MB	~500 MB	Use when you already rely on external services for embeddings and reranking (OpenAI, Cohere, TEI). Significantly smaller image, faster deploys. Requires external providers.

The slim image corresponds to the hindsight-api-slim pip package. See Configuration for external provider options.

Available Tags

# Standalone (API + Control Plane)
ghcr.io/vectorize-io/hindsight:latest        # Full, latest release
ghcr.io/vectorize-io/hindsight:latest-slim          # Slim, latest release
ghcr.io/vectorize-io/hindsight:0.4.9         # Full, specific version
ghcr.io/vectorize-io/hindsight:0.4.9-slim    # Slim, specific version

# API only
ghcr.io/vectorize-io/hindsight-api:latest
ghcr.io/vectorize-io/hindsight-api:latest-slim

# Control Plane only
ghcr.io/vectorize-io/hindsight-control-plane:latest

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Helm / Kubernetes

Best for: Production deployments, auto-scaling, cloud environments

# Install with built-in PostgreSQL
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \
  --set api.llm.provider=groq \
  --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
  --set postgresql.enabled=true

# Or use external PostgreSQL
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \
  --set api.llm.provider=groq \
  --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
  --set postgresql.enabled=false \
  --set api.database.url=postgresql://user:pass@postgres.example.com:5432/hindsight

# Install a specific version
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight --version 0.1.3

# Upgrade to latest
helm upgrade hindsight oci://ghcr.io/vectorize-io/charts/hindsight

Requirements:

• Kubernetes cluster (GKE, EKS, AKS, or self-hosted)
• Helm 3.8+

Distributed Workers

For high-throughput deployments, enable dedicated worker pods to scale task processing independently:

helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \
  --set worker.enabled=true \
  --set worker.replicaCount=3

See Services - Worker Service for configuration details and architecture.

See the Helm chart values.yaml for all chart options.

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Bare Metal (pip)

Best for: Running Hindsight as a standalone service on a host machine.

Install

pip install hindsight-api        # Full — works out of the box
pip install hindsight-api-slim   # Slim — requires external services for embeddings, reranking, and the database

When using hindsight-api-slim, you must configure external providers for all model operations. See Configuration for details.

Run with Embedded Database

For development and testing, Hindsight can run with an embedded PostgreSQL (pg0):

export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx

hindsight-api

This creates a database in ~/.hindsight/data/ and starts the API on http://localhost:8888.

Run with External PostgreSQL

For production, connect to your own PostgreSQL instance:

export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx

hindsight-api

Note: The database must exist and have pgvector enabled (CREATE EXTENSION vector;).

CLI Options

hindsight-api --port 9000          # Custom port (default: 8888)
hindsight-api --host 127.0.0.1     # Bind to localhost only
hindsight-api --workers 4          # Multiple worker processes
hindsight-api --log-level debug    # Verbose logging

Control Plane

The Control Plane (Web UI) can be run standalone using npx:

npx @vectorize-io/hindsight-control-plane --api-url http://localhost:8888

This connects to your running API server and provides a visual interface for managing memory banks, exploring entities, and testing queries.

Options

Option	Environment Variable	Default	Description
-p, --port	PORT	9999	Port to listen on
-H, --hostname	HOSTNAME	0.0.0.0	Hostname to bind to
-a, --api-url	HINDSIGHT_CP_DATAPLANE_API_URL	http://localhost:8888	Hindsight API URL

Examples

# Run on custom port
npx @vectorize-io/hindsight-control-plane --port 9999 --api-url http://localhost:8888

# Using environment variables
export HINDSIGHT_CP_DATAPLANE_API_URL=http://api.example.com
npx @vectorize-io/hindsight-control-plane

# Production deployment
PORT=80 HINDSIGHT_CP_DATAPLANE_API_URL=https://api.hindsight.io npx @vectorize-io/hindsight-control-plane

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Windows

Best for: Running Hindsight natively on Windows without Docker

Hindsight works on Windows with the embedded database (pg0) out of the box — just install and run:

pip install hindsight-api

set HINDSIGHT_API_LLM_PROVIDER=openai
set HINDSIGHT_API_LLM_API_KEY=sk-xxx
set HINDSIGHT_API_LLM_MODEL=gpt-4o-mini

hindsight-api

Using External PostgreSQL (optional)

If you prefer to use your own PostgreSQL instance instead of the embedded database:

# Install PostgreSQL
winget install PostgreSQL.PostgreSQL.17

# Build pgvector (requires Visual Studio Build Tools)
git clone https://github.com/pgvector/pgvector.git
cd pgvector

# Open "x64 Native Tools Command Prompt for VS" and run:
set PGROOT=C:\Program Files\PostgreSQL\17
nmake /F Makefile.win
nmake /F Makefile.win install

# Create the database and enable the vector extension
psql -U postgres -c "CREATE DATABASE hindsight;"
psql -U postgres -d hindsight -c "CREATE EXTENSION vector;"

Then run Hindsight pointing to your database:

pip install hindsight-api

set HINDSIGHT_API_DATABASE_URL=postgresql://postgres@localhost:5432/hindsight
set HINDSIGHT_API_LLM_PROVIDER=openai
set HINDSIGHT_API_LLM_API_KEY=sk-xxx
set HINDSIGHT_API_LLM_MODEL=gpt-4o-mini

hindsight-api

• API Server: http://localhost:8888

tip

You can also use the slim package (pip install hindsight-api-slim) if you configure external providers for embeddings and reranking. See Configuration for details.

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Embedded in a Python Application

Best for: Using Hindsight programmatically from Python without running a separate server process.

pip install hindsight-all        # Full — works out of the box
pip install hindsight-all-slim   # Slim — requires external services for embeddings, reranking, and the database

hindsight-all supports two modes of embedding:

In-process (HindsightServer): the server runs in a background thread inside your application. Best when you want the tightest integration and are already managing your own process lifecycle.

from hindsight import HindsightServer, HindsightClient

with HindsightServer(llm_provider="openai", llm_api_key="sk-xxx") as server:
    client = HindsightClient(base_url=server.url)
    client.retain(bank_id="alice", content="Alice prefers concise answers.")
    results = client.recall(bank_id="alice", query="How should I respond to Alice?")

Managed subprocess (HindsightEmbedded): the server runs as a background daemon process, shared across multiple Python processes or sessions. The daemon starts on first use and shuts down automatically after an idle timeout.

from hindsight import HindsightEmbedded

client = HindsightEmbedded(llm_provider="openai", llm_api_key="sk-xxx")
client.retain(bank_id="alice", content="Alice prefers concise answers.")
results = client.recall(bank_id="alice", query="How should I respond to Alice?")

See the Python SDK for the full API reference.

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Next Steps

• Configuration — Environment variables and settings
• Models — ML models and providers
• Monitoring — Metrics and observability