Hindsight API

Memory System for AI Agents — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.

Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.

Installation

pip install hindsight-api

Quick Start

Run the Server

# Set your LLM provider
export HINDSIGHT_API_LLM_PROVIDER=openai
export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx

# Start the server (uses embedded PostgreSQL by default)
hindsight-api

The server starts at http://localhost:8888 with:

  • REST API for memory operations
  • MCP server at /mcp for tool-use integration

Use the Python API

from hindsight_api import MemoryEngine

# Create and initialize the memory engine
memory = MemoryEngine()
await memory.initialize()

# Create a memory bank for your agent
bank = await memory.create_memory_bank(
    name="my-assistant",
    background="A helpful coding assistant"
)

# Store a memory
await memory.retain(
    memory_bank_id=bank.id,
    content="The user prefers Python for data science projects"
)

# Recall memories
results = await memory.recall(
    memory_bank_id=bank.id,
    query="What programming language does the user prefer?"
)

# Reflect with reasoning
response = await memory.reflect(
    memory_bank_id=bank.id,
    query="Should I recommend Python or R for this ML project?"
)

CLI Options

hindsight-api --help

# Common 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

Configuration

Configure via environment variables:

Variable Description Default
HINDSIGHT_API_DATABASE_URL PostgreSQL connection string pg0 (embedded)
HINDSIGHT_API_LLM_PROVIDER openai, anthropic, gemini, groq, ollama, lmstudio openai
HINDSIGHT_API_LLM_API_KEY API key for LLM provider -
HINDSIGHT_API_LLM_MODEL Model name gpt-4o-mini
HINDSIGHT_API_HOST Server bind address 0.0.0.0
HINDSIGHT_API_PORT Server port 8888

Example with External PostgreSQL

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

Docker

docker run -it --name hindsight --restart unless-stopped -p 8888:8888 \
  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
  ghcr.io/vectorize-io/hindsight:latest

MCP Server

For local MCP integration without running the full API server:

hindsight-local-mcp

This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.

Key Features

  • Multi-Strategy Retrieval (TEMPR) — Semantic, keyword, graph, and temporal search combined with RRF fusion
  • Entity Graph — Automatic entity extraction and relationship tracking
  • Temporal Reasoning — Native support for time-based queries
  • Disposition Traits — Configurable skepticism, literalism, and empathy influence opinion formation
  • Three Memory Types — World facts, experience facts (the bank's own actions), and observations

Documentation

Full documentation: https://hindsight.vectorize.io

License

Apache 2.0

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.9.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for vectorize-io from gravatar.com vectorize-io

Requires

Python >=3.11

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