c5700ff5b4
* feat: Windows native support — run Hindsight without Docker on Windows
Four compatibility fixes that allow Hindsight to run natively on Windows
with an external PostgreSQL + pgvector installation:
1. **pyproject.toml**: Conditional event loop dependency
- `winloop` on Windows (sys_platform == 'win32')
- `uvloop` on Linux/macOS (sys_platform != 'win32')
2. **main.py**: winloop integration via `winloop.install()`
- Patches asyncio event loop policy globally before uvicorn starts
- uvicorn sees "asyncio" but runs winloop underneath (same perf as uvloop)
- Falls back to default asyncio if winloop unavailable
3. **metrics.py**: Guard `resource` module import
- `resource` is Unix-only (getrusage, getrlimit)
- Conditional import with None fallback
- Skip process metrics collection on Windows
4. **fact_storage.py**: Cross-platform strftime
- `%-d` (no-padding day) is glibc-only, fails on Windows
- Replaced with `%d` + `.replace(" 0", " ")` for same output
## Windows Setup Guide
### Prerequisites
- Python 3.11+
- PostgreSQL 17 with pgvector extension
- Ollama (for local embeddings) or external embedding provider
### Install PostgreSQL + pgvector on Windows
```bash
winget install PostgreSQL.PostgreSQL.17
# Build pgvector from source (requires Visual Studio Build Tools)
git clone https://github.com/pgvector/pgvector.git
# In x64 Native Tools Command Prompt:
set PGROOT=C:\Program Files\PostgreSQL\17
nmake /F Makefile.win
nmake /F Makefile.win install
# Enable extension
psql -U postgres -d hindsight -c "CREATE EXTENSION IF NOT EXISTS vector;"
```
### Install and Run Hindsight
```bash
pip install -e ".[embedded-db]"
# Set environment variables
set HINDSIGHT_API_LLM_PROVIDER=openai
set HINDSIGHT_API_LLM_API_KEY=your-api-key
set HINDSIGHT_API_LLM_BASE_URL=https://your-llm-endpoint/v1
set HINDSIGHT_API_LLM_MODEL=your-model
set HINDSIGHT_API_DATABASE_URL=postgresql://postgres@localhost:5432/hindsight
set HINDSIGHT_API_EMBEDDING_PROVIDER=ollama
set HINDSIGHT_API_PORT=8889
hindsight-api
```
Data persists in PostgreSQL on your local disk — survives reboots,
updates, and anything that would wipe a Docker volume.
Tested on Windows 11 with PostgreSQL 17.9, pgvector 0.8.2,
Python 3.11, RTX 5080 (CUDA embeddings + reranking).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: handle strftime ValueError on Windows in fact_storage
The strftime call on occurred_start/occurred_end can raise ValueError
on Windows when the datetime object has unexpected format properties.
Wrap in try/except to gracefully skip date signal rather than crash
the entire retain batch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
|
||
|---|---|---|
| .. | ||
| hindsight_api | ||
| tests | ||
| pyproject.toml | ||
| README.md | ||
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
/mcpfor 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 --rm -it -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, bank actions, and formed opinions with confidence scores
Documentation
Full documentation: https://hindsight.vectorize.io
License
Apache 2.0