mirror of
https://github.com/vectorize-io/hindsight.git
synced 2026-09-14 19:31:49 +08:00
Nicolò Boschi
b05546b57d
Recall resolves the attachments behind each fact by reading memory_units.attachment_ids, on
every recall. For a bank whose memories store owns its rows that table holds none of them, so
the read can only return nothing -- and it is not the cheap empty read it looks like.
memory_units carries partial vector indexes per bank, and the planner opens and locks every
index on a table to plan any statement against it. In a tenant with 5,286 banks that is 15,877
indexes: planning took 434-484 ms and 15,880 locks (15,863 on the slow path) for a statement
that executed in 0.04 ms, against ~2 ms and 22 locks in a tenant with a few banks. Twenty
concurrent recalls saturated LWLock:LockManager for the whole database: a 2 vCPU API pod fell
from 180 to ~13 recalls/s, and unrelated pods' readiness checks slowed from 6 ms to ~870 ms.
The guard uses the same store_owned_for check as the other store-owned paths. Store-owned banks
report no attachments on recall until the store can carry the ids itself.
The test asserts the lookup returns before it reads the bank profile or takes a connection,
not merely that it returns {}: an empty result is what the expensive read produced as well.
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 |
LLM provider, including openai, anthropic, gemini, groq, ollama, lmstudio, and github-copilot |
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