f903948a26
* feat: support timestamp="unset" to retain content without a date When callers retain timeless content (e.g. fictional documents, static reference material), passing timestamp="unset" now skips the utcnow() default so mentioned_at is stored as NULL instead of an artificial date. - HTTP: validate_timestamp recognises "unset" sentinel and threads it through api_retain as event_date=None (key present, value None), which the orchestrator distinguishes from key-absent (still defaults to now) - Orchestrator: new branching logic separates "key absent" → utcnow() from "key present but None" → no date - types.py: RetainContent.event_date and ProcessedFact.mentioned_at are now datetime | None; removed the unused _now_utc factory - fact_extraction.py: all event_date params accept datetime | None; _build_user_message emits "Event Date: Unknown" when None; removed mentioned_at from the Fact LLM response model (LLM never sets it) - embedding_processing: skip date suffix when fact_date is None - entity_resolver: COALESCE(event_date, now()) for first_seen/last_seen so entities table NOT NULL constraint is preserved - link_utils: skip temporal linking for units without event_date - Migration aa2b3c4d5e6f: DROP NOT NULL on memory_units.event_date - Tests: test_retain_no_timestamp and test_retain_omit_timestamp_defaults_to_now - Docs + OpenAPI + TypeScript client updated * refactor: replace _TIMESTAMP_UNKNOWN sentinel with plain string comparison The sentinel object() was only needed to distinguish "unset" from None at the boundary — but since the field type is datetime | str | None, "unset" can pass through the validator unchanged and be compared directly. * chore: regenerate OpenAPI spec and clients after timestamp type change timestamp field is now datetime | str | None to accept the "unset" sentinel value. |
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| 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