8b1a46585d
* Fix reflect based_on population and enforce full hierarchical retrieval Problem 1: based_on field was incomplete - search_observations results were never extracted into based_on, so observations used by the agent were invisible to callers - search_mental_models and get_mental_model used non-existent fields (summary/description) instead of the actual content field, producing empty text in based_on entries - A duplicate unreachable elif block for search_mental_models was dead code (the first identical condition always matched) Problem 2: mental models could produce "I don't have information" - When a bank has mental models, the agent's tool_choice forcing only covered iteration 0 (search_mental_models). Iterations 1+ were auto, allowing the LLM to short-circuit without ever searching observations or raw facts. Combined with the LOW budget prompt encouraging speed, this meant the agent would often stop after a single tool call. - This created a self-reinforcing failure loop: if a mental model refresh produced "I don't have information" (e.g. due to the agent skipping recall), subsequent reflects would find that content and trust it, never searching deeper. Fix: extend forced tool_choice to cover the full hierarchical retrieval path before allowing auto mode: - With mental models: search_mental_models(0) → search_observations(1) → recall(2) → auto(3+) - Without mental models: search_observations(0) → recall(1) → auto(2+) This matches the retrieval strategy documented in the system prompt and ensures all three knowledge levels are always consulted. The agent still has 2-3 auto iterations (with LOW budget, max_iterations=5) for additional searches or calling done(). * Add Umami analytics tracking to docs site Add conditional Umami script injection to docusaurus.config.ts and pass UMAMI_URL/UMAMI_WEBSITE_ID env vars in the GitHub Pages deploy workflow. The tracking script only loads when both env vars are set. |
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| 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