083295dc6f
* feat: add audit log for feature usage tracking
Add full auditability for all mutating and core API operations across
HTTP, MCP, and system (worker) transports. Audit entries record raw
request/response as JSONB, timing (started_at/ended_at), action, and
transport type.
Backend:
- New audit_log table with JSONB columns for expandability without
future migrations (merge migration of 3 existing heads)
- AuditLogger with fire-and-forget writes via asyncio.create_task
- @audited decorator on 28 HTTP route handlers
- MCP tool audit wrapping for 16 auditable tools
- Worker task execution wrapped with audit_context
- List endpoint with action, transport, date range filters + pagination
- Stats endpoint with per-day counts for charting
- Configurable retention sweep (concurrent-safe DELETE)
Config (env-only, static):
- HINDSIGHT_API_AUDIT_LOG_ENABLED (default: false)
- HINDSIGHT_API_AUDIT_LOG_ACTIONS (comma-separated allowlist, empty=all)
- HINDSIGHT_API_AUDIT_LOG_RETENTION_DAYS (default: -1, keep forever)
Control Plane:
- New "Audit Logs" tab on bank configuration page
- Line chart showing request volume (today/7d/30d) with action filter
- Filterable table with action, transport, date range filters
- Paginated list with detail dialog showing raw request/response JSON
Tests:
- 13 tests covering list, filters, pagination, stats, disabled mode,
action allowlist, and ordering
* fix: split 3-way merge migration into two 2-way merges
Alembic doesn't support 3-parent merge migrations. Split into a no-op
merge of 2 heads (b1c2d3e4f5g6) followed by the audit_log table
migration merging the third head.
* fix: correct merge migration to merge actual 2 heads
The original analysis incorrectly identified 3 heads. There were only 2
(a3b4c5d6e7f8 and c8e5f2a3b4d1). Remove the unnecessary intermediate
merge migration and fix the audit_log migration to merge these 2 heads.
* fix: use 'heads' instead of 'head' in migration runner
Alembic's upgrade('head') fails when multiple heads exist (e.g. from
namespace package overlaps between hindsight-api and hindsight-api-slim).
Using 'heads' (plural) handles this gracefully by upgrading all branches.
* chore: regenerate OpenAPI spec with audit log endpoints
* chore: regenerate TypeScript client and docs skill OpenAPI spec
Python and Go clients still need regeneration (requires Docker).
* chore: regenerate all client SDKs (Python, Go, TypeScript)
Adds generated audit log API clients for Python (audit_api.py),
Go (api_audit.go), and TypeScript client type updates.
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|---|---|---|
| .. | ||
| 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