tealtiger-hindsight

Importance-weighted governance decision memory using Hindsight — contextual recall and natural decay for AI agent compliance.

Part of the TealTiger ecosystem — deterministic AI agent governance.

What it does

tealtiger-hindsight stores governance decisions with importance-weighted retention:

• 🔴 Critical DENYs (PII detected, secrets blocked) → importance 0.90 → retained for months
• 🟡 Notable MONITORs (flagged but allowed) → importance 0.70 → retained for weeks
• 🟢 Routine ALLOWs (passed all checks) → importance 0.55 → natural decay within days

Enables contextual recall: "what governance decisions were made for this agent in similar situations?" — informing future policy evaluation without overriding deterministic enforcement.

Design Principle

Storage = evidence/continuity, NOT authority.

A stored ALLOW from yesterday cannot authorize today's action. Every new request gets a fresh deterministic evaluation. Storage informs; it doesn't permit.

Installation

pip install tealtiger-hindsight

Quick Start

from hindsight_client import Hindsight
from tealtiger_hindsight import HindsightGovernanceMemory

# Connect to Hindsight
client = Hindsight(base_url="http://localhost:8888")

# Create governance memory
memory = HindsightGovernanceMemory(
    client=client,
    bank_id="governance",
)

# Store a governance decision (from TealTiger's on_decision callback)
memory.store({
    "action": "DENY",
    "correlation_id": "dec-001",
    "agent_id": "research-agent",
    "tool_name": "send_email",
    "reason_codes": ["PII_DETECTED:ssn"],
    "risk_score": 90,
    "mode": "ENFORCE",
})

# Recall past decisions for context
past_decisions = memory.recall(
    agent_id="research-agent",
    context="tool:send_email",
    limit=5,
)

# Reflect on governance patterns
insights = memory.reflect(
    agent_id="research-agent",
    query="What are the most common denial reasons?"
)

With TealTiger observe()

from tealtiger import observe
from tealtiger_hindsight import HindsightGovernanceMemory
from hindsight_client import Hindsight
from openai import OpenAI

client = Hindsight(base_url="http://localhost:8888")
memory = HindsightGovernanceMemory(client=client)

# Every governance decision auto-stored with importance weighting
llm = observe(
    OpenAI(),
    guardrails={"pii_detection": True},
    on_decision=memory.store,
)

Custom Importance Function

Override the default importance mapping for richer decay behavior:

def custom_importance(decision) -> float:
    """Custom importance: risk_score + recurrence boost."""
    base = {"DENY": 0.85, "MONITOR": 0.65, "ALLOW": 0.50}
    score = base.get(decision.get("action", "ALLOW"), 0.55)

    # Risk contributes but doesn't dominate
    risk_boost = (decision.get("risk_score", 0) / 100) * 0.15

    # Repeated patterns are more important to remember
    if decision.get("is_recurring"):
        score += 0.10

    return min(score + risk_boost, 1.0)

memory = HindsightGovernanceMemory(
    client=client,
    importance_fn=custom_importance,
)

How Memory Decay Works

Hindsight's importance-based memory means routine ALLOWs naturally fade from recall context while critical DENYs remain easily retrievable for compliance audits.

Use Cases

1. Contextual governance: Before evaluating a new tool call, recall similar past decisions. If an agent was denied 5 times for PII in web_search results, that context is available.
2. Anomaly detection: Query "agents whose denial rate spiked vs. historical baseline" — natural with importance-weighted memory.
3. Compliance audits: Critical DENYs persist indefinitely. recall(min_importance=0.85) retrieves only security-relevant events.
4. Storage efficiency: Routine ALLOWs decay naturally — no manual cleanup needed.

Requirements

• Python 3.10+
• hindsight-client >= 0.4.0
• A running Hindsight server (or Hindsight Cloud)

Links

• 📖 TealTiger Docs
• 🧠 Hindsight Docs
• 🐙 GitHub
• 📦 PyPI
• Issue: vectorize-io/hindsight#2284

License

Apache 2.0 — see LICENSE.