tripartite-memory 0.1.4

The official AEGIS OS Tripartite Memory SDK for Autonomous Agents.

AEGIS Tripartite Memory SDK 🧠

Available on PyPI as tripartite-memory

Most LLM agents fail in the same way: they forget what already happened. They retry failed approaches, ignore system state, and confidently suggest things that already broke production.

This is AI Amnesia.

tripartite-memory is a unified async Python SDK that gives AI agents persistent, structured memory across three distinct layers. Before an agent takes action, it can answer:

“Has this failed before?”
“What will this impact?”
“Is this safe to execute?”

Instead of guessing, it knows.

Memory & Context Optimization ⚡

tripartite-memory significantly reduces the cost and improves the performance of running large models:

• 60-80% Token Reduction: Instead of dumping massive chat histories into the prompt, recall() injects only the 3-5 most relevant precedents.
• VRAM Relief: By keeping context windows lean, models consume less VRAM (which scales quadratically with sequence length). Run larger models (32B/70B) on consumer-grade hardware.
• Improved Reasoning: Providing specific "Hard Constraints" from the Ledger prevents the LLM from making up rules, leading to deterministic and reliable outputs.

What This Fixes

Without memory:

• Agents loop on failed solutions.
• Context windows explode with irrelevant history.
• Risky actions happen without awareness of dependencies.

With tripartite-memory:

• Agents avoid known failure paths.
• Context stays small and relevant.
• Actions are informed by real system state and "trace the real blast radius."

The Tripartite Architecture

To make an LLM safe for production, it needs an operating-system-level memory stack:

1. The Ledger (Postgres): Immutable state, strict constraints, and audit logs.
2. The Semantic Engine (Qdrant): High-dimensional vector search for historical precedents and documentation.
3. The Capability Graph (Neo4j): Dependency mapping to understand how modifying Component A impacts System B.

Installation

pip install tripartite-memory

Quickstart

Initialize the MemoryCore with your database credentials (or use a .env file).

import asyncio
from tripartite_memory.core import MemoryCore

async def main():
    # Automatically loads from .env
    memory = MemoryCore()

    # 1. Unified Ingestion (Write to all 3 databases simultaneously)
    await memory.ingest(
        content="Modified the Nginx reverse proxy to route /api/v2 traffic to staging.",
        actor="agent:InfrastructureOps",
        tags=["nginx", "networking", "staging"]
    )

    # 2. Pre-Action Context Check (The Blast Radius)
    # Give your agent complete situational awareness before it touches production.
    context = await memory.recall(
        intent="Restart the Nginx service to apply new SSL certificates.",
        graph_depth=2
    )

    print(context.status) # "KNOWN", "ADJACENT", or "UNKNOWN"
    print(context.blast_radius) # Neo4j dependent nodes
    print(context.historical_precedents) # Qdrant vector matches

if __name__ == "__main__":
    asyncio.run(main())

The Agent Protocol 🛡️

tripartite-memory works best when the agent is "forced" to use it. We recommend adding a Memory Protocol to your agent's system prompt. See SYSTEM_PROMPT.md for the exact snippet.

Universal Integration

• Local Models (Ollama/LM Studio): Inject the recall() JSON directly into the context window before the user's prompt.
• CLI Clients (Claude Code/Gemini CLI): Wrap the SDK in a tool or use the provided Bridge Script.

Bi-directional Memory Bridge 🔄

We provide a ready-to-use bridge in examples/bridge.py that works on Linux, Mac, and Windows.

# Get Context
python examples/bridge.py recall "How do I optimize VRAM on Pascal?"

# Store Knowledge
python examples/bridge.py ingest "Successfully tuned batch size to 4 for Qwen-32B." --tags optimization

Remote Connection Guide (LAN) 🌐

If testing from a remote machine, point the SDK to your server's IP in your .env:

POSTGRES_URL=postgresql://user:password@10.0.0.100:5432/aegis_local
QDRANT_URL=http://10.0.0.100:6333
NEO4J_URI=bolt://10.0.0.100:7687
NEO4J_PASSWORD=your-secure-password
OLLAMA_URL=http://10.0.0.100:11434

Managed Cloud Support ☁️

tripartite-memory is compatible with major managed database providers. Just update your .env with the cloud connection strings:

• Vector (Qdrant): Works with Qdrant Cloud. Set QDRANT_API_KEY in your environment.
• Graph (Neo4j): Works with Neo4j AuraDB. Use your provided bolt:// URI and password.
• Ledger (Postgres): Works with Neon or Supabase.

# Cloud Example
QDRANT_URL=https://your-cluster.qdrant.tech
QDRANT_API_KEY=your-api-key
NEO4J_URI=bolt+s://your-instance.databases.neo4j.io

SBOM & Transparency 🛡️

In alignment with AEGIS OS security standards, this repository includes a Software Bill of Materials (SBOM) in CycloneDX format.

• View SBOM: sbom.json
• Generate Fresh SBOM: python scripts/generate_sbom.py

Why We Built This

We built this SDK as the foundational memory layer for AEGIS OS—a bare-metal orchestration layer designed to govern AI agents on real infrastructure using deterministic safety tiers (T0/T1/T2).

While the core OS uses a Business Source License (BSL), we believe fundamental agentic memory should be open and standardized. tripartite-memory is 100% open-source (Apache 2.0).

Contributing

PRs are welcome. If you are building agentic systems that require strict intent multiplexing and deterministic safety, we'd love to collaborate.