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Aegis Memory

Aegis Memory is an open-source, agent-native memory engine for multi-agent AI systems. It gives your agents persistent, semantic memory that survives context window resets.

Quick Start

Get running in 5 minutes

Why Aegis?

Understand the problem we solve

Core Concepts

Learn the fundamentals

Tutorials

Build real-world examples

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The Problem

Every AI agent framework promises memory. Few deliver. 
# What happens today
agent.chat("I'm John, a Python developer")
# ... context window resets ...
agent.chat("What's my name?")  # "I don't know your name"
After analyzing 200+ production agent deployments, we found 40-80% fail due to memory coordination issues—not model capability.
ProblemReality
Context loss mid-taskAgents forget what they learned
No shared stateMulti-agent teams can’t coordinate
Memory bloatEverything saved, nothing useful retrieved
Session boundariesLong tasks restart from zero

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The Solution

Aegis Memory provides:
1

Semantic Memory

Store and retrieve memories by meaning, not just keywords
2

ACE Patterns

Self-improving agents that learn what works (voting, reflections, playbooks)
3

Multi-Agent Coordination

Scoped memory sharing with structured handoffs
4

Session Persistence

Resume work across context window resets

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Quick Example

from aegis_memory import AegisClient

client = AegisClient(api_key="your-key")

# Store a memory
client.add(
    content="User prefers dark mode and Python",
    agent_id="assistant",
    user_id="john_123"
)

# Later, retrieve relevant context
memories = client.query(
    "What are John's preferences?",
    user_id="john_123"
)
# Returns: "User prefers dark mode and Python"

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Framework Integrations

Aegis works with your existing agent stack:

CrewAI

Multi-agent teams

LangChain

Chains & agents

LangGraph

Stateful workflows

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Open Source

Aegis Memory is fully open source under the Apache 2.0 license.

GitHub Repository

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