Per-agent memory protocol for multi-agent systems
The problem: shared memory = shared perspective = no real team cognition. But fully isolated memory = no collaboration, no learning from each other.
Current agent swarms share a single memory system. Every agent draws from the same pool of context, producing the same perspective. This creates the illusion of collaboration — agents divide tasks but never truly think differently from each other. Shared memory = shared perspective = no real team cognition.
The opposite — fully isolated agents — breaks collaboration entirely. Agents cannot learn from each other, cannot build on shared experience, and cannot develop institutional knowledge over time.
eachmind solves this by giving each agent its own memory — privately encoded, individually shaped — while defining a protocol for what gets selectively shared, when, and how. Agents develop genuine perspectives. Teams develop genuine collective intelligence.
A standalone, framework-agnostic Python library that defines how memory is stored, differentiated, and selectively shared across agents in a multi-agent system. It is a memory protocol — not an agent framework, not a task runner. Any agent system can adopt it.
| Primitive | Description |
|---|---|
| PrivateMemory | Each agent's own store. Encoded from its perspective. Never automatically shared. |
| SharedMemory | What agents explicitly publish to the collective. Opt-in, not default. |
| MemoryEvent | A discrete experience. Same event, encoded differently per agent based on its context. |
| Perspective | The lens through which an agent encodes events — shaped by its history and role. |
| Consolidation | How repeated private experiences abstract into durable beliefs over time. |
| Drift | Agents in the same team naturally diverge in perspective over time. Measurable. |
Memory is private unless explicitly shared. Sharing is a deliberate act, not an automatic sync.
When agents observe the same event, each encodes it through its own perspective. Divergence is the feature, not the bug.
Works alongside OpenAI Swarm, CrewAI, LangGraph, or a hand-written agent loop. No lock-in.
Over time, what agents repeatedly share consolidates into team-level knowledge — without forcing a single shared brain.
pip install eachmindfrom eachmind import Agent, MemoryEvent, SharedMemory
# Create agents with their own private memory
analyst = Agent(name="analyst", role="data analysis")
writer = Agent(name="writer", role="content creation")
# Both agents observe the same event
event = MemoryEvent(
content="Q1 revenue grew 23% YoY",
source="quarterly_report",
timestamp="2026-04-10T09:00:00Z"
)
# Each encodes it through their own perspective
analyst.observe(event) # Encodes: statistical significance, trend implications
writer.observe(event) # Encodes: narrative angle, audience framing
# Analyst decides to share a finding
analyst.share(
content="Revenue growth acceleration suggests market expansion",
to=SharedMemory.TEAM
)
# Writer can now access shared knowledge
shared = writer.recall(source=SharedMemory.TEAM)
# Over time, perspectives naturally drift — and that's measurable
drift = analyst.perspective.drift_from(writer.perspective)
- Not a vector database or RAG system — eachmind defines memory behavior, not storage engines.
- Not an agent framework or task runner — it doesn't orchestrate agents or assign tasks.
- Not a replacement for mem0, Zep, or MemGPT — those are memory backends; eachmind is a protocol layer above them.
- Not a multi-agent orchestrator — it doesn't manage agent coordination or communication routing.
Foundation complete. Project 1 of 2. All core primitives, protocol specification, storage backends, visualizations, and integration examples are implemented. Project 2 is an agent architecture built on top of eachmind that demonstrates genuine team cognition — agents that challenge, review, disagree, and accumulate institutional knowledge over time.
- Core primitives implementation
- Protocol specification
- Storage backend adapters (in-memory, SQLite, Redis)
- Integration examples (OpenAI Agents SDK, CrewAI, LangGraph)
- Drift measurement and visualization
- Consolidation algorithms
- Project 2: Team cognition demo
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
MIT License — see LICENSE for details.