The Memory & Knowledge Engine for Multi-Agent Systems
MeshOS is a developer-first framework for building multi-agent AI-driven operations with structured memory, knowledge retrieval, and real-time collaboration. Unlike generic memory stores, MeshOS is purpose-built for:
- Autonomous Agents & Teams – Agents and humans evolve a shared memory over time.
- Graph-Based Memory – Track relationships, dependencies, and evolving knowledge.
- Fast Semantic Search – Vector-based retrieval with pgvector.
- Event-Driven Execution – Automate workflows based on evolving context.
- Versioned Knowledge – Track updates, past decisions, and historical context.
- Open & Portable – Runs on PostgreSQL + Hasura with no vendor lock-in.
Most frameworks give you a blob of memories—MeshOS gives you structured, evolving intelligence with deep relationships and versioning.
| Feature | MeshOS | Mem0 / Letta / Zep |
|---|---|---|
| Multi-Agent Memory | ✅ Yes | ❌ No |
| Structured Taxonomy | ✅ Yes | ❌ No |
| Versioned Knowledge | ✅ Yes | ❌ No |
| Graph-Based Relationships | ✅ Yes | ❌ No |
| Semantic & Vector Search | ✅ Yes | ✅ Partial |
| Event-Driven Execution | ✅ Yes | ❌ No |
| Open-Source & Portable | ✅ Yes | ✅ Partial |
✅ Builders of AI-powered operations – Structured memory and decision-making for AI-driven systems.
✅ Multi-agent system developers – AI agents that need to store, process, and evolve shared knowledge.
✅ Developers & engineers – Wanting an open-source, PostgreSQL-powered framework with no lock-in.
flowchart LR
%% Main System
subgraph MeshOS[MeshOS System]
direction LR
%% Taxonomy Details
subgraph Taxonomy[Memory Classification]
direction TB
subgraph DataTypes[Data Types]
direction LR
knowledge[Knowledge Type]
activity[Activity Type]
decision[Decision Type]
media[Media Type]
end
subgraph Subtypes[Example Subtypes]
direction LR
k_types[Research/Mission/Vision]
a_types[Conversations/Logs/Events]
d_types[Policies/Strategies]
m_types[Documents/Images]
knowledge --> k_types
activity --> a_types
decision --> d_types
media --> m_types
end
subgraph Relations[Edge Types]
direction LR
basic[related_to/version_of]
semantic[influences/depends_on]
temporal[follows_up/precedes]
end
end
%% Memory Operations
subgraph MemoryEngine[Memory Operations]
direction LR
rememberAction[Store/Remember]
recallAction[Search/Recall]
linkAction[Link Memories]
versioning[Version Control]
rememberAction --> recallAction
recallAction --> linkAction
linkAction --> versioning
end
end
%% Organization & Agents
subgraph Organization[Organization & Agents]
direction TB
%% Company Memory
subgraph CompanyMemory[Company-Wide Memory]
direction LR
corpVision[Company Vision]
corpMission[Company Mission]
corpData[Knowledge Base]
end
%% Agents
subgraph Agent1[Research Agent]
a1Mem[Research Memories]
end
subgraph Agent2[Service Agent]
a2Mem[Service Memories]
end
end
%% System Connections
Taxonomy --> MemoryEngine
MemoryEngine --> Organization
%% Memory Connections
corpVision -.->|influences| a1Mem
corpMission -.->|guides| a2Mem
a1Mem -.->|shares| a2Mem
a2Mem -.->|feedback| corpData
a1Mem -.->|versions| corpData
%% Styling
classDef system fill:#dfeff9,stroke:#333,stroke-width:1.5px
classDef engine fill:#fcf8e3,stroke:#333
classDef taxonomy fill:#e7f5e9,stroke:#333
classDef types fill:#f8f4ff,stroke:#333
classDef org fill:#f4f4f4,stroke:#333
class MeshOS system
class MemoryEngine engine
class Taxonomy,DataTypes,Subtypes,Relations taxonomy
class Organization org
pip install mesh-os
mesh-os create my-project && cd my-project
mesh-os upfrom mesh_os import MeshOS
# Initialize MeshOS
os = MeshOS()
# Register an agent
agent = os.register_agent(name="AI_Explorer")
# Store structured knowledge
memory = os.remember(
content="The Moon has water ice.",
agent_id=agent.id,
metadata={
"type": "knowledge",
"subtype": "fact",
"tags": ["astronomy", "moon"],
"version": 1
}
)
# Retrieve similar knowledge
results = os.recall(query="Tell me about the Moon.")✅ Memory for Multi-Agent Systems – Let agents store, retrieve, and link structured knowledge.
✅ Fast Semantic Search – pgvector-powered similarity matching across all memories.
✅ Graph-Based Knowledge – Build evolving relationships between facts, ideas, and actions.
✅ Versioning Built-In – Track updates, past decisions, and context shifts.
✅ Event-Driven Execution – Automate workflows based on new knowledge.
✅ Open & Portable – Runs anywhere PostgreSQL does. No black-box infrastructure.
MeshOS enforces structured knowledge with memory classification and versioning:
| Memory Type | Examples |
|---|---|
| Knowledge | Research reports, datasets, concepts |
| Activity | Agent workflows, logs, system events |
| Decision | Policy updates, business strategy |
| Media | Documents, images, AI-generated content |
Memories evolve over time, with full versioning and relationship tracking.
# Required
OPENAI_API_KEY=your_api_key_here
# Optional (defaults shown)
POSTGRES_PASSWORD=mysecretpassword
HASURA_ADMIN_SECRET=meshos
POSTGRES_PORT=5432
HASURA_PORT=8080
HASURA_ENABLE_CONSOLE=truegit clone https://github.com/yourusername/mesh-os.git
cd mesh-os
poetry install
poetry run pytestContributions are welcome! Please submit a Pull Request.
This project is licensed under the Apache 2.0 License – see LICENSE for details.