What is consensus validation in Sage?

Consensus validation ensures that institutional memory entries are verified and agreed upon before storage, preventing unreliable or conflicting information from being persisted. The exact validation mechanism is determined by your agent configuration.

Does Sage require external services or cloud infrastructure?

No. Sage runs entirely locally via Docker, storing all institutional memory on your machine. This keeps your agent context private and eliminates external dependencies.

How do I install and run Sage?

Pull and run the Docker image: `docker run ghcr.io/l33tdawg/sage:4.0.0`. It communicates via stdio, so it integrates directly with MCP-compatible clients and agent frameworks.

Can multiple AI agents share the same institutional memory?

Yes. Sage is designed for multi-agent systems, allowing agents to store and retrieve shared, consensus-validated context. This enables teams of agents to build on each other's knowledge.

What happens if agents disagree on a fact—how does consensus work?

Sage validates entries through consensus before persisting them, but the specific consensus rules depend on your agent setup. Refer to the server configuration and agent orchestration layer for details on dispute resolution.

MCP ServerSTDIOOfficialv4.0.0

L33tdawg Sage MCP Server

Store and retrieve consensus-validated institutional memory for AI agents with persistent local storage. Ideal for teams building multi-agent systems that need reliable, shared context across conversations.

io.github.l33tdawg/sage

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Hosted URL

Local install

Transport

STDIO

Auth

No auth required

What the L33tdawg Sage MCP server does

How models use it and what it is built for.

Connect to L33tdawg Sage

Local install — runs as a subprocess.

docker run ghcr.io/l33tdawg/sage:4.0.0

Resources

Where to find authoritative docs and source for L33tdawg Sage.

Source repository

Example prompts for L33tdawg Sage

Paste any of these into Agent Studio after connecting L33tdawg Sage.

Store this decision about our API versioning strategy in institutional memory
Retrieve all consensus-validated facts about our deployment process
What institutional knowledge do we have about handling rate limits
Add this architecture pattern to our shared agent memory with consensus validation

L33tdawg Sage MCP server — FAQ

Common questions about connecting and running L33tdawg Sage.

What is consensus validation in Sage?
Consensus validation ensures that institutional memory entries are verified and agreed upon before storage, preventing unreliable or conflicting information from being persisted. The exact validation mechanism is determined by your agent configuration.
Does Sage require external services or cloud infrastructure?
No. Sage runs entirely locally via Docker, storing all institutional memory on your machine. This keeps your agent context private and eliminates external dependencies.
How do I install and run Sage?
Pull and run the Docker image: `docker run ghcr.io/l33tdawg/sage:4.0.0`. It communicates via stdio, so it integrates directly with MCP-compatible clients and agent frameworks.
Can multiple AI agents share the same institutional memory?
Yes. Sage is designed for multi-agent systems, allowing agents to store and retrieve shared, consensus-validated context. This enables teams of agents to build on each other's knowledge.
What happens if agents disagree on a fact—how does consensus work?
Sage validates entries through consensus before persisting them, but the specific consensus rules depend on your agent setup. Refer to the server configuration and agent orchestration layer for details on dispute resolution.

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