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  1. Home
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  3. Sage
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Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 9:16:47 PM

Sage

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View Repository246 GitHub StarsTotal stargazers on GitHub for the source repository (246 stars).Visit Website
memorymulti-agentmcpconsensusdeveloper-tools

Stores persistent agent memory with consensus validation, confidence scores, semantic recall, tasks, inboxes, and federation controls.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON â–¾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for l33tdawg/sage, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

The l33tdawg/sage MCP server gives AI agents persistent institutional memory with consensus validation, attribution, confidence scores, semantic recall, and time-based decay. It runs locally as a SAGE node, storing authoritative chain state in BadgerDB and projecting memory content into SQLite or PostgreSQL with pgvector for recall. It also exposes durable tasks, inbox and reply workflows, runtime handoff, access controls, and controlled federation. Reach for it when agents need governed memory and coordination across sessions rather than a simple file or unvalidated vector store.

Use cases

•Store validated institutional memory across agent sessions
•Coordinate exact-agent tasks and retained replies
•Recall memories semantically with confidence and lifecycle controls
•Federate bounded memory access between separate SAGE chains

Key features

•Consensus-validated persistent memory
•Semantic recall with confidence scores
•Durable agent-assigned tasks
•Unified inbox and reply workflows
•Access controls and bounded federation
•Local dashboard and recovery snapshots

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Sage.

Extracted Tool Capabilities
Consensus-validated persistent memory
Semantic recall with confidence scores
Durable agent-assigned tasks
Unified inbox and reply workflows
Access controls and bounded federation
Local dashboard and recovery snapshots

How Sage works

What l33tdawg/sage MCP server does

The l33tdawg/sage MCP server provides persistent memory infrastructure for AI agents. Memories can carry attribution and confidence, pass through consensus validation, be recalled semantically, and decay over time. The system is designed for institutional memory that remains available across conversations and agent sessions.

Beyond memory, SAGE supports durable tasks assigned to specific agents, a unified inbox, retained replies, runtime handoff, access groups, ownership checks, classification controls, and bounded federation between separate SAGE chains. These functions support multi-agent workflows where assignment, claiming, completion, and reply are distinct states.

How it works

A SAGE node accepts requests from agents through MCP, SDK, or REST and applies authentication and local policy before processing them. Consensus state is maintained through CometBFT and ABCI, with BadgerDB serving as the authoritative chain store. Memory content and vectors are projected at commit time into SQLite for personal deployments or PostgreSQL with pgvector for clusters.

Personal mode uses one CometBFT validator with a per-node memory auto-voter, so it does not provide Byzantine redundancy. Multi-validator deployments use a shared chain. Separate chains can be connected through explicit federation policies that limit reading and copying; federation does not automatically grant local membership or write access.

Setup and configuration

The project can be built from source with Go 1.25.13 or newer. The documented source workflow builds the sage-gui binary, uses sage-gui setup to generate MCP configuration for a selected AI, and starts the service with sage-gui serve. The service and dashboard use port 8080 by default.

Release downloads are available for macOS, Windows, and Linux. A Docker image is published at ghcr.io/l33tdawg/sage. For a containerized stdio connection, the MCP process runs inside the already-running SAGE container and receives SAGE_PROVIDER, SAGE_PROJECT, and SAGE_IDENTITY_PATH. The SAGE data directory should remain mounted so agent keys and ledger state persist.

The l33tdawg/sage MCP server also exposes HTTP MCP endpoints at /v1/mcp/sse and /v1/mcp/streamable; these endpoints require a bearer token or OAuth. The base URL at port 8080 is a REST endpoint, not an unauthenticated MCP endpoint.

Tools and capabilities

The documented capability set includes:

  • Governed memory with validation, semantic recall, confidence, and lifecycle controls
  • Durable, idempotent tasks assigned to exact agents
  • Unified inboxes for local or federated requests and assignment notices
  • Retained replies and reply pagination
  • Session- and revision-fenced runtime handoff
  • Enrollment, roles, profiles, ownership, access groups, grants, and classification checks
  • Explicit federation with bounded read and copy policies
  • Recovery snapshots, in-place upgrades, and retained message claims

The repository description reports 13 MCP tools, while the excerpt does not enumerate every tool name or parameter.

Limitations and notes

Consensus acceptance and block inclusion are different events. Agent requests and results are treated as untrusted data and cannot expand the user’s instructions. Wake notifications are hints without payload, delivery guarantees, or claim evidence.

Adding more agents in personal mode does not create additional consensus voters. Runtime handoff keeps work within the same signed agent identity and does not reassign a task to another agent. HTTP MCP requires authentication, and a separate docker run ... mcp container is not the documented setup because its localhost network is isolated from the running SAGE node.

The l33tdawg/sage MCP server is open source in a public GitHub repository, but the provided material does not state its license.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
246
Stargazers on the source repository.
Last commit
2d ago
Most recent push to the default branch.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Sage

Download a release for macOS, Windows, or Linux, or build from source with Go 1.25.13 or newer. The documented source flow builds `sage-gui`, then uses `sage-gui setup` to create MCP configuration.

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Technical Specs & Signals

Category🧠Knowledge & Memory
PricingFree
More technical detailsExpand â–¾
AuthOther
Last updatedSep 9, 2026
10/10 checks healthy over the last 31d
Views3
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars246
GitHub Star CountTotal stargazers on GitHub representing community popularity (246 stars).
Last commit2d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 9, 2026
52Quality signal: Good · 52/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools20/30
Adoption & activity9/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Sage →Install in Claude DesktopInstall in CursorInstall in VS Code