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l33tdawg/sage

v9.2.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

agentic-ai artificial-intelligence bft-consensus bft-network distributed-systems governance

Summary

AI summary

Updates Quick Start (sage-gui) ```bash, sage-gui, and mcp across a mixed release.

Changes in this release

Feature Medium

Adds corroboration integrity guard and on-chain author field to app-v10.

Adds corroboration integrity guard and on-chain author field to app-v10.

Source: llm_adapter@2026-06-04

Confidence: high

Feature Medium

Adds sage_corroborate tool exposing corroboration on the MCP surface.

Adds sage_corroborate tool exposing corroboration on the MCP surface.

Source: llm_adapter@2026-06-04

Confidence: high

Dependency Low

Bumps SDK version from 9.1.0 to 9.2.0.

Bumps SDK version from 9.1.0 to 9.2.0.

Source: llm_adapter@2026-06-04

Confidence: high

Full changelog

SAGE v9.2.0

Sovereign Agent Governed Experience — persistent, governed memory for AI agents.

Quick Start (sage-gui)

# Download and extract for your platform, then:
./sage-gui setup    # Interactive setup wizard
./sage-gui serve    # Start your personal memory node

See the README for full documentation.

Changelog

  • 80d62a4e48c03d8da5e1f8345f58e47efcd97944 feat(abci,store): app-v10 fork — corroboration integrity guard + on-chain author field
  • 0a28c8afac7ca37c23cf304db76f947c41e724a2 feat(mcp): sage_corroborate tool exposing corroboration on the MCP surface (issue #31, by @ihubanov)
  • 43e4fc29d16d3f4b9d1b82a4a485dd9518d70932 release(v9.2.0): app-v10 corroboration integrity guard + on-chain author field + sage_corroborate MCP tool; bump SDK 9.1.0 -> 9.2.0

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About l33tdawg/sage

Institutional memory for AI agents with real BFT consensus. 4 application validators vote on every memory before it's committed — no more storing garbage. 13 MCP tools, runs locally, works with any MCP-compatible model. Backed by 4 published research papers.

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Beta — feedback welcome: [email protected]