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lithtrix/lithtrix-mcp

by Various

Memory Consolidation for AI agents across vendors, owners, and time. Persistent memory, credibility-scored web search, browser fetch, and a shared Commons pool — all under a stable

L

MCP

lithtrix/lithtrix-mcp

Added 1 June 2026

Overview

Memory consolidation for AI agents across vendors, owners, and time. Provides persistent memory, credibility-scored web search, browser fetch, and a shared Commons pool. Uses a stable ltx key that survives tool switches, session resets, and orchestrator changes.

Best for

Best for
Developers building multi-agent systems that need persistent, cross-session memory

Use cases

  • Persist agent memory across sessions and tool switches
  • Search the web with credibility scoring for agent context
  • Share a common memory pool among multiple agents

Notes

Memory consolidation for AI agents across vendors, owners, and time. Provides persistent memory, credibility-scored web search, browser fetch, and a shared Commons pool. Uses a stable ltx key that survives tool switches, session resets, and orchestrator changes.

0 stars on GitHub. Last updated 2026-05-13.

Use cases

  • Persist agent memory across sessions and tool switches
  • Search the web with credibility scoring for agent context
  • Share a common memory pool among multiple agents

Pros

  • Persistent memory that survives session resets and orchestrator changes
  • Shared Commons pool enables multi-agent collaboration
  • Credibility-scored web search reduces irrelevant results

Cons

  • Zero stars on GitHub indicates very early stage with minimal community validation
  • Requires Node.js runtime and npx setup
  • Credibility scoring mechanism is not publicly documented

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Persistent memory that survives session resets and orchestrator changes
  • Shared Commons pool enables multi-agent collaboration
  • Credibility-scored web search reduces irrelevant results

Cons

  • Zero stars on GitHub indicates very early stage with minimal community validation
  • Requires Node.js runtime and npx setup
  • Credibility scoring mechanism is not publicly documented