Solving AI Agent Amnesia: Building a Self-Hosted Shared Memory Mesh for Coding LLMs
Every developer using AI coding agents (Claude Code, Cursor, Codex CLI, Antigravity) knows the feeling: You spend three hours with an AI assistant debugging a distributed state machine, isolating an obscure race condition, and fine-tuning an Nginx reverse proxy. The agent understands every nuance o
Posted on Oct 30 • Originally published at github.com
Every developer using AI coding agents (Claude Code, Cursor, Codex CLI, Antigravity) knows the feeling:
You spend three hours with an AI assistant debugging a distributed state machine, isolating an obscure race condition, and fine-tuning an Nginx reverse proxy. The agent understands every nuance of your architecture.
The next morning, you open a new session: Your agent has complete amnesia.
You re-explain the database schema. You re-explain why port 5432 is reserved. You re-explain your branching rules.
The standard workaround today is creating a CLAUDE.md or memory.md file in your repository root.
It works fine for the first week. But after three weeks of active development:
We built and open-sourced Transcendence Memory to solve this once and for all.
Transcendence Memory provides a self-hosted cloud memory service that turns isolated AI sessions into a collaborative, persistent knowledge network:
Agent A (Frontend / Claude) Agent B (Backend / Codex) Agent C (DevOps / Terminal) | | | | store & search own | store & search own | store & search own | cross-query B, C | cross-query A, C | cross-query A, B | | | +----------------------------------+----------------------------------+ | Transcendence Memory Server +-------------------------+ | Container: frontend | | Container: backend | | Container: devops | | Container: shared | +-------------------------+ Enter fullscreen mode Exit fullscreen mode Key Architecture Highlights Strict Container Isolation: Each agent writes exclusively to its own namespace ( frontend, backend, etc.). No accidental context contamination. Cross-Query Mesh: Agents can perform semantic queries across peer containers without polluting their working prompt state.
Multi-Tier RAG & Knowledge Graph: Tier 0: Ultra-low latency vector retrieval via LanceDB (<15ms, zero LLM inference token cost). Tier 1: Hybrid BM25 + dense vector ranking. Tier 2: Entity-relationship Knowledge Graph extraction powered by LightRAG.
Tier 3: Multimodal parsing for system diagrams, PDFs, and database schemas. Lifecycle Hooks for Zero-Effort Capture: Integrates with your host CLI. When you run git commit, an automatic hook summarizes the architectural intent and saves it without manual note-taking.
100% Self-Hosted & Private: Runs via a clean Docker Compose stack. Zero telemetry, full data sovereignty. Works with any OpenAI-compatible endpoint or local Ollama models.
Deploy in 60 Seconds with an AI Prompt You don't even need to read through configuration docs. The repository README includes an AI-Assisted Setup Prompt: simply copy the prompt into Claude Code or Cursor, and your agent will install, configure, and health-check the Docker container automatically!
Explore the code and join the project on GitHub:
MIT Licensed. If you find this helpful for your daily AI workflow, leave a ⭐️ on GitHub!