setup
Skill624 starsChanged 6 months agoApache-2.0View on GitHub
Install Memoria and configure MCP for AI tools (Kiro, Cursor, Claude Code, Codex, Gemini CLI). Decision tree for Cloud vs self-hosted mode, database, embedding provider. Use when helping users set up Memoria.
- Pipes a download into a shell
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191 other projects use a version of this.
Adopted by
Most recently taken up by
- hydra-db/hydradb-claude-codetheir own version, first seen Nov 8, 2026
- WALKERKILLER/Look-Tongji-Notestheir own version, first seen Nov 8, 2026
- steerlabs/opensteertheir own version, first seen Nov 8, 2026
- ansvisor/ansvisortheir own version, first seen Nov 8, 2026
- dsifry/metaswarmtheir own version, first seen Nov 8, 2026
- anombyte93/prd-taskmastertheir own version, first seen Nov 8, 2026
Used with
Projects with it also use add-voice-transcription, add-discord and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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- +8 more from microsoft
Other versions191 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 191
- anthropics/knowledge-work-plugins★ 35ksame name
- garrytan/gbrain★ 32ksame name
- gavrielc/nanoclaw★ 31ksame name
- nanocoai/nanoclaw★ 31ksame name
- qwibitai/nanoclaw★ 31ksame name
- Q00/ouroboros★ 6.3ksame name
- pytorch/executorch★ 5.2ksame name
- agenticnotetaking/arscontexta★ 3.5ksame name
- aws/agent-toolkit-for-aws★ 3.4ksame name
- spotify/portal-ai-plugins★ 2.8ksame name
181 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- .cursor/rules/memory-branching-patterns.mdcCursor rule
- .cursor/rules/memory.mdcCursor rule
- api-referenceSkill
- architectureSkill
- deploymentSkill
- local-embeddingSkill
- plugin-developmentSkill
- releaseSkill
The file
We split this skill into the rules it gives an agent
7 rules. 0 are common, 4 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 170 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Follow these steps in order.
- Very specific: line 12
Ask: "Use Memoria Cloud, or run your own instance?"
- Distinctive: line 19
Ask: "Which AI tool are you using — Kiro, Cursor, Claude Code, Codex, or Gemini CLI?"
- Distinctive: line 35
Ask: "Do you have a MatrixOne database running?"
- Distinctive: line 46
Ask: "Do you have an OpenAI-compatible embedding endpoint?"
- Very specific: line 128
After running memoria init, tell user to:
- Distinctive: line 131
Ask the AI: *"Do you have memory tools available?"*
“Common” is measured: kept by at least 6 in 10 of the 170 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Decision Tree
Follow these steps in order. Steps marked "Self-Hosted only" can be skipped for Memoria Cloud users.
Step 1: Memoria Cloud or Self-Hosted?
Ask: "Use Memoria Cloud, or run your own instance?"
- Memoria Cloud (recommended) → sign up at thememoria.ai, get API URL + token, then proceed to Step 2
- Self-hosted → proceed to Step 2
Step 2: Which AI tool?
Ask: "Which AI tool are you using — Kiro, Cursor, Claude Code, Codex, or Gemini CLI?"
The --tool flag value and config files generated per tool:
| Tool | --tool value | Config files |
| Kiro | kiro | .kiro/settings/mcp.json + .kiro/steering/memory.md |
| Cursor | cursor | .cursor/mcp.json + .cursor/rules/memory.mdc |
| Claude Code | claude | .mcp.json + CLAUDE.md |
| Codex | codex | ~/.codex/config.toml + AGENTS.md |
| Gemini CLI | gemini | .gemini/settings.json + GEMINI.md |
Step 3: Database (Self-Hosted only)
Skip this step if user chose Memoria Cloud in Step 1.
Ask: "Do you have a MatrixOne database running?"
- Already have one → get connection URL (format:
mysql+pymysql://<user>:<pass>@<host>:<port>/<db>) - No → run
docker compose up -din the Memoria repo root (wait 30-60s for first start)
Step 4: Embedding provider (Self-Hosted only)
Skip this step if user chose Memoria Cloud in Step 1.
⚠️ Hard to reverse. Embedding dimension is locked into schema on first startup.
Ask: "Do you have an OpenAI-compatible embedding endpoint?"
- Yes → collect: base URL, API key, model, dimension
- No → suggest SiliconFlow (free tier) or Ollama. Local embedding requires
--features local-embeddingbuild.
Step 5: Install Memoria CLI
The memoria binary is required for all modes — it serves as the MCP bridge between the AI tool and the Memoria server.
One-line install (recommended):
curl -sSL https://raw.githubusercontent.com/matrixorigin/Memoria/main/scripts/install.sh | bashOr download manually from GitHub Releases.
Platform-specific manual install:
# Linux x86_64
curl -LO https://github.com/matrixorigin/Memoria/releases/latest/download/memoria-x86_64-unknown-linux-musl.tar.gz
tar xzf memoria-x86_64-unknown-linux-musl.tar.gz && sudo mv memoria /usr/local/bin/
# macOS Apple Silicon
curl -LO https://github.com/matrixorigin/Memoria/releases/latest/download/memoria-aarch64-apple-darwin.tar.gz
tar xzf memoria-aarch64-apple-darwin.tar.gz && sudo mv memoria /usr/local/bin/
# From source (required for local embedding)
cd Memoria/memoria && cargo build --release -p memoria-cli
# With local embedding: cargo build --release -p memoria-cli --features local-embedding
sudo cp target/release/memoria /usr/local/bin/Verify: memoria --version
Step 6: Configure
Memoria Cloud (Remote Mode)
Sign up at thememoria.ai — after login you will receive the API URL and token.
cd <user-project>
memoria init --tool <tool> --api-url '<API URL from thememoria.ai>' --token '<your token>'Replace <tool> with the value from Step 2 (e.g., kiro, cursor, claude, codex, gemini).
Self-Hosted: Local Docker
docker compose up -d # Start MatrixOne
docker ps --filter name=matrixone # Verify (wait 30-60s)
cd <user-project>
memoria init --tool <tool> # + embedding flags belowSelf-Hosted: Existing DB
cd <user-project>
memoria init --tool <tool> --db-url 'mysql+pymysql://<user>:<pass>@<host>:<port>/<db>'Embedding Flags (Self-Hosted only)
# Local (default, no flags)
memoria init --tool <tool>
# OpenAI-compatible
memoria init --tool <tool> \
--embedding-provider openai \
--embedding-base-url https://api.siliconflow.cn/v1 \
--embedding-api-key sk-... \
--embedding-model BAAI/bge-m3 \
--embedding-dim 1024Step 7: Verify
After running memoria init, tell user to:
- Restart their AI tool
- Ask the AI: "Do you have memory tools available?"
- Or run:
memoria status
Expected: memory_retrieve("test") → "No relevant memories found".
Post-Setup
memoria rules --force # After upgrading Memoria binary, re-sync steering rulesMCP Server Modes (Reference)
These are the underlying commands that memoria init configures. Users normally don't need to run them directly.
# Embedded mode (direct DB connection, self-hosted)
memoria mcp --db-url "mysql+pymysql://root:111@localhost:6001/memoria" --user alice
# Remote mode (proxy to Memoria API server, Cloud or self-hosted API)
memoria mcp --api-url "<API URL>" --token "<token>"
# SSE transport (alternative to default stdio)
memoria mcp --transport sseTroubleshooting
| Problem | Fix |
| MatrixOne won't start | docker logs memoria-matrixone |
| Port 6001 in use | Change MO_PORT in .env |
| Can't connect to DB | Wait 30-60s on first start |
| Docker permission denied | sudo usermod -aG docker $USER && newgrp docker |
| Docker not available | Use Memoria Cloud instead (no Docker needed) |
| First query slow | Normal with local embedding (~3-5s). Use openai provider for faster response |
local-embedding not compiled | Use OpenAI-compatible service, or build from source with --features local-embedding |
| AI tool doesn't see memory tools | 1. Run which memoria to verify CLI installed 2. Restart AI tool 3. Test MCP server directly |
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