GoodMem Claude Code Plugin
A Claude Code plugin for GoodMem — memory infrastructure for AI agents.
With this plugin, you can operate GoodMem's memory infrastructure in plain English — create and manage embedders, rerankers, and LLMs, ingest memories from files, base64 strings, and plain text, and build knowledge-powered agents for RAG and Deep Research. Also includes Python and Java SDK references for writing GoodMem code.
Use cases
- Deep Research agent — Create an embedder and space, ingest research papers and documents, then ask complex questions that retrieve and synthesize information across your entire knowledge base.
- RAG pipeline — Configure embedders, rerankers, and LLMs, store source materials as memories, and retrieve relevant context for answering questions.
- Self-improving support agent — Ingest historical issue tickets and resolutions, search for solutions matching a new problem description, and iteratively store new discoveries and fixes as memories so future troubleshooting gets smarter over time.
Installation
From the official marketplace:
/plugin install goodmem
From this repo:
/plugin marketplace add PAIR-Systems-Inc/goodmem-claude-code-plugin
/plugin install goodmem@goodmem-plugins
Configuration
What you need to configure
GoodMem credentials (required):
GOODMEM_BASE_URL — GoodMem server URL (e.g., https://your-server.example.com)GOODMEM_API_KEY — GoodMem API key (starts with gm_)
TLS / self-signed certificates (optional):
This is an advanced topic. If you do not even know what that is, skip this section.
If the GoodMem server uses self-signed CA certificates, set one and only one of the following:
NODE_EXTRA_CA_CERTS=/path/to/rootCA.pem — adds the CA to the trusted store. More secure, but requires you to copy the certificate file from the GoodMem server to your machine.NODE_TLS_REJECT_UNAUTHORIZED=0 — disables certificate verification entirely. Easy and hassle-free, but not recommended for production.
How to configure
1. In English from chat (GoodMem credentials only; TLS variables cannot be set this way)
Just tell Claude your server details — it will call goodmem_configure automatically:
> "Configure GoodMem with base URL https://my-server.com and API key gm_abc123"
Credentials persist for the session. You can reconfigure anytime to switch servers.
2. Shell export (before launching Claude Code; works for all configurations)
export GOODMEM_BASE_URL="https://your-server.example.com"
export GOODMEM_API_KEY="gm_..."
export NODE_EXTRA_CA_CERTS="/path/to/rootCA.pem" # only if needed for self-signed certs
export NODE_TLS_REJECT_UNAUTHORIZED=0 # only if you want to skip cert verification
Reloading after updates
After installing a new version or pulling the latest changes, run /reload-plugins inside Claude Code to pick up the updates without restarting. If you are using the plugin from this repo, you can run git pull to get the latest changes.
What's included
| Component | Description |
|---|
skills/help/ | Setup guide, available skills overview, example workflows |
skills/python/ | Python SDK reference — method signatures, parameters, examples |
skills/java/ | Java SDK usage reference — setup, builders, streaming, pagination, examples |
skills/mcp/ | MCP tools reference — all 41+ tools with parameters |
.mcp.json | MCP server with auto-inference from 79 model registries |
Skills
goodmem:help — Overview of all skills, setup instructions, example workflowsgoodmem:python — Python SDK reference for writing GoodMem codegoodmem:java — Java SDK reference for writing GoodMem codegoodmem:mcp — MCP tools reference for direct operations
MCP tools
The MCP server exposes all GoodMem API operations as tools:
- goodmem_configure — set server credentials from chat
- goodmem_lookup_model — look up model info from the registry (79 models: 29 embedders, 34 LLMs, 16 rerankers)
- embedders — create, list, get, update, delete embedding models
- llms — create, list, get, update, delete LLM configurations
- rerankers — create, list, get, update, delete reranker models
- spaces — create, list, get, update, delete memory spaces
- memories — create, list, get, update, delete, retrieve, batch operations
- ocr — extract text from documents
- users, apikeys, system, admin — manage users, API keys, server
Auto-inference
When creating embedders, LLMs, or rerankers, just provide model_identifier and the plugin auto-fills provider, endpoint, dimensions, and other fields from the built-in model registry. You only need to provide display_name, model_identifier, and credentials — everything else is inferred.
Explicit values always override inferred defaults.
Credential validation
