gjalla runs on your own machine — the client starts it, so there is no endpoint to ping. 3 685 installs a week from pypi.
Self-curating shared memory that keeps your agents working like a high-performing team
Today is the operative word: we check gjalla every 15 minutes and re-read its code on every release. Watch it and you find out the day that stops being true.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add mcp-server -- uvx gjalla
{
"mcpServers": {
"mcp-server": {
"args": [
"gjalla"
],
"command": "uvx"
}
}
}
[mcp_servers.mcp-server]
command = "uvx"
args = ["gjalla"]
{
"mcpServers": {
"mcp-server": {
"args": [
"gjalla"
],
"command": "uvx"
}
}
}
{
"mcpServers": {
"mcp-server": {
"args": [
"gjalla"
],
"command": "uvx"
}
}
}
This one needs environment variables set before it will start:
GJALLA_API_KEY (Your gjalla API key. Required for Startup and Custom tiers (MCP/CLI agent integration). Get one at https://gjalla.io.).
The author declared them in the registry entry; get the values from the project itself.
Hosted self-curating shared memory that keeps your agents working like a high-performing team
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Shared, git-tracked working memory for AI agents on the same codebase.
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Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path.
shared AI-context layer for teams — persistent memory your agents search and update over MCP
Shared, persistent memory for AI agents — works with Claude, Kiro, Cursor, or any MCP client.
Shared memory + orchestration for your coding agents. Local-first MCP, vector RAG.
Answers built from our own checks of this server.