Junê (june-mcp)
Give your agent a memory. june-mcp is the official MCP server for Junê — it connects any MCP host (Claude Desktop, Claude Code, and friends) to a June knowledge graph, so your agent can ask, search, and remember against a shared, cited, tenant-isolated memory. This package is a thin, zero-logic connector: all retrieval, graph assembly, and answering happen on the June endpoint you point it at. No engine code lives here — which is why it's small enough to read in one sitting. june-mcp speaks to any June service. Three ways to have one:
Ads
Overview
Give your agent a memory. june-mcp is the official MCP server for Junê — it connects any MCP host (Claude Desktop, Claude Code, and friends) to a June knowledge graph, so your agent can ask, search, and remember against a shared, cited, tenant-isolated memory. This package is a thin, zero-logic connector: all retrieval, graph assembly, and answering happen on the June endpoint you point it at. No engine code lives here — which is why it's small enough to read in one sitting. june-mcp speaks to any June service. Three ways to have one:
Capabilities
- june_answer
Ask cited questions — have your agent call `june_answer` to get grounded, cited answers from your knowledge graph, abstaining when uncertain.
- june_search
Search with evidence — use `june_search` to retrieve ranked, multi-hop evidence for any query across your connected memory.
- june_remember
Remember new facts — write notes or facts into the graph via `june_remember`, making them immediately retrievable and citable.
- june_enumerate
List everything — run `june_enumerate` to get recall-complete results for "list ALL X" queries, not just top-k matches.
- june_ingest_file
Ingest local files — upload PDFs, docs, images, or audio from an approved folder with `june_ingest_file` (when `JUNE_FILES_ROOT` is set).
- june_neighborhood
Explore graph structure — inspect the neighborhood around a node with `june_neighborhood` or export a bounded `june_subgraph`.
Installation
pip install june-mcp # just the connector (or: pipx install june-mcp)
Configuration
{
"mcpServers": {
"june": {
"command": "june-mcp",
"env": {
"JUNE_BASE_URL": "http://localhost:8000",
"JUNE_API_KEY": "your-key",
"JUNE_CANVAS": "work",
"JUNE_LLM_KEY": "your-llm-provider-key"
}
}
}
}Paste into ~/.cursor/mcp.json or .cursor/mcp.json
Related MCP servers
Knowledge & Memory
Memory MCP Server
Persist assistant memory as a lightweight knowledge graph across sessions.
2 tools · create_entities, search_nodes
Knowledge & Memory
OpenLore
Sponsored by <a href="https://oiya.ai/?utm_source=github&utm_medium=referral&utm_campaign=openlore&utm_content=sponsor_logo"><img src="assets/oiya-logo.svg" alt="Oiya" height="24" align="absmiddle"></a> OpenLore is a minimal, extensible, agent-native knowledge base that keeps shared context current and inspectable. AI coding agents already know how to explore files with ls, cat, grep, find, pipes, and shell loops. OpenLore gives them that same interface over SSH, backed by your documentation instead of a real machine.
5 tools · Manage identity-scoped access, publish
Knowledge & Memory
liza-studio/skillmem
Self-improving skill memory for coding agents: skills that help get reinforced, unused ones fade on an Ebbinghaus decay curve. $0 write path (SQLite FTS5 + local ONNX embeddings, no LLM calls), bilingual EN/RU hybrid search, SHA256 tamper-evident history, deterministic LongMemEval benchmark in-repo (hit@5 0.871). 8 mem_* tools; deep Claude Code hook integration optional. `pip install skillmem`
Knowledge & Memory
yubinkim444/repo-memory
Shared, git-tracked memory for AI agents on the same codebase. Stores facts/decisions/gotchas in a `.ai-memory/` directory you commit to the repo — sync is git, no DB, no daemon. What one Claude/Cursor/Cline learns, the next one picks up automatically across users and machines. 5 MCP tools (`get_repo_memory`, `add_fact`, `list_facts`, `add_decision`, `add_gotcha`). Install: `uvx repo-memory-mcp --repo /path/to/repo`.
Knowledge & Memory
Mnemosyne-OS/Mnemosyne-Neural-OS
Memory the human governs: vaults separated by domain (code, notes, journal) on your own machine, hybrid retrieval (BM25 fused with vectors by RRF), and a declared vault list, so a vault left out cannot be read by mistake. Also reads what the OTHER coding agents on this machine wrote to disk, and flags when two are live in the same git worktree. Needs the Mnemosyne OS desktop app running. `npx -y @mnemosyne_os/mcp`
Knowledge & Memory
kunickiaj/codemem
Persistent coding memory across sessions, machines, and teammates. Provides MCP tools for searching and managing memories, optional cross-machine sync, and project sharing. OpenCode, Claude Code, and Codex integrations add automatic capture and context injection.
Knowledge & Memory
kskurtveit/echocache
Cache expensive LLM results — HTTP-style freshness (TTL / stale-while-revalidate) plus semantic recall of related past answers
Knowledge & Memory
pipeshub-ai/mcp-server
Permission-aware enterprise search and RAG that gives agents context from your business apps (Google Workspace, Microsoft 365, Slack, Jira, and others). Streamable HTTP or local stdio (`npx @pipeshub-ai/mcp`). Official registry: `io.github.pipeshub-ai/mcp`.