j5ed-knowledge-graph

A knowledge graph memory server for the Model Context Protocol (MCP). Gives AI assistants persistent, structured memory across sessions using a simple entity-relation-observation graph.

Forked from the official @modelcontextprotocol/server-memory and significantly enhanced with multi-agent isolation, tiered search, index navigation, backup rotation, and three transport modes.

What's different from upstream

Feature	Upstream	j5ed
read_graph()	Returns full graph every time	Returns lightweight index stubs by default; force=true for full dump
search_nodes()	Flat substring match	Tokenized multi-word queries, tiered results by match count, field-priority ranking, per-tier caps
open_nodes()	Returns relations between opened nodes only	Also returns inbound relations to index entities (table-of-contents navigation)
Multi-agent	None	Tenant isolation via authored_by: provenance — agents only see their own entities
Multi-user	None	Two-dimensional isolation with user_id: tags — users share agent memory but not private entities
Auth	None	Bearer token authentication for HTTP/SSE transports
Read grants	None	Cross-agent read visibility via AGENT_READ_GRANTS
Write safety	None	Async mutex serializes all mutations — no lost writes under concurrency
Backups	None	Automatic backup rotation (last 5 copies) on every save
Transports	stdio only	stdio, SSE, and Streamable HTTP
Param handling	Strict	Gracefully handles double-serialized JSON from flaky clients

Installation

npm install j5ed-knowledge-graph

Or run directly:

npx j5ed-knowledge-graph

MCP Configuration

stdio (local, default)

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "j5ed-knowledge-graph"],
      "env": {
        "MEMORY_FILE_PATH": "/path/to/memory.json"
      }
    }
  }
}

Streamable HTTP (network)

npx j5ed-knowledge-graph --http --port 3100

{
  "mcpServers": {
    "memory": {
      "url": "http://localhost:3100/mcp"
    }
  }
}

SSE (legacy network)

npx j5ed-knowledge-graph --sse --port 3100

Environment Variables

Variable	Default	Description
MEMORY_FILE_PATH	./memory.json (relative to install)	Path to the NDJSON graph file
DEFAULT_AGENT_ID	default	Agent identity used for stdio connections and when no auth header is provided
AGENT_CREDENTIALS	(empty)	Comma-separated agentId:token pairs for bearer auth. When set, all HTTP/SSE connections require Authorization: Bearer <token>
AGENT_READ_GRANTS	(empty)	Comma-separated readerId:sourceId pairs. Grants readerId read access to entities authored by sourceId

Multi-Agent Setup

When multiple agents share a graph, each agent's writes are tagged with authored_by:<agentId> and reads are filtered to show only entities owned by (or granted to) that agent.

Example: two agents with cross-read access

AGENT_CREDENTIALS="assistant:token-abc,researcher:token-xyz" \
AGENT_READ_GRANTS="assistant:researcher" \
npx j5ed-knowledge-graph --http --port 3100

• assistant authenticates with Bearer token-abc, sees its own entities + researcher's
• researcher authenticates with Bearer token-xyz, sees only its own entities
• Writes are always scoped — agents can only delete what they authored

Tools

Tool	Description
create_entities	Create new entities with name, type, and observations
create_relations	Create directed relations between entities
add_observations	Append observations to existing entities
delete_entities	Delete entities and their relations (agent-scoped)
delete_observations	Remove specific observations from entities
delete_relations	Remove specific relations
read_graph	Returns index stubs by default; force: true for full graph
search_nodes	Tiered search across entity names, types, and observations
open_nodes	Retrieve full entities by name, with index navigation

Index Navigation Pattern

The graph supports an index-first navigation pattern for large knowledge bases:

1. read_graph() — returns lightweight stubs for index entities (name, type, summary)
2. Pick relevant index — open_nodes(["MY_INDEX"])
3. See inbound relations — entities with indexed_in relations to the index are its table of contents
4. Drill into specifics — open_nodes(["SPECIFIC_ENTITY"])

Index entities are detected by entityType containing "index" (case-insensitive) or entity name ending with _INDEX.

Search Behavior

Multi-word queries are tokenized. Results are grouped into tiers by how many tokens matched:

• Tier 1: entities matching any 1 token (broadest)
• Tier N: entities matching all N tokens (most specific)

Within each tier, entities are ranked by field priority: index entities first, then name matches (+30), type matches (+20), observation matches (+10). Per-tier caps prevent noise.

Results are returned as lightweight stubs (name, type, matchedIn fields, optional snippet). Use open_nodes() to get full observations.

Storage Format

NDJSON (newline-delimited JSON). Each line is either:

{"type":"entity","name":"MY_ENTITY","entityType":"service","observations":["fact 1","fact 2"]}
{"type":"relation","from":"MY_ENTITY","to":"MY_INDEX","relationType":"indexed_in"}

Docker

docker build -t j5ed-knowledge-graph .
docker run -i -v graph-data:/app/dist --rm j5ed-knowledge-graph

Development

git clone https://github.com/arktechnwa/j5ed-knowledge-graph.git
cd j5ed-knowledge-graph
npm install
npm run build
npm test        # 7 suites, 80 tests
npm run dev     # stdio mode with ts-node

License

MIT