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ReasonGraph Cloud memory MCP server

ai.primaxiom.memory · Updated

Graph memory for AI agents: entities, cause-effect links, cross-session recall, time travel.

At a glance

ReasonGraph Cloud memory is an MCP server in the AI & Memory category. It is hosted remotely and connects over HTTP, and needs Authorization. It scores B (75/100) on the MCP Skills Hub health score as of Oct 28, 2026.

Source 3GitHub stars

One-click install

Opens your editor and pre-fills the config. Your editor will ask before adding it.

How to install ReasonGraph Cloud memory in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install ReasonGraph Cloud memory in Claude CodeAnthropic's agentic CLI

  1. Add the server

    bash
    claude mcp add --transport http reasongraph https://memory.primaxiom.ai/mcp --header 'Authorization: Bearer <YOUR_TOKEN>'
  2. Fill in your credentials

    Replace Bearer <YOUR_TOKEN> with your real value. Keep secrets out of any config file you commit.

  3. Verify it's connected

    bash
    claude mcp list
  4. Sharing it with your team?

    Add `--scope project` to write it to .mcp.json in the repository root, which you can commit (with the secrets left as environment variables).

  5. Use it in a session

    Ask Claude to use the tools this server exposes. It discovers them automatically.

What does ReasonGraph Cloud memory need from you?

Bearer token — Bearer <your rgm_ API key> from https://memory.primaxiom.ai/console

  • AuthorizationrequiredsecretHTTP header

    Bearer <your rgm_ API key> from https://memory.primaxiom.ai/console

What tools does ReasonGraph Cloud memory expose?

This server requires credentials before it will list its tools, so we cannot enumerate them here. Connect it in your client to see what it offers.

What the code can do

This is a remote server — nothing is installed or run on your machine, so there is no local code to analyze. What it can reach is described above: your requests go to the provider's host.

Before you install

Signals gathered from the source repo and package registry — not a safety verdict.

What it can reach

  • Third-party hosted service — Your requests and any data in them are sent to memory.primaxiom.ai.
  • No local execution — Nothing is installed or run on your machine.
  • Last commit 28 days ago — Actively maintained.
  • 3 GitHub stars — Very little community usage so far.

Client compatibility

Claude Code: NativeClaude Desktop: Via mcp-remoteCursor: NativeCodex CLI: Via mcp-remoteWindsurf: NativeCline: NativeVS Code: Native

Health score

B · 75

Maintenance, adoption, transparency and packaging — not a safety audit. How this is calculated

  • Maintenance30%100%

    Last commit 28 days ago.

  • Adoption20%18%

    3 stars.

  • Transparency20%75%

    3/4: ✓ public source repo, ✓ declared license, ✓ documentation, ✗ discoverable tool list.

  • Provenance15%75%

    Links to its own repository; package linkage unverified.

  • Packaging15%100%

    Remote endpoint with documented authentication.

Documentation

Full README

ReasonGraph

A graph-based memory for AI agents: it ingests facts, auto-extracts entities and cause->effect relations, and discovers connections across independent documents *and* across agent sessions -- with conflict resolution, time-travel, causal tracing, and counterfactuals.

Why ReasonGraph?

Standard RAG retrieves documents similar to your query. ReasonGraph is a persistent, updatable memory that discovers connections *between* facts that were written independently.

When you feed text into add_texts(), ReasonGraph automatically extracts entities (via GLiNER) and cause-effect relations (via a dedicated causal model) that become nodes and typed edges in a graph. Facts that share entities or causal chains get connected -- even if they never reference each other. Multi-hop traversal then walks these connections to build reasoning chains that span multiple sources.

On top of retrieval it works as agent memory: scopes/sessions (agents discover into each other's memory through shared entities), contradiction resolution (a new fact soft-supersedes what it contradicts), time-travel (query(as_of=...)), causal tracing (trace_effects / root_causes / causal_chain), counterfactuals (what_if), and a shippable MemoryService over HTTP and MCP.

Zero config, strong defaults. ReasonGraph() picks the best available entity extractor, causal model, embedder, and reranker automatically -- the eval numbers below come from these defaults. For the SOTA causal model (~0.70 F1) use pip install reasongraph[causal] and the graph uses it automatically. The configuration sections are optional depth, not required reading.

Use it in 60 seconds

Claude Code / Cursor / any MCP client, hosted (EU, no LLM in the loop):

bash
claude mcp add --transport http memory https://memory.primaxiom.ai/mcp \
  --header "Authorization: Bearer rgm_YOUR_KEY"

Local MCP server (stdio, everything on your machine, SQLite):

bash
pip install "reasongraph[service,gliner,fastembed,sqlite]"
claude mcp add memory -- reasongraph-mcp

Python, in-process:

bash
pip install "reasongraph[all]"

Excerpt — the full document is on GitHub.

Maintain ReasonGraph Cloud memory? Add a badge to your README

Shows its health grade, and links readers to install steps for every client.

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