ReasonGraph Cloud memory MCP server

ReasonGraph

<!-- mcp-name: ai.primaxiom.memory/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 addtexts(), 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(asof=...)), causal tracing (traceeffects / rootcauses / causalchain), counterfactuals (whatif), 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):

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):

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

Python, in-process:

pip install "reasongraph[all]"

from reasongraph import ReasonGraph

graph = ReasonGraph()
graph.initialize_sync()
graph.add_texts_sync(["TSMC is building a chip fab in Phoenix, Arizona.",
                      "Arizona ordered water cuts for industrial users in Maricopa County."])
print(graph.discover_sync("water and chips"))   # a path: water cuts -> Arizona -> TSMC fab

Any language, over HTTP (self-hosted or hosted):

curl -X POST https://memory.primaxiom.ai/sessions/notes/memory \
  -H "Authorization: Bearer rgm_YOUR_KEY" -H "Content-Type: application/json" \
  -d '{"text": "Apple sources M-series chips from TSMC in Arizona."}'

Ready-to-copy agents (Groq/OpenAI-compatible research agent, two agents sharing one memory, Claude Code with persistent memory, LangGraph) live in examples/agents/.

Hosted: ReasonGraph Cloud

memory.primaxiom.ai runs this library as a service: sign in, get a free key (10k requests a month), remote MCP endpoint, browser console and playground. Extraction runs with small models on servers PrimAxiom operates (currently in the EU); facts are only sent to an LLM provider if you ask for a synthesized answer. Early access.

JavaScript and TypeScript

Shortened. The full README is on GitHub.