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Health: ActiveRecent health check succeeded.Last checked 9/28/2026, 1:46:16 PM

ReasonGraph Cloud memory

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository1 GitHub StarsTotal stargazers on GitHub for the source repository (1 stars).Visit Website

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "reasongraph-cloud-memory": {
      "command": "uvx",
      "args": [
        "reasongraph"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Documentation Overview

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.

PyPI version Python 3.11+ License: MIT

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):

Terminal
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):

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

Python, in-process:

Terminal
pip install "reasongraph[all]"
server.ts
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):

Terminal
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

Agents that are not written in Python talk to the hosted service through a dependency-free client (clients/typescript). It uses the platform fetch, so it runs on Node 18+, Bun, Deno, Cloudflare Workers and the browser. npm install reasongraph.

server.ts
import { Memory, memoryTools } from "reasongraph";

const mem = new Memory({ apiKey: process.env.REASONGRAPH_API_KEY });
await mem.remember("scout", "TSMC is building a chip fab in Phoenix, Arizona.");
const facts = await mem.discover("What could disrupt the Phoenix fab?");

memoryTools(mem) returns OpenAI-style tool definitions with their executors attached, for agents that call functions directly without a framework: remember, recall and why.

Installation

Terminal
pip install reasongraph[all]        # everything included

Or install only what you need:

Terminal
pip install reasongraph             # core: in-memory backend, NER extraction, embeddings
pip install reasongraph[gliner]     # + GLiNER entity extraction + hybrid causal (default, recommended)
pip install reasongraph[causal]     # + SOTA span-pointer causal model (~0.70 F1) + hybrid fallback
pip install reasongraph[gliner2]    # + GLiNER2 alternative (single model does entities + causal)
pip install reasongraph[sqlite]     # + SQLite backend with sqlite-vec
pip install reasongraph[postgres]   # + PostgreSQL + pgvector backend
pip install reasongraph[service]    # + HTTP + MCP memory service
pip install reasongraph[fastembed]  # + pure-ONNX embedder / reranker (faster cold start)

Cross-Source Discovery

Two reports about different topics. Source A covers TSMC's semiconductor plant. Source B covers Arizona's water crisis. Neither mentions the other's subject.

server.ts
import asyncio
from reasongraph import ReasonGraph

source_a = [  # Tech industry report
    "TSMC announced plans to build a $40 billion semiconductor fabrication plant in Phoenix, Arizona.",
    "The Phoenix fab requires 10 million gallons of purified water daily to cool wafers during the chip etching process.",
    "TSMC signed a long-term supply agreement with Apple to manufacture next-generation M-series processors at the Arizona facility.",
    "Construction delays at the Phoenix site pushed first production to late 2025, raising concerns among TSMC's major customers.",
]

source_b = [  # Environmental report -- never mentions TSMC, semiconductors, or chips
    "Arizona declared a water emergency after Lake Mead dropped to its lowest level since the 1930s, threatening water supply for millions.",
    "The Arizona Department of Water Resources ordered mandatory water cuts for all industrial users in Maricopa County, where Phoenix is located.",
    "Intel paused expansion of its Chandler, Arizona chip plant citing water availability concerns and rising operational costs.",
    "Apple warned investors that component shortages from its Asian and North American suppliers could impact iPhone production timelines through 2026.",
]

async def main():
    async with ReasonGraph() as graph:
        await graph.add_texts(source_a)
        await graph.add_texts(source_b)
        results = await graph.query("How does the Arizona water crisis affect semiconductor manufacturing?")
        for i, text in enumerate(results, 1):
            source = "A" if text in source_a else "B"
            print(f"{i}. [Source {source}] {text}")

asyncio.run(main())
Code
1. [Source B] Intel paused expansion of its Chandler, Arizona chip plant citing water availability concerns and rising operational costs.
2. [Source B] The Arizona Department of Water Resources ordered mandatory water cuts for all industrial users in Maricopa County, where Phoenix is located.
3. [Source A] The Phoenix fab requires 10 million gallons of purified water daily to cool wafers during the chip etching process.
4. [Source B] Arizona declared a water emergency after Lake Mead dropped to its lowest level since the 1930s.
5. [Source A] TSMC announced plans to build a $40 billion semiconductor fabrication plant in Phoenix, Arizona.
6. [Source A] TSMC signed a long-term supply agreement with Apple to manufacture M-series processors at the Arizona facility.

Results come from both sources. No single document contains this chain. Here is what happens under the hood:

ReasonGraph extracts entities and causal relations from each text (requires an entity+causal extractor, e.g. pip install reasongraph[gliner] or [all]):

Text (abbreviated)EntitiesCausal relations
TSMC to build fab in Phoenix, Arizona...TSMC, Phoenix, Arizona--
Phoenix fab requires 10M gallons water...Phoenix--
TSMC supply agreement with Apple...TSMC, Apple, Arizona--
Construction delays at Phoenix site...TSMC, PhoenixConstruction delays -> first production
Arizona water emergency, Lake Mead...Arizona, Lake MeadLake Mead dropped -> water emergency
Mandatory water cuts in Maricopa County...Arizona Dept. of Water Resources, Phoenix, Maricopa County--
Intel paused Arizona chip plant...Intel, Chandler, Arizona--
Apple warned of component shortages...Applecomponent shortages -> iPhone production timelines

Three entities appear in both sources, creating bridge nodes:

Bridge entitySource A connectionsSource B connections
ArizonaTSMC fab, TSMC-Apple dealwater emergency, Intel pause, water cuts
PhoenixTSMC fab, water usage, delayswater cuts for industrial users
AppleTSMC supply agreementcomponent shortage warning

The query traversal path:

Water crisis query -> finds water-related texts from both sources via embeddings -> follows Arizona and Phoenix entity edges to discover TSMC's water-intensive fab -> follows Apple entity edge from TSMC supply agreement to Apple's component shortage warning. The causal relation Lake Mead dropped -> water emergency connects the environmental trigger to the industrial impact.

Full demo: uv run python examples/cross_source_discovery.py

Quick Start

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
1
Stargazers on the source repository.
Last commit
10d ago
Most recent push to the default branch.

Reviews

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Frequently Asked Questions about ReasonGraph Cloud memory

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "reasongraph-cloud-memory": { "command": "uvx", "args": ["reasongraph"] } }

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Technical Specs & Signals

Category🧠Knowledge & Memory
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimePython
Last updatedSep 18, 2026
Views0
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars1
GitHub Star CountTotal stargazers on GitHub representing community popularity (1 stars).
Last commit10d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 18, 2026
40Quality signal: Fair Β· 40/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 5h ago via OSV.dev Β· reasongraph (PyPI)

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