ReasonGraph Cloud 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 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):

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

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.

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

pip install reasongraph[all]        # everything included

Or install only what you need:

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.

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())

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]):

Three entities appear in both sources, creating bridge nodes:

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

Using a built-in dataset

from reasongraph import ReasonGraph

graph = ReasonGraph()
graph.initialize_sync()
graph.load_dataset_sync("financial")

results = graph.query_sync("What caused the 2008 financial crisis?")
for i, text in enumerate(results, 1):
    print(f"{i}. {text}")

graph.close_sync()

Output -- a connected reasoning chain, not just keyword matches:

1. Lehman Brothers filed for bankruptcy in September 2008 after massive MBS losses.
2. Loose lending standards fueled a housing price bubble across the United States.
3. Lehman's collapse triggered a global credit freeze as interbank lending stopped.
4. Mortgage-backed securities built on subprime loans collapsed when defaults surged.
5. The U.S. government enacted TARP, a $700 billion bailout to stabilize the financial system.
6. Banks issued subprime mortgages to borrowers with poor credit histories.

Async API

import asyncio
from reasongraph import ReasonGraph

async def main():
    async with ReasonGraph() as graph:
        await graph.load_dataset("financial")
        results = await graph.query("What caused the 2008 crisis?")
        for text in results:
            print(text)

asyncio.run(main())

Features

• Cross-source discovery -- connect facts across independent documents through shared entities and causal relations
• Automatic extraction -- entities (GLiNER gliner_small-v2.5 by default) and cause->effect relations (a dedicated span-pointer / hybrid causal model) are extracted on add, both on by default; falls back to GLiNER2 then BERT NER when gliner is not installed
• Agent memory -- scopes/sessions with cross-session discovery, contradiction resolution (soft-supersede), time-travel (as_of), semantic dedup, and auto-forget
• Causal reasoning -- trace downstream effects, root causes, and directed causal paths; ask counterfactual what_if
• Hybrid search -- combine embedding similarity, keyword (trigram) matching, or both
• Multi-hop traversal -- follow graph edges to discover connected reasoning chains
• Cross-encoder reranking -- rerank results at each hop with a cross-encoder (ms-marco-MiniLM-L-6-v2 by default, multilingual mMARCO in the hosted service)
• Memory service -- ready HTTP + MCP server so agents share and query memory
• Built-in datasets -- load curated reasoning graphs for immediate use
• Async-first -- native async API with sync convenience wrappers
• Pluggable backends -- in-memory (zero-config default), SQLite, or PostgreSQL with pgvector

Causal eval cases

tests/data/causal_cases.jsonl (40 reviewed cases), tests/data/causal_cases_batch2.jsonl (40 more, 8 domains x 5, 12 non-English) tests/data/causal_cases_batch3.jsonl (60 more: 20 each German, Spanish, French) and tests/data/causal_cases_batch4_trnl.jsonl (20 Turkish, 20 Dutch) each hold multi-source why-questions with a gold chain. Run python tests/eval_causal_cases.py --cases tests/data/causal_cases_batch2.jsonl. Baseline on batch2 with the default models: chain recovered 100%, ordered 47%, answer 100%, causal chain 65%.

Models

Every model slot is pluggable; these are the defaults and what ReasonGraph Cloud runs. All of them are small and run on CPU.

Evaluation scripts for each slot are in tests/ (eval_causal_extraction.py, eval_causal_cases.py) and results are quoted next to the options below.

Built-in Datasets

graph.load_dataset_sync("financial")

Search Modes

embedding (the default) finds seeds by meaning. hybrid fuses that with a word-level trigram channel (pg_trgm strict word similarity, index-assisted on Postgres): a name, a code, a number or a compound in the question matches the fact that contains it even when the embedder never saw the word. keyword is the trigram channel alone, for known-term lookups. The memory loop takes the same option (MemoryLoop(search_mode="hybrid"), or REASONGRAPH_LOOP_SEARCH=hybrid), and stays embedding by default.

Measured, so you do not have to guess: on the causal-root eval (180 cases, six languages, one busy tenant) hybrid does not help and slightly hurts: root recall 9% -> 7%, English 15% -> 8%, p95 latency +22%. The reason is structural, not a tuning failure: a root cause is phrased nothing like the question, so matching the question's words surfaces mid-chain filler that then competes for the context budget. Hybrid's own case (exact names, codes, part numbers, identifiers) is real but is a lookup task, which that eval does not measure. Turn it on for lookup-shaped workloads; leave it off for "why" questions.

How far the walk goes, and in which direction

hops bounds the walk's depth. causal_hops=(forward, backward) bounds it per direction: forward follows cause -> effect (consequences), backward follows effect -> cause (what led here). Entity bridges are unaffected. "Why" is a backward question, so a walk that spends its depth asymmetrically can reach a root cause a symmetric one misses:

loop = MemoryLoop(graph, session="chat", causal_hops=(3, 2))   # or REASONGRAPH_CAUSAL_HOPS=3,2

The default is symmetric (None), and on our own causal eval the split makes no difference at all: no path there runs more than two causal edges in one direction before the overall hops limit binds, so a per-direction cap never fires. Reach for this only when your graph has genuinely long one-directional chains, four or more hops of pure cause-to-cause. Raising hops itself was also measured: depth 5 buys two more correct answers in 180 for roughly twice the database work, which is why the default stays 3.

Linking two wordings of one event

The same event turns up written two ways: "costs were reduced" in one sentence, "the cost reduction" in the next. Cosine over a general retrieval embedder does not put those together, and when it fails the causal chain breaks, which is measurably where root causes get lost.

That decision is its own job, so it can use its own model. span_linker accepts either shape:

ReasonGraph(span_linker="bi:my-org/same-event-multilingual")   # a similarity model, scored by cosine
ReasonGraph(span_linker="my-org/same-event-cross-encoder")     # a cross-encoder, scored pairwise

or REASONGRAPH_SPAN_LINKER with the same values. Both see only direction-aware candidates: an effect span is only ever compared with a cause span. span_link_top_k sets how many near neighbours a span is compared against before anything judges them. It defaults to 6. It used to widen to 10 whenever a linker was configured, and that was measurably wrong: on 341 cases the wider shortlist floods the walk with look-alikes, halving the gain on rephrased chains and turning a small gain on ordinary chains into a small loss. Narrowing it back was the single largest improvement in this whole line of work, and it costs nothing.

By default a linker replaces the embedder's own cosine, which means it can also lose links cosi