Graph memory for AI agents: entities, cause-effect links, cross-session recall, time travel.
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π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
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.
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.
Claude Code / Cursor / any MCP client, hosted (EU, no LLM in the loop):
Local MCP server (stdio, everything on your machine, SQLite):
Python, in-process:
Any language, over HTTP (self-hosted or hosted):
Ready-to-copy agents (Groq/OpenAI-compatible research agent, two agents sharing one
memory, Claude Code with persistent memory, LangGraph) live in
examples/agents/.
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.
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.
memoryTools(mem) returns OpenAI-style tool definitions with their executors attached, for agents
that call functions directly without a framework: remember, recall and why.
Or install only what you need:
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.
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) | Entities | Causal 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, Phoenix | Construction delays -> first production |
| Arizona water emergency, Lake Mead... | Arizona, Lake Mead | Lake 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... | Apple | component shortages -> iPhone production timelines |
Three entities appear in both sources, creating bridge nodes:
| Bridge entity | Source A connections | Source B connections |
|---|---|---|
| Arizona | TSMC fab, TSMC-Apple deal | water emergency, Intel pause, water cuts |
| Phoenix | TSMC fab, water usage, delays | water cuts for industrial users |
| Apple | TSMC supply agreement | component 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
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