Shortest path
Most efficient route to a goal.
Graph-native reasoning
Agents That Plan. Not Prompt.
The core question for any agent is: what should I do next? A reasoning graph makes the answer explicit - queryable action space, scored paths, inspectable traces.
Run on Linux/MacOS
curl -sSL https://install.memgraph.com/agentic-ai | bash
Run on Windows (PowerShell)
iwr https://install.memgraph.com/agentic-ai/windows -useb | iex
Memgraph and MCP, pre-loaded with a reasoning graph and multi-agent shared state. Requires Docker and git; no API keys.
Prompting isn't planning.
Most agent frameworks answer "what next?" by prompting the LLM to reason in natural language consuming tokens, producing variable results, and offering no guarantees. A reasoning graph makes the action space explicit and traversable.
Reasoning graphs.
Nodes represent states or decision points. Edges represent available actions. Properties encode scores, expected value, feasibility, historical success rate. The planning problem becomes a graph traversal.
Centrality
Identify critical intermediate states worth validating.
Community detection
Group related sub-tasks for parallel execution.
Weighted traversal
Evaluate multi-step plans without consuming LLM calls.
CUSTOMER SUPPORT · AGENT REASONING GRAPH
Ticket receivedTicket received
Assess severityAssess severity
score0.31
Auto-resolveAuto-resolve
score0.87
Escalate to agentEscalate to agent
score0.54
Request more infoRequest more info
Done
Done
Ticket receivedTicket received
Assess severityAssess severity
score0.31
Auto-resolveAuto-resolve
score0.54
Request more infoRequest more info
score0.87
Escalate to agentEscalate to agent
Done
Done
Chosen path
Alternative path
Auditability as structure.
When the agent acts, the traversed path is an inspectable trace. Alternative paths can be scored and compared. The basis for the decision is examinable through graph structure and edge scores, not through interpretation of token probabilities.
Capabilities
Why Memgraph.
Sub-millisecond traversals. Native vector search. Real-time writes. Built for AI pipelines that can't afford to wait.
Sub-millisecond scoring and traversal
Rapid concurrent reads and writes
Multi-agent coordination
Real-time state updates
Integrations
Build with your existing stack.
LangGraph
Memgraph toolkit with 7+ tools for building stateful, multi-actor agent applications with graph-backed state management.
Read docsMemgraph MCP Server
Connect any MCP-compatible client - Claude, VS Code, custom agents, directly to Memgraph for Cypher queries and graph analysis.
Read docsMemgraph Lab MCP Client
Connect Memgraph Lab to external MCP servers - Stripe, Elasticsearch, Slack, and others, to combine graph insights with live data from across your stack.
Read docsGet started
Try it in one command.
Everything already included: the database, the sample data, and working examples. Spin up Memgraph and its MCP server, loaded with a reasoning graph and multi-agent shared state, ready to run. Requires Docker and git; no API keys.
Read detailed docsRun on Linux/MacOS
curl -sSL https://install.memgraph.com/agentic-ai | bash
Run on Windows (PowerShell)
iwr https://install.memgraph.com/agentic-ai/windows -useb | iex
