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.
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
Agent reasoning loops are latency-sensitive. Every millisecond querying state is a millisecond added to response time.
Rapid concurrent reads and writes
State evolves with every action. Memgraph handles the read/write velocity of an active agent without queuing or batching.
Multi-agent coordination
Multiple agents or sub-agents working in parallel, accessing shared state concurrently.
Real-time state updates
The reasoning graph isn't static. As the agent acts and new information arrives, the graph updates in real time.
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 docs→Memgraph MCP Server
Connect any MCP-compatible client - Claude, VS Code, custom agents, directly to Memgraph for Cypher queries and graph analysis.
Read docs→Memgraph 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 docs→AI Workloads
