"XState" matching MCP servers:

An MCP-native state sidecar that externalises workflow state for distributed agent deployments.

Enables AI agents to persist and restore checkpoint state, supporting crash recovery and replay for LangGraph-based workflows.

Enables AI agents to arbitrate typed state-action schemas and dispatch deterministic actions locally, so routine or reflexive decisions are resolved in sub-millisecond fashion instead of incurring expensive frontier-LLM calls. It also applies token-saving pre-escalation filters, escalating only ambiguous or multi-hop plans to larger models while enforcing reversible execution checkpoints, strict token budgets, and local-first memory retention.

Enables AI agents to capture and commit stateful workflow checkpoints during autonomous execution, computing deterministic state hashes and rollback points. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients for reliable, low-latency checkpoint management.

Single source of truth and control for agent-operated companies, managing business state with deterministic policy enforcement, seat identity, and hash-chained audit trail.

Provides state and log management tools designed for long-lived AI agents that may be interrupted and resumed. It enables tracking agent progress and maintaining an append-only event history to ensure continuity across multiple sessions.

Enables MCP-compatible agents to arbitrate typed state-action schemas, dispatch routine actions deterministically, and apply pre-escalation filters that reduce unnecessary frontier LLM calls. It supports low-latency, schema-enforced routing with safety guardrails and local-first memory retention.

Enables AI agents to commit stateful workflow checkpoints by calculating state hashes and rollback points through the Model Context Protocol. It provides deterministic, dependency-free checkpoint management for autonomous agent execution.