Architecture comparisons
Which part of customer-facing AI architecture does each option solve?
Agent runtimes, multi-agent frameworks, Skills, and long context each solve a different problem. Ask what each option executes, then ask where the customer’s goals, decisions, artifacts, and next steps live after the run ends.
The layer ThruWire solves
ThruWire is the product-owned work layer: a living, domain-specific model that agents can orchestrate and your customers can inspect and steer. It can coexist with the framework or model provider your team already uses.
ThruWire vs LangGraph
LangGraph helps engineers orchestrate long-running, stateful agents. ThruWire helps a software product own the customer work those agents advance.
Compare an agent orchestration runtime with the customer-owned work model a software product maintains across agents and time.
Read comparison →ThruWire vs CrewAI
CrewAI coordinates agents and execution. ThruWire maintains the customer work those agents create, revise, and move toward an outcome.
Compare multi-agent crews and flows with the customer-owned work model a software product maintains between executions.
Read comparison →ThruWire vs Claude Agent SDK + Skills
Claude Agent SDK and Skills make Claude more capable. ThruWire gives your product a durable model of the work Claude—or another agent—performs.
Compare Claude-native agent execution and reusable capabilities with product-owned customer work that persists across providers.
Read comparison →ThruWire vs Long Context
Long context helps an AI assistant see more. ThruWire helps your product know what the work is, what changed, and what should happen next.
Compare a large model context window with structured, customer-owned work that remains maintainable as information changes.
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