Agent Frameworks Compared: Tool-Calling Schema Handling

Tool-calling schemas vary substantially among OpenAI, Anthropic, Google, and other model providers, affecting tool definitions, response formats, argument encoding, and result handling, so changing models can require translation code or introduce failures. The comparison finds that LangChain and LangGraph provide provider-specific schema translation, CrewAI delegates it to underlying SDKs or LiteLLM, Microsoft Agent Framework relies on its selected connector, and provider-native SDKs such as OpenAI Agents SDK, Claude Agent SDK, and Google ADK primarily use their own ecosystems’ formats, with external-model support often depending on compatibility layers. All reviewed frameworks support MCP to differing degrees, but MCP standardizes tool discovery rather than model wire formats. Common model-switching problems include schema validation errors, plain-text rather than structured calls from models lacking native support, inconsistent repair behavior, and differing tool-call reliability between providers hosting the same model. OpenRouter presents an API-layer alternative by accepting OpenAI-style tool definitions and returning standardized tool_calls across supported models, while its Auto Exacto routing uses endpoint throughput, tool-calling success rates, and benchmark data to prioritize providers; applications still need to manage execution loops, validation, retries, state, and stopping conditions.

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