A team built FetchSandbox, a testing tool that replays recorded response patterns from real service providers instead of simulated mocks. It exists because agents passed tests against behavioral mocks yet failed on live webhooks where providers respond differently. For practitioners, it addresses the gap between agent test success and production reliability.
FetchSandbox is a sandbox for testing agent-built workflows without real accounts, so no real emails, invites, or Slack messages are sent during testing. It verifies resulting state rather than just success status, replays webhooks, and catches duplicate side effects from retries. It targets developers building agent workflows who currently must connect real accounts to test.
FetchSandbox is a sandbox for testing agent-built workflows without real accounts, so no real emails, invites, or Slack messages are sent during testing. It verifies resulting state rather than just success status, replays webhooks, and catches duplicate side effects from retries. It targets developers building agent workflows who currently must connect real accounts to test.
FetchSandbox is a sandbox layer that runs the full integration lifecycle for AI agents before production. It tests real workflows, real webhooks, and failure scenarios on demand, providing a public receipt URL as proof of survival. It solves the problem of AI agent integrations that pass tests but break on real webhooks due to duplicate events, non-idempotent handlers, and retries hitting stale state.
FetchSandbox is a sandbox layer that runs the full integration lifecycle for AI agents before production. It tests real workflows, real webhooks, and failure scenarios on demand, providing a public receipt URL as proof of survival. It solves the problem of AI agent integrations that pass tests but break on real webhooks due to duplicate events, non-idempotent handlers, and retries hitting stale state.
FetchSandbox MCP is a tool that lets AI agents test API integrations against a live sandbox environment, running full request/response cycles and simulating webhook edge cases. It helps developers catch integration failures before shipping by providing a realistic testing environment.
FetchSandbox MCP is a tool that lets AI agents test API integrations against a live sandbox environment, running full request/response cycles and simulating webhook edge cases. It helps developers catch integration failures before shipping by providing a realistic testing environment.
A Reddit post announces MockAgent, a free local-dev tool that intercepts agent tool calls and validates JSON parameters in real time, targeting parameter hallucination and retry loops in frameworks like CrewAI and AutoGen. It returns structured AJV validation errors and supports chaos testing to verify agent self-correction.
A Reddit post announces MockAgent, a free local-dev tool that intercepts agent tool calls and validates JSON parameters in real time, targeting parameter hallucination and retry loops in frameworks like CrewAI and AutoGen. It returns structured AJV validation errors and supports chaos testing to verify agent self-correction.
A developer built GhostAPI, an open-source local API sandbox and MCP control plane that emulates common APIs (Stripe, GitHub, OpenAI, Twilio) so AI agents can run tool calls, fail, and retry entirely offline without leaking keys or wasting money. It addresses the common pain point of agents getting stuck in retry loops and hitting live endpoints during development.
A developer built GhostAPI, an open-source local API sandbox and MCP control plane that emulates common APIs (Stripe, GitHub, OpenAI, Twilio) so AI agents can run tool calls, fail, and retry entirely offline without leaking keys or wasting money. It addresses the common pain point of agents getting stuck in retry loops and hitting live endpoints during development.