Agent-ready API mocking and service virtualization

Your AI agents drive Traffic Parrot, write mocks for it and get help using it. They bring the model; Traffic Parrot gives each one a deterministic backend, self-hosted on your network.

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Give your AI coding agents a backend to test against

Real backend

Traffic Parrot simulator

If the agent cannot reach the backend, it improvises. It hard-codes a response or rewrites the test so it passes, then reports the code as working even though nothing was really tested.

Every call gets the response you defined: green means the code was tested, red means it is broken.

Sandboxes were sized for people. An agent burns the quota, then waits.

Simulated third-party APIs, local to the agent: no quota, no waiting, no partner keys.

A dozen agents share one environment. Nobody provisions a queue manager per agent.

One simulator per agent, IBM MQ, JMS and gRPC as well as HTTP.

Someone still has to write the mocks.

Claude Code, GitHub Copilot CLI, Cursor or Antigravity writes them over MCP or as JSON files, validated against the schema.

51.9% of code is now AI-authored (DX survey of over 400 companies, June 2026). 60% of organisations ship untested code due to AI-accelerated velocity (Tricentis Quality Transformation Report, June 2026). Testing has to get cheap enough for the agent to do it on every run.

What Traffic Parrot provides

Deterministic self-hosted simulators

HTTP(S), gRPC, JMS, IBM® MQ, RabbitMQ/AMQP, Thrift and file transfers. One per agent: a laptop, a CI job or a Kubernetes container. Responses come from mappings you control, so a failing test points at the code, not at the environment.

All features

Built-in MCP server

Your assistant reads and changes what a running instance serves: mappings, received requests, scenarios. Nothing extra to install. Start with the AGENTS.md brief.

MCP endpoint reference

Your agent writes the mocks, checked before they load

Point Claude Code, GitHub Copilot CLI, Cursor, Antigravity or any agent that can call an HTTP API at a running instance. It fetches the schema, writes the mapping, validates it and loads it. A safety policy blocks the remote-code-execution helpers, and an opt-in check gives CI a second gate.

Generate mocks with your own AI agent

AI support agents

Claude or ChatGPT, set up to explain features and troubleshoot.

Pick a support assistant

Why enterprises pick it for agent workflows

Runs where your agents run. Laptops, CI agents, VMs, Kubernetes, air-gapped networks. No new SaaS to assess.
Nothing leaves your network. Contracts, test data and recordings stay on infrastructure you run. There is no model inside Traffic Parrot, so no new AI vendor either.
One instance per agent. 512MB RAM and 200MB disk, so each agent, pipeline or developer gets its own, in a container if you like.
Beyond HTTP. IBM® MQ, JMS, gRPC, Thrift, RabbitMQ/AMQP and file transfers are simulated too, so agents can test messaging code, not only REST.
Mocks are files, reviewed like code. The agent's mappings land in a directory you keep in git and review in the same pull request as its code.
Previews before changes. MCP tools default to a dry run, and the endpoint accepts only the local machine unless you configure a token.
Guards on generated mocks. Validated against the schema of the version you run, a safety policy blocks the remote-code-execution helpers, an opt-in check gates them in CI, and you review before loading.

Which file does your assistant need?

	AGENTS.md	llms.txt
Where	In the install directory	On this site
For	Your coding agent	Any assistant
What	How to work a running instance: endpoint, tools, ground rules, workflows	What Traffic Parrot is, where the docs are, how to answer questions about it. Does not drive an instance
Use it when	The agent works in your repository	The assistant needs to know about Traffic Parrot
Get it	How to hand it over	llms.txt, and llms-trial-onboarding.txt for a fresh trial install

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