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aimock

Mock infrastructure for AI application testing — point your SDK at one local port and every provider, protocol, and service answers deterministically.

Quick start · The suite · Record & replay · Docs

One package, one port, zero dependencies. LLM APIs, image generation and editing, text-to-speech, transcription, audio translation and generation, video generation, embeddings, MCP tools, A2A agents, AG-UI event streams, vector databases, search, rerank, and moderation — no keys, no network, no surprise bills.

Quick Start

The aimock Suite

aimock mocks everything your AI app talks to:

| Tool | What it mocks | Docs |

| LLMock | OpenAI (Chat/Responses/Realtime GA/GPT-Live), Claude, Gemini (REST/Live/Interactions/Embeddings), Bedrock, Azure, Vertex AI, Ollama (chat/embeddings), Cohere (chat/embed), OpenRouter (chat/router/video), BytePlus Ark (chat/images/video), Grok (video), Veo (video), fal.ai, ElevenLabs (TTS/Voice Design) | Providers | | MCPMock | MCP tools, resources, prompts with session management | MCP | | A2AMock | Agent-to-agent protocol with SSE streaming | A2A | | AGUIMock | AG-UI agent-to-UI event streams for frontend testing | AG-UI | | VectorMock | Pinecone, Qdrant, ChromaDB compatible endpoints | Vector | | Services | Tavily search, Cohere rerank, OpenAI moderation | Services |

Run them all on one port with npx @copilotkit/aimock --config aimock.json, or use the programmatic API to compose exactly what you need.

Features

• Record & Replay — Proxy real APIs, save as fixtures, replay deterministically forever • Timing-aware recording and replay — Recorded fixtures capture per-frame arrival timestamps; replay uses recorded timings for approximate timing reproduction based on recorded TTFT and inter-frame cadence (replay chunk count may differ from recording — TTFT and average pace are preserved, not per-token fidelity) with configurable --replay-speed multiplier • Recorded token usage and cost — Recording captures the final usage frame of a streaming completion, so replayed fixtures serve real prompt_tokens / completion_tokens instead of a length estimate; OpenRouter's provider-reported usage.cost and its cost_details / *_tokens_details breakdowns are captured too, so an app that bills from real provider cost can e2e-test its wallet path from a tape • Multi-turn Conversations — Record and replay multi-turn traces with tool rounds; match distinct turns via turnIndex, hasToolResult, toolCallId, toolResultContains (gate on the tool-result payload), sequenceIndex, systemMessage (gate on host-supplied agent context), or custom predicates • 11 providers across 22 API surfaces — OpenAI Chat, OpenAI Responses, OpenAI Realtime (GA; an OpenAI-Beta: realtime=v1 request gets the real sunset rejection), GPT-Live, Claude, Gemini REST, Gemini embedContent, Gemini Live, Gemini Interactions, Azure, Bedrock, Vertex AI, Ollama (chat + embeddings), Cohere (chat + embed), OpenRouter (chat + router failover), BytePlus Ark (OpenAI-compatible chat + images under /api/v3), ElevenLabs TTS and Voice Design — full streaming support • Multimedia APIs — image generation (gpt-image-1 default, Imagen; /v1/images/variations replays OpenAI's removal 404), image editing (/v1/images/edits), text-to-speech (OpenAI + ElevenLabs TTS and Voice Design), audio transcription, audio translation (/v1/audio/translations), video generation, OpenRouter video generation (/api/v1/videos with async job lifecycle), Google Veo video generation (:predictLongRunning + /v1beta/operations async lifecycle), Grok Imagine video generation (/v1/videos/generations with async job lifecycle), BytePlus Ark video generation (Seedance, /api/v3/contents/generations/tasks with async task lifecycle), fal.ai (image / video / audio with queue lifecycle) • OpenAI platform APIs — Files (byte-exact uploads, purpose validation), Vector Stores (CRUD, file attach with deterministic poll progression, file batches, deterministic direct search; hosted file_search execution is not supported), fine-tuning jobs (deterministic lifecycle, events, cursor pages) and Batches (/v1/batches create/list/retrieve/cancel with deterministic poll progression and real output files) • MCP / A2A / AG-UI / Vector — Mock every protocol your AI agents use • MCP Scenario Fakes — An mcpFakes block in a fixture file scripts each test's MCP tool answers next to its LLM turns: ordered answers, exact or any-argument matching, scripted tool errors, and a closed-world mode that fails on any undeclared tool. Works with any MCP client over Streamable HTTP • MCP Recording — Record a live MCP server into mcpFakes files (--mcp-record /mcp=, or llm.record.mcp with llm.enableMcpRecording: true in a config) and replay them offline, with the recorded tool list, progress notifications and timing; known secrets are redacted before anything is written • MCP Fake Reports — GET /__aimock/mcp/fakes/report lists each test's served, unused and failed fakes; with useAimock({ fakesReport: "fail" }) a swallowed fake error or an unused fake fails the test, and fakesFor() gives the MCP URL and headers for the current test. Works for agents, workflows and pipelines alike • Chaos Testing — 500 errors, malformed JSON, mid-stream disconnects and 429 rate limits (--chaos-ratelimit, with Retry-After) at any probability, plus a fixed pre-handling delay (--chaos-latency ); read, set and clear the rates at runtime via GET / POST / DELETE /__aimock/chaos, scoped to the caller's X-Test-Id so one test cannot destabilise the suite running beside it • Model Misbehavior — Test invalid tool arguments, schema violations, unknown tools, duplicate IDs, length stops, empty responses, refusals, content filtering, and reasoning-only responses. Supported wires include OpenAI Chat, Responses, Realtime GA, Anthropic, Bedrock InvokeModel and Converse, Gemini generateContent (including the Vertex wrapper), Gemini Live, Gemini Interactions, Cohere, and Ollama chat. Fault and mode support varies by wire. Azure and…

Description from the npm listing.