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Text of the page (random words):
mcpmock onto llmock so they share one port import llmock mcpmock from copilotkit aimock const llm new llmock port 5555 const mcp new mcpmock mcp addtool name calc description calculator mcp ontoolcall calc args 42 llm mount mcp mcp await llm start mcp available at http 127 0 0 1 5555 mcp enter fullscreen mode exit fullscreen mode it supports tools register handlers that return strings or rich content resources static files and data your agent can read via uri prompts reusable prompt templates with arguments session management full mcp session id lifecycle per the streamable http spec full docs here a2amock test multi agent workflows without live agents a2a agent2agent is how agents discover and talk to each other it handles agent cards message routing and streaming responses between agents testing a multi agent system is hard when every agent is live one agent goes down your whole test suite breaks a2amock gives you a local a2a server with full agent card discovery message routing task management and sse streaming register your agents define how they respond and test your multi agent workflows end to end without anything actually running import a2amock from copilotkit aimock a2a const a2a new a2amock a2a registeragent name translator description translates text between languages skills id translate name translate a2a onmessage translator translate text translated text const url await a2a start agent card at url well known agent card json json rpc at url enter fullscreen mode exit fullscreen mode here is the pattern for long running tasks with incremental updates a2a onstreamingtask agent long task type status state task_state_working type artifact parts text partial result name output type artifact parts text final result lastchunk true name output 50 50ms delay between events enter fullscreen mode exit fullscreen mode for task management agent cards and json rpc methods see the full docs here vectormock deterministic retrieval for rag pipelines if your app does retrieval augmented generation your tests depend on what is in your vector database right now your dev index is messy your staging index does not match prod and results shift every time someone upserts new vectors vectormock is a mock vector database server that supports pinecone qdrant and chromadb api formats with collection management upsert query and delete operations import vectormock from copilotkit aimock vector const vector new vectormock vector addcollection docs dimension 1536 vector onquery docs id doc 1 score 0 95 metadata title getting started id doc 2 score 0 87 metadata title api reference const url await vector start point your vector db client at url enter fullscreen mode exit fullscreen mode if you need results to vary based on the query use a dynamic handler instead vector onquery docs query const topk query topk 10 return array from length topk _ i id result i score 1 i 0 1 enter fullscreen mode exit fullscreen mode here are compatible endpoints for pinecone qdrant and chromadb apis consistent retrieval results every test run full docs here services search rerank and moderation these are the apis most people forget to mock and the ones that quietly make your test suite non deterministic services within aimock provides built in mocks for web search reranking and content moderation register fixture patterns on your llmock instance and requests are matched by query input text no separate server needed tavily search mock web search results by query pattern at post search cohere reranking mock reranked document lists at post v2 rerank openai moderation mock moderation decisions at post v1 moderations unmatched requests return unflagged by default import llmock from copilotkit aimock const mock new llmock string pattern case insensitive substring match mock onsearch weather title weather report url https example com weather content sunny today regexp pattern mock onsearch stock s price i title acme stock url https example com stocks content 42 00 score 0 95 catch all empty results for unmatched queries mock onsearch mock onrerank machine learning index 0 relevance_score 0 99 index 2 relevance_score 0 85 mock onmoderate violent flagged true categories violence true hate false category_scores violence 0 95 hate 0 01 enter fullscreen mode exit fullscreen mode if you just need catch all responses to stop live requests enable all three in config services search true rerank true moderate true enter fullscreen mode exit fullscreen mode string patterns use case insensitive substring matching regexp patterns do full regex testing first match wins all service requests are recorded in the journal so you can inspect exactly what got called full docs here drift detection catch provider changes before your users do this is one of the things no other mocking tool does a mock that does not match reality is worse than no mock here is the problem mocks are a snapshot of how an api behaved when you wrote the fixture openai adds a field claude changes a default gemini tweaks its streaming format your mocks still pass ci is green then your app breaks in production drift detection runs a three way comparison in ci every day sdk types what typescript type definitions say the shape should be real api responses actual live requests to openai anthropic gemini aimock output what the mock returns for the same request if any of those three disagree you know within 24 hours not when a user files a bug here is what the output looks like when drift is detected pnpm test drift critical aimock drift field in sdk real api but missing from mock path choices message refusal sdk null real null mock absent critical type mismatch real api and mock disagree on type path content input sdk object real object mock string warning provider added field in real api but not in sdk or mock path choices message annotations sdk absent real array mock absent 2 critical test fails 1 warning logged detected before any user reported it enter fullscreen mode exit fullscreen mode severity levels critical mock mismatches the real api test fails immediately warning provider added a new field that neither the sdk nor the mock knows about yet logged as early warning ok all three sources agree nothing to do here is how the three way comparison works under the hood import extractshape triangulate formatdriftreport shouldfail from schema 1 get the sdk shape what typescript says const sdkshape openaichatcompletionshape 2 call the real api and the mock in parallel const realres mockres await promise all openaichatnonstreaming config role user content say hello httppost instance url v1 chat completions 3 extract response shapes const realshape extractshape realres body const mockshape extractshape json parse mockres body 4 three way comparison const diffs triangulate sdkshape realshape mockshape const report formatdriftreport openai chat non streaming text diffs 5 critical diffs fail the test if shouldfail diffs expect soft report toequal diffs filter d d severity critical enter fullscreen mode exit fullscreen mode run it yourself against live endpoints run drift checks against live endpoints pnpm vitest config vitest config drift ts enter fullscreen mode exit fullscreen mode msw vidaimock mokksy none of them do this your mocks should never silently go stale full docs here record and replay stop hand writing fixtures writing fixtures by hand is fine for simple cases it gets painful fast when you are dealing with multi turn agent conversations with tool calls streaming responses and branching logic msw vidaimock mock llm piyook none of the llm mocking tools solve this record and replay works like a vcr here is exactly what happens client sends a request to aimock aimock attempts fixture matching as usual on miss the request is forwarded to the configured upstream provider the upstream response is relayed back to the client immediately the response is collapsed if streaming and saved as a fixture to disk and memory subsequent identical requests match the newly recorded fixture quick start using cli npx aimock fixtures fixtures record provider openai https api openai com provider anthropic https api anthropic com enter fullscreen mode exit fullscreen mode recorded fixtures are saved automatically and look like this fixtures match usermessage what is the weather response content i don t have real time weather data enter fullscreen mode exit fullscreen mode aimock handles stream collapsing automatically across six formats openai sse anthropic sse gemini sse cohere sse ollama ndjson and bedrock eventstream auth headers are forwarded to the upstream provider but never saved in fixtures it also supports a programmatic api with enablerecording and requesttransform for normalizing dynamic data like timestamps in ci add strict so that any unmatched request returns a 503 and fails immediately rather than slipping through silently without it missing fixtures return 404 responses and your test suite may never tell you that a live api call was attempted full docs here chaos testing prove your app handles failure your app will eventually get a 500 from openai a malformed json response from a tool a mid stream disconnect from a vector database the question is whether you find out in testing or in production chaos testing lets you inject failures at configurable probabilities at three levels server wide per fixture or per individual request three failure modes drop returns http 500 with error message chaos request dropped code chaos_drop malformed returns http 200 with invalid json disconnect destroys the tcp connection immediately with no response server level chaos applies to all requests import llmock from copilotkit aimock const mock new llmock mock setchaos droprate 0 1 10 of requests return 500 malformedrate 0 05 5 return broken json disconnectrate 0 02 2 drop the connection remove all chaos later mock clearchaos enter fullscreen mode exit fullscreen mode fixture level chaos targets specific responses only fixtures match usermessage unstable response content this might fail chaos droprate 0 3 malformedrate 0 2 disconnectrate 0 1 enter fullscreen mode exit fullscreen mode per request headers override everything else useful for forcing a specific failure in a single test force 100 disconnect on this specific request await fetch mock url v1 chat completions method post headers content type application json x aimock chaos disconnect 1 0 body json stringify model gpt 4 messages role user content hello enter fullscreen mode exit fullscreen mode all chaos events are tracked in the journal with a chaosaction field and counted in prometheus metrics full docs here what else is included beyond the core modules aimock offers a lot more worth knowing embeddings post v1 embeddings is fully supported return explicit vectors via fixtures or let aimock generate them deterministically from a hash of the input text same input always produces the same vector default dimensions are 1536 websocket apis openai realtime openai responses over websocket and gemini live are all supported using raw rfc 6455 framing if your app uses voice or real time streaming agents these are covered sequential responses return different responses for the same prompt on each successive call useful for testing retry logic and multi turn workflows where the same message should behave differently over time match usermessage retry sequenceindex 0 response content first attempt match usermessage retry sequenceindex 1 response content second attempt match usermessage retry response content fallback enter fullscreen mode exit fullscreen mode streaming physics configure ttft time to first token tps tokens per second and jitter to simulate realistic timing pre built profiles cover fast models reasoning models and overloaded systems prometheus metrics request counts latency histograms and current fixture count at metrics enable with metrics flag docker and helm the official docker image is available at ghcr io copilotkit aimock github container registry for ci cd it runs as a plain http server so any language can use it no node js needed on the test runner side ag ui uses aimock in production ag ui is the open protocol that connects ai agents to frontend applications adopted by langgraph crewai mastra google adk aws bedrock agentcore and more it uses aimock for its end to end test suite verifying ai agent behavior across llm providers with fixture driven responses ag ui is a protocol used in production if you want to see what a real aimock setup looks like at scale that is a good place to start migrating to aimock the docs include step by step migration guides for msw vidaimock mock llm python mocks and mokksy you will find a side by side comparison and a breakdown of what you gain and keep if you are on msw you do not have to replace everything you can keep msw for general rest and graphql mocking and use aimock only for ai endpoints if you are already using copilotkit llmock the upgrade is a find and replace pnpm remove copilotkit llmock pnpm add copilotkit aimock enter fullscreen mode exit fullscreen mode the llmock class all fixture formats and the programmatic api are unchanged your existing tests will work as is get started npm install copilotkit aimock enter fullscreen mode exit fullscreen mode docs aimock copilotkit dev github github com copilotkit aimock npm copilotkit aimock cli aimock cli your test suite should be as complete as your stack that is what aimock is for you can connect me on github twitter and linkedin follow copilotkit on twitter and say hi and if you d like to build something cool join the discord community copilotkit follow react ui elegant infrastructure for ai copilots in app ai agents ai chatbots and ai powered textareas top comments 28 subscribe personal trusted user create template templates let you quickly answer faqs or store snippets for re use submit preview dismiss collapse expand cyber safety zone cyber safety zone cyber safety zone follow cybersecurity content writerblogger at cyber safety zone helping freelancers and small businesses stay secure in the digital age i write about ai risks cyber threats and budget friendly security email admin cybersafetyzone com location united states san francisco joined aug 17 2025 apr 11 dropdown menu copy link hide great concept this is exactly the kind of tooling ai teams need as stacks get more complex love how aimock goes beyond just llm mocking and actually covers the entire agentic pipeline llm mcp vector db search rerank etc that s a huge gap in current testing setups where people mock one layer and unknowingly leave the rest non deterministic the record replay drift detection combo is especially powerful feels like a practical way to keep tests realistic without silently breaking when providers change apis curious how it performs at scale in large ci pipelines and whether teams mix it with tools like msw or fully replace their existing mocks like comment like comment 2 likes like comment b...