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Text of the page (random words):
for rag pipelines services search rerank and moderation drift detection catch provider changes before your users do record and replay stop hand writing fixtures chaos testing prove your app handles failure what else is included ag ui uses aimock in production migrating to aimock aimock is open source and free ai apps in 2026 call a lot more than just an llm a realistic agent request looks like this user message llm decides to use a tool tool call via mcp file system database calendar rag retrieval from pinecone or qdrant web search via tavily cohere reranker to sort results back to llm with full context enter fullscreen mode exit fullscreen mode each of these is a live network call in your test environment everyone can fail return something slightly different or cost you tokens we looked at every tool out there some handled llm mocking some handled one protocol none covered the full picture you would have to stitch together three or four libraries each with its own config format and still have gaps here is how aimock compares to other options out there introducing aimock mock your entire agentic stack it mocks everything your ai app talks to here is what is inside llmock 11 providers full streaming tool calls reasoning models mcpmock local mcp server with full json rpc 2 0 session management tools resources prompts a2amock agent card discovery message routing sse streaming between agents vectormock mock vector database for deterministic rag retrieval services search rerank and moderation the apis everyone forgets to mock on top of that aimock does three things no other mocking tool does drift detection runs daily against real provider apis and catches response format changes within 24 hours before your users do record and replay proxies real api calls saves them as fixtures replays them forever in ci without touching live apis again chaos testing inject 500s malformed json and mid stream disconnects to prove your app handles failure run all of them on one port with a single config file llm fixtures fixtures llm providers openai claude gemini mcp tools fixtures mcp tools json a2a agents fixtures a2a agents json vector path vector collections enter fullscreen mode exit fullscreen mode get started with the aimock cli npx aimock config aimock json port 4010 enter fullscreen mode exit fullscreen mode let s go through each one in brief llmock full streaming tool calls reasoning across every major provider llmock runs a real http server on a real port not in process patching any process on the machine can reach it your next js app agent workers langgraph processes anything that speaks http it has native support for 10 providers openai claude gemini bedrock azure vertex ai ollama cohere openrouter and anthropic azure reasoning models are supported across all of them any openai compatible endpoint such as mistral groq together ai or vllm works out of the box too full streaming tool calls structured outputs extended thinking multi turn conversations and websocket apis are all built in aimock handles the translation internally so one fixture format works across all providers using the programmatic api with vitest register a fixture and assert on the response beforeall starts the mock server once for the suite afterall tears it down and mock on registers a fixture that maps a user message to a deterministic response import llmock from copilotkit aimock import describe it expect beforeall afterall from vitest let mock llmock beforeall async mock new llmock await mock start afterall async await mock stop it non streaming text response async mock on usermessage hello content hello how can i help const res await fetch mock url v1 chat completions method post headers content type application json body json stringify model gpt 4 messages role user content hello stream false const body await res json expect body choices 0 message content tobe hello how can i help expect body object tobe chat completion expect body id tomatch chatcmpl enter fullscreen mode exit fullscreen mode point your existing openai client at the mock url nothing else in your code changes no api keys no network calls the same response every time full provider docs here mcpmock a mock mcp server for testing tool integrations mcp is how agents call tools if your agent uses mcp every tool call in your test suite hits a live server with real latency and no control over the response mcpmock gives you a local server that speaks the full mcp protocol over json rpc 2 0 your agent connects to it exactly like a real mcp server you control what comes back import mcpmock from copilotkit aimock mcp const mcp new mcpmock mcp addtool name search description search the web inputschema type object properties query type string mcp ontoolcall search args const query args as query string return found 3 results for query const url await mcp start point your mcp client at url enter fullscreen mode exit fullscreen mode in a real agent test your llm and mcp tool server run together mount 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...
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