Q: How does the token counting work across different providers?
Answer: Token counting is provider-specific because each LLM provider uses different tokenization schemes. For OpenAI, the system uses the tiktoken library for accurate counting. For Anthropic, it uses the Anthropic SDK's token counting capabilities or a custom implementation. For Gemini, it uses Google's token counting API or estimates based on character counts. The function determines the provider from the API key using regex patterns, then calls the appropriate counting function. It calculates (system prompt + messages), (based on model specs), and (input + reserved output). It also fetches the for the specific model. Pre-validation happens before each agent turn to prevent context limit errors.
Q: Why fallback to legacy completions endpoint for OpenAI?
Answer: The OpenAI integration includes a fallback to the legacy completions endpoint to maintain compatibility with older OpenAI models that don't support the newer Responses API. In streamResponse and generateResponse, when the OpenAI client request fails, the error is checked using isLegacyCompletionsHint which looks for specific error codes indicating the model doesn't support the new API. If detected, the system logs a warning and falls back to the legacy completions.create endpoint. The legacy endpoint requires different input formatting - the system uses buildLegacyCompletionPrompt to combine the system prompt and messages into a single prompt string. The fallback also adjusts the max_tokens parameter using getLegacyCompletionsMaxTokens because the legacy API has different limits.
Q: How does the agent loop decide when to stop?
Answer: The agent loop has multiple termination conditions: (1) No tool results were returned from the LLM, (2) The LLM indicates completion via a completion signal, (3) A tool execution fails with a non-recoverable error, (4) The context window is exceeded, (5) The user aborts the run. The loop also has a hard limit of MAX_AGENT_TURNS (50 turns) - if reached without natural termination, the stop reason is set to MAX_TURNS_REACHED. The loop checks for abort signals via abortController.signal.aborted after each turn. Context limit is checked before each LLM call using isOverContextLimit. The loop also tracks noProgressContinuations - if the agent keeps executing the same tool without making progress, it will eventually stop.
Q: What's the install task queue pattern in agent loop?
Answer: The install task queue serializes package installation operations across turns to prevent race conditions. The pattern uses a promise chain where each install operation is appended to the tail of a chain of promises. The installTaskQueue object has enqueue(task) which adds a task to the chain, and waitForIdle() which returns a promise that resolves when all enqueued tasks complete. The implementation maintains a installQueueTail promise representing the end of the chain. When enqueue is called, it creates a new promise that executes the task and catches errors (to prevent chain breakage), then updates the tail. This ensures that even if multiple turns request package installations, they execute sequentially rather than concurrently, which would cause npm/pnpm conflicts.
Q: How does context window management work?
Answer: Context window management uses multiple strategies: (1) composePrompt uses different prompt profiles (COMPACT, VERBOSE) to control verbosity, (2) For follow-up conversations, buildConversationMessages can truncate older messages to stay within limits, (3) Skill compaction merges similar consecutive messages, (4) Pre-verified dependencies from planning workflow avoid including dependency analysis in the prompt, (5) Reserved output tokens are calculated based on model specifications, (6) Context limit validation happens before each LLM call. If the context would exceed the limit, the system can truncate history, reduce system prompt verbosity, increase reserved output tokens, or emit an error and terminate the run.
Sandbox & Docker
Q: How does sandbox recovery work after API restart?
Answer: Sandbox recovery uses Docker labels to rehydrate Redis state from running containers. The getActiveSandbox function first checks Redis for an active sandbox state. If not found, it queries Docker for containers with labels: com.edward.sandbox=true, com.edward.chat={chatId}, and State=running. If found, it extracts the sandboxId and userId from labels and reconstructs a SandboxInstance object. It saves this state to Redis and transitions the lifecycle state to ACTIVE. This ensures that if the API server restarts, running sandboxes aren't orphaned - the API can reconnect to existing containers. The function also includes a cached container status check with a 5-minute TTL to avoid frequent Docker inspect calls.
Q: Why scaffold .gitignore in containers?
Answer: The system scaffolds a default .gitignore file in sandbox containers to prevent committing unnecessary files to git when users push their projects. The ensureScaffoldGitignore function checks if .gitignore exists, and if not, creates one with standard exclusions: node_modules, build artifacts (dist, build, .next), coverage reports, log files, environment files (.env), and OS-specific files (.DS_Store). This is done for all frameworks except vanilla. The scaffold happens during sandbox provisioning after container creation. By providing a sensible default, the system prevents users from accidentally committing large node_modules directories or sensitive .env files.
Q: How does network connection management work during builds?
Answer: Network connection management ensures containers can access package registries during dependency installation but are isolated during other operations. The buildAndUploadUnified function calls connectToNetwork(containerId) before starting the build process to connect the container to a Docker network that provides internet access. After the build completes (successfully or with failure), it calls disconnectContainerFromNetwork(containerId, sandboxId) in a finally block to ensure disconnection even on error. This pattern is important for security - sandboxes should not have unrestricted network access. Network is only granted temporarily for the specific operation that needs it.
Q: What's the purpose of the container status cache?
Answer: The container status cache reduces the overhead of frequent Docker inspect calls by caching container alive/dead status in Redis with a 5-minute TTL. The getContainerStatus and setContainerStatus functions wrap Redis get/set operations with a timestamp. Before calling Docker to inspect a container, the function checks if there's a cached status that's less than CONTAINER_STATUS_CACHE_MS old. If the cache is fresh and indicates the container is alive, it skips the Docker inspect call entirely. This caching is important because Docker inspect operations are relatively expensive. During normal operation, the system might check container status dozens of times per minute.
Q: How does framework detection work?
Answer: Framework detection determines the build commands and runtime configuration for a sandbox. The primary mechanism is detectFrameworkFromPackageJson which reads the package.json from the container and inspects dependencies and scripts. It checks for known framework indicators: "next" for Next.js, "react" + "react-dom" for React, "vue" for Vue, "svelte" for Svelte, etc. It also checks the scripts section for build commands like "next build", "vite build", or "npm run build". The detected framework is cached in the sandbox state's scaffoldedFramework field. For frameworks that can't be auto-detected, the system falls back to "vanilla" which uses a simple static file server.
Build Pipeline
Q: How does the unified build orchestrator handle different frameworks?
Answer: The unified build orchestrator handles different frameworks through a template registry and runtime detection. It first attempts to use the scaffoldedFramework from the sandbox state. If the framework is unknown, it falls back to "vanilla". The orchestrator calls runUnifiedBuild which uses the template registry to get framework-specific build commands. For Next.js, it runs pnpm run build and expects output in .next. For Vite projects, it runs the build script and expects output in dist. For vanilla projects, it might skip the build step entirely. For SPAs, it uploads a 404.html fallback to handle client-side routing. The base-path injection logic ensures previews work correctly when hosted under a path prefix.
Q: Why merge and install dependencies before build?
Answer: The merge and install step ensures all required dependencies are available for the build process. The mergeAndInstallDependencies function combines packages requested by the AI with existing dependencies in the sandbox's package.json. It reads the current package.json, merges the dependencies section using semver-compatible merging (keeping the highest version for conflicts), and writes the updated package.json back. Then it runs pnpm install to install all dependencies. This is critical because the AI might generate code that uses new packages that weren't in the original project. By merging and installing before the build, the build process has access to all required packages.
Q: How does S3 upload handle partial failures?
Answer: The S3 upload process handles partial failures gracefully. The function iterates through build output files and uploads them to S3. Each upload is wrapped in a try-catch block - if an individual file upload fails, it logs the error but continues with other files. The function tracks both successful count and totalFiles count. After all uploads complete, if successful < totalFiles, it logs a warning with the failure count but doesn't treat it as a fatal error. This non-fatal handling is important because a single file upload failure shouldn't prevent the entire preview from being available. The uploaded keys are used for cleanup - cleanupS3FolderExcept deletes all files in the preview prefix except the ones that were successfully uploaded.
Q: What's the CloudFront invalidation strategy?
Answer: CloudFront invalidation is performed after successful builds to ensure the CDN serves the new content immediately. The invalidatePreviewCache function creates a CloudFront invalidation request for the preview path (e.g., /userId/chatId/preview/*). This invalidation is non-blocking - it's called with .catch() so that if it fails, the build is still considered successful. The invalidation uses a wildcard pattern to invalidate all files under the preview prefix, which is simpler and faster than invalidating individual files. The function uses the CloudFront distribution ID from configuration. The non-fatal error handling is intentional - CloudFront invalidations can occasionally fail due to rate limits, but the preview should still be available.
Q: How does subdomain registration differ from path-based preview?
Answer: Subdomain registration and path-based preview are two deployment modes configured via EDWARD_DEPLOYMENT_TYPE. In path-based mode (default), previews are accessed via URLs like https://assets.example.com/userId/chatId/preview/index.html. No additional infrastructure is needed. In subdomain mode, previews are accessed via subdomains like https://chatId-userId.preview.example.com. This requires Cloudflare integration - the registerPreviewSubdomain function writes routing rules to Cloudflare KV, mapping subdomains to S3 paths. A CloudFront Function or Cloudflare Worker rewrites requests from the subdomain to the S3 path. Subdomain mode requires additional configuration (CLOUDFLARE_API_TOKEN, CLOUDFLARE_ACCOUNT_ID, CLOUDFLARE_KV_NAMESPACE_ID, PREVIEW_ROOT_DOMAIN).
