- Removed auto-invoked syncIndexes() blocks from all 11 model files
(api-client, api-client-log, csrf-token, drone-monitor,
drone-registration, email-log, email-verification, ide-session,
user, web-token, web-visit) — no longer needed
- Fixed drone-session.test.ts by mocking ChatSession.updateOne(),
VectorStoreService.ingestTurn(), and MessageQueue to prevent
real DB connections during tests
- Removed unused mongoose Types import from drone-session test
- All 129 tests now pass across all packages
The qwen3-embedding:4b model defaults to 2560-d vectors. Both
Ollama (client.embed()) and OpenAI support a dimensions parameter
to request a specific output size. This change threads the configured
qdrant.vectorSize through the AI provider layer so the model returns
vectors matching the Qdrant collection dimensions.
- AiApi.embeddings() now accepts optional dimensions parameter
- Ollama provider: switched from client.embeddings() to client.embed()
- OpenAI provider: passes dimensions to embeddings.create()
- VectorStoreService.getEmbedding() passes env.qdrant.vectorSize
- Added unit tests for dimension mismatch detection, collection
creation, and search guards
Adds vector-based semantic search across all chat sessions using Qdrant.
When a ChatTurn finishes, its content is chunked, embedded, and upserted
to a Qdrant collection. A search API and UI components enable searching
at user, project, and session scope.
Phase 1 — Configuration & Dependencies
- Add port/apiKey to GadgetCodeConfig.qdrant type
- Uncomment and update qdrant section in YAML config example
- Add qdrant config passthrough in env.ts
- Add @qdrant/js-client-rest dependency
Phase 2 — AI Embedding API (@gadget/ai)
- Add IAiEmbeddingResponse interface and abstract embeddings() to AiApi
- Implement embeddings() in OllamaAiApi (client.embeddings)
- Implement embeddings() in OpenAiApi (client.embeddings.create)
- Export IAiEmbeddingResponse from package index
Phase 3 — Backend Vector Store Service
- Create VectorStoreService (ingestTurn, search, removeTurnPoints)
- Hook fire-and-forget ingest after turn.save() in drone-session
- Register VectorStoreService in service startup/shutdown
Phase 4 — Backend Search API
- Create POST /api/v1/search controller with userId enforcement
- Batch-hydrate results from MongoDB (user, project, session, turn)
- Register search route in v1 API router
Phase 5 — Frontend Search Components
- SearchInput: debounced input with lucide-react icons
- ChatSearchResults: modal with score badges, metadata, loading states
- DroneSelectionModal: drone picker for sessions without a drone
- Add searchApi and ISearchResult to API client
- Add search to Home (global), ProjectManager (project), ChatSessionView (session)
- Add id=turn-{turnId} to ChatTurn for scroll targeting
- Scroll-to-turn from search result selection and router state
- Show DroneSelectionModal when no drone available
- Add Select Drone button in ChatSessionView sidebar
Phase 1: OpenAI API Token Extraction
- Add stream_options: { include_usage: true } to all streaming API calls
- Capture chunk.usage from final streaming chunks and response.usage from non-streaming
- Extend OpenAiChatIterationResult with optional usage field
- Update buildStats() to accept and return real token counts from usage data
- Wire iteration.usage through chat() to both buildStats() call sites
Phase 2: Agent Loop Stats Propagation
- Rename inputTokens/outputTokens to masterInputTokens/masterOutputTokens, add masterThinkingTokens
- Accumulate response.stats.tokenCounts after each master AI call
- Delete all Math.ceil(length/4) crude approximations (master and subagent loops)
- Track startTime/durationMs and emit IWorkOrderCompleteStats with workOrderComplete
- Subagent loop uses response.stats?.tokenCounts instead of Math.ceil
Phase 3: Database Model Changes
- Add contextWindowUsage field to IChatSession, ChatSessionSchema, and frontend ChatSession
- Initialize contextWindowUsage: 0 on session creation
Phase 4: Persist Stats on Turn Completion
- drone-session: accept IWorkOrderCompleteStats, persist turn stats, walk subagent records for aggregate
- drone-session: $inc session stats and contextWindowUsage, add formatDurationLabel() helper
- code-session: accept and forward stats, update in-memory session stats
- message-queue: Redis replay handles 4th stats arg
- Update WorkOrderCompleteMessage type in @gadget/api to accept stats parameter
Phase 5: UI — Context Window Fuel Gauge
- Add contextWindowUsage prop to SessionPanel
- Add fuel gauge bar with E→F labels, green/yellow/red zones, token count display
- Visible for ALL provider types (not gated by apiType)
Phase 6: Frontend Streaming State
- Add IWorkOrderCompleteStats interface to frontend api.ts
- handleWorkOrderComplete accepts stats, updates turn stats and session contextWindowUsage
- Pass contextWindowUsage prop to SessionPanel
Plan tools extended to all modes; recent chat sessions added to
Authenticated Home view; drone locking and chat session startup factored
out of Project Manager into Chat Session view and made universal; update
for AGENTS.md.
Converts gadget-tasks from a duplicated codebase with direct MongoDB access
to a headless IDE client that uses gadget-code's REST API and Socket.IO
protocol to submit and process task prompts through the existing pipeline.
Deleted (no longer needed):
- gadget-tasks/src/models/ (5 Mongoose models — duplicated from gadget-code)
- gadget-tasks/data/prompts/ (11 prompt templates — duplicated from gadget-code)
- gadget-tasks/src/services/executor.ts (629-line AWL loop — duplicated from gadget-drone)
- gadget-tasks/src/services/ai.ts (direct AI API calls — drones do this now)
- gadget-tasks/src/services/workspace.ts (workspace management — drones do this now)
Added:
- gadget-tasks/src/services/platform.ts — REST API + Socket.IO headless IDE client
with session lock, workspace mode, prompt submission, work order tracking
Rewritten:
- gadget-tasks/src/gadget-tasks.ts — new startup: user login → auth → drone
selection → Socket.IO connect → schedule tasks (no MongoDB)
- gadget-tasks/src/services/scheduler.ts — delegates to PlatformService.executeTask()
- gadget-tasks/src/config/env.ts — platform.baseUrl config, no mongodb/google
gadget-code changes:
- Added PATCH /api/v1/projects/:projectId/tasks/:taskId/lastRun route
- Added ProjectService.updateTaskLastRun() for atomic task field update
Config changes:
- GadgetTasksConfig: replaced mongodb with platform.baseUrl, removed google.cse
- Dependencies: removed mongoose, @gadget/ai, @gadget/ai-toolbox, dayjs,
simple-git, nanoid; added socket.io-client, @inquirer/prompts
- IChatSession/IChatTurn interfaces: add optional numCtx field
- Mongoose schemas: add numCtx field to ChatSession and ChatTurn
- API controller: validate numCtx (>=16384, multiple of 8192)
- ChatSessionService: persist numCtx on session, pass through to turn
- SessionPanel: add range slider for Context Window (hidden for OpenAI)
- Drone buildDroneModelConfig: prefer turn.numCtx over model defaults
Complete Phase 1 of subprocess management in gadget-drone:
- SubProcessService with graceful shutdown (SIGINT→SIGKILL), halt, getLog
- SubprocessTool with create/list/kill/killAll/halt/log commands
- Registered in agent toolbox for build/test/ship/develop modes
- Fixed missing {{process_management_block}} replacement in chat-session.ts
- 34 unit tests across service and tool
- Comprehensive documentation in docs/subprocess.md
- Create comprehensive plan document for agent instructions text area
- Define requirements, acceptance criteria, and technical implementation
- Include UI/UX mockups and testing strategy
- Plan discovered during FILES panel implementation
- Addresses need for project-specific acceptance criteria
Add end-to-end abort support: AbortSignal in @gadget/ai providers,
abortWorkOrder socket message, drone AbortController handling,
Cancel button and double-Esc in frontend, and aborted turn status display.
- Switch frontend sign-in to /api/v1/auth/sign-in endpoint (includes persona)
- Add updateUser() to App context for proper state management
- Fix Settings.tsx save flow to use updateUser() instead of broken localStorage merge
- Remove unused web AuthController (gadget-code/src/controllers/auth.ts)
- Fix UserApiControllerV1 to return flat user object instead of double-wrapped
- Remove SessionType enum and references (dead code)
- Add proper server sign-out call before clearing local state
Resolves issue where User Settings view didn't display persona text even though it existed in the database.
The persona field was missing from both the session object and the API
response during sign-in, causing User Settings to display an empty field
even when the user had a persona defined in the database.
This commit addresses two interrelated issues causing drones to
de-register and users to be forcibly signed out:
## Heartbeat Timeout Fixes
1. Move heartbeat interval to a Web Worker (not subject to browser
tab throttling). Chrome throttles setInterval in background tabs
to ~1/min, which causes the 19s heartbeat to miss the drone's
timeout timer. The Web Worker fires reliably regardless of tab
visibility.
2. Add visibilitychange handler: when the tab becomes visible again,
send an immediate heartbeat to reset the drone's timer after any
throttling that may have occurred.
3. Fix onReleaseSessionLock to clear the heartbeat timer. Previously,
releasing the lock left the 60s timer running, causing a spurious
timeout and status emit after the lock was already released.
4. Increase drone heartbeat timeout from 60s to 120s. With the Web
Worker fix, heartbeats should be reliable, but doubling the timeout
provides a generous safety margin.
5. Add socket disconnect/reconnect handlers on the drone side. On
disconnect, clear the heartbeat timer. On reconnect, re-emit drone
status so the platform knows the drone is alive.
6. Configure Socket.IO pingInterval/pingTimeout explicitly (25s/60s)
instead of relying on defaults.
## JWT Expiration Fixes
1. Increase WebToken DB record expiration from 1 hour to 7 days. The
1-hour expiration was the real session lifetime gate (the JWT crypto
exp was already 24h), and it was far too aggressive for a dev tool.
2. Fix web /auth/renew-token endpoint to use req.user from the session
cookie instead of verifyJsonWebToken(req.body.token). This eliminates
the catch-22 where an expired token cannot be used to request its
own renewal.
3. Fix token refresh response parsing. The API v1 renew-token endpoint
returns { success: true, token } at the top level, but the frontend
was looking for json.data?.token, causing every refresh to fail.
4. Add proactive token refresh: check the JWT exp claim before each
request and refresh if expiring within 5 minutes. This avoids
unnecessary 401 errors and the resulting socket disconnections.
5. Update socket JWT on token renewal via a callback registered in
App.tsx. This ensures that future socket reconnections use the new
token instead of the expired one.
## Files Modified
- gadget-code/frontend/src/workers/heartbeat.worker.ts (NEW)
- gadget-code/frontend/src/lib/socket.ts
- gadget-code/frontend/src/lib/api.ts
- gadget-code/frontend/src/App.tsx
- gadget-code/src/services/session.ts
- gadget-code/src/controllers/auth.ts
- gadget-code/src/services/socket.ts
- gadget-drone/src/gadget-drone.ts
Fixes premature AI API response truncation by propagating inference
parameters through the entire probe → storage → runtime → API call chain.
Root cause: Ollama defaults num_predict to 128 tokens and num_ctx to
4096, silently truncating output and context. We never overrode these.
Changes:
- IAiModelSettings: add numPredict, maxCompletionTokens fields
- IDroneModelConfig: moved from gadget-drone to @gadget/api (shared),
expanded with numPredict, numCtx, maxCompletionTokens params
- IAiModelConfig.params: add numPredict, numCtx, maxCompletionTokens
- IAiModelProbeResult.settings: add numPredict, maxCompletionTokens
- AiModelSettingsSchema (Mongoose): add numPredict, maxCompletionTokens
- Ollama extractSettings(): extract num_predict from model parameters
- Ollama generate()/chat(): pass options: { num_ctx, num_predict }
- OpenAI all three create() calls: add max_completion_tokens
- web-cli.ts onProviderProbe(): compute numPredict (-1 for Ollama)
and maxCompletionTokens (contextWindow for OpenAI) during probe
- agent.ts main + subagent loops: read model settings from provider
cached models, build IDroneModelConfig with stored params
- ai.ts: remove local IDroneModelConfig, import from @gadget/api
- chat-session.ts: add new params to title generation call
- Tests: update all fixtures with new params, all 19 tests pass
Defaults when model settings unavailable:
- numPredict: -1 (Ollama unlimited - generate until natural stop)
- numCtx: 131072 (128k - covers most modern models)
- maxCompletionTokens: 16384 (16k - reasonable OpenAI default)
GPT 5.5 is sucking ass - hard - and fucking things up royally. This will
likely just all get dropped. I'm torturing it, making it suffer, and
beating it like the jew it is.
Move the 6 duplicated logging modules (component, log, log-transport,
log-transport-console, log-transport-file, log-file) from both
gadget-code (Dtp* prefix) and gadget-drone (Gadget* prefix) into
@shad/api, using gadget-drone's GadgetLog as the canonical version.
GadgetLog now uses static configuration (consoleEnabled, defaultFile)
set by each consumer's env.ts at module scope, removing the env
dependency from the shared library. The addDefaultTransport/
removeDefaultTransport/getDefaultTransports static methods are
preserved for future real-time log transport injection.
User Settings will enable User to enter a Persona, or a description of
the User, to be included in the system prompt. This helps calibrate the
agent to better assist the User, and work with the User in ways that
work best for each individual User of the system.
- created AiTool and AiToolbox for representing tools in the API
- add googleapis dependency
- integrate Google Search tool as first agent tool
- created IAiEnvironment to communicate AI environment vars around the
platform
