Enable tagging chats and filtering by tags

Summary Something I find myself reaching for frequently is the ability to assign custom tags/labels to chats, and to be able to filter the chat list by these tags. As a bonus, it would be nice to have such tags included in markdown chat exports. Problem As chat history grows, finding related conversations across different topics or projects becomes difficult using only keyword search or chronological browsing. Proposed Solution Chat Tagging: Allow assigning one or more tags to a chat (via sidebar context menu, chat settings, or inline header). Tag Filtering: Add a tag filter/selector in the sidebar or search bar to quickly narrow down chats by tag. Markdown Export Integration: BoltAI already uses YAML front matter for permalinks on export. Include the assigned tags directly in the front matter: --- title: macOS App Design permalink: "boltai://chat/..." tags: - SwiftUI - macOS - development --- This would make organizing workflows within the app much cleaner and integrate seamlessly with personal note systems (like Obsidian) upon export.

Sync via Apple CloudKit (private iCloud database) as an alternative to BoltAI Cloud

Hi, Thanks for BoltAI. Having the same chats on Mac, iPhone and iPad is great. A question and a suggestion: why not use Apple's CloudKit (https://developer.apple.com/icloud/cloudkit/), i.e. each user's private iCloud database, to sync BoltAI data, instead of or alongside BoltAI Cloud? From the v2 announcement, BoltAI Cloud runs on Supabase with row-level security, and end-to-end encryption covers API keys. That's a sensible design. It still means my conversations, which often contain sensitive personal and work material, are stored in readable form on a third-party server. For a privacy-focused app, that's the part I'm not comfortable with. What CloudKit would bring: Data stays in the user's own iCloud account. BoltAI never stores it, so there's no extra server to trust, secure, breach or be legally compelled to hand over. Real end-to-end encryption. With Advanced Data Protection turned on, CloudKit fields marked as encrypted (CKRecord.encryptedValues) and all CKAssets are end-to-end encrypted, with keys held only on the user's devices. No separate account. The Apple ID already signed in on the device is the identity. Storage counts against the user's iCloud plan rather than a quota you have to host and pay for, which could also lower your infrastructure costs. Native fit. BoltAI runs on Mac, iPhone and iPad, which is exactly CloudKit's ecosystem, and CKSyncEngine (macOS 14 / iOS 17) handles much of the sync plumbing. Thanks!

Programmatic ability to make full backup of local Bolt AI data on Mac

Ability to specify endpoint provider for a model on OpenRouter

A lot of the coding extensions give a UI option to select the API-call-level variable of which provider you want: eg: novita-fp8, deepinfra-fp4, etc. This would be highly useful on Bolt as OpenRouter has the following non-ideal behavior when this variable is not set: 1) Flitting between different providers even with a heavy cache load. Today, four requests went to four different providers in immediate succession. 2) OpenRouter’s autorouting (or even what is possible by manually configuring guardrails) is limited and often leaves you with endpoints that are more expensive or with a worse quantization than is ideal. This is best solved by utilizing the API-call level config OpenRouter allows. This could be implemented with an optional “choose endpoint” in the model configuration, and/or the ability to switch endpoints in the right chat sidebar.

Delete all prior/preceding messages

You can select a message in a chat and delete all subsequent messages. I’d like to have a similar option to delete all prior messages.

Button to jump to beginning of chat

There’s a button to jump to the end of a chat. It would be great if there was a similar button to jump to the beginning of the chat — the very first message (such that older messages would automatically be added).

Give BoltAI a Real Knowledge Base, Not Just Document Dumping

Why I'm Writing This I use BoltAI for quick tasks, writing, and code help, and it's good at that. But I also run a personal knowledge base of over 1,100 Markdown documents — 491 YouTube video transcripts, a published book, legal case files, product research, and travel guides — and BoltAI can't touch it. The reason is simple: BoltAI doesn't do retrieval at all. When you attach a document, the whole thing gets stuffed into the context window. That works for a single short file. It breaks down completely the moment someone has a real knowledge base rather than a handful of documents, because there's no way to attach 1,100 files to a single chat, and no mechanism to find the two or three that actually answer a question. I've built a system from plain files and SQLite that finds the right document out of that 1,100 and answers correctly almost every time. Nothing in it is exotic. Most of it would translate directly into BoltAI. Here's how it works and what I'd suggest you build. How My System Works (The Reference Architecture) The core idea: retrieval and context are two different jobs. Search finds the right document. The full document — not a chunk of it — is what goes to the model. The setup: One folder, plain Markdown, as the source of truth. Every piece of knowledge is a structured .md file in a numbered folder hierarchy. No proprietary format, no lock-in. YAML frontmatter on every file. Each document carries kb_id, title, domain, and tags. These are first-class retrieval signals, not decoration. A query about "Simpson Desert tyre pressure" matches on domain and tags before any embedding maths happens. A database that stores complete documents. A local SQLite database holds every document in full — title, full text, metadata, one row per document. It rebuilds from the files every 2 hours via upsert, so it never drifts and never duplicates. A cloud copy (Supabase) adds vector embeddings for semantic search. Universal ingest with a complete internal scrape. Anything can be dropped in: PDF, Word, plain text, email, web page, video. Videos are transcribed with Whisper into one MD file per video. PDFs and other documents are converted into a complete structured text representation — the full content, restructured with headings, not a sampled extract. The original goes to archive; the complete text version lives in the knowledge base forever. Hybrid search over a section index, full documents in context. The index knows about 11,700+ sections across the documents and searches them with combined lexical (BM25) and semantic scoring. Sections are only signposts. When the model answers, it reads the entire matched document. Used with Claude, this setup finds the right information across 1,100+ documents with an accuracy BoltAI has no way to approach today — not because BoltAI's model is worse, but because it never gets to see the right document in the first place. Where BoltAI Falls Short Today BoltAI's document handling is attach-and-dump: the full content of whatever you attach goes into the prompt. There's no index, no search step, no concept of "which of my documents is relevant to this question." That's fine for one document. It's not a knowledge base feature — it's a paste-a-file feature with extra steps. Ask BoltAI "summarise what I've written about the Simpson Desert" or "what tyre press

Bolt Hangs

Hello, Any idea why Bolt Ai now constantly hangs when using my Ai workflow shortcut for text and spelling correction?? Cheers Stefan

Can I dictate in Chinese?

Is there any way to do that, whether on a local model or via API to other providers?

iOS App doesn't send image properly to llama-server

I share the same extract text from image prompt for gemma4 from macOS and iOS, but iOS never “gets” the file.

Allow Ollama provider to support Ollama Cloud directly

Currently I have added Ollama Cloud to BoltAI 2 as a custom OpenAI provider, howevr it would be nice (cosmetically) if the endpoint https://ollama.com could be added to the Ollama provider, along with an API key so that the Ollama icon is used.

Add support for Cursor API

https://cursor.com/en-US/docs/cloud-agent/api/endpoints#create-an-agent describes how to access the Cursor via API-Key. Yet we’re not able to access their models, e.g. composer 2.5. Please add Cursor in the list of AI-Providers

View Thoughts

I love to review the reasoning behind the output for any given post. I really like the way you have the reasoning/thoughts displayed while the model is processing, but upon completion, I see that the “View Thoughts” section is then constricted to a very small popup window. I’d love to be able to expand the “View Thoughts” section and read it in the main window along with the output. Maintaining the same formatting as it has while streaming would be really cool.

Quick open chat modal palette

I’d like a shortcut-triggered palette with type-ahead title search for opening one of my chats. Examples can be found in most IDEs/code editors, e.g. ⌘⇧O in Xcode.

Compact conversation?

Lots of more capable agents like coding agents have a compact conversation feature that takes the conversation above it and condenses it down to something that reduces context use. I’ve found that context overflow / poisoning can be a problem in long dialogues. I usually kind of do this manually, have it summarize and then make a new discussion, but a way to compact and then a feature to include the previous N up to and including the most recent compacting would be nice.

option to Archive a Project

So I'm getting more and more Project folders. When I start a new project, I create a new Project, and let's say I work on it for two weeks with 20+ conversations, files, etc. Now the project is finished, but I don't want to delete it, as I "might" need something from it in the future. A collapsible Archive folder that holds Archived Projects would be great.

Support Gemini 3.1 and 3.5 Flash for Google Vertex AI Provider

Problem Currently, the Google Vertex AI provider in BoltAI is missing recent models, such as the Gemini 3.1 series (including 3.1 Pro Preview) and Gemini 3.5 Flash. Additionally, other non-Gemini models offered through Vertex AI are not supported. Because the AI models evolves rapidly, it is difficult to maintain and update a hardcoded list every time a new model is released. Proposed Solution Add Gemini 3.1 Pro and 3.5 Flash next release for a quick fix. For the long run, add a custom "Model ID" input field within the Google Vertex AI provider settings. This would allow users to manually enter the exact ID of the language model they wish to use. Benefits This approach future-proofs the integration. Instead of waiting for Daniel to release an update with a new model, users can instantly access the latest models (like Gemini 3.1 Pro Preview and 3.5 Flash) or any other supported Vertex AI model simply by entering the corresponding model ID.

Plugins

It would be great if you could add a plugin that supports Tavily web search. What would be even better is that you would provide the possibility to add custom plugins

Cross-Chat References

Allow referencing or including context from previous chats in a new conversation. What I'd like: Ability to attach/link a previous chat (or multiple chats) as context to a new one Ideally searchable chat history so finding the right one is easy attach/link selectable or all history from Projects Why it matters: Work often spans multiple sessions — being able to pull in context from a past conversation without copy-pasting saves time and avoids losing important decisions/outcomes.

Skills.md

It will be great if BoltAi could support SKILLS.MD spec: https://agentskills.io/home This allows the user to add in prompts or scripts in a folder, and BoltAI will trigger the respective skills automatically when the model detected certain trigger words.