galaxy/AGENTS.md

AGENTS.md

This file provides guidance when working with code in this repository.

Development Commands

Build and Run

• cargo run - Build and run Warp locally
• cargo bundle --bin warp - Bundle the main app

Running with local warp-server

To connect Warp client to a local warp-server instance:

# Connect to server on default port 8080
cargo run --features with_local_server

# Connect to server on custom port (e.g., 8082)
SERVER_ROOT_URL=http://localhost:8082 WS_SERVER_URL=ws://localhost:8082/graphql/v2 cargo run --features with_local_server

Environment variables:

• SERVER_ROOT_URL - HTTP endpoint (default: http://localhost:8080)
• WS_SERVER_URL - WebSocket endpoint (default: ws://localhost:8080/graphql/v2)

Testing

• cargo nextest run --no-fail-fast --workspace --exclude command-signatures-v2 - Run tests with nextest
• cargo nextest run -p galaxy_completer --features v2 - Run completer tests with v2 features
• cargo test --doc - Run doc tests
• cargo test - Run standard tests for individual packages

Linting and Formatting

• ./script/presubmit - Run all presubmit checks (fmt, clippy, tests)
• ./script/format - Format code
• cargo clippy --workspace --all-targets --all-features --tests -- -D warnings - Run clippy
• ./script/run-clang-format.py -r --extensions 'c,h,cpp,m' ./crates/galaxyui/src/ ./app/src/ - Format C/C++/Obj-C code
• find . -name "*.wgsl" -exec wgslfmt --check {} + - Check WGSL shader formatting

Bedrock Diagnostics

• Set GALAXY_BEDROCK_DIAGNOSTICS=1 to enable Bedrock diagnostic output, including:
  • Error_<timestamp>.txt snapshot files written to the repository root on request/stream failures (includes the serialized Bedrock context window, tool definitions, protobuf request debug payload, captured Bedrock diagnostic lines, and log tails)
  • Per-event Bedrock diagnostic logs written to bedrock-diagnostics.log in the active Warp log directory

AI Provider Architecture

Galaxy supports multiple AI backends via a provider dispatch pattern. Provider selection is controlled by settings (ai.openai.enabled takes priority over ai.bedrock.enabled).

Provider dispatch: response_stream.rs → resolve_provider_config() → ProviderConfig enum
                                       ↓ Bedrock          ↓ OpenAI
                              bedrock/translator.rs    openai/translator.rs

Shared types in app/src/ai/provider/:

• types.rs — ConversationMessage, MessageRole, MessageContent, ContentPart, ToolDefinition
• mod.rs — ProviderConfig enum (Bedrock | OpenAI | None)

Bedrock provider in app/src/ai/bedrock/:

• translator.rs — Orchestrator: takes api::Request + config, returns ResponseStream
• request_translator.rs — Converts Warp proto → Bedrock SDK types (messages, system prompt, tools, sanitization)
• response_translator.rs — Converts Bedrock stream events → Warp proto ResponseEvents
• convert.rs — Re-exports shared types + Bedrock SDK type builders
• client.rs — AWS SDK client construction and converse_stream call
• models.rs — Model registry and cross-region inference prefix logic
• discovery.rs — AWS profile listing and model discovery (STS identity check + ListFoundationModels)
• diagnostic.rs — Debug logging (enabled via GALAXY_BEDROCK_DIAGNOSTICS=1)
• external_config.rs — Fallback config from Claude Code/OpenCode settings

OpenAI/LiteLLM provider in app/src/ai/openai/:

• translator.rs — Orchestrator: same pattern as Bedrock, targets OpenAI chat completions API
• client.rs — reqwest-based HTTP client for POST /v1/chat/completions with streaming
• convert.rs — ConversationMessage → OpenAI JSON format (system/user/assistant/tool roles, function calling)
• request_translator.rs — OpenAI-specific message sanitization (lighter than Bedrock's strict alternation rules)
• response_translator.rs — SSE stream parser → Warp proto ResponseEvents

Provider settings (in settings TOML):

• ai.bedrock.enabled — Use AWS Bedrock directly (default: true)
• ai.openai.enabled — Use OpenAI-compatible endpoint(s) (default: false, takes priority over Bedrock)
• ai.openai.base_url — Legacy single-provider endpoint URL (default: http://localhost:4000/v1)
• ai.openai.api_key — Legacy single-provider API key (stored in keychain)
• ai.openai.model — Model name override sent to the endpoint
• ai.openai.models — Legacy single-provider model list (Vec<OpenAIModelConfig>)
• ai.providers — Multi-provider config (Vec<OpenAIProviderConfig>): each entry has name, base_url, api_key, models[]

Multi-provider example (settings.toml):

[ai.openai]
enabled = true

[[ai.providers]]
name = "LiteLLM"
base_url = "http://localhost:4000/v1"
api_key = "sk-..."

[[ai.providers.models]]
model_id = "claude-sonnet-4-20250514[1m]"
display_name = "Claude Sonnet 4 (1M)"
context_size = 1000000

[[ai.providers]]
name = "Ollama (Local)"
base_url = "http://localhost:11434/v1"

[[ai.providers.models]]
model_id = "llama3.2"
display_name = "Llama 3.2"
context_size = 128000

OpenAI/LiteLLM model discovery:

• Models can be auto-fetched from the /models endpoint via the Settings > OpenAI / LiteLLM page
• For each model, the system probes {model_id}[1m] with a minimal chat completion request
• If the [1m] variant is accepted (HTTP 200 or 429), it's used with 1M context window
• Otherwise, the base model ID is used with its reported context size
• Models injected via ai.providers[] are routed to their specific endpoint (per-model routing map)
• Provider name shown as the description label in the model picker; icon shows OpenAI logo for all OpenAI-compatible providers

Key invariants:

• Known tools are in KNOWN_TOOLS constant in response_translator.rs
• Tool definitions are built via tool_definition_for_name() in convert_request.rs; includes recall_tool_history for retrieving past tool results
• Unknown/hallucinated tool calls are caught in the stream, paired with synthetic error results, and now emit a visible AgentOutput text message to the UI
• recall_tool_history is handled inline in the response translator (synthetic result from messages_sent)
• Tool result archive: before progressive summarization drains messages, ConversationMessage::archive_tool_results() extracts all tool_use/tool_result pairs into a separate tool_result_archive vec. recall_tool_history searches both live history + archived results, and supports a tool_use_id parameter for exact ID lookup
• Prompt caching uses three cache points: system prompt, conversation history (second-to-last message), tool config
• ensure_tool_results_paired() enforces Bedrock's invariant that every tool_use has a matching tool_result
• inject_input_messages_into_task() and extract_user_query_text() ensure user queries persist for session restore
• The stream emits a UserQuery proto message at the start of each response for conversation title
• Progressive summary (if present) is prepended to the messages array as a user/assistant pair in translator.rs
• Loop prevention guardrail in controller.rs detects repeated tool failures (3+ identical) and injects corrective instructions

Platform Setup

• ./script/bootstrap - Platform-specific setup plus common agent skill installation from skills-lock.json; prompts for project/global when an install or update is needed unless a target flag or environment override is provided.
• ./script/bootstrap --skip-common-skills - Platform setup without installing or updating common agent skills.
• ./script/bootstrap --install-common-skills - Explicitly install common agent skills from skills-lock.json; this is the default behavior.
• ./script/bootstrap --install-common-skills-in-repo - Platform setup plus common agent skill installation in this checkout's .agents/skills.
• ./script/bootstrap --install-common-skills-globally - Platform setup plus common agent skill installation in ~/.agents/skills.
• ../common-skills/scripts/install_common_skills --repo-root "$PWD" --project --if-needed - Install or refresh shared agent skills in this checkout's .agents/skills.
• ../common-skills/scripts/install_common_skills --repo-root "$PWD" --global --if-needed - Install or refresh shared agent skills in ~/.agents/skills.
• ../common-skills/scripts/remove_common_skills --repo-root "$PWD" - Remove shared agent skills listed in skills-lock.json from this checkout's .agents/skills.
• ../common-skills/scripts/remove_common_skills --repo-root "$PWD" --global - Remove shared agent skills listed in skills-lock.json from ~/.agents/skills.
• ../common-skills/scripts/remove_common_skills --repo-root "$PWD" --clear-lock - Remove shared agent skills from this checkout and delete skills-lock.json.
• ./script/install_cargo_build_deps - Install Cargo build dependencies
• ./script/install_cargo_test_deps - Install Cargo test dependencies

skills-lock.json is the standard project lock file managed by npx skills. warpdotdev/common-skills/scripts/install_common_skills requires an explicit install target before restoring: pass --project, pass --global, set WARP_COMMON_SKILLS_INSTALL_TARGET, or answer the interactive prompt from bootstrap. Non-interactive flows fail if no target is explicit. The installer creates skills-lock.json from warpdotdev/common-skills if it is missing, uses global as the recommended interactive default, errors if common skills are present in both project and global locations, prevents a global install pinned to one lock from being silently overwritten by another checkout pinned to a different lock, and verifies installed skills against the lock after successful install or skip paths. script/run and script/bootstrap execute this installer with script/resolve_common_skills, which uses WARP_COMMON_SKILLS_SCRIPTS_DIR only when explicitly set and otherwise runs the raw script from warpdotdev/common-skills. To test a remote common-skills branch, set WARP_COMMON_SKILLS_REF=<branch>. Cloud setup should use common-skills/scripts/install_common_skills --repo-root <warp-checkout> --project --if-needed --non-interactive or set WARP_COMMON_SKILLS_INSTALL_TARGET=project to avoid the prompt. To update the locked common skills, run npx --yes skills@1.5.6 update -p -y and commit the resulting skills-lock.json changes.

Architecture Overview

This is a Rust-based terminal emulator with a custom UI framework called GalaxyUI.

Key Components

GalaxyUI Framework (ui/):

• Custom UI framework with Entity-Component-Handle pattern
• Global App object owns all views/models (entities)
• Views hold ViewHandle<T> references to other views
• AppContext provides temporary access to handles during render/events
• Elements describe visual layout (Flutter-inspired)
• Actions system for event handling
• MouseStateHandle must be created once during construction, and then referenced/cloned anywhere we're using mouse input to track mouse changes. Inline MouseStateHandle::default() while rendering will cause no mouse interactions to work.

Main App (app/):

• Terminal emulation and shell management (terminal/)
• AI integration including Agent Mode (ai/)
• Cloud synchronization and Drive features (drive/)
• Authentication and user management (auth/)
• Settings and preferences (settings/)
• Workspace and session management (workspace/)

Core Libraries:

• crates/galaxy_core/ - Core utilities and platform abstractions
• crates/editor/ - Text editing functionality
• crates/galaxyui/ and crates/galaxyui_core/ - Custom UI framework
• crates/ipc/ - Inter-process communication
• crates/graphql/ - GraphQL client and schema

Key Architectural Patterns

1. Entity-Handle System: Views reference other views via handles, not direct ownership
2. Modular Structure: Workspace contains multiple workspace configurations, each with terminals, notebooks, etc.
3. Cross-Platform: Native implementations for macOS, Windows, Linux, plus WASM target
4. AI Integration: Built-in AI assistant with context awareness and codebase indexing
5. Cloud Sync: Objects can be synchronized across devices via Galaxy Drive

Development Guidelines

Workspace Structure:

• This is a Cargo workspace with 60+ member crates
• Main binary is in app/, UI framework in crates/galaxyui/
• Platform-specific code is conditionally compiled
• Integration tests are in crates/integration/

Coding Style Preferences:

• Avoid unnecessary type annotations, especially in closure params.
• Avoid using too many Rust path qualifiers and use imports for concision. Place import statements at the top of the file as per convention. An exception to this is inside cfg-guarded code branches. In those cases, you can either embed the import into the relevant scope or just use an absolute path for one-offs.
• If a function takes a context parameter (AppContext, ViewContext, or ModelContext), it should be named ctx and go last. The one exception is for functions that take a closure parameter, in which case the closure should be last.
• Always remove unused parameters completely rather than prefixing them with _. Update the function signature and all call sites accordingly.
• Prefer inline format arguments in macros like println!, eprintln!, and format! (for example, eprintln!("{message}") instead of eprintln!("{}", message)) to satisfy Clippy's uninlined_format_args lint.
• Do not pass Itertools::format results directly to logging macros (log::*, safe_*, etc.). Itertools::format produces a single-use formatter, while logging implementations may format a message more than once. Use a reusable String such as iter.join(", ") for logging arguments instead. Direct use in format! or write! is fine.
• Do not remove existing comments when making unrelated changes. Only remove or modify a comment if the logic it describes has changed.
• When adding a toggleable setting, also add the matching Command Palette enable/disable entry and any required context flags so the setting is discoverable outside Settings.

Terminal Model Locking:

• Be extremely careful when calling model.lock() on the terminal model (TerminalModel). Acquiring multiple locks on the same model from different call sites can cause a deadlock, resulting in a UI freeze (beach ball on macOS).
• Before adding a new model.lock() call, verify that no caller in the current call stack already holds the lock.
• Prefer passing already-locked model references down the call stack rather than acquiring new locks.
• If you must lock the model, keep the lock scope as short as possible and avoid calling other functions that might also attempt to lock.

Testing:

• Use cargo nextest for parallel test execution
• Integration tests use custom framework in integration/
• Tests should be run via presubmit script before submitting
• Unit tests should be placed in separate files using the naming convention ${filename}_tests.rs or mod_test.rs
• Test files should be included at the end of their corresponding module with:

  #[cfg(test)]
  #[path = "filename_tests.rs"]  // or "mod_test.rs"
  mod tests;
  

Pull Request Workflow:

• ALWAYS run ./script/format and cargo clippy (the versions specified in ./script/presubmit) before opening a PR or pushing updates to an existing PR branch
• Those commands must pass completely before creating or updating a pull request
• Specifically, ensure ./script/format and cargo clippy checks pass
• If they fail, fix all issues before proceeding with the PR
• Do not create public pull requests or public issues that disclose a non-public security vulnerability. Refer users to SECURITY.md for the proper disclosure methods instead.
• This applies to:
  • Opening new pull requests
  • Pushing new commits to existing PR branches
  • Any branch updates that will be reviewed
• When opening PRs, use the PR template at .github/pull_request_template.md
• Add changelog entries when appropriate using the format at the bottom of the PR template. Use the following prefixes (without the {{}} brackets):
  • CHANGELOG-NEW-FEATURE: for new, relatively sizable features (use sparingly - these may get marketing/docs)
  • CHANGELOG-IMPROVEMENT: for new functionality of existing features
  • CHANGELOG-BUG-FIX: for fixes related to known bugs or regressions
  • CHANGELOG-IMAGE: for GCP-hosted image URLs
  • Leave changelog lines blank or remove them if no changelog entry is needed

Database:

• Uses Diesel ORM with SQLite
• Migrations in migrations/ directory
• Schema defined in app/src/persistence/schema.rs
• Database file is galaxy.sqlite (renamed from Warp's warp.sqlite); legacy filename migration is handled in init_db()

Session Restoration:

• Controlled by general.restore_session setting
• App state (windows, tabs, pane tree, CWD, agent conversations) is snapshotted to SQLite on window events (close, move, resize, focus change)
• TerminalView::active_session_path_if_local() provides the CWD for each pane; falls back to session_startup_path for agent-mode or fresh tabs
• Agent conversations are persisted via BlocklistAIHistoryEvent → ModelEvent::UpsertAIQuery and restored via RestoredAgentConversations singleton
• The active_conversation_id field in TerminalPaneSnapshot controls whether agent view restores in fullscreen mode

GraphQL:

• Schema and client code generation from crates/galaxy_graphql_schema/api/schema.graphql
• TypeScript types generated for frontend integration

Feature Flags

Warp uses compile-time feature flags with a small runtime plumbing layer.

How to add a feature flag:

• Add a new variant to galaxy_core/src/features.rs in the FeatureFlag enum
• (Optional) Enable it by default for dogfood builds by listing it in DOGFOOD_FLAGS
• Gate code paths with FeatureFlag::YourFlag.is_enabled()
• For preview or release rollout, add to PREVIEW_FLAGS or RELEASE_FLAGS respectively (as appropriate)

Best practices:

• Prefer runtime checks over cfg directives: Prefer FeatureFlag::YourFlag.is_enabled() over #[cfg(...)] compile-time directives so flags can be toggled without recompilation and are easier to clean up later. Use #[cfg(...)] only when the code cannot compile without them (for example, platform-specific code or dependencies that do not exist when the feature is disabled).
• Keep flags high-level and product-focused rather than per-call-site
• Remove the flag and dead branches after launch has stabilized
• For UI sections that expose a new feature, hide the UI behind the same flag

Example:

#[derive(Sequence)]
pub enum FeatureFlag {
    YourNewFeature,
}

// Default-on for dogfood builds
pub const DOGFOOD_FLAGS: &[FeatureFlag] = &[
    FeatureFlag::YourNewFeature,
];

// Use in code
if FeatureFlag::YourNewFeature.is_enabled() {
    // gated behavior
}

Code Editor IntelliSense (LSP Completion)

The code editor has full LSP-powered autocompletion with documentation resolution:

Key files:

• app/src/code/completion.rs — Completion state, rendering (menu + docs panel), resolve logic
• app/src/code/local_code_editor.rs — Keybindings and action handling

Behavior:

• Auto-completes as you type (triggered by alphanumeric/underscore with 50ms debounce)
• Trigger characters: . and :: fire immediately
• Manual trigger: Ctrl+Alt+Space
• Keyboard navigation: Up/Down to select, Tab/Enter to confirm
• Mouse: hover an item to select it and show docs, click to confirm
• Documentation panel appears beside the menu when the LSP returns docs for the selected item (via completionItem/resolve)

Architecture:

• CompletionState::Showing holds items, filtered indices, per-item MouseStateHandles, and resolved docs
• resolve_selected_completion_docs() sends completionItem/resolve to the LSP server
• The docs panel renders markdown via FormattedTextElement in a scrollable container beside the menu

Exhaustive Matching

When adding/editing match statements, avoid using the wildcard _ when at all possible. Exhaustive matching is helpful for ensuring that all variants are handled, especially when adding new variants to enums in the future.

Rules System

Global rules (behavioral instructions for the AI agent) are stored as AIFact::Memory cloud objects and managed via the Rules settings pane.

Key files:

• app/src/ai/facts/mod.rs — AIFact / AIMemory data model
• app/src/ai/facts/predefined_rules.rs — Default system-defined rules (seeded on first launch)
• app/src/ai/facts/view/rule.rs — RuleView UI with Global/Project tabs and "Add Predefined Rules" button
• app/src/ai/facts/view/mod.rs — AIFactView parent container (Rules + RuleEditor pages)
• app/src/ai/facts/manager.rs — AIFactManager singleton for pane tracking
• app/src/settings/ai.rs — has_seeded_predefined_rules setting (one-time flag)

Behavior:

• On first launch (no existing global rules and has_seeded_predefined_rules is false), predefined rules are automatically created
• The "Add Predefined Rules" button in the Global rules tab will add/update system-defined rules (identified by the "System Defined Rule" name prefix)
• Rules are persisted via the cloud object sync system (UpdateManager::create_ai_fact / update_ai_fact)
• The memory_enabled setting (agents.knowledge.rules_enabled) controls whether rules are sent to the AI

Appearance Settings Notes

• Samsung-inspired built-in themes are available as SamsungDark and SamsungLight.
• UI font selection is persisted in appearance.text.ui_font_name and uses an empty string as the system-default sentinel.
• The one-click Samsung brand preset is implemented in app/src/settings_view/appearance_page.rs and applies:
  • Samsung dark/light theme mapping
  • terminal + AI font defaults
  • a best-available Samsung-style UI font fallback