- Add context_window_size() method to LLMProvider trait
- Implement for EmbeddedProvider to return the auto-detected context length
- Update Agent to query provider directly instead of using hardcoded defaults
- Removes need for model-specific context length mappings
Eliminate code-path aliasing in Agent construction methods by introducing
a single `build_agent()` helper that all constructors delegate to.
Before: 3 nearly-identical `Ok(Self { ... })` blocks (~30 lines each)
with subtle differences in auto_compact, is_autonomous, quiet, and
computer_controller fields - prone to drift over time.
After: Single canonical `build_agent()` method that constructs Agent
with all fields. All public constructors delegate to this single path:
- new_for_test() -> new_for_test_with_readme() -> build_agent()
- new_with_mode_and_readme() -> build_agent()
Changes:
- Add `build_agent()` private helper method (single source of truth)
- Simplify `new_for_test()` to delegate to `new_for_test_with_readme()`
- Update `new_for_test_with_readme()` to use `build_agent()`
- Update `new_with_mode_and_readme()` to use `build_agent()`
Net reduction: ~43 lines (-109/+66)
All 190 tests pass.
Agent: fowler
- Extend Usage struct with cache_creation_tokens and cache_read_tokens fields
- Parse Anthropic cache_creation_input_tokens and cache_read_input_tokens
- Parse OpenAI prompt_tokens_details.cached_tokens for automatic prefix caching
- Add CacheStats struct to Agent for cumulative tracking across API calls
- Add "Prompt Cache Statistics" section to /stats output showing:
- API call count and cache hit count
- Hit rate percentage
- Total input tokens and cache read/creation tokens
- Cache efficiency (% of input served from cache)
- Update all provider implementations and test files
- Fix test_rehydrate_success race condition by using UUID for unique session IDs
- Add #[serial] attribute to prevent parallel execution conflicts
- Improve cleanup to remove entire session directory tree
- Add characterization test for resize_image_to_dimensions fallback behavior
(documents fix from commit af8b849 for media type preservation)
Agent: hopper
Add test_project_content_survives_compaction() to verify that project
content loaded via /project command persists through context compaction.
This is a CHARACTERIZATION test that validates:
- Project content appended to README message survives compaction
- The README message (containing project content) is preserved as message[1]
- PROJECT INSTRUCTIONS, ACTIVE PROJECT markers, Brief and Status sections
all survive the compaction process
Agent: hopper
The previous implementation added the summary as a System message, which
caused "Conversation must start with a user message" errors because the
first non-system message after compaction was Assistant (the preserved
last assistant message).
Fix: Change summary from System to User message, creating valid alternation:
[System Prompt] -> [Summary as USER] -> [Last Assistant] -> [Latest User]
This also prevents system message bloat across multiple compactions since
the summary is now part of the conversation flow and gets replaced on
each compaction.
Added test_second_compaction_no_bloat to verify no accumulation.
When context window compaction occurs, the last assistant message is now
preserved in addition to the system prompt, README, and summary. This
improves continuity after compaction by keeping the LLM's most recent
response, which often contains important context about what was just
done or what comes next.
New message order after compaction:
[System Prompt] -> [README/AGENTS.md] -> [ACD Stub?] -> [Summary] -> [Last Assistant] -> [Latest User?]
Changes:
- Add last_assistant_message field to PreservedMessages struct
- Modify extract_preserved_messages() to find last assistant message
- Modify reset_with_summary_and_stub() to include last assistant message
- Add comprehensive integration tests using MockProvider
Tests cover edge cases:
- No assistant message exists
- Tool-call-only assistant messages (still preserved)
- Multiple assistant messages (only last one preserved)
- No trailing user message
When resize_image_to_dimensions() returns a larger file than the original,
we fall back to using the original bytes. Previously, was_resized was set
to true if the original dimensions exceeded MAX_IMAGE_DIMENSION, which
caused final_media_type to be set to 'image/jpeg' even though we were
using the original PNG bytes.
This caused Anthropic API errors like:
'Image does not match the provided media type image/jpeg'
Fix: Set was_resized=false when falling back to original bytes, so the
original media type (detected from magic bytes) is preserved.
Rename all references from "Project Memory" to "Workspace Memory" to avoid
future conflation if a "project" concept is introduced later.
Changes:
- Rename read_project_memory() -> read_workspace_memory()
- Update all prompts, tool descriptions, and comments
- Update header parsing in memory.rs to use "# Workspace Memory"
- Update display detection for "=== Workspace Memory ==="
- Update documentation and analysis/memory.md
11 files changed, ~36 occurrences updated.
Warnings fixed:
- Remove unused 'warn' import from retry.rs
- Prefix unused 'output' param with underscore
- Prefix unused 'rel_start' with underscore
- Add #[allow(dead_code)] to G3Status::info()
Message format tweaked per feedback:
- 'g3: model overloaded [error]' (no attempt info)
- 'g3: retrying in 2.2s (1/3) ... [done]' (attempt info moved here)
- Handle empty error message in Status::Error to show just '[error]'
The prefix was causing duplication when users typed 'Task: ...' themselves,
resulting in '📋 Task: Task: ...' in context dumps.
User messages are now stored as-is without any prefix.
Change from multi-line verbose format to single-line compact format:
Before:
⚡ DEHYDRATED CONTEXT (fragment_id: 188c7ac71613)
• 8 messages (4 user, 4 assistant)
• 3 tool calls (shell ×3)
• ~299 tokens saved
To restore this history, call: rehydrate(fragment_id: "188c7ac71613")
After:
⚡ DEHYDRATED CONTEXT: 3 tool calls (shell x3), 8 total msgs. To restore, call: rehydrate(fragment_id: "188c7ac71613")
- Combine all info into single line
- Remove tokens saved (not essential for rehydration decision)
- Use ASCII 'x' instead of '×' for simplicity
- Add 'no tool calls' case for fragments without tools
- Update related tests
Centralize tool output formatting logic that was duplicated/scattered in
stream_completion_with_tools(). This eliminates code-path aliasing where
tool type checks were done in multiple places.
Changes:
- Add ToolOutputFormat enum (SelfHandled, Compact, Regular)
- Add format_tool_result_summary() for centralized formatting decisions
- Add is_compact_tool() and is_self_handled_tool() helper functions
- Move parse_diff_stats() from lib.rs to streaming.rs
- Simplify tool execution display logic in lib.rs using new helpers
Net effect: -86 lines in lib.rs, +112 lines in streaming.rs
The streaming.rs additions are reusable, well-named functions.
All 585+ workspace tests pass.
Agent: fowler
Consolidate scattered state variables in the 834-line stream_completion_with_tools()
function to use the existing StreamingState and IterationState structs from
streaming.rs. This eliminates code-path aliasing where state was tracked in
multiple places and makes the streaming loop easier to reason about.
Changes:
- Add assistant_message_added field to StreamingState
- Add stream_stop_reason field to IterationState
- Replace 8 inline state variables with StreamingState::new()
- Replace 7 iteration-local variables with IterationState::new()
- All 585 workspace tests pass
This is a pure refactor with no behavior changes. The state structs were already
defined in streaming.rs but not used in the main streaming loop.
Agent: fowler
Extract a new g3_status module in g3-cli that provides consistent formatting
for all 'g3:' prefixed system status messages.
Key changes:
- Add G3Status struct with methods for progress, done, failed, error, etc.
- Add Status enum with Done, Failed, Error, Resolved, Insufficient, NoChanges
- Add ThinResult struct in g3-core for semantic thinning data
- Update UiWriter trait with print_thin_result() method
- Refactor context thinning to return ThinResult instead of formatted strings
- Update all callers to use the new centralized formatting
- Session resume/decline messages now use G3Status
- Compaction status messages now use G3Status
This maintains clean separation of concerns: g3-core emits semantic data,
g3-cli handles all terminal formatting and colors.
Adds 8 unit tests verifying:
- Research tool has 20-minute timeout
- All other tools (shell, read_file, write_file, str_replace, code_search,
webdriver_*, etc.) have standard 8-minute timeout
- Comprehensive test_only_research_has_extended_timeout covers 19 tools
This ensures future changes don't accidentally affect other tool timeouts.
The research tool often runs past 8 minutes due to web browsing and
analysis. Increased its timeout to 20 minutes while keeping other
tools at 8 minutes.
Changes:
- Tool timeout is now tool-specific (20 min for research, 8 min for others)
- Timeout error message now shows the correct duration for each tool
Adds test_llm_repeats_text_before_each_tool_call() which documents the
scenario where the LLM re-outputs the same preamble text before each
tool call in a multi-tool response.
Analysis showed this is LLM behavior, not a g3 bug:
- Each assistant message is correctly stored with different tool calls
- The duplicate display is the LLM choosing to repeat context
- Storage is correct, display accurately reflects LLM output
Decision: Accept as LLM behavior (Option B). Future LLM improvements
may resolve this naturally without g3 code changes.
Adds 3 new tests to json_parsing_stress_test.rs:
- test_tool_result_with_json_not_parsed: Full agent integration test proving
that JSON in tool results (sent TO the LLM) is never parsed by the
streaming parser (which only sees LLM output)
- test_parser_only_processes_completion_chunks: Documents that StreamingToolParser
only accepts CompletionChunk, not Message objects
- test_architectural_separation_documented: Documents the data flow showing
tool results flow TO the LLM while the parser only sees FROM the LLM
This proves the architectural guarantee: there is no code path where
tool result content could be parsed as a tool call, because:
1. Tool results are Message objects added to context_window
2. The streaming parser only processes CompletionChunk from provider.stream_completion()
3. These are completely separate data types flowing in opposite directions
Total: 41 JSON parsing stress tests now pass.
Added 6 new integration tests for stream_completion_with_tools:
- test_text_before_tool_call_preserved: text before native tool call is saved
- test_native_tool_call_execution: native tool calls execute correctly
- test_duplicate_tool_calls_skipped: sequential duplicates are detected
- test_json_fallback_tool_calling: JSON tool calls work without native support
- test_text_after_tool_execution_preserved: follow-up text is saved
- test_multiple_tool_calls_executed: multiple tool calls in sequence work
Also added MockResponse helper methods:
- text_then_native_tool(): text followed by native tool call
- duplicate_native_tool_calls(): same tool call twice (for dedup testing)
Fixed text_with_json_tool() to ensure "tool" key comes before "args"
(serde_json alphabetizes keys, breaking pattern detection).
Total: 18 integration tests covering historical bugs and core behaviors.
The bug was caused by mark_tool_calls_consumed() being called after
displaying each chunk, which advanced last_consumed_position to the
end of the current buffer. When the next chunk arrived with JSON,
the unchecked_buffer started at position 0 of the slice, causing
is_on_own_line() to return true (position 0 is always "on its own line").
Removed the problematic mark_tool_calls_consumed() call from the
"no tool executed" branch. The remaining call after actual tool
execution is correct and necessary.
Added integration test that verifies inline JSON in prose is not
detected as a tool call.
Adds a configurable mock LLM provider that can simulate various behaviors:
- Text-only responses (single or multi-chunk streaming)
- Native tool calls
- JSON tool calls in text
- Truncated responses (max_tokens)
- Multi-turn conversations
Features:
- Builder pattern for easy test setup
- Request tracking for verification
- Preset scenarios for common patterns
- Full LLMProvider trait implementation
Also adds integration tests that use MockProvider to test the
stream_completion_with_tools code path, including:
- test_butler_bug_scenario: reproduces the exact bug where text-only
responses were not saved to context, causing consecutive user messages
This enables testing complex streaming behaviors without real API calls.
Bug: When the LLM responded with text-only (no tool calls), the assistant
message was sometimes not saved to the context window. This caused consecutive
user messages where the LLM would lose track of previous responses.
Root causes found and fixed:
1. Early return path (line ~2535): When stream finishes with no tools executed
in previous iterations (any_tool_executed=false), the code returned early
without saving the assistant message. Fixed by adding save before return.
2. Post-loop path (line ~2657): When raw_clean was empty but current_response
had content, no message was saved. Fixed by falling back to current_response.
Both paths now properly save the assistant message before returning.
The assistant_message_added flag prevents any duplication.
Added tests:
- missing_assistant_message_test.rs: verifies the fallback logic
- assistant_message_dedup_test.rs: verifies no duplicate messages
- consecutive_assistant_message_test.rs: verifies alternation invariant
The Anthropic API was rejecting requests with multiple high-resolution images
(~2000x3000 pixels each) even though individual file sizes were under limits.
Root cause: Code only checked per-image file size (3.75MB), not dimensions.
Claude recommends images ≤1568px on longest edge and has 32MB total request limit.
Changes:
- Add MAX_IMAGE_DIMENSION (1568px) and MAX_TOTAL_IMAGE_PAYLOAD (20MB) constants
- Trigger resize when dimensions > 1568px (not just file size > 3.75MB)
- Add new resize_image_to_dimensions() for dimension-constrained resizing
- Track cumulative payload size across multiple images
- Warn if total payload exceeds recommended limit
Test results with Walking Dead comic images:
- WD_0001_0001.jpg: 800KB 1987x3057 → 321KB 1019x1568
- WD_0001_1064.png: 150KB 1988x3057 → 143KB 1020x1568
- WD_0002_0001.jpg: 1023KB 1988x3056 → 292KB 1020x1568
- Total payload: ~2.5MB → ~1MB base64
Removed the persistent_chrome config flag - chromedriver is now always
kept running after webdriver_quit. This eliminates startup latency for
subsequent WebDriver sessions.
Safaridriver is still killed on quit since it doesn't benefit from
persistence in the same way.
Updated quit message to correctly indicate chromedriver remains running.
When webdriver_start is called, now checks if chromedriver is already
running on the configured port and reuses it instead of spawning a new
process. This significantly reduces startup time for subsequent sessions.
New config option:
[webdriver]
persistent_chrome = true # Keep chromedriver running between sessions
When enabled, webdriver_quit closes the browser session but leaves
chromedriver running for reuse by the next session.
Change format from verbose emoji-based message to cleaner status line:
Before: ✨🥒 Context thinned at 70%: 7 tool results, ~33839 chars saved ✨
After: g3: thinning context ... 70% -> 40% ... [done]
The new format shows before/after percentages and uses bold green for
'g3:' and '[done]' to match other status messages.
Also removes unused emoji() and label() methods from ThinScope.
The Anthropic API has a 5MB limit on base64-encoded images, not raw file
size. Base64 encoding increases size by ~33% (4/3 ratio), so a 4MB raw
image becomes ~5.3MB encoded, exceeding the limit.
Changed MAX_IMAGE_SIZE from 5MB to ~3.75MB (5MB * 3/4) to trigger
resizing before the base64-encoded result exceeds the API limit.
Also updated target resize size to 3.6MB to leave margin.
Images >= 5MB are now automatically resized to < 4.9MB using ImageMagick
before being sent to the LLM. This prevents API errors from oversized images.
- Uses iterative quality/scale reduction to find optimal size
- Converts to JPEG for better compression
- Shows original and resized size in terminal output (e.g., '6.2 MB → 4.1 MB (resized)')
- Falls back to original if ImageMagick fails or isn't available
Adds tests to verify that:
- All streaming chunks are processed before control returns to caller
- Both tool calls in a multi-tool-call stream are executed
- The finished signal properly terminates stream processing
Also adds Agent::new_for_test() to allow injecting mock providers.
The JSON filter only suppresses tool calls at line boundaries. When
"Memory checkpoint: " was printed without a trailing newline, the LLM
response `{"tool": "remember", ...}` appeared on the same line and
leaked through to the UI.
Fix:
- Add trailing newline to "Memory checkpoint:" message
- Reset JSON filter state before streaming the response
Added test: test_tool_call_not_at_line_start_passes_through
Documents the filter behavior and references the fix location.
- Shell outputs > 8KB are truncated to first 500 chars
- Full output saved to .g3/sessions/<session_id>/tools/shell_stdout_<id>.txt
- LLM can use read_file with start/end to paginate through large outputs
- read_file now uses seek() for O(1) random access instead of reading entire file
- UTF-8 safe: reads extra bytes at boundaries to find valid char positions
- Falls back to lossy conversion for binary files (no panics)
Files changed:
- paths.rs: get_tools_output_dir(), generate_short_id()
- shell.rs: truncate_large_output() integration
- file_ops.rs: seek-based read_file_range() helper
- New test: read_file_utf8_test.rs
- Fix aliasing issue where resolve_max_tokens() used fallback_default_max_tokens
(8192) instead of provider-specific defaults
- Update fallback_default_max_tokens from 8192 to 32000
- Set provider-specific max_tokens defaults:
- Anthropic: 32000
- OpenAI: 32000 (was 16000)
- Databricks: 32000 (was 50000, now matches Anthropic as passthru)
- Embedded: 2048
- Context window lengths unchanged:
- OpenAI: 400,000
- Anthropic: 200,000
- Databricks (Claude): 200,000
This fixes the 'LLM response was cut off due to max_tokens limit' error
in agent mode that occurred because 8192 was being used instead of 32000.
Agent: carmack
Changes:
- streaming_parser.rs: Unified find_first/last_tool_call_start into single
find_tool_call_start with SearchDirection enum, reducing duplication.
Simplified is_json_invalidated from 45 to 20 lines with clearer logic.
Fixed redundant !escape_next check in find_complete_json_object_end.
- filter_json.rs: Simplified check_tool_pattern from 40 to 24 lines.
Replaced repetitive prefix checks with loop over ["t", "to", "too", "tool"].
Reduced trailing return statements with direct expression returns.
- ui_writer_impl.rs: Added ansi module for duration color constants.
Simplified duration_color function by removing redundant comments.
- language_prompts.rs: Fixed test assertions to match actual prompt content
("obvious, readable Racket" instead of "RACKET-SPECIFIC GUIDANCE").
All 174+ tests pass. No behavior changes.
