The agent would stop mid-task because native tool calls were stored as
inline JSON text in Message.content. When sent back to the Anthropic API
via convert_messages(), they went as plain text instead of structured
tool_use/tool_result blocks. The model would occasionally get confused
and emit text describing what it wanted to do instead of invoking the
tool mechanism.
Changes:
- Add MessageToolCall struct and tool_calls/tool_result_id fields to Message
- Add id field to core ToolCall struct to preserve provider tool call IDs
- Update Anthropic convert_messages() to emit tool_use and tool_result blocks
- Add ToolResult variant to AnthropicContent enum
- Store tool calls structurally in tool message construction (not inline JSON)
- Fix add_message() to preserve empty-content messages with tool_calls
- Fix check_duplicate_in_previous_message() to check structured tool_calls
- Generate valid IDs for JSON fallback tool calls (Anthropic pattern requirement)
- Update planner create_tool_message() to use structured tool calls
When the LLM emits identical JSON tool calls as text content (JSON
fallback mode), the raw duplicate JSON was being stored in the assistant
message in conversation history. This confused the model on subsequent
turns, causing it to stall or repeat itself.
Root cause: raw_content_for_log used get_text_content() which returns
the full parser buffer including all duplicate tool call JSONs.
Fix: Added get_text_before_tool_calls() to StreamingToolParser that
returns only the text before the first JSON tool call. Changed
raw_content_for_log to use this method so the assistant message only
contains the preamble text + the single executed tool call.
Added 5 integration tests covering stuttered duplicates, triple
stutter, cross-turn dedup, and different-args boundary case.
Added MockResponse helpers for simulating LLM stutter patterns.
Root cause: ActionEnvelope.to_yaml_value() creates a Mapping from the
facts HashMap without a 'facts:' wrapper key, but rulespec selectors
may include a 'facts.' prefix (e.g. 'facts.feature.done' instead of
'feature.done'). This caused zero facts to be extracted, making all
predicate evaluations fail.
Fix: extract_facts() now tries the selector against the unwrapped
envelope value first, and if empty, retries against a facts-wrapped
version as fallback.
Also:
- Strengthened write_envelope tool description to require top-level
facts: key, file paths for evidence, and allow free-form notes
- Updated system prompt with matching rules
- Added 6 new tests (4 unit, 2 integration)
- Strengthened existing integration test to verify fact count > 0
- New crates/g3-core/src/tools/envelope.rs with execute_write_envelope()
and verify_envelope() (moved from shadow_datalog_verify in plan.rs)
- write_envelope accepts YAML facts, writes envelope.yaml to session dir,
then runs datalog verification against analysis/rulespec.yaml in shadow mode
- plan_verify() now only checks envelope existence (no longer runs datalog)
- Tool count: 13 -> 14
- Updated system prompt to instruct agents to call write_envelope before
marking last plan item done
- Updated integration tests to use write_envelope tool directly
Workflow: write_envelope -> verify_envelope -> datalog shadow artifacts
plan_write(done) -> plan_verify -> checks envelope exists
- Remove rulespec parameter from plan_write tool definition and execution
- Remove rulespec compilation from plan_approve (no longer pre-compiles)
- Remove write_rulespec, get_rulespec_path, format_rulespec_yaml/markdown
from invariants.rs; read_rulespec() now takes &Path working dir
- Remove save/load_compiled_rulespec, get_compiled_rulespec_path from datalog.rs
- Update shadow_datalog_verify() to compile on-the-fly from
analysis/rulespec.yaml, writing rulespec.compiled.dl and
datalog_evaluation.txt to session dir
- Remove rulespec display from plan_read output
- Remove Invariants/Rulespec section from native.md system prompt
- Remove rulespec from prompts.rs plan_write format and examples
- Update existing tests to remove rulespec from plan_write calls
- Add 3 integration tests for on-the-fly rulespec verification
Removed redundant and vague content from prompts/system/native.md:
- Simplified intro from 17 lines to 3 lines
- Reduced Code Search section to one line
- Removed duplicate Plan Mode example (kept one)
- Removed Action Envelope section (rarely used correctly)
- Removed verbose Memory Format details (tool description covers it)
- Removed Response Guidelines (obvious to modern LLMs)
Size: 8,620 chars -> 4,498 chars
Also updated:
- G3_IDENTITY_LINE constant for agent mode compatibility
- Test assertions to check for new prompt markers
- System prompt validation to use new marker string
Solves the tautology problem where the LLM would write invariants after
implementation, making them match what was done rather than constrain it.
Changes:
- plan_write now accepts 'rulespec' parameter
- New plans REQUIRE rulespec (fails with helpful error if missing)
- Plan updates don't require rulespec (backward compatible)
- Rulespec is parsed, validated, and written atomically with plan
- Updated system prompt with clear examples for new vs update
- Updated tool definition schema
- Updated all affected tests
New flow: task → plan+rulespec → user reviews BOTH → approve → implement
- Add in_plan_mode flag to Agent struct
- Add set_plan_mode() and is_plan_mode() methods
- Gate check now only runs when in_plan_mode is true
- CLI calls set_plan_mode(true) on /plan command and EnterPlanMode
- CLI calls set_plan_mode(false) on approval and CTRL-D exit
- Update integration test to enable plan mode
- Fix test YAML to use Vec<Check> for negative/boundary checks
- Add check_plan_approval_gate() in tools/plan.rs that runs after each tool call
- Detects file changes via git status --porcelain when plan exists but not approved
- Reverts changes: git checkout for modified files, rm for new untracked files
- Returns blocking message instructing LLM to create/approve plan first
- Add ApprovalGateResult enum with Allowed/Blocked/NotGitRepo variants
- Add set_session_id() and set_working_dir() methods on Agent for testing
- Add integration test using MockProvider to simulate blocked write_file
Plan Mode is a cognitive forcing system that requires reasoning about:
- Happy path
- Negative case
- Boundary condition
New tools:
- plan_read: Read current plan for session
- plan_write: Create/update plan with YAML content (validates structure)
- plan_approve: Mark current revision as approved
New command:
- /feature <description>: Start Plan Mode for a new feature
Plan schema requires:
- plan_id, revision, approved_revision
- items with id, description, state, touches, checks (happy/negative/boundary)
- evidence and notes required when marking items done
Verification:
- plan_verify() called automatically when all items are done/blocked
Removed:
- todo_read, todo_write tools
- todo.rs module and related tests
Add characterization tests for the streaming parser stuttering bug fix (fa3c920).
These tests verify that when an LLM "stutters" and emits incomplete tool call
fragments followed by complete tool calls, the parser:
1. Does not get stuck waiting for the incomplete fragment to complete
2. Successfully parses complete tool calls that appear after the fragment
Tests cover:
- The exact pattern from butler session butler_c6ab59af2e4f991c
- Edge cases that should NOT trigger invalidation (nested JSON, patterns in strings)
- Recovery behavior after reset
- Multiple complete tool calls
- Boundary conditions (chunk boundaries, minimal patterns)
Agent: hopper
Named after David Huffman, inventor of Huffman coding -
compression that preserves information with fewer bits.
Fits the agent's purpose: compact memory, preserve semantics.
README.md is no longer auto-loaded into the LLM context at startup.
This saves ~4,600 tokens per session while AGENTS.md and memory.md
still provide all critical information for code tasks.
Changes:
- Delete read_project_readme() function
- Remove readme_content parameter from combine_project_content()
- Rename extract_readme_heading() -> extract_project_heading()
- Rename Agent constructors: *_with_readme_* -> *_with_project_context_*
- Update context preservation to only check for Agent Configuration
- Remove has_readme field from LoadedContent
- Update all tests to use new markers and function names
The LLM can still read README.md on-demand via read_file when needed.
- 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
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
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 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
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.
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.
- 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
- Add ToolParsingHint enum (Detected/Active/Complete) for UI feedback
- New UiWriter methods: print_tool_streaming_hint(), print_tool_streaming_active()
- Refactor ConsoleUiWriter state to use atomics in ParsingHintState
- Add tool_call_streaming field to CompletionChunk for provider hints
- Anthropic provider sends streaming hints when tool name detected
- New streaming helpers: make_tool_streaming_hint(), make_tool_streaming_active()
Parser improvements:
- Add is_json_invalidated() to detect false positive tool patterns
- Fix tool result poisoning when file contents contain partial JSON
- Unescaped newlines in strings or prose after JSON invalidates detection
User sees ' ● tool_name |' immediately when tool call starts streaming,
with blinking indicator while args are received.
When partial JSON tool call patterns appear in LLM output (e.g., from
quoting file content), the parser would incorrectly report them as
"incomplete tool calls", triggering auto-continue loops.
Fix: Added is_json_invalidated() to detect when partial JSON has been
invalidated by subsequent content that cannot be valid JSON:
- Unescaped newline inside a string (invalid JSON)
- Newline followed by prose text outside a string
The check is only applied to incomplete JSON - complete tool calls
with trailing text are still correctly detected.
Added 6 new tests covering:
- Tool results with partial JSON patterns
- LLM quoting file content inline vs on own line
- Comment prefixes (// # -- etc) with partial patterns
- Real incomplete tool calls (should still be detected)
The streaming parser was incorrectly detecting tool call patterns that
appeared inline in prose (e.g., when explaining the format), causing
g3 to return control mid-task.
Fix: Modified find_first_tool_call_start() and find_last_tool_call_start()
to only recognize patterns that appear on their own line (at start of
buffer or after newline with only whitespace before the pattern).
Changes:
- Added is_on_own_line() helper to check line-boundary conditions
- Updated detection methods to skip inline patterns
- Removed sanitize_inline_tool_patterns() and LBRACE_HOMOGLYPH (no longer needed)
- Rewrote tests for new behavior
- Added streaming_repro tests that use process_chunk() to verify the exact bug scenario
28 tests covering: streaming repro, line boundaries, Unicode, code contexts, edge cases
- Rename take_screenshot -> screenshot, code_coverage -> coverage (shorter names)
- Align | character across all compact tools (pad to 11 chars for str_replace)
- Make code_search a compact tool with summary display
- Show language and search name in code_search output (e.g., rust:"find structs")
- Add format_code_search_summary() to extract match/file counts from JSON response
Agent: hopper
Adds 56 new integration tests covering the observable end-of-turn
behaviors in the streaming module:
- Timing footer formatting (5 tests): verifies user-facing timing display
with various durations, token counts, and context percentages
- Tool call duplicate detection (6 tests): ensures identical sequential
tool calls are detected while different tools/args are not
- Empty response detection (9 tests): validates detection of empty,
whitespace-only, and timing-only responses that trigger auto-continue
- Connection error classification (5 tests): verifies EOF, connection,
chunk, and body errors are correctly identified for graceful recovery
- Tool output summary formatting (17 tests): covers read_file, write_file,
str_replace, remember, screenshot, coverage, and rehydrate summaries
- Duration formatting (4 tests): milliseconds, seconds, minutes, zero
- Text truncation (4 tests): short/long strings, multiline, flag behavior
- LLM token cleaning (3 tests): removal of stop tokens like <|im_end|>
- Edge cases (4 tests): empty inputs, unicode handling, large numbers
All tests are blackbox/characterization style - they test observable
outputs through stable public interfaces without encoding internal
implementation details. Tests remain stable under refactoring that
preserves behavior.
This change removes the legacy logs/ directory and consolidates all
session data, error logs, and discovery files under the .g3/ directory.
New directory structure:
- .g3/sessions/<session_id>/session.json - session logs
- .g3/errors/ - error logs (was logs/errors/)
- .g3/background_processes/ - background process logs
- .g3/discovery/ - planner discovery files (was workspace/logs/)
Changes:
- paths.rs: Remove get_logs_dir()/logs_dir(), add get_errors_dir(),
get_background_processes_dir(), get_discovery_dir()
- session.rs: Anonymous sessions now use .g3/sessions/anonymous_<ts>/
- error_handling.rs: Errors now saved to .g3/errors/
- project.rs: Remove logs_dir() and ensure_logs_dir() methods
- feedback_extraction.rs: Remove logs_dir field and fallback logic
- planner: Use .g3/ for workspace data and .g3/discovery/ for reports
- flock.rs: Look for session metrics in .g3/sessions/
- coach_feedback.rs: Remove fallback to logs/ path
- Update all tests to use new paths
- Update README.md and .gitignore
Agent: hopper
Added 4 new test files with blackbox/characterization-style integration tests:
- compaction_behavior_test.rs (14 tests): Token cap calculation, thinking mode
disable logic, summary message building, CompactionResult behavior
- retry_behavior_test.rs (17 tests): RetryConfig presets and customization,
RetryResult state handling, retry_operation behavior with simulated errors
- tool_execution_roundtrip_test.rs (16 tests): End-to-end tool execution through
Agent interface for read_file, write_file, shell, str_replace, and TODO tools
- error_classification_test.rs (25 tests): Recoverable vs non-recoverable error
classification, retry delay calculation, edge cases and priority handling
All tests follow integration-first philosophy:
- Test through stable public interfaces
- Assert observable behavior, not implementation details
- Use characterization style to document current behavior
- Enable refactoring by not encoding internal structure
ACD (Aggressive Context Dehydration) fixes:
- Fixed dehydrate_context() to extract turn summary from context window
instead of using the passed-in final_response (which contained only
the timing footer, not the actual LLM response)
- Removed final_response parameter from dehydrate_context() since it
now self-extracts the last assistant message as the summary
- This ensures the actual turn summary is preserved after dehydration,
not just the timing footer
New /dump command:
- Added /dump command to dump entire context window to tmp/ for debugging
- Shows message index, role, kind, content length, and full content
- Available in both console and machine modes
UTF-8 safety:
- Fixed truncate_to_word_boundary() to use character indices instead of
byte indices, preventing panics on multi-byte UTF-8 characters
- Added UTF-8 string slicing guidance to AGENTS.md
Agent: g3
