Fixes three bugs in the input formatter introduced in 4e16942:
1. Bug 2 & 3 (missing newline, line duplication):
- Changed print! to println! to add trailing newline
- Calculate visual lines based on terminal width instead of
logical line count, fixing duplication for wrapped lines
2. Bug 1 (^M on non-interactive prompts):
- Added TTY check to skip formatting when stdout is not a terminal
- Prevents terminal state corruption for stdin prompts
The research tool now spawns the scout agent in a background tokio task
and returns immediately with a research_id placeholder. This allows the
agent to continue working while research runs (30-120 seconds).
Key changes:
- New PendingResearchManager for tracking async research tasks
- research tool returns immediately with placeholder containing research_id
- research_status tool to check progress of pending research
- Auto-injection of completed research at natural break points:
- Start of each tool iteration (before LLM call)
- Before prompting user in interactive mode
- /research CLI command to list all research tasks
- Updated system prompt to explain async behavior
The agent can:
- Continue with other work while research runs
- Check status with research_status tool
- Yield turn to user if results are critical before continuing
When users type prompts in interactive mode, the input is now
reformatted in place with enhanced highlighting:
- ALL CAPS words (2+ chars) become bold green (e.g., FIX, BUG, HTTP2)
- Quoted text ("..." or ...) becomes cyan
- Standard markdown formatting is also supported
New module: input_formatter.rs with 10 unit tests
Integrated into interactive.rs for both single-line and multiline input
- Add CommonFlags struct to group flags that apply across all modes
- Refactor run_agent_mode() to accept CommonFlags instead of individual params
- Add project loading logic for agent chat mode
- Add integration tests for --project with agent mode
This refactor prevents future bugs where new flags work in one mode
but are forgotten in another.
GLM-4 models wrap tool calls in markdown code fences and inline backticks,
which prevents the streaming parser from detecting them. This adapter:
- Strips ```json and ``` code fence markers during streaming
- Strips inline backticks from tool call JSON
- Handles chunked streaming correctly (buffers potential fence lines)
- Transforms GLM native format (<|assistant|>tool_name) to g3 JSON format
Also refactors embedded provider into module structure:
- embedded/mod.rs - module exports
- embedded/provider.rs - main EmbeddedProvider (moved from embedded.rs)
- embedded/adapters/mod.rs - ToolFormatAdapter trait
- embedded/adapters/glm.rs - GLM-specific adapter
Includes 22 unit tests covering edge cases like nested JSON in strings,
chunk boundary handling, and false pattern detection.
Updates README to show GLM-4 9B now works (⭐⭐) for agentic tasks.
embedded.rs (937→789 lines, -16%):
- Extract duplicated inference setup into prepare_context() helper
- Extract stop sequence handling into find_stop_sequence() and truncate_at_stop_sequence()
- Add InferenceParams struct to consolidate request parameter extraction
- Add clear section markers for code organization
- Tests now use module-level format functions directly (no duplication)
gemini.rs:
- Extract common request building into build_request() method
- Reduces duplication between complete() and stream() methods
All 399 unit tests pass. Behavior unchanged.
Agent: carmack
Agent: hopper
Added two new integration test files:
1. cache_stats_integration_test.rs (g3-core)
- Tests CacheStats accumulation through streaming completion flow
- Verifies cache hit detection (cache_read_tokens > 0)
- Tests multi-request accumulation of cache statistics
- Verifies cache efficiency and hit rate calculations
- Uses MockProvider to simulate provider usage data
2. gemini_serialization_test.rs (g3-providers)
- Tests Gemini API message format conversion
- Verifies system messages become system_instruction
- Verifies assistant role maps to "model" (Gemini terminology)
- Tests tool conversion to function_declarations format
- Characterizes multi-system-message behavior (last wins)
Both test files follow blackbox/integration testing principles:
- Test observable behavior through stable surfaces
- Do not assert internal implementation details
- Include documentation of what is/is not asserted
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.
Adds a new --project <PATH> flag that loads project files (brief.md,
contacts.yaml, status.md) at startup, similar to the /project command
but WITHOUT auto-executing the project status prompt.
Changes:
- Add --project flag to cli_args.rs
- Add load_and_validate_project() helper in project.rs (shared by both
--project flag and /project command)
- Modify run_interactive() to accept optional initial_project parameter
- Wire up --project in lib.rs to load project before interactive mode
- Refactor /project command to use shared helper (reduces duplication)
- Add 4 new tests for load_and_validate_project()
Added a new section documenting local LLM performance on complex agentic
tasks (comic book repacking test case). Includes:
- Cloud model baseline (Claude Opus 4.5, Sonnet 4.5, Claude 4 family)
- Local model ratings (Qwen3-32B, Qwen3-14B, GLM-4 9B, Qwen3-4B)
- Key findings about MoE vs dense models
- Configuration example for embedded providers
- Add GeminiProvider with streaming and native tool calling
- Support gemini-2.5-pro, gemini-2.0-flash, gemini-1.5-pro/flash models
- Model-specific context window detection (1M-2M tokens)
- Message conversion: assistant -> model role mapping
- System messages extracted to system_instruction field
- Tool schema conversion with functionCall/functionResponse parts
- SSE streaming with JSON array buffer parsing
- 8 unit tests for conversion and parsing logic
- Register provider in g3-core and validate in g3-cli
The resolve_max_tokens() function was returning 2048 for embedded providers,
which caused responses to be truncated prematurely. Increased to 8192 to
allow the provider's own effective_max_tokens() calculation to work properly.
- 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
- Use global OnceLock for llama.cpp backend to prevent BackendAlreadyInitialized error
- Suppress verbose llama.cpp stderr logging during model loading
- Fix provider validation to accept "embedded.name" format (extract type before dot)
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
Both multiline and single-line input paths in interactive.rs had identical
code for:
- Template processing (process_template)
- Task execution (execute_task_with_retry)
- Auto-memory reminder with error handling
Extracted to a single execute_user_input() helper function that handles
all three steps. This eliminates code-path aliasing where the two paths
could drift over time.
File reduced from 401 to 393 lines (-2%).
All 106 g3-cli tests pass.
Agent: fowler
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.
