Manual Slop

A high-density GUI orchestrator for local LLM-driven coding sessions. Manual Slop bridges high-latency AI reasoning with a low-latency ImGui render loop via a thread-safe asynchronous pipeline, ensuring every AI-generated payload passes through a human-auditable gate before execution.

Design Philosophy: Full manual control over vendor API metrics, agent capabilities, and context memory usage. High information density, tactile interactions, and explicit confirmation for destructive actions.

Tech Stack: Python 3.11+, Dear PyGui / ImGui Bundle, FastAPI, Uvicorn, tree-sitter Providers: Gemini API, Anthropic API, DeepSeek, Gemini CLI (headless), MiniMax Platform: Windows (PowerShell) — single developer, local use

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Key Features

Multi-Provider Integration

• Gemini SDK: Server-side context caching with TTL management, automatic cache rebuilding at 90% TTL
• Anthropic: Ephemeral prompt caching with 4-breakpoint system, automatic history truncation at 180K tokens
• DeepSeek: Dedicated SDK for code-optimized reasoning
• Gemini CLI: Headless adapter with full functional parity, synchronous HITL bridge
• MiniMax: Alternative provider support

4-Tier MMA Orchestration

Hierarchical task decomposition with specialized models and strict token firewalling:

• Tier 1 (Orchestrator): Product alignment, epic → tracks
• Tier 2 (Tech Lead): Track → tickets (DAG), persistent context
• Tier 3 (Worker): Stateless TDD implementation, context amnesia
• Tier 4 (QA): Stateless error analysis, no fixes

Strict Human-in-the-Loop (HITL)

• Execution Clutch: All destructive actions suspend on threading.Condition pending GUI approval
• Three Dialog Types: ConfirmDialog (scripts), MMAApprovalDialog (steps), MMASpawnApprovalDialog (workers)
• Editable Payloads: Review, modify, or reject any AI-generated content before execution

26 MCP Tools with Sandboxing

Three-layer security model: Allowlist Construction → Path Validation → Resolution Gate

• File I/O: read, list, search, slice, edit, tree
• AST-Based (Python): skeleton, outline, definition, signature, class summary, docstring
• Analysis: summary, git diff, find usages, imports, syntax check, hierarchy
• Network: web search, URL fetch
• Runtime: UI performance metrics

Parallel Tool Execution

Multiple independent tool calls within a single AI turn execute concurrently via asyncio.gather, significantly reducing latency.

AST-Based Context Management

• Skeleton View: Signatures + docstrings, bodies replaced with ...
• Curated View: Preserves @core_logic decorated functions and [HOT] comment blocks
• Targeted View: Extracts only specified symbols and their dependencies
• Heuristic Summaries: Token-efficient structural descriptions without AI calls

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Architecture at a Glance

Four thread domains operate concurrently: the ImGui main loop, an asyncio worker for AI calls, a HookServer (HTTP on :8999) for external automation, and transient threads for model fetching. Background threads never write GUI state directly — they serialize task dicts into lock-guarded lists that the main thread drains once per frame (details).

The Execution Clutch suspends the AI execution thread on a threading.Condition when a destructive action (PowerShell script, sub-agent spawn) is requested. The GUI renders a modal where the user can read, edit, or reject the payload. On approval, the condition is signaled and execution resumes (details).

The MMA (Multi-Model Agent) system decomposes epics into tracks, tracks into DAG-ordered tickets, and executes each ticket with a stateless Tier 3 worker that starts from ai_client.reset_session() — no conversational bleed between tickets (details).

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Documentation

Guide	Scope
Readme	Documentation index, GUI panel reference, configuration files, environment variables
Architecture	Threading model, event system, AI client multi-provider architecture, HITL mechanism, comms logging
Tools & IPC	MCP Bridge 3-layer security, 26 tool inventory, Hook API endpoints, ApiHookClient reference, shell runner
MMA Orchestration	4-tier hierarchy, Ticket/Track data structures, DAG engine, ConductorEngine, worker lifecycle, abort propagation
Simulations	live_gui fixture, Puppeteer pattern, mock provider, visual verification, ASTParser / summarizer
Meta-Boundary	Application vs Meta-Tooling domains, inter-domain bridges, safety model separation

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Setup

Prerequisites

• Python 3.11+
• uv for package management

Installation

git clone <repo>
cd manual_slop
uv sync

Credentials

Configure in credentials.toml:

[gemini]
api_key = "YOUR_KEY"

[anthropic]
api_key = "YOUR_KEY"

[deepseek]
api_key = "YOUR_KEY"

Running

uv run sloppy.py                        # Normal mode
uv run sloppy.py --enable-test-hooks    # With Hook API on :8999

Running Tests

uv run pytest tests/ -v

Note: See the Structural Testing Contract for rules regarding mock patching, live_gui standard usage, and artifact isolation (logs are generated in tests/logs/ and tests/artifacts/).

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MMA 4-Tier Architecture

The Multi-Model Agent system uses hierarchical task decomposition with specialized models at each tier:

Tier	Role	Model	Responsibility
Tier 1	Orchestrator	gemini-3.1-pro-preview	Product alignment, epic → tracks, track initialization
Tier 2	Tech Lead	gemini-3-flash-preview	Track → tickets (DAG), architectural oversight, persistent context
Tier 3	Worker	gemini-2.5-flash-lite / deepseek-v3	Stateless TDD implementation per ticket, context amnesia
Tier 4	QA	gemini-2.5-flash-lite / deepseek-v3	Stateless error analysis, diagnostics only (no fixes)

Key Principles:

• Context Amnesia: Tier 3/4 workers start with ai_client.reset_session() — no history bleed
• Token Firewalling: Each tier receives only the context it needs
• Model Escalation: Failed tickets automatically retry with more capable models
• WorkerPool: Bounded concurrency (default: 4 workers) with semaphore gating

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Module by Domain

src/ — Core implementation

File	Role
src/gui_2.py	Primary ImGui interface — App class, frame-sync, HITL dialogs, event system
src/ai_client.py	Multi-provider LLM abstraction (Gemini, Anthropic, DeepSeek, MiniMax)
src/mcp_client.py	26 MCP tools with filesystem sandboxing and tool dispatch
src/api_hooks.py	HookServer — REST API on `127.0.0.1:8999 for external automation
src/api_hook_client.py	Python client for the Hook API (used by tests and external tooling)
src/multi_agent_conductor.py	ConductorEngine — Tier 2 orchestration loop with DAG execution
src/conductor_tech_lead.py	Tier 2 ticket generation from track briefs
src/dag_engine.py	TrackDAG (dependency graph) + ExecutionEngine (tick-based state machine)
src/models.py	Ticket, Track, WorkerContext, Metadata, Track state
src/events.py	EventEmitter, AsyncEventQueue, UserRequestEvent
src/project_manager.py	TOML config persistence, discussion management, track state
src/session_logger.py	JSON-L + markdown audit trails (comms, tools, CLI, hooks)
src/shell_runner.py	PowerShell execution with timeout, env config, QA callback
src/file_cache.py	ASTParser (tree-sitter) — skeleton, curated, and targeted views
src/summarize.py	Heuristic file summaries (imports, classes, functions)
src/outline_tool.py	Hierarchical code outline via stdlib ast
src/performance_monitor.py	FPS, frame time, CPU, input lag tracking
src/log_registry.py	Session metadata persistence
src/log_pruner.py	Automated log cleanup based on age and whitelist
src/paths.py	Centralized path resolution with environment variable overrides
src/cost_tracker.py	Token cost estimation for API calls
src/gemini_cli_adapter.py	CLI subprocess adapter with session management
src/mma_prompts.py	Tier-specific system prompts for MMA orchestration
src/theme_*.py	UI theming (dark, light modes)

Simulation modules in simulation/:

File	Role
simulation/sim_base.py	BaseSimulation class with setup/teardown lifecycle
simulation/workflow_sim.py	WorkflowSimulator — high-level GUI automation
simulation/user_agent.py	UserSimAgent — simulated user behavior (reading time, thinking delays)

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Setup

The MCP Bridge implements a three-layer security model in mcp_client.py:

Every tool accessing the filesystem passes through _resolve_and_check(path) before any I/O.

Layer 1: Allowlist Construction (configure)

Called by ai_client before each send cycle:

1. Resets _allowed_paths and _base_dirs to empty sets
2. Sets _primary_base_dir from extra_base_dirs[0]
3. Iterates file_items, resolving paths, adding to allowlist
4. Blacklist check: history.toml, *_history.toml, config.toml, credentials.toml are NEVER allowed

Layer 2: Path Validation (_is_allowed)

Checks run in order:

1. Blacklist: history.toml, *_history.toml → hard deny
2. Explicit allowlist: Path in _allowed_paths → allow
3. CWD fallback: If no base dirs, allow cwd() subpaths
4. Base containment: Must be subpath of _base_dirs
5. Default deny: All other paths rejected

Layer 3: Resolution Gate (_resolve_and_check)

1. Convert raw path string to Path
2. If not absolute, prepend _primary_base_dir
3. Resolve to absolute (follows symlinks)
4. Call _is_allowed()
5. Return (resolved_path, "") on success or (None, error_message) on failure

All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.

Security Model

The MCP Bridge implements a three-layer security model in mcp_client.py. Every tool accessing the filesystem passes through _resolve_and_check(path) before any I/O.

Layer 1: Allowlist Construction (configure)

Called by ai_client before each send cycle:

1. Resets _allowed_paths and _base_dirs to empty sets.
2. Sets _primary_base_dir from extra_base_dirs[0] (resolved) or falls back to cwd().
3. Iterates file_items, resolving each path to an absolute path, adding to _allowed_paths; its parent directory is added to _base_dirs.
4. Any entries in extra_base_dirs that are valid directories are also added to _base_dirs.

Layer 2: Path Validation (_is_allowed)

Checks run in this exact order:

1. Blacklist: history.toml, *_history.toml, config, credentials → hard deny
2. Explicit allowlist: Path in _allowed_paths → allow
3. CWD fallback: If no base dirs, any under cwd() is allowed (fail-safe for projects without explicit base dirs)
4. Base containment: Must be a subpath of at least one entry in _base_dirs (via relative_to())
5. Default deny: All other paths rejected All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.

Layer 3: Resolution Gate (_resolve_and_check)

Every tool call passes through this:

1. Convert raw path string to Path.
2. If not absolute, prepend _primary_base_dir.
3. Resolve to absolute.
4. Call _is_allowed().
5. Return (resolved_path, "") on success, (None, error_message) on failure All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.

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Conductor SystemThe project uses a spec-driven track system in conductor/ for structured development:

conductor/
├── workflow.md           # Task lifecycle, TDD protocol, phase verification
├── tech-stack.md         # Technology constraints and patterns
├── product.md            # Product vision and guidelines
├── product-guidelines.md # Code standards, UX principles
└── tracks/
    └── <track_name>_<YYYYMMDD>/
        ├── spec.md       # Track specification
        ├── plan.md       # Implementation plan with checkbox tasks
        ├── metadata.json # Track metadata
        └── state.toml    # Structured state with task list

Key Concepts:

• Tracks: Self-contained implementation units with spec, plan, and state
• TDD Protocol: Red (failing tests) → Green (pass) → Refactor
• Phase Checkpoints: Verification gates with git notes for audit trails
• MMA Delegation: Tracks are executed via the 4-tier agent hierarchy

See conductor/workflow.md for the full development workflow.

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Project Configuration

Projects are stored as <name>.toml files. The discussion history is split into a sibling <name>_history.toml to keep the main config lean.

[project]
name = "my_project"
git_dir = "./my_repo"
system_prompt = ""

[files]
base_dir = "./my_repo"
paths = ["src/**/*.py", "README.md"]

[screenshots]
base_dir = "./my_repo"
paths = []

[output]
output_dir = "./md_gen"

[gemini_cli]
binary_path = "gemini"

[agent.tools]
run_powershell = true
read_file = true
# ... 26 tool flags

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Quick Reference

Hook API Endpoints (port 8999)

Endpoint	Method	Description
/status	GET	Health check
/api/project	GET/POST	Project config
/api/session	GET/POST	Discussion entries
/api/gui	POST	GUI task queue
/api/gui/mma_status	GET	Full MMA state
/api/gui/value/<tag>	GET	Read GUI field
/api/ask	POST	Blocking HITL dialog

MCP Tool Categories

Category	Tools
File I/O	read_file, list_directory, search_files, get_tree, get_file_slice, set_file_slice, edit_file
AST (Python)	py_get_skeleton, py_get_code_outline, py_get_definition, py_update_definition, py_get_signature, py_set_signature, py_get_class_summary, py_get_var_declaration, py_set_var_declaration, py_get_docstring
Analysis	get_file_summary, get_git_diff, py_find_usages, py_get_imports, py_check_syntax, py_get_hierarchy
Network	web_search, fetch_url
Runtime	get_ui_performance

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