Phillip Tarrant
90a38f12d1
push_screen_wait requires a worker context, but on_mount is a plain lifecycle callback. Extract session resume logic into a @work-decorated method so the modal can be awaited without triggering NoActiveWorker.
SneakyCode
A privacy-first, locally-running Python coding agent that uses a local LLM (via Ollama) to perform autonomous coding tasks inside a project directory.
SneakyCode accepts natural language tasks and executes them using a defined toolset for filesystem operations, shell execution, code search, and file manipulation. It runs a ReAct-style tool-call loop: send conversation history to the LLM, receive tool calls, execute them with permission checks, and feed results back until the task is complete.
Prerequisites
- Python 3.11+
- Ollama running locally with a model that supports function calling (e.g.,
qwen3.5,llama3.1,mistral-nemo) - uv (recommended) or pip
Installation
# Clone the repository
git clone <repo-url>
cd SneakyCode
# Install dependencies
uv sync --dev
# Or with pip
pip install -e ".[dev]"
Configuration
Edit config/config.yaml to configure the agent. Key settings:
llm:
model: "qwen3.5:latest" # Ollama model name
endpoint: "http://localhost:11434" # Ollama endpoint
max_retries: 3 # Retry attempts on transient errors
retry_backoff_base: 1.0 # Exponential backoff base (seconds)
agent:
max_iterations: 25 # Max tool-call iterations per turn
max_conversation_tokens: 32000 # Token budget for conversation
workspace_root: "." # Project directory for file operations
truncation_keep_recent: 10 # Messages preserved during truncation
truncation_threshold: 0.85 # Budget fraction that triggers truncation
session:
auto_save: true # Save session after each turn
max_session_age_hours: 72 # Auto-cleanup old sessions
offer_resume: true # Offer to resume on startup
permissions:
auto_approve: [read_file, list_dir, grep_files, find_files, finish]
prompt_user: [write_file, delete_file, run_command, str_replace, patch_apply, make_dir]
deny: []
Environment variable SNEAKYCODE_CONFIG can override the config file path.
Usage
# Start the interactive REPL
sneakycode
# Or run directly
python -m app.main
# With options
sneakycode --config path/to/config.yaml --verbose --log-file sneakycode.log
REPL Commands
| Command | Description |
|---|---|
/quit |
Save session and exit |
/history |
Show conversation history |
/clear |
Clear conversation history |
/save |
Manually save session |
/session |
Show session info (messages, tokens) |
Session Persistence
Sessions are automatically saved after each agent turn and on exit. On startup, SneakyCode offers to resume the most recent session for the current workspace.
Session files are stored in .sneakycode/sessions/ within the workspace root.
Available Tools
SneakyCode provides 11 tools across 5 categories. See docs/tools.md for the full reference.
| Category | Tools | Permission |
|---|---|---|
| Read | read_file, list_dir |
Auto-approved |
| Search | grep_files, find_files |
Auto-approved |
| Write | write_file, make_dir, delete_file |
User confirm |
| Edit | str_replace, patch_apply |
User confirm |
| Shell | run_command |
User confirm |
| Control | finish |
Auto-approved |
Development
# Run tests
.venv/bin/python -m pytest tests/ -v
# Run with coverage
.venv/bin/python -m pytest tests/ --cov=app
# Lint
.venv/bin/ruff check app/ tests/
# Format
.venv/bin/ruff format app/ tests/
Project Structure
app/
├── agent/ # Agent loop and session context
├── models/ # Pydantic config and message schemas
├── services/ # LLM client, streaming, permissions, session persistence
├── tools/ # Tool implementations (one file per group)
└── utils/ # Logging, display, file helpers, token counter
config/
└── config.yaml # Application configuration
tests/
├── unit/ # Unit tests for individual components
└── integration/ # End-to-end workflow tests with mocked LLM
Architecture
SneakyCode follows a ReAct-style agent pattern:
- User provides a task in natural language
- Agent sends conversation history + tool schemas to the LLM
- LLM responds with either text (task complete) or tool calls
- Agent executes tool calls with permission checks
- Results are fed back to the LLM for the next iteration
- Loop continues until the LLM produces a plain-text response or calls
finish
The LLM client is abstracted behind an OpenAI-compatible interface, so any endpoint implementing the /v1/chat/completions SSE streaming protocol works as a backend.
License
MIT