do_it-0.2.0 is not a library.

do_it

An autonomous coding agent that runs local LLMs via Ollama to read, write, and fix code in your repositories. Works on Windows and Linux with no shell dependency, no Python, no cloud APIs.

Features

Local-first — runs entirely on your machine via Ollama
Cross-platform — Windows (MSVC) and Linux, no shell operators
Agent roles — restrict tools and prompts per task type (developer, navigator, qa, boss, research, memory)
Persistent memory — .ai/ hierarchy: plan, last session notes, knowledge base
Rich tool set — filesystem, git, web search, code intelligence (AST), Telegram notifications
Model routing — use different models per role (e.g. a large coder model for developer, a small fast model for navigator)

Quick Start

# 1. Pull a model
ollama pull qwen3.5:9b

# 2. Install
cargo install do_it

# 3. Run
do_it run --task "Find and fix the bug in src/parser.rs" --repo /path/to/project

# With a role (recommended for smaller models)
do_it run --task "Add input validation to handlers.rs" --role developer

Roles

Each role restricts the agent to a focused set of tools and a role-specific system prompt. This is critical for smaller models — 6–8 tools instead of 20+ significantly improves output quality.

Role	Purpose	Key tools
`developer`	Write and edit code	read/write file, str_replace, run_command, git, AST
`navigator`	Explore codebase structure	tree, find_files, search, outline, find_references
`research`	Find information	web_search, fetch_url, memory
`qa`	Run tests, verify changes	run_command, diff_repo, git_log, search
`boss`	Plan and orchestrate	memory, tree, web_search, ask_human
`memory`	Manage `.ai/` state	memory_read, memory_write

do_it roles   # list all roles and their tool allowlists

Tools

Filesystem: read_file, write_file, str_replace, list_dir, find_files, search_in_files, tree

Execution: run_command, diff_repo

Git: git_status, git_commit, git_log, git_stash

Internet: web_search (DuckDuckGo, no API key), fetch_url

Code intelligence (Rust, TypeScript, JavaScript, Python, C++, Kotlin): get_symbols, outline, get_signature, find_references

Memory (.ai/ hierarchy): memory_read, memory_write

Communication: ask_human (Telegram or console), finish

Configuration

# config.toml
ollama_base_url  = "http://localhost:11434"
model            = "qwen3.5:9b"
temperature      = 0.0
max_tokens       = 4096
history_window   = 8
max_output_chars = 6000

# Optional: different models per role
[models]
coding    = "qwen3-coder-next"
search    = "qwen3.5:4b"
execution = "qwen3.5:4b"

# Optional: Telegram notifications for ask_human
# telegram_token   = "..."
# telegram_chat_id = "..."

do_it config   # show resolved config

Memory hierarchy

The agent maintains persistent state in .ai/ at the repository root:

.ai/
├── prompts/          ← custom role prompts (override built-ins)
├── state/
│   ├── current_plan.md
│   ├── last_session.md    ← agent reads this on startup
│   └── session_counter.txt
├── logs/history.md
└── knowledge/             ← agent-written notes about the project

At session start, last_session.md is automatically injected into context so the agent remembers what it did before.

Custom role prompts: create .ai/prompts/developer.md to override the built-in developer prompt for a specific project.

CLI

do_it run    --task <text|file|image>
             --repo <path>          (default: .)
             --role <role>          (default: unrestricted)
             --config <path>        (default: config.toml)
             --system-prompt <text|file>
             --max-steps <n>        (default: 30)

do_it config [--config <path>]
do_it roles

Roadmap

spawn_agent — boss delegates subtasks to role-specific sub-agents
git_push / git_pull structured tools
Web search providers beyond DuckDuckGo
Tree-sitter backend for more accurate AST analysis

Authors

Built by Claude Sonnet 4.6 with Oleksandr.

License

MIT

do_it 0.2.0