bonsaiaf 0.1.0

An adaptive agent orchestration framework inspired by tree growth. Structure emerges from the problem, not the other way around.

BonsAI

An adaptive agent orchestration framework inspired by tree growth.

Structure emerges from your project. Agents justify their existence. Everything that doesn't earn its place gets pruned.

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The Idea

Most agent frameworks make you design the structure upfront. You define a graph, a pipeline, a hierarchy. Then you wire agents into it.

BonsAI works the other way.

You describe what you're building. BonsAI reads your codebase, derives domain boundaries, assigns agents, and grows a structure that fits your project — not a generic template.

Like a bonsai tree, the shape that emerges reflects the environment it grew in.

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How It Works

BonsAI has two systems:

The Root System — a set of .md files that live inside your project. They track what exists, what's been tried, what failed, and where the project is going. Every agent reads from and writes to the roots. This is how agents stay in sync without sharing a context window.

The Tree — the agents themselves. Each agent owns a domain. No overlap. They run concurrently where possible, branch when tasks are complex, and prune themselves when they're not earning their budget.

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Quickstart

Install:

pip install bonsaif

Initialize on an existing project:

cd your-project
bonsai init .

BonsAI will scan your codebase, identify domain boundaries, ask you one question (what is this project for?), and propose an agent roster.

Run a task:

bonsai run "implement user authentication
in src/auth/" --budget 20.0

Check status:

bonsai status

Generate reports:

bonsai report runs
bonsai report health
bonsai report tree --run-id <id>

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Commands

Command	Description
bonsai init <path>	Initialize BonsAI on a project
bonsai run <task>	Run a task with a single agent
bonsai run-multi <task>	Run with multi-agent orchestration
bonsai status	Live project dashboard
bonsai report <type>	Generate run/budget/health/tree reports

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Executor Backends

BonsAI supports two backends:

Claude Code (default) Uses your Claude Max plan. No API key needed.

bonsai run "your task" --executor claude_code

Anthropic API Precise token tracking. Requires ANTHROPIC_API_KEY.

pip install bonsaif[api]
bonsai run "your task" --executor api

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What Gets Created

Running bonsai init adds two things to your project:

your-project/
├── roots/           # BonsAI's context map
│   ├── ROOT.md      # Master index
│   ├── project/     # Vision, state, decisions
│   ├── agents/      # Agent definitions
│   ├── context/     # Codebase map, patterns
│   ├── quality/     # Repetitions, debt, pruning log
│   └── flows/       # Flow contracts
└── .bonsai          # Config (executor, roster)

Your source code is untouched. The roots/ directory is committed to your repo — it's documentation that stays in sync with your code.

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The Pruning Agent

BonsAI ships with a quality agent that continuously monitors for:

• Repeated components across the codebase
• Oversized files that should be decomposed
• Dead code with no recent activity
• Structural debt

It never acts unilaterally. It proposes. You decide.

bonsai run "find duplicate components
and propose consolidations" \
--agent quality

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Multi-Agent Runs

For complex tasks BonsAI can spawn child agents that work in parallel:

bonsai run-multi \
  "implement and test the payment flow" \
  --budget 40.0 \
  --max-depth 2

The root agent decides whether to branch based on task complexity and available budget. Child agents get focused context — only what they need for their subtask.

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Observability

Every run is recorded. BonsAI tracks:

• Which agents ran and for how long
• Budget consumed per run
• Success rates over time
• Which tasks produced pruning proposals

bonsai status          # live dashboard
bonsai report health   # health overview
bonsai report budget   # spend by agent

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Philosophy

Agents should earn their existence. Every agent tracks its contribution relative to its cost. Low-value agents get fewer resources. Consistently low-value agents get pruned.

Structure should emerge, not be imposed. BonsAI derives agent boundaries from your actual codebase — not from a template you fill in.

The roots are the memory. Agents don't share a context window. They share a file system. The roots/ directory is the project's long-term memory — readable by humans and agents alike.

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Requirements

• Python 3.10+
• Claude Code CLI (for claude_code executor) — install from claude.ai/code
• Claude Max, Pro, or Team plan (or set ANTHROPIC_API_KEY for api executor mode)

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Status

BonsAI is in active development. Current version: 0.1.0 (Alpha)

What works:

• bonsai init on greenfield and brownfield projects
• bonsai run with claude_code and api executors
• Multi-agent runs with branching
• Full observability and reporting
• 128 unit tests passing

What's coming:

• check_intent_coherence invariant (requires embedding similarity)
• Web dashboard
• More agent types
• Plugin system for custom executors

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Contributing

BonsAI is open source under Apache 2.0.

Before contributing read ARCHITECTURE.md — it explains the design principles behind every decision. PRs that conflict with the architecture will be declined not because they're wrong but because they need to be reasoned through first.

git clone https://github.com/ejharv/BonsAI
cd BonsAI
python3 -m unittest discover tests/ -v

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License

Apache 2.0 — see LICENSE file.

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Structure emerges. Agents earn their place. Everything else gets pruned.