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# Pyreact

Pyreact is a Python multi‑agent framework with React‑style components and hooks. It coordinates agents via a router and input bus, runs on web and terminal, and integrates with DSPy for LLM‑powered agents.

## React ↔ Multi‑agent analogy (how it works)

### 1) Component ↔ Agent
- In React, a component is a function that declares UI and behavior.
- In Pyreact, an agent is a component function decorated with `component` (see `pyreact/core/core.py`). Calling it normally returns a `VNode`; the runtime renders it inside a `HookContext`.
- The `HookContext` (in `pyreact/core/hook.py`) is the agent’s execution context: it stores state, effects, children, and service references.

Key pieces:
- `component(fn)`: wraps your function so it becomes a declarative node (`VNode`) until rendered.
- `HookContext.render()`: executes the function body, reconciles children, and queues effects.
- `HookContext.run_effects()`: runs effect cleanups then new effects after commit.

### 2) Props ↔ Agent inputs
- React passes data via props; Pyreact does the same. Props are stored on `VNode.props` and copied into child `HookContext` during reconciliation (see `HookContext.render`).

### 3) State/hooks ↔ Agent internal memory
- React `useState`/`useReducer` manage local state and rerender on change.
- Pyreact mirrors this in `HookContext`:
  - `use_state(initial)`: returns `[state, set_state]`; calls `schedule_rerender` when value changes.
  - `use_reducer(reducer, initial, init_fn=None, deps=None)`: returns `[state, dispatch]`; supports lazy init and dependency‑based re‑init.
  - `use_memo(factory, deps)`, `use_callback(fn, deps)`: memoization primitives.

### 4) Effects ↔ Agent side‑effects and async work
- React `useEffect` runs after commit when dependencies change.
- Pyreact `use_effect(effect_fn, deps)`: schedules the effect; on commit, `run_effects()` executes cleanups first, then runs the effect (awaiting if it’s async), storing a new cleanup if returned.

### 5) Context/Provider ↔ Shared agent state/services
- React Context shares values across the tree with Providers.
- Pyreact `create_context` (in `pyreact/core/provider.py`) returns a context object with:
  - `Context(value=..., children=[...])`: provider component.
  - `get()`/`set(value)`: read/update. `set` notifies subscribed `HookContext`s via `schedule_rerender`.
- Agents read with `hooks.use_context(Context)`. Subscriptions are tracked so unmount cleans them up.

### 6) Reconciler/scheduler ↔ Agent commit loop
- React batches state updates and commits.
- Pyreact scheduling (in `pyreact/core/runtime.py`):
  - `schedule_rerender(ctx, reason)`: enqueues a context once and signals the loop.
  - `run_renders()`: drains the queue; for each context calls `render()` then `run_effects()`; finally marks idle with `get_render_idle().set()`.

### 7) Events ↔ Environment inputs
- React gets DOM events; agents often react to external inputs.
- Pyreact uses `InputBus` (in `pyreact/input/bus.py`):
  - `subscribe(fn)`: register a listener; returns `unsubscribe()`.
  - `emit(ev)`: deliver `{type, value, source, ts}` to all listeners.
- The `AppRunner` (in `pyreact/boot/app_runner.py`) bridges terminal and web inputs into the `InputBus` and advances the render loop.

### 8) Routing/orchestration ↔ Agent coordination
- React apps route to components; multi‑agent systems route tasks/messages.
- Pyreact Router (in `pyreact/router/router.py`) and `NavService` coordinate which agent subtree is active. Hooks like `use_route`, `use_navigate`, `use_query_params` expose navigation state and helpers.
- Agents can drive navigation imperatively (e.g., after an effect completes) by calling `navigate(...)` from `use_navigate`.

### Lifecycle overview
1. A root agent (component) is wrapped in a `HookContext` by the runner.
2. State changes or context updates call `schedule_rerender`.
3. The loop `run_renders` renders contexts, reconciles children, then runs effects.
4. On unmount, `HookContext.unmount()` runs effect cleanups, removes context subscriptions, and unmounts the subtree.

## DSPy Provider Setup

### OpenAI API Key Configuration

To use this project's AI resources, you need to configure your OpenAI API key.

### 1. Get an API Key

1. Access [OpenAI Platform](https://platform.openai.com/)
2. Log in or create an account
3. Go to "API Keys" in the sidebar menu
4. Click "Create new secret key"
5. Copy the generated key

### 2. Configure the API Key

#### Option A: Environment Variable (Recommended)

```bash
export OPENAI_API_KEY="your_api_key_here"
```

#### Option B: .env File

Create a `.env` file in the project root:

```bash
# .env
OPENAI_API_KEY=your_api_key_here
```

**Note:** The `.env` file is in `.gitignore` for security. Never commit your API key to the repository.

### 3. Verify Configuration

To check if the configuration is working:

```bash
python -c "import os; print('API Key configured:', bool(os.getenv('OPENAI_API_KEY')))"
```

### 4. Run the Project

After configuring the API key, run the project:

```bash
# For web version
python main_web.py

# For terminal version
python main_terminal.py
```

## Troubleshooting

### Error: "DSPy context not available"

This error indicates that:
1. The API key is not configured
2. The API key is invalid
3. The DSPyProvider was not initialized correctly

**Solutions:**
1. Check if the `OPENAI_API_KEY` variable is defined
2. Confirm that the API key is valid
3. Restart the application after configuring the API key

### Error: "No language model configured"

This error indicates that no language model was configured in the DSPyProvider.

**Solution:** Check if the `DSPyProvider` is being used correctly in the `Root` component.

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