# Architecture Overview
## Layered Design
yoagent is organized as three conceptual layers within a single crate. Dependencies flow strictly downward — upper layers use lower layers, never the reverse.
```
┌─────────────────────────────────────────────┐
│ Layer 3: Orchestration (partly planned) │
│ Delegation today; work modes planned │
├─────────────────────────────────────────────┤
│ Layer 2: Agent + Providers │
│ Concrete providers, tools, retry, caching, │
│ context management, MCP │
├─────────────────────────────────────────────┤
│ Layer 1: Core Loop │
│ agent_loop, types, traits │
│ Provider-agnostic. Tool-agnostic. │
└─────────────────────────────────────────────┘
```
### Layer 1: Core Loop
The pure agent loop. No opinions about LLMs, no built-in tools. Just the control flow.
**Modules:** `types.rs`, `agent_loop.rs`, `provider/traits.rs`
**Owns:**
- `agent_loop()` / `agent_loop_continue()` — the loop itself
- `AgentTool` trait — interface tools must implement
- `StreamProvider` trait — interface providers must implement
- `AgentMessage`, `AgentEvent`, `StreamDelta` — message & event types
- `AgentContext` — system prompt + messages + tools
- Tool execution strategies (parallel/sequential/batched)
- Streaming tool output (`ToolUpdateFn`)
- Steering & follow-up message injection
**Does not own:** Any concrete provider or tool implementation.
### Layer 2: Agent + Providers
Batteries-included single-agent layer. Most users interact with this.
**Modules:** `agent.rs`, `context.rs`, `retry.rs`, `provider/*.rs`, `tools/*.rs`, `mcp/*.rs`
**Adds on top of Layer 1:**
- Concrete providers — Anthropic, OpenAI-compat, OpenAI Responses, Azure OpenAI, Google Gemini, Vertex, Bedrock (7 protocols)
- Provider registry — dispatch by API protocol
- Prompt caching — automatic cache breakpoint placement
- Retry with backoff — exponential, jitter, respects retry-after
- Context management — token estimation, smart truncation, execution limits
- Built-in tools — bash, read_file, write_file, edit_file, list_files, search (feature `native`)
- MCP client — stdio (feature `native`) + HTTP transports, tool adapter
- `ToolSource` — per-run tool sources, consulted at the start of every run
- `rt` — runtime shims (Tokio on native targets, the host executor on wasm32)
- `Agent` struct — stateful builder wrapping it all together
### Layer 3: Orchestration (partly planned)
Delegation exists today as `SubAgentTool` (a tool that runs a child loop with its own model, tools and limits; nestable) with `SharedState` for passing artifacts by reference. A higher-level `Orchestrator` with work modes is not implemented.
**Planned capabilities:**
- `Orchestrator` struct — spawn, delegate, and coordinate multiple agents
- Work modes:
- **Interactive** — multi-turn, human in the loop (current default)
- **Autonomous** — runs to completion without input (background tasks, CI)
- **Pipeline** — input → output, chainable (scan → fix → verify)
- **Supervisor** — delegates to other agents, synthesizes results
- Fan-out — same task to multiple agents (different providers for diversity)
- Pipeline chaining — output of agent A feeds input of agent B
- Agent communication through the orchestrator event bus
**Why not yet:** Multi-agent orchestration adds complexity. The single-agent loop handles 95% of use cases. Layer 3 will be built when a concrete use case drives it, not speculatively.
---
## Module Layout
```
yoagent/
├── src/
│ ├── lib.rs # Public re-exports
│ │
│ │── Layer 1: Core Loop ─────────────────────
│ ├── types.rs # Message, Content, AgentTool, AgentEvent
│ ├── agent_loop.rs # Core loop: prompt → LLM → tools → repeat
│ │
│ ├── rt.rs # spawn/sleep/timeout/Instant, MaybeSend/MaybeSync
│ ├── tool_source.rs # ToolSource (tools resolved per run)
│ ├── extension.rs # Extension / RunHooks, Budget (the plug-in contract)
│ │
│ │── Layer 2: Agent + Providers ─────────────
│ ├── agent.rs # Agent struct (stateful wrapper)
│ ├── context.rs # Token estimation, compaction, limits
│ ├── llm_compaction.rs # LlmCompaction (background LLM summaries)
│ ├── retry.rs # Retry with exponential backoff
│ ├── sub_agent.rs # SubAgentTool (delegation to a child loop)
│ ├── shared_state.rs # SharedState + backends
│ ├── session.rs # Session trees, JSONL persistence
│ ├── skills.rs # AgentSkills SKILL.md loading
│ ├── gasp.rs # GASP run recording (feature gasp)
│ ├── provider/
│ │ ├── traits.rs # StreamProvider trait, StreamEvent, ProviderError
│ │ ├── model.rs # ModelConfig, ApiProtocol, OpenAiCompat
│ │ ├── registry.rs # ProviderRegistry (protocol → provider)
│ │ ├── anthropic.rs # Anthropic Messages API
│ │ ├── openai_compat.rs # OpenAI Chat Completions (15+ providers)
│ │ ├── openai_responses.rs # OpenAI Responses API
│ │ ├── azure_openai.rs # Azure OpenAI
│ │ ├── responses_request.rs # Request body shared by Responses and Azure
│ │ ├── responses_stream.rs # SSE parser shared by Responses and Azure
│ │ ├── google.rs # Google Generative AI
│ │ ├── google_vertex.rs # Google Vertex AI
│ │ ├── bedrock.rs # AWS Bedrock ConverseStream
│ │ ├── eventstream.rs # AWS binary eventstream decoder
│ │ ├── sigv4.rs # SigV4 request signing
│ │ ├── turn_hook.rs # TurnHookProvider
│ │ ├── tool_args.rs # Tool-argument parsing
│ │ ├── prices.rs # PriceTable (+ prices/global.rs, prices/fetch.rs)
│ │ ├── prices.json # Bundled price snapshot (applied only on opt-in)
│ │ ├── mock.rs # Mock provider for testing
│ │ └── sse.rs # SSE utilities (crate-private)
│ ├── tools/
│ │ ├── bash.rs # BashTool (native)
│ │ ├── file.rs # ReadFileTool, WriteFileTool (native)
│ │ ├── edit.rs # EditFileTool (native)
│ │ ├── list.rs # ListFilesTool (native)
│ │ ├── search.rs # SearchTool (native)
│ │ ├── sandbox.rs # PathSandbox
│ │ └── shared_state_tool.rs # SharedStateTool
│ ├── mcp/
│ │ ├── client.rs # MCP client (stdio on native, HTTP everywhere)
│ │ ├── tool_adapter.rs # McpToolAdapter (MCP tool → AgentTool)
│ │ ├── transport.rs # Transport implementations
│ │ └── types.rs # MCP protocol types
│ ├── openapi/ # OpenAPI 3.0 → tools (feature openapi)
│ └── decision/ # Decision models (feature decision)
```
## Data Flow
```
┌─────────────┐
│ Caller │
└──────┬──────┘
│ prompt / prompt_messages
┌──────▼──────┐
│ Agent │ Layer 2: stateful wrapper
│ (agent.rs) │ Manages queues, tools, state
└──────┬──────┘
│
┌──────▼──────┐
│ agent_loop │ Layer 1: core loop
│ │ Prompt → LLM → Tools → Repeat
└──┬───────┬──┘
│ │
┌────────▼──┐ ┌──▼────────┐
│ Provider │ │ Tools │ Layer 2: implementations
│ .stream() │ │ .execute()│
└────────┬──┘ └──┬────────┘
│ │
┌────────▼──┐ ┌──▼────────┐
│ LLM API │ │ OS / FS │
│ (HTTP) │ │ (shell) │
└───────────┘ └───────────┘
Events flow back via mpsc::UnboundedSender<AgentEvent>
```
## How Providers Plug In
1. Implement `StreamProvider` trait (Layer 1 interface)
2. Register with `ProviderRegistry` under an `ApiProtocol` (Layer 2)
3. Set `ModelConfig.api` to match that protocol
4. The registry dispatches `stream()` calls to the right provider
Each provider translates between yoagent's `Message`/`Content` types and the provider's native API format. All providers emit `StreamEvent`s through the channel for real-time updates.
## How Tools Plug In
1. Implement `AgentTool` trait (Layer 1 interface)
2. Add to the tools vec (via `default_tools()` or custom)
3. The agent loop converts tools to `ToolDefinition` (name, description, schema) for the LLM
4. When the LLM returns `Content::ToolCall`, the loop finds the matching tool and calls `execute()`
5. Results are wrapped in `Message::ToolResult` and added to context
Tools receive a `CancellationToken` child token — they should check it for cooperative cancellation during long operations.
## Design Principles
- **Layers are conceptual, not physical.** One crate, clean module boundaries. Feature flags only gate host capabilities (`native`) and optional integrations (`openapi`, `gasp`, `decision`).
- **Dependencies flow down.** Layer 1 never imports from Layer 2. Layer 2 never imports from Layer 3.
- **Layer 1 is stable.** The core loop and traits change rarely. New features are added in Layer 2 or 3.
- **Build what's needed.** Layer 3's `Orchestrator` is designed but not implemented. It will be built when a use case demands it, not speculatively.
- **Simple over clever.** A straightforward loop with good defaults beats an elegant abstraction nobody can debug.