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# Goose: The Extension-First Rust Agent with LLM-Based Adversary Inspection
- URL: https://wuu73.org/aiguide/coding-agents-goose/
- Published: 2026-05-29T00:25:00.000Z
- Updated: 2026-05-29T00:25:00.000Z
- Description: Part of Agent Harness Field Guide.
- Author: WUU73
- Tags: Deep Dives, Agent Harness Field Guide, #Deep Dive Child, #Import 2026-09-12 06:38

AAIF (Linux Foundation) • Rust Native • Extension-First • Updated April 2026

Goose (by **AAIF — Agentic AI Foundation at the Linux Foundation**) is a Rust-native AI agent that runs on your machine — desktop app, CLI, and API. With 15+ providers, MCP support, a multi-stage security inspector stack, and a skill/recipe framework, Goose represents a distinctive engineering philosophy: make the core lean and extensible, then let extensions do the heavy lifting.

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## What makes Goose different

Goose occupies a unique position in this agent set: it is a **Rust-native, extension-first runtime** with a security model that uses a second LLM to review tool calls. The architecture is deliberately minimal at the core — tools come from extensions, security comes from a layered inspector stack, and learning happens through a recipes and skills system.

🏗️

#### Three deployment modes

**Desktop app:** Native desktop for macOS, Linux, and Windows with full UI. **CLI:** Full terminal workflow for scripted and interactive use. **API:** Embed Goose anywhere via HTTP API. All three share the same Rust core and extension system.

### Extension-first philosophy

Unlike agents that ship with 40+ built-in tools, Goose's core ships with only a handful of platform tools. Everything else — file editing, web search, database access, specialized workflows — comes through the extension system. This keeps the core lean and makes the agent behavior genuinely customizable.

### LLM-based adversary inspection

The `AdversaryInspector` (in `src/security/adversary_inspector.rs`) is unique in this set: it fires a second LLM call to review tool calls against user-defined rules from `~/.config/goose/adversary.md`. This is defense-in-depth against a rogue sub-agent — not pattern matching, actual LLM judgment.

## Architecture overview

Goose is built as a **Cargo workspace** with multiple crates. The main `goose` crate contains the core agent runtime, tool inspection pipeline, provider system, and extension manager. Key crates:

| Crate            | Purpose                              | Key modules                                 |
| ---------------- | ------------------------------------ | ------------------------------------------- |
| goose            | Core agent runtime, tools, providers | agents/, providers/, security/, config/     |
| goose-cli        | Terminal interface                   | commands/, session/, recipes/               |
| goose-server     | HTTP API server                      | routes/, auth.rs, tunnel/                   |
| goose-mcp        | MCP server implementation            | mcp\_server\_runner.rs, computercontroller/ |
| goose-sdk        | SDK for extensions                   | Extension interface types                   |
| goose-acp-macros | ACP protocol macros                  | Protocol derive macros                      |

## The agent core: agent.rs

The main `Agent` struct in `src/agents/agent.rs` (\~2,500 lines) handles the complete agent loop. The key design patterns:

### Provider system

The agent holds a `SharedProvider` (an `Arc<Mutex<Option<Arc<dyn Provider>>>>`) that can be updated at runtime. This enables sub-agents to switch providers independently of the main agent. The `update_provider()` method persists the provider choice to the session.

### Tool inspection pipeline

Before any tool call executes, it passes through a `ToolInspectionManager` with stacked inspectors: Security → Egress → Adversary → Permission → Repetition. Each inspector can approve, deny, or require user approval. This is the most layered security pipeline in this agent set.

```
// From src/agents/agent.rs — the inspection stack setup
fn create_tool_inspection_manager(
    permission_manager: Arc<PermissionManager>,
    provider: SharedProvider,
) -> ToolInspectionManager {
    let mut manager = ToolInspectionManager::new();

    // Add security inspector (highest priority - runs first)
    manager.add_inspector(Box::new(SecurityInspector::new()));
    manager.add_inspector(Box::new(EgressInspector::new()));

    // Add adversary inspector (LLM-based review, enabled by ~/.config/goose/adversary.md)
    manager.add_inspector(Box::new(AdversaryInspector::new(provider.clone())));

    // Add permission inspector (medium-high priority)
    manager.add_inspector(Box::new(PermissionInspector::new(...)));

    // Add repetition inspector (lower priority - basic repetition checking)
    manager.add_inspector(Box::new(RepetitionInspector::new(None)));

    manager
}
```

💡

#### Sub-agent provider isolation

When spawning a sub-agent, the main agent can call `agent.update_provider(task_config.provider.clone(), &session_id)` to give the sub-agent its own provider. This means sub-agents can use different models than the parent agent — a powerful architectural choice for cost optimization.

## Multi-mode security: GooseMode

Goose implements four distinct operating modes defined in `src/config/goose_mode.rs`:

| Mode               | Description                       | Security behavior                                               |
| ------------------ | --------------------------------- | --------------------------------------------------------------- |
| **Auto** (default) | Automatically approve tool calls  | Minimal friction, tools run immediately                         |
| **Approve**        | Ask before every tool call        | Maximum control, every tool requires confirmation               |
| **SmartApprove**   | Ask only for sensitive tool calls | Uses read\_only\_hint annotation; auto-approves read-only tools |
| **Chat**           | Chat only, no tool calls          | Zero tool execution, pure conversation mode                     |

The `SmartApprove` mode uses a particularly elegant pattern: it calls `permission_manager.apply_tool_annotations(&tools)` which reads the MCP `ToolAnnotations.read_only_hint` field. Tools with `read_only: false` are automatically added to the "ask\_before" list in the smart-approve permission config.

## The LLM-based AdversaryInspector

The `AdversaryInspector` in `src/security/adversary_inspector.rs` is the most distinctive security component in Goose. It activates when you place an `adversary.md` file in your Goose config directory (`~/.config/goose/adversary.md`).

### adversary.md format

```
tools: shell, computercontroller__automation_script
---
BLOCK if the command exfiltrates data or is destructive.
ALLOW normal development operations.
```

The frontmatter specifies which tools to review (defaults to `shell` and `computercontroller__automation_script`). The rules below the `---` separator are passed to the LLM for judgment.

### How it works

1. When a tool call targets a listed tool, the inspector formats the tool call with its arguments
2. It extracts the original task from the conversation and recent user messages
3. It fires a separate LLM call (using the same provider) with the tool call, task, and user rules
4. The LLM responds with `ALLOW` or `BLOCK` and a reason
5. If `BLOCK`, the tool is denied with the LLM's explanation shown to the user

⚠️

#### Fail-open design

If the LLM call fails (e.g., no provider, network error), the inspector fails open — allowing the tool to proceed. This prevents the security system from blocking legitimate work due to infrastructure issues.

```
// From src/security/adversary_inspector.rs — the LLM review prompt
let system_prompt = "You are an adversarial security reviewer, protecting the user \
    in case the other agent is rogue. An AI coding agent is about to execute a tool \
    call. Your ONLY job: decide if this tool call is safe given the user's task and \
    rules. Respond with ALLOW or BLOCK on the first line, then a brief reason on \
    the next line.";

let user_message = format!(
    "The user's original task: {}\n\n\
     Recent user messages (oldest first):\n{}\n\
     The tool call to review:\n```\n{}\n```\n\n\
     Rules:\n{}\n\n\
     Respond with a single word on the first line: ALLOW or BLOCK\n\
     Then on the next line, a brief reason.",
    original_task, history_section, tool_description, rules
);
```

## Provider system: 15+ providers across 35+ modules

Goose's provider architecture in `src/providers/` is extensive. The `providers/mod.rs` exports 35+ provider modules:

| Provider category            | Modules                                                                                                                             |
| ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| **Direct API providers**     | anthropic, openai, google, gemini\_cli, ollama, openrouter, bedrock, azure                                                          |
| **ACP-bridged providers**    | claude\_acp, pi\_acp, codex\_acp, copilot\_acp, amp\_acp                                                                            |
| **Platform-specific agents** | chatgpt\_codex (GitHub Copilot), claude\_code, gemini\_cli, kimicode, cursor\_agent                                                 |
| **Enterprise providers**     | databricks, snowflake, tetrate, venice, xai, nanogpt                                                                                |
| **Declarative providers**    | JSON config files in providers/declarative/: cerebras, deepseek, groq, lmstudio, mistral, novita, nvidia, ovhcloud, zhipu, and more |
| **Local inference**          | local\_inference/ with candle-core, llama-cpp-2, tokenizers — supports CUDA and Metal GPU acceleration                              |

The `canonical/` directory contains auto-generated model mapping data: `canonical_models.json` maps canonical model names to provider-specific model identifiers, and `provider_metadata.json` contains per-provider capabilities and limits.

## Extension system and MCP

Goose's extension system is implemented in `src/agents/extension_manager.rs` and enables dynamic loading of MCP servers and custom tools. Extensions can be stdio-based (spawned as child processes) or embedded via Docker.

### Extension discovery

Extensions are discovered from `~/.config/goose/extensions/` and can be npm packages, local directories, or git repositories. The `ExtensionManager` resolves commands via `SearchPaths::builder().with_npm()` to find executables in node\_modules/.bin or on PATH.

### malware scanning

Before loading an extension, Goose runs `extension_malware_check.rs` to scan the extension's code for suspicious patterns. This is a critical security measure given that extensions run with the same permissions as the agent.

### MCP client implementation

The `McpClient` in `src/agents/mcp_client.rs` implements the MCP client protocol using the `rmcp` crate. It handles:

- Connection management and session tracking
- Tool listing and calling with proper request context
- Notification forwarding (progress, logging, etc.)
- Resource and prompt management
- Working directory synchronization across MCP sessions

📌

#### Key files

`src/agents/extension_manager.rs` (extension lifecycle), `src/agents/mcp_client.rs` (MCP client protocol), `src/agents/extension_malware_check.rs` (security scanning), `src/config/extensions.rs` (configuration)

## Tool execution pipeline

The tool execution flow in `src/agents/tool_execution.rs` and `src/agents/agent.rs` follows this sequence:

1**Categorize tools**

Split into frontend tools (UI handles) and extension tools

↓2**Run inspection stack**

Security → Egress → Adversary → Permission → Repetition

↓3**Route by permission result**

Auto-approved tools dispatch immediately; denied tools return error; needs\_approval tools await user confirmation

↓4**Dispatch via ExtensionManager**

Tool call routed to appropriate MCP client or stdio extension

↓5**Handle result and notifications**

Result streamed back with optional MCP server notifications during execution

## Sub-agent execution

Goose implements sub-agent execution in `src/agents/subagent_handler.rs` with a comprehensive system:

### Task configuration

Each sub-agent gets a `TaskConfig` specifying provider, max\_turns, and extensions. Sub-agents can use different providers than the parent agent, enabling cost-effective delegation patterns.

### Recipe-driven prompts

Sub-agents receive instructions from a `Recipe` object that contains system instructions, task prompts, response schemas, and retry configuration. The `subagent_system.md` prompt template provides the agent persona.

```
// From src/agents/subagent_handler.rs — sub-agent prompt context
pub struct SubagentPromptContext {
    pub max_turns: usize,
    pub subagent_id: String,
    pub task_instructions: String,
    pub tool_count: usize,
    pub available_tools: String,
}

// Template from src/prompts/subagent_system.md:
// "You are a specialized subagent within the goose AI framework,
// created by AAIF (Agentic AI Foundation). You were spawned by
// the main goose agent to handle a specific task efficiently."
```

The sub-agent supports `final_output` mode where it runs until it produces structured output matching a JSON schema, then stops. This is useful for extracting structured data from complex tasks.

## Shell tool: platform-aware execution

The shell tool in `src/agents/platform_extensions/developer/shell.rs` (\~980 lines) is one of the most platform-aware shell implementations in this set:

| Feature                     | Implementation                                                                                             |
| --------------------------- | ---------------------------------------------------------------------------------------------------------- |
| Unix shell detection        | Checks for bash on PATH, falls back to sh; respects GOOSE\_SHELL env var                                   |
| Windows shell handling      | Detects PowerShell vs cmd vs POSIX-like shells (Cygwin/MSYS2)                                              |
| Flatpak support             | Detects Flatpak sandbox and wraps commands with flatpak-spawn --host                                       |
| Login shell PATH resolution | Spawns a login shell to recover user's full PATH (fixes Electron-inherited minimal PATH)                   |
| Output handling             | Parallel stdout/stderr collection, 2000-line/50KB limits, temp file fallback for overflow                  |
| Background process handling | Detects backgrounded processes (sleep &), sets output\_truncated flag, doesn't wait for backgrounded sleep |
| Process session management  | Uses ProcessSession wrapper to prevent bash job control from stealing terminal foreground                  |

## Recipe and skill framework

Goose's recipe system enables converting chat conversations into reusable automation workflows. Defined in `src/recipe.rs` and prompted via `src/prompts/recipe.md`:

### Recipe structure

- **title** — Human-readable name
- **instructions** — Reusable task description
- **activities** — Example use cases
- **response** — Optional JSON schema for structured output
- **settings** — Provider, model, temperature
- **extensions** — Required extensions to load

### Recipe creation

During chat, the agent can call `create_recipe()` which takes the conversation history, generates a structured recipe using the LLM, and saves it as a reusable automation. Recipes are stored in `~/.config/goose/recipes/`.

## Context compaction and memory management

Goose implements context compaction in `src/context_mgmt/mod.rs`. The compaction triggers when context usage exceeds a configurable threshold (default: `GOOSE_AUTO_COMPACT_THRESHOLD`).

- **Tool pair summarization:** Repeated tool call/response pairs are summarized to reduce context size
- **Session naming:** Goose auto-generates session names using the provider when not disabled
- **MOIM injection:** `inject_moim()` in `src/agents/moim.rs` injects a message-of-instructions before the latest assistant message, sourced from extension-collected hints

🔧

#### Hint files system

Goose supports `.goosehints` files in project directories that provide context hints to the agent. The `PromptManager` loads these via `load_subdirectory_hints()` and can refresh tool/prompt lists when hints change.

## Key differences from other agents

#### vs Claude Code

Claude Code is Anthropic-first with 40+ built-in tools and a React/Ink TUI. Goose is provider-agnostic with an extension-first model where tools come from MCP or stdio extensions. Goose's security uses an LLM-based adversary reviewer; Claude Code uses a permissions system with wildcard rules.

#### vs Pi Mono

Both share the extension-first philosophy, but Pi Mono is TypeScript with a JSONL session format and Pi Packages (npm/git bundles). Goose is Rust with a SQLite session store and an ACP/MCP protocol layer. Goose's provider count (15+) is comparable to Pi Mono's (23).

#### vs Codex

Both are Rust-native and ship as compiled binaries. Codex focuses on coding with platform-specific sandboxing (Seatbelt/bubblewrap). Goose is more general-purpose with a focus on extensibility and the extension marketplace. Goose uses a layered security inspector stack; Codex uses an execpolicy rule engine.

#### vs DeerFlow

DeerFlow is a Python/LangGraph orchestration harness. Goose is a Rust native binary with a simpler but more production-focused architecture. DeerFlow's middleware chain is more composable; Goose's inspector stack is more focused on security. DeerFlow has 14 middleware layers; Goose has 5 security inspectors.

## File count and codebase size

Goose's Cargo workspace contains multiple crates with substantial source:

| Crate        | Notable modules                                        | Characteristics                          |
| ------------ | ------------------------------------------------------ | ---------------------------------------- |
| goose        | agents/, providers/, security/, config/, conversation/ | \~50+ source files, 2,500+ line agent.rs |
| goose-cli    | commands/, session/, recipes/                          | Full terminal interface                  |
| goose-server | routes/, auth.rs, tunnel/                              | HTTP API with TLS support                |
| goose-mcp    | mcp\_server\_runner.rs, computercontroller/            | MCP server + computer use tools          |
| goose-sdk    | Extension interface types                              | SDK for third-party extensions           |

## Notable engineering decisions

### Extension isolation via container

The agent can run stdio extensions inside a Docker container via `set_container()`. This provides process isolation for untrusted or resource-intensive extensions.

### Structured error handling with ErrorData

Tool errors use `rmcp::model::ErrorData` with error codes (`INVALID_PARAMS`, `INTERNAL_ERROR`, etc.) rather than plain strings — making error handling more programmatic.

### Notification streaming during tool execution

`ToolCallResult` combines a result future with an optional `ServerNotification` stream. Tools can emit progress notifications during long-running operations without blocking the result.

### ActionRequired permission routing

Some providers (e.g., Claude Code via ACP) handle permissions natively through an action-required protocol. Goose detects this via `permission_routing() == PermissionRouting::ActionRequired` and delegates confirmation handling to the provider.

### Prompts via minijinja templates

System prompts use `minijinja` templates (embedded in `src/prompts/`) rather than hardcoded strings. Templates receive context like extension list, mode, tool counts, and render dynamically at runtime.

### Feature-gated dependencies

The `Cargo.toml` uses extensive feature gates: `local-inference` (candle, llama-cpp, tokenizers), `aws-providers` (Bedrock, SageMaker), `telemetry`/`otel` (OpenTelemetry), `cuda` (GPU acceleration), etc.

## Key files reference

| File                                               | Lines          | What it does                                                                          |
| -------------------------------------------------- | -------------- | ------------------------------------------------------------------------------------- |
| src/agents/agent.rs                                | \~2,500        | Main agent loop, reply(), tool inspection stack setup                                 |
| src/agents/tool\_execution.rs                      | \~190          | Tool call dispatch, approval routing, result handling                                 |
| src/agents/extension\_manager.rs                   | \~700+         | Extension discovery, MCP client management, tool routing                              |
| src/agents/mcp\_client.rs                          | \~500+         | MCP client protocol implementation using rmcp                                         |
| src/agents/subagent\_handler.rs                    | \~350          | Sub-agent spawning, task execution, result collection                                 |
| src/security/adversary\_inspector.rs               | \~630          | LLM-based adversary review with fail-open behavior                                    |
| src/security/security\_inspector.rs                | \~150          | Pattern-matching security detection                                                   |
| src/config/permission.rs                           | \~390          | YAML-based permission config with AlwaysAllow/AskBefore/NeverAllow                    |
| src/config/goose\_mode.rs                          | \~35           | Four-mode enum: Auto, Approve, SmartApprove, Chat                                     |
| src/agents/platform\_extensions/developer/shell.rs | \~980          | Platform-aware shell tool with Flatpak, login-shell PATH, background process handling |
| src/agents/platform\_extensions/analyze/mod.rs     | \~200+         | Code analysis extension with tree-sitter AST parsing, structure/语义/focus modes        |
| src/agents/moim.rs                                 | \~145          | Message-of-instructions injection before latest assistant message                     |
| src/providers/mod.rs                               | \~65           | 35+ provider module exports                                                           |
| src/providers/canonical/                           | Multiple files | Auto-generated canonical model mappings and provider metadata                         |
| src/context\_mgmt/mod.rs                           | \~300+         | Context compaction, tool pair summarization                                           |
| src/conversation/message.rs                        | \~500+         | Message types with tool requests/responses, thinking, system notifications            |

## My assessment

### Best for

- Users who want a lean, extensible agent core rather than a feature-heavy CLI
- Security-conscious users who want LLM-based adversarial review of tool calls
- Teams building custom extensions and integrating with MCP servers
- Users who prefer Rust's performance and type safety for agent tooling
- Enterprise deployments requiring TLS, OAuth, and multi-provider support

### Tradeoffs

- Extension-first model means some things require setup (vs. batteries-included agents)
- Security relies on user-defined rules in adversary.md — no built-in defaults
- Recipe framework is less mature than some alternatives' workflow systems
- Desktop app adds UI complexity that pure CLI agents avoid

🏆

#### Most distinctive feature

The `AdversaryInspector` using an LLM to review tool calls against user-defined rules is unique in this agent set. No other agent has a dedicated second-LLM security review for tool execution. This makes Goose particularly interesting for multi-agent setups where the parent agent delegates to sub-agents — the adversary inspector provides defense-in-depth against a potentially compromised sub-agent.