Provider System

The Provider system abstracts model inference backends behind a uniform interface, enabling ZeroClaw to work with any LLM provider (OpenAI, Anthropic, local models, etc.) through a single consistent API.

Architecture Overview

Provider Trait

All providers implement the Provider trait from src/providers/traits.rs:

#[async_trait]
pub trait Provider: Send + Sync {
    /// Query provider capabilities
    fn capabilities(&self) -> ProviderCapabilities {
        ProviderCapabilities::default()
    }

    /// Convert tool specs to provider-native format
    fn convert_tools(&self, tools: &[ToolSpec]) -> ToolsPayload {
        ToolsPayload::PromptGuided {
            instructions: build_tool_instructions_text(tools),
        }
    }

    /// Simple one-shot chat (single user message)
    async fn simple_chat(
        &self,
        message: &str,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String>;

    /// Chat with optional system prompt
    async fn chat_with_system(
        &self,
        system_prompt: Option<&str>,
        message: &str,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String>;

    /// Multi-turn conversation with history
    async fn chat_with_history(
        &self,
        messages: &[ChatMessage],
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String>;

    /// Structured chat API for agent loop
    async fn chat(
        &self,
        request: ChatRequest<'_>,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<ChatResponse>;

    /// Whether provider supports native tool calls
    fn supports_native_tools(&self) -> bool {
        self.capabilities().native_tool_calling
    }

    /// Whether provider supports vision/image inputs
    fn supports_vision(&self) -> bool {
        self.capabilities().vision
    }

    /// Warm up HTTP connection pool
    async fn warmup(&self) -> anyhow::Result<()> {
        Ok(())
    }
}

Provider Capabilities

Providers declare capabilities to enable intelligent adaptation:

#[derive(Debug, Clone, Default, PartialEq, Eq)]
pub struct ProviderCapabilities {
    /// Native tool calling via API primitives
    pub native_tool_calling: bool,
    /// Vision / image input support
    pub vision: bool,
}

Native Tool Calling

Providers that support native tool calling return structured tool calls:

pub struct ChatResponse {
    pub text: Option<String>,
    pub tool_calls: Vec<ToolCall>,
    pub usage: Option<TokenUsage>,
    pub reasoning_content: Option<String>,
    pub stop_reason: Option<NormalizedStopReason>,
}

pub struct ToolCall {
    pub id: String,
    pub name: String,
    pub arguments: String, // JSON-encoded
}

Native Tool Calling Providers:

OpenAI (GPT-4, GPT-3.5)
Anthropic (Claude)
Gemini
Azure OpenAI

Prompt-Guided Tool Calling

Providers without native tool support use prompt injection:

pub enum ToolsPayload {
    /// Native formats
    OpenAI { tools: Vec<serde_json::Value> },
    Anthropic { tools: Vec<serde_json::Value> },
    Gemini { function_declarations: Vec<serde_json::Value> },
    
    /// Fallback: inject as text in system prompt
    PromptGuided { instructions: String },
}

Prompt-Guided Example:

## Tool Use Protocol

To use a tool, wrap a JSON object in <tool_call></tool_call> tags:

<tool_call>
{"name": "shell", "arguments": {"command": "ls -la"}}
</tool_call>

### Available Tools

**shell**: Execute a shell command in the workspace directory
Parameters: `{"type":"object","properties":{"command":{"type":"string"}}}`

**file_read**: Read contents of a file
Parameters: `{"type":"object","properties":{"path":{"type":"string"}}}`

OpenAI Provider Implementation

Real implementation from src/providers/openai.rs:

pub struct OpenAiProvider {
    base_url: String,
    credential: Option<String>,
    max_tokens_override: Option<u32>,
}

impl OpenAiProvider {
    pub fn new(base_url: impl Into<String>, api_key: Option<String>) -> Self {
        Self {
            base_url: base_url.into(),
            credential: api_key,
            max_tokens_override: None,
        }
    }
}

#[async_trait]
impl Provider for OpenAiProvider {
    fn capabilities(&self) -> ProviderCapabilities {
        ProviderCapabilities {
            native_tool_calling: true,
            vision: true,
        }
    }

    fn convert_tools(&self, tools: &[ToolSpec]) -> ToolsPayload {
        let native_tools: Vec<_> = tools
            .iter()
            .map(|spec| {
                serde_json::json!({
                    "type": "function",
                    "function": {
                        "name": spec.name,
                        "description": spec.description,
                        "parameters": spec.parameters,
                    }
                })
            })
            .collect();
        
        ToolsPayload::OpenAI { tools: native_tools }
    }

    async fn chat_with_system(
        &self,
        system_prompt: Option<&str>,
        message: &str,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String> {
        let mut messages = Vec::new();
        
        if let Some(sys) = system_prompt {
            messages.push(Message {
                role: "system".into(),
                content: sys.into(),
            });
        }
        
        messages.push(Message {
            role: "user".into(),
            content: message.into(),
        });
        
        let client = Client::new();
        let request = ChatRequest {
            model: model.to_string(),
            messages,
            temperature,
            max_tokens: self.max_tokens_override,
        };
        
        let response = client
            .post(format!("{}/chat/completions", self.base_url))
            .header("Authorization", format!("Bearer {}", self.credential.as_deref().unwrap_or("")))
            .json(&request)
            .send()
            .await?
            .json::<ChatResponse>()
            .await?;
        
        let content = response
            .choices
            .first()
            .and_then(|c| c.message.content.as_deref())
            .unwrap_or("");
        
        Ok(content.to_string())
    }
    
    async fn chat(
        &self,
        request: ChatRequest<'_>,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<ChatResponse> {
        // Convert tools to OpenAI format
        let tools = request.tools.map(|tools| {
            match self.convert_tools(tools) {
                ToolsPayload::OpenAI { tools } => tools,
                _ => vec![],
            }
        });
        
        // Build native request with tool definitions
        let native_request = NativeChatRequest {
            model: model.to_string(),
            messages: convert_messages(request.messages),
            temperature,
            max_tokens: self.max_tokens_override,
            tools,
            tool_choice: None,
        };
        
        // Send to API and parse response
        let response = self.send_request(native_request).await?;
        
        // Extract tool calls
        let tool_calls = response.choices
            .first()
            .and_then(|c| c.message.tool_calls.as_ref())
            .map(|calls| {
                calls.iter().map(|tc| ToolCall {
                    id: tc.id.clone().unwrap_or_default(),
                    name: tc.function.name.clone(),
                    arguments: tc.function.arguments.clone(),
                }).collect()
            })
            .unwrap_or_default();
        
        Ok(ChatResponse {
            text: response.choices.first().and_then(|c| c.message.content.clone()),
            tool_calls,
            usage: response.usage.map(|u| TokenUsage {
                input_tokens: u.prompt_tokens,
                output_tokens: u.completion_tokens,
            }),
            reasoning_content: None,
            quota_metadata: None,
            stop_reason: response.choices.first()
                .and_then(|c| c.finish_reason.as_deref())
                .map(NormalizedStopReason::from_openai_finish_reason),
            raw_stop_reason: response.choices.first()
                .and_then(|c| c.finish_reason.clone()),
        })
    }
}

Provider Factory

Providers are instantiated via factory function in src/providers/mod.rs:

pub fn create_provider(
    name: &str,
    base_url: Option<&str>,
    api_key: Option<String>,
    config: &Config,
) -> Result<Box<dyn Provider>> {
    match name {
        "openai" => {
            let url = base_url.unwrap_or("https://api.openai.com/v1");
            Ok(Box::new(OpenAiProvider::new(url, api_key)))
        }
        
        "anthropic" => {
            let url = base_url.unwrap_or("https://api.anthropic.com");
            Ok(Box::new(AnthropicProvider::new(url, api_key)))
        }
        
        "ollama" => {
            let url = base_url.unwrap_or("http://localhost:11434");
            Ok(Box::new(OllamaProvider::new(url)))
        }
        
        "gemini" => {
            let url = base_url.unwrap_or("https://generativelanguage.googleapis.com");
            Ok(Box::new(GeminiProvider::new(url, api_key)))
        }
        
        _ => Err(anyhow::anyhow!("Unknown provider: {}", name)),
    }
}

Reliable Provider Wrapper

The ReliableProvider wrapper adds fallback chains and retry logic:

pub struct ReliableProvider {
    primary: Box<dyn Provider>,
    fallbacks: Vec<Box<dyn Provider>>,
    max_retries: u32,
}

impl ReliableProvider {
    pub fn new(
        primary: Box<dyn Provider>,
        fallbacks: Vec<Box<dyn Provider>>,
    ) -> Self {
        Self {
            primary,
            fallbacks,
            max_retries: 3,
        }
    }
}

#[async_trait]
impl Provider for ReliableProvider {
    async fn chat_with_system(
        &self,
        system_prompt: Option<&str>,
        message: &str,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String> {
        // Try primary
        match self.primary.chat_with_system(system_prompt, message, model, temperature).await {
            Ok(response) => return Ok(response),
            Err(e) => eprintln!("Primary provider failed: {}", e),
        }
        
        // Try fallbacks
        for (i, fallback) in self.fallbacks.iter().enumerate() {
            match fallback.chat_with_system(system_prompt, message, model, temperature).await {
                Ok(response) => {
                    eprintln!("Fallback {} succeeded", i);
                    return Ok(response);
                }
                Err(e) => eprintln!("Fallback {} failed: {}", i, e),
            }
        }
        
        Err(anyhow::anyhow!("All providers failed"))
    }
}

Stop Reason Normalization

Providers normalize stop reasons to a common enum:

#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NormalizedStopReason {
    EndTurn,
    ToolCall,
    MaxTokens,
    ContextWindowExceeded,
    SafetyBlocked,
    Cancelled,
    Unknown(String),
}

impl NormalizedStopReason {
    pub fn from_openai_finish_reason(raw: &str) -> Self {
        match raw.trim().to_ascii_lowercase().as_str() {
            "stop" => Self::EndTurn,
            "tool_calls" | "function_call" => Self::ToolCall,
            "length" | "max_tokens" => Self::MaxTokens,
            "content_filter" => Self::SafetyBlocked,
            "cancelled" | "canceled" => Self::Cancelled,
            _ => Self::Unknown(raw.trim().to_string()),
        }
    }
    
    pub fn from_anthropic_stop_reason(raw: &str) -> Self {
        match raw.trim().to_ascii_lowercase().as_str() {
            "end_turn" | "stop_sequence" => Self::EndTurn,
            "tool_use" => Self::ToolCall,
            "max_tokens" => Self::MaxTokens,
            "model_context_window_exceeded" => Self::ContextWindowExceeded,
            "safety" => Self::SafetyBlocked,
            _ => Self::Unknown(raw.trim().to_string()),
        }
    }
}

Token Usage Tracking

#[derive(Debug, Clone, Default)]
pub struct TokenUsage {
    pub input_tokens: Option<u64>,
    pub output_tokens: Option<u64>,
}

pub struct ChatResponse {
    pub text: Option<String>,
    pub tool_calls: Vec<ToolCall>,
    pub usage: Option<TokenUsage>, // Populated if provider returns it
    // ...
}

Configuration

Providers are configured in zeroclaw.toml:

[provider]
name = "openai"
api_key = "${OPENAI_API_KEY}"
model = "gpt-4"
temperature = 0.7

# Optional fallback chain
[[provider.fallback]]
name = "anthropic"
api_key = "${ANTHROPIC_API_KEY}"
model = "claude-3-opus-20240229"

[[provider.fallback]]
name = "ollama"
model = "llama2"
base_url = "http://localhost:11434"

Built-in Providers

Provider	Native Tools	Vision	Streaming	Base URL
OpenAI	✅	✅	✅	https://api.openai.com/v1
Anthropic	✅	✅	✅	https://api.anthropic.com
Gemini	✅	✅	✅	https://generativelanguage.googleapis.com
Ollama	❌	❌	✅	http://localhost:11434
Azure OpenAI	✅	✅	✅	https://.openai.azure.com
AWS Bedrock	✅	✅	✅	Runtime endpoint
OpenRouter	✅	Varies	✅	https://openrouter.ai/api/v1

Adding a New Provider

From AGENTS.md §7.1:

Create provider file: src/providers/new_provider.rs

use crate::providers::traits::*;
use async_trait::async_trait;

pub struct NewProvider {
    base_url: String,
    api_key: Option<String>,
}

impl NewProvider {
    pub fn new(base_url: impl Into<String>, api_key: Option<String>) -> Self {
        Self {
            base_url: base_url.into(),
            api_key,
        }
    }
}

#[async_trait]
impl Provider for NewProvider {
    fn capabilities(&self) -> ProviderCapabilities {
        ProviderCapabilities {
            native_tool_calling: false, // or true if supported
            vision: false,
        }
    }
    
    async fn chat_with_system(
        &self,
        system_prompt: Option<&str>,
        message: &str,
        model: &str,
        temperature: f64,
    ) -> anyhow::Result<String> {
        // Implement API call
        todo!()
    }
}

Register in factory: src/providers/mod.rs

"new_provider" => {
    let url = base_url.unwrap_or("https://api.example.com");
    Ok(Box::new(NewProvider::new(url, api_key)))
}

Add tests: Test factory wiring and error paths

#[test]
fn factory_creates_new_provider() {
    let provider = create_provider("new_provider", None, None, &Config::default())
        .expect("factory should create provider");
    assert_eq!(provider.name(), "new_provider");
}

Update docs: Add to docs/providers-reference.md

Best Practices

Error Handling

Return structured errors with context
Distinguish retryable vs. non-retryable errors
Log API errors at debug level, not error

Rate Limiting

Respect provider rate limits
Implement exponential backoff
Surface rate limit errors to user

Security

Never log API keys or tokens
Use environment variables for credentials
Validate API responses before parsing

Performance

Reuse HTTP clients (connection pooling)
Implement warmup() for connection pre-warming
Stream responses when possible

Next Steps

Channels - Channel system architecture
Tools - Tool system and capabilities
Security - Security policy and validation

Getting Started

Core Concepts

Guides

Operations

Provider System

Provider System

Architecture Overview

Provider Trait

Provider Capabilities

Native Tool Calling

Prompt-Guided Tool Calling

OpenAI Provider Implementation

Provider Factory

Reliable Provider Wrapper

Stop Reason Normalization

Token Usage Tracking

Configuration

Built-in Providers

Adding a New Provider

Best Practices

Error Handling

Rate Limiting

Security

Performance

Next Steps

​Provider System

​Architecture Overview

​Provider Trait

​Provider Capabilities

​Native Tool Calling

​Prompt-Guided Tool Calling

​OpenAI Provider Implementation

​Provider Factory

​Reliable Provider Wrapper

​Stop Reason Normalization

​Token Usage Tracking

​Configuration

​Built-in Providers

​Adding a New Provider

​Best Practices

​Error Handling

​Rate Limiting

​Security

​Performance

​Next Steps

Provider System

Architecture Overview

Provider Trait

Provider Capabilities

Native Tool Calling

Prompt-Guided Tool Calling

OpenAI Provider Implementation

Provider Factory

Reliable Provider Wrapper

Stop Reason Normalization

Token Usage Tracking

Configuration

Built-in Providers

Adding a New Provider

Best Practices

Error Handling

Rate Limiting

Security

Performance

Next Steps