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llm

Pure MoonBit LLM client library

llm
ai
client
moon add mizchi/llm@0.3.2
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Version
0.3.2
License
MIT
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15 hours ago
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Dependencies

README

#
SchemaBuilder

source

SchemaBuilder helps construct JSON Schema for tool input_schema.

#
ToolChoice

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ToolChoice controls how the model selects tools.

#
ToolDef

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using @mizchi/llm/tools { type ToolDef }

ToolDef defines a tool that can be called by the model.

#
ToolExecuteError

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#
ToolRegistry

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ToolRegistry manages available tools and their handlers.

#
Provider

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pub(open) trait Provider {
  fn stream(Self, Array[Message], Array[
ToolDef], StreamHandler) -> Unit
  fn name(Self) -> String
}

Provider is the core trait for LLM providers.

#
AgentEvent

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pub(all) enum AgentEvent {
  Stream(StreamEvent)
  ToolExecute(name~ : String, input~ : Json)
  ToolResult(name~ : String, result~ : String, is_error~ : Bool)
  StepComplete(step~ : Int)
  Done(reason~ : String)
}

AgentEvent represents events emitted during agent execution.

#
BoxedProvider

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pub struct BoxedProvider {
  inner : &Provider
}

BoxedProvider wraps a Provider for dynamic dispatch.

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BoxedProvider::name

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fn BoxedProvider::name(self : BoxedProvider) -> String

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BoxedProvider::new

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fn BoxedProvider::new(provider : &Provider) -> BoxedProvider

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BoxedProvider::stream

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fn BoxedProvider::stream(self : BoxedProvider, messages : Array[Message], tools : Array[
ToolDef], handler : StreamHandler) -> Unit

#
CollectResult

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pub(all) struct CollectResult {
  text : String
  tool_calls : Array[ToolCall]
  finish_reason : FinishReason
  usage : Usage?
}

CollectResult holds the full result of a collected stream.

#
ContentBlock

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pub(all) enum ContentBlock {
  Text(String)
  ToolUse(id~ : String, name~ : String, input~ : Json)
  ToolResult(tool_use_id~ : String, content~ : String, is_error~ : Bool)
}

ContentBlock represents typed content within a message.

#
ContentBlock::to_anthropic_json

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fn ContentBlock::to_anthropic_json(self : ContentBlock) -> Json

Convert a ContentBlock to JSON (Anthropic format).

#
ContentBlock::to_openai_json

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fn ContentBlock::to_openai_json(self : ContentBlock) -> Json

Convert a ContentBlock to JSON (OpenAI format).

#
FinishReason

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pub(all) enum FinishReason {
  Stop
  ToolUse
  Length
  ContentFilter
  Error
  Unknown(String)
} derive(
Debug)

FinishReason indicates why the model stopped generating.

#
FinishReason::from_string

source
fn FinishReason::from_string(s : String) -> FinishReason

#
JsonChatLoopResult

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pub(all) struct JsonChatLoopResult {
  final_content : String
  messages : Array[Json]
  iterations : Int
}

JsonChatLoopResult is the final result of run_json_chat_loop.

#
JsonChatResponse

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pub(all) struct JsonChatResponse {
  content : String
  tool_calls : Array[JsonChatToolCall]
  reasoning_content : String?
}

JsonChatResponse is the per-step response from a model callback.

#
JsonChatResponse::new

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fn JsonChatResponse::new(content : String, tool_calls : Array[JsonChatToolCall], reasoning_content : String?) -> JsonChatResponse

#
JsonChatToolCall

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pub(all) struct JsonChatToolCall {
  id : String
  name : String
  arguments : Json
}

JsonChatToolCall represents a function call in JSON chat loop.

#
JsonChatToolCall::new

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fn JsonChatToolCall::new(id : String, name : String, arguments : Json) -> JsonChatToolCall

#
Message

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pub(all) struct Message {
  role : Role
  content : Array[ContentBlock]
}

Message represents a single message in a conversation.

#
Message::assistant

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fn Message::assistant(text : String) -> Message

Create an assistant text message.

#
Message::estimate_tokens

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fn Message::estimate_tokens(self : Message) -> Int

Estimate token count for a message (rough: ~4 chars per token).

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Message::get_text

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fn Message::get_text(self : Message) -> String

Get the text content of a message (concatenated from all Text blocks).

#
Message::system

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fn Message::system(text : String) -> Message

Create a system text message.

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Message::text

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fn Message::text(role : Role, text : String) -> Message

Create a simple text message.

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Message::to_anthropic_json

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fn Message::to_anthropic_json(self : Message) -> Json

Convert a Message to Anthropic JSON format.

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Message::to_openai_json

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fn Message::to_openai_json(self : Message) -> Json

Convert a Message to OpenAI JSON format.

#
Message::user

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fn Message::user(text : String) -> Message

Create a user text message.

#
MockProvider

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pub struct MockProvider {
  responses : Array[Array[StreamEvent]]
  call_count : Int
}

#
MockProvider::boxed

source

#
MockProvider::new

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fn MockProvider::new(responses : Array[Array[StreamEvent]]) -> MockProvider

#
ProviderConfig

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pub(all) struct ProviderConfig {
  api_key : String
  model : String
  max_tokens : Int
  system_prompt : String
  base_url : String
  timeout_sec : Int
  max_retries : Int
  retry_delay_ms : Int
}

ProviderConfig holds common configuration for providers.

#
ProviderConfig::new

source
fn ProviderConfig::new(api_key : String, model : String, base_url : String, max_tokens? : Int, system_prompt? : String, timeout_sec? : Int, max_retries? : Int, retry_delay_ms? : Int) -> ProviderConfig

Create a ProviderConfig with defaults.

#
Role

source
pub(all) enum Role {
  User
  Assistant
  System
  Tool
} derive(Eq, 
Debug)

Role represents the sender of a message in a conversation.
impl Show for Role

#
Role::from_string

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fn Role::from_string(s : String) -> Role

#
Role::to_string

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fn Role::to_string(self : Role) -> String

#
SkillEntry

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pub(all) struct SkillEntry {
  name : String
  path : String
  source : String
  content : String
}

SkillEntry is a raw skill definition with content.

#
SkillEntry::new

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fn SkillEntry::new(name : String, path : String, source : String, content : String) -> SkillEntry

#
SkillInfo

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pub(all) struct SkillInfo {
  name : String
  path : String
  source : String
  available : Bool
}

SkillInfo is a projected skill metadata view for listings.

#
SkillInfo::new

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fn SkillInfo::new(name : String, path : String, source : String, available : Bool) -> SkillInfo

#
SkillsLoader

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pub struct SkillsLoader {
  workspace_skills : Map[String, SkillEntry]
  builtin_skills : Map[String, SkillEntry]
}

#
SkillsLoader::build_skills_summary

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fn SkillsLoader::build_skills_summary(self : SkillsLoader, bins? : Map[String, Bool], env? : Map[String, String]) -> String

#
SkillsLoader::get_always_skills

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fn SkillsLoader::get_always_skills(self : SkillsLoader, bins? : Map[String, Bool], env? : Map[String, String]) -> Array[String]

#
SkillsLoader::get_skill_description

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fn SkillsLoader::get_skill_description(self : SkillsLoader, name : String) -> String

#
SkillsLoader::get_skill_metadata

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fn SkillsLoader::get_skill_metadata(self : SkillsLoader, name : String) -> Map[String, String]?

#
SkillsLoader::list_skills

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fn SkillsLoader::list_skills(self : SkillsLoader, filter_unavailable? : Bool, bins? : Map[String, Bool], env? : Map[String, String]) -> Array[SkillInfo]

#
SkillsLoader::load_skill

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fn SkillsLoader::load_skill(self : SkillsLoader, name : String) -> String?

#
SkillsLoader::load_skills_for_context

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fn SkillsLoader::load_skills_for_context(self : SkillsLoader, skill_names : Array[String]) -> String

#
SkillsLoader::new

source

#
SkillsLoader::register_builtin_skill

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fn SkillsLoader::register_builtin_skill(self : SkillsLoader, name : String, content : String, path? : String) -> Unit

#
SkillsLoader::register_workspace_skill

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fn SkillsLoader::register_workspace_skill(self : SkillsLoader, name : String, content : String, path? : String) -> Unit

#
StopCondition

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pub(all) enum StopCondition {
  MaxSteps(Int)
  OnFinish
}

StopCondition controls when the agent loop should stop.

#
StreamEvent

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pub(all) enum StreamEvent {
  TextDelta(String)
  ToolCallStart(id~ : String, name~ : String)
  ToolCallDelta(id~ : String, input_delta~ : String)
  ToolCallEnd(id~ : String, name~ : String, input~ : Json)
  MessageStart
  MessageEnd(finish_reason~ : FinishReason, usage~ : Usage?)
  Error(String)
}

StreamEvent represents events during streaming generation.
impl Show for StreamEvent

#
StreamHandler

source
pub(all) struct StreamHandler {
  on_event : (StreamEvent) -> Unit
}

StreamHandler receives streaming events.

#
ToolCall

source
pub(all) struct ToolCall {
  id : String
  name : String
  input : Json
}

ToolCall represents a tool invocation from the model.

#
Usage

source
pub(all) struct Usage {
  input_tokens : Int
  output_tokens : Int
} derive(
Debug)

Usage tracks token consumption.
impl Show for Usage

#
collect

source
fn collect(provider : &Provider, messages : Array[Message], tools? : Array[
ToolDef]) -> CollectResult

Collect full response including text, tool calls, finish reason, and usage.

#
collect_text

source
fn collect_text(provider : &Provider, messages : Array[Message], tools? : Array[
ToolDef]) -> String

Collect all text from a streaming provider call into a single string.

#
default_anthropic_model

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let default_anthropic_model : String

Default models for each provider.

#
default_ollama_model

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let default_ollama_model : String

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default_openai_model

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let default_openai_model : String

#
default_openrouter_model

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let default_openrouter_model : String

#
json_get_array

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fn json_get_array(j : Json, field : String) -> Array[Json]?

Helper to get a JSON array field.

#
json_get_int

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fn json_get_int(j : Json, field : String) -> Int?

Helper to get an int field from a JSON object.

#
json_get_object

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fn json_get_object(j : Json, field : String) -> Json?

Helper to get a JSON object field.

#
json_get_string

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fn json_get_string(j : Json, field : String) -> String?

Helper to get a string field from a JSON object.

#
parse_skill_frontmatter

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fn parse_skill_frontmatter(content : String) -> (Map[String, String]?, String)

Parse a markdown skill file frontmatter and return (metadata, body).

#
run_agent

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fn run_agent(provider : BoxedProvider, registry : 
ToolRegistry, messages : Array[Message], stop : StopCondition, on_event : (AgentEvent) -> Unit) -> Unit

run_agent executes a tool loop with the given provider and tools.

#
run_agent_cancellable

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fn run_agent_cancellable(provider : BoxedProvider, registry : 
ToolRegistry, messages : Array[Message], stop : StopCondition, should_cancel : () -> Bool, on_event : (AgentEvent) -> Unit) -> Unit

#
run_json_chat_loop

source
fn run_json_chat_loop(messages : Array[Json], tools : Array[Json], max_iterations : Int, chat : (Array[Json], Array[Json]) -> JsonChatResponse, execute_tool : (String, Json) -> String, fallback_content? : String) -> JsonChatLoopResult

#
skill_metadata_description

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fn skill_metadata_description(metadata : Map[String, String]?, fallback : String) -> String

Resolve human-readable skill description from metadata.

#
skill_metadata_is_always

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fn skill_metadata_is_always(metadata : Map[String, String]?, scope_key? : String) -> Bool

always may be specified either at root frontmatter or namespaced metadata.

#
skill_metadata_is_available

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fn skill_metadata_is_available(metadata : Map[String, String]?, bins? : Map[String, Bool], env? : Map[String, String], scope_key? : String) -> Bool

Evaluate whether a skill is runnable in the current environment.

#
skill_metadata_missing_requirements

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fn skill_metadata_missing_requirements(metadata : Map[String, String]?, bins? : Map[String, Bool], env? : Map[String, String], scope_key? : String) -> String

Collect missing requirement text (e.g. CLI: tmux, ENV: OPENAI_API_KEY).

#
strip_skill_frontmatter

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fn strip_skill_frontmatter(content : String) -> String

Remove frontmatter and return trimmed skill markdown body.

#
truncate_messages

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fn truncate_messages(messages : Array[Message], max_tokens : Int) -> Array[Message]

Truncate messages to fit within a token budget. Keeps system messages and the last N messages within budget.

#
validate_params

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fn validate_params(input : Json, schema : Json) -> Array[String]

#
validate_tool_input

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fn validate_tool_input(input : Json, schema : Json) -> Array[String]

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