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majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

LLM Sdk Guide by majiayu000 — Agent Skill

Name: LLM Sdk Guide
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

File contents

LLM SDK Guide

Two SDKs for LLM integration:

U-llm-sdk: Multi-provider (Claude, Codex, Gemini) with unified LLMResult
claude-only-sdk: Claude-specific advanced features (agents, sessions, orchestration)

Architecture: CLI-based (NOT API)

IMPORTANT: These SDKs wrap CLI tools, NOT direct API calls!

SDK Layer (Python)
    │
    ▼
asyncio.create_subprocess_exec()  ← Spawns CLI process
    │
    ▼
CLI Execution (e.g., `claude -p "prompt" --mcp-config ...`)
    │
    ▼
Separate Session with own context window

This enables:

MCP Isolation: Each spawned process has its own MCP config (--mcp-config)
Context Separation: Child process context doesn't pollute parent session
Session Independence: Each call creates isolated Claude Code session

U-llm-sdk Quick Start

from u_llm_sdk import LLM, LLMConfig
from llm_types import Provider, ModelTier, AutoApproval

config = LLMConfig(
    provider=Provider.CLAUDE,
    tier=ModelTier.HIGH,
    auto_approval=AutoApproval.EDITS_ONLY,
)

async with LLM(config) as llm:
    result = await llm.run("Your prompt")
    print(result.text)

claude-only-sdk Quick Start

from claude_only_sdk import ClaudeAdvanced, SessionTemplate

async with ClaudeAdvanced() as client:
    result = await client.run_with_template(
        "Review src/auth.py",
        SessionTemplate.SECURITY_ANALYST,
    )

MCP Isolation Pattern

Use mcp_config to run isolated sessions with specific MCP servers:

from claude_only_sdk import ClaudeAdvanced, ClaudeAdvancedConfig

config = ClaudeAdvancedConfig(
    mcp_config="./config/serena.mcp.json",  # Only Serena MCP loaded
    timeout=300.0,
)

async with ClaudeAdvanced(config) as client:
    # This runs in separate session with only Serena MCP
    result = await client.run("Use Serena to analyze code")

This pattern is useful for:

Keeping heavy MCP servers out of main session context
Running specialized tools in isolated environments
Reducing token usage in main conversation

Core Components

U-llm-sdk

Component	Purpose
`LLM` / `LLMSync`	Async/Sync clients
`LLMConfig`	Unified configuration
`BaseProvider`	Provider abstraction
`InterventionHook`	RAG integration protocol

claude-only-sdk

Component	Purpose
`ClaudeAdvanced`	Extended Claude client
`AgentDefinition`	Specialized agent config
`SessionManager`	Virtual session injection
`TaskExecutor`	Parallel task execution
`AutonomousOrchestrator`	Prompt-based parallelization

Key Patterns

Provider Selection

async with LLM.auto() as llm:  # Claude > Codex > Gemini
    result = await llm.run("prompt")

Session Continuity

llm = LLM(config).resume(session_id)
async with llm:
    result = await llm.run("Continue...")

Agent Definition (Claude)

planner = AgentDefinition(
    name="planner",
    system_prompt="You are a planning specialist...",
    tier=ModelTier.HIGH,
    allowed_tools=["Read", "Grep", "Glob"],
)

Output Handling

result: LLMResult
result.text              # Response text (may be empty for FILE_EDIT!)
result.files_modified    # List[FileChange]
result.commands_run      # List[CommandRun]
result.session_id        # For continuation
result.result_type       # TEXT/CODE/FILE_EDIT/COMMAND/MIXED

For detailed patterns: [references/u-llm-sdk.md] For Claude features: [references/claude-only-sdk.md] For types reference: [references/llm-types.md]

---name: llm-sdk-guidedescription: U-llm-sdk and claude-only-sdk patterns. Use when working on projects with LLM service, designing LLM integrations, or implementing AI-powered features.--- # LLM SDK Guide Two SDKs for LLM integration: - **U-llm-sdk**: Multi-provider (Claude, Codex, Gemini) with unified `LLMResult` - **claude-only-sdk**: Claude-specific advanced features (agents, sessions, orchestration) ## Architecture: CLI-based (NOT API) **IMPORTANT**: These SDKs wrap CLI tools, NOT direct API calls! ``` SDK Layer (Python) │ ▼ asyncio.create_subprocess_exec() ← Spawns CLI process │ ▼ CLI Execution (e.g., `claude -p "prompt" --mcp-config ...`) │ ▼ Separate Session with own context window ``` This enables: - **MCP Isolation**: Each spawned process has its own MCP config (`--mcp-config`) - **Context Separation**: Child process context doesn't pollute parent session - **Session Independence**: Each call creates isolated Claude Code session ## U-llm-sdk Quick Start ```python from u_llm_sdk import LLM, LLMConfig from llm_types import Provider, ModelTier, AutoApproval config = LLMConfig( provider=Provider.CLAUDE, tier=ModelTier.HIGH, auto_approval=AutoApproval.EDITS_ONLY, ) async with LLM(config) as llm: result = await llm.run("Your prompt") print(result.text) ``` ## claude-only-sdk Quick Start ```python from claude_only_sdk import ClaudeAdvanced, SessionTemplate async with ClaudeAdvanced() as client: result = await client.run_with_template( "Review src/auth.py", SessionTemplate.SECURITY_ANALYST, ) ``` ## MCP Isolation Pattern Use `mcp_config` to run isolated sessions with specific MCP servers: ```python from claude_only_sdk import ClaudeAdvanced, ClaudeAdvancedConfig config = ClaudeAdvancedConfig( mcp_config="./config/serena.mcp.json", # Only Serena MCP loaded timeout=300.0, ) async with ClaudeAdvanced(config) as client: # This runs in separate session with only Serena MCP result = await client.run("Use Serena to analyze code") ``` This pattern is useful for: - Keeping heavy MCP servers out of main session context - Running specialized tools in isolated environments - Reducing token usage in main conversation ## Core Components ### U-llm-sdk | Component | Purpose | |-----------|---------| | `LLM` / `LLMSync` | Async/Sync clients | | `LLMConfig` | Unified configuration | | `BaseProvider` | Provider abstraction | | `InterventionHook` | RAG integration protocol | ### claude-only-sdk | Component | Purpose | |-----------|---------| | `ClaudeAdvanced` | Extended Claude client | | `AgentDefinition` | Specialized agent config | | `SessionManager` | Virtual session injection | | `TaskExecutor` | Parallel task execution | | `AutonomousOrchestrator` | Prompt-based parallelization | ## Key Patterns ### Provider Selection ```python async with LLM.auto() as llm: # Claude > Codex > Gemini result = await llm.run("prompt") ``` ### Session Continuity ```python llm = LLM(config).resume(session_id) async with llm: result = await llm.run("Continue...") ``` ### Agent Definition (Claude) ```python planner = AgentDefinition( name="planner", system_prompt="You are a planning specialist...", tier=ModelTier.HIGH, allowed_tools=["Read", "Grep", "Glob"], ) ``` ## Output Handling ```python result: LLMResult result.text # Response text (may be empty for FILE_EDIT!) result.files_modified # List[FileChange] result.commands_run # List[CommandRun] result.session_id # For continuation result.result_type # TEXT/CODE/FILE_EDIT/COMMAND/MIXED ``` For detailed patterns: [references/u-llm-sdk.md] For Claude features: [references/claude-only-sdk.md] For types reference: [references/llm-types.md]

Frequently asked questions

LLM Sdk Guide