Awesome-omni-skill langchain-agents

Instructions

You are an expert LangChain developer helping users build agents in LangConfig. Follow these guidelines based on official LangChain documentation and LangConfig patterns.

LangChain Core Concepts

LangChain is a framework for building LLM-powered applications with these key components:

1. Models - Language models (ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI)
2. Messages - Structured conversation data (HumanMessage, AIMessage, SystemMessage)
3. Tools - Functions agents can call to interact with external systems
4. Memory - Context persistence within and across conversations
5. Retrievers - RAG systems for accessing external knowledge

Agent Configuration in LangConfig

Supported Models (December 2025)

# OpenAI
"gpt-5.1"              # Latest GPT-5 series
"gpt-4o", "gpt-4o-mini" # GPT-4o series

# Anthropic Claude 4.5
"claude-opus-4-5-20250514"    # Most capable
"claude-sonnet-4-5-20250929"  # Balanced
"claude-haiku-4-5-20251015"   # Fast/cheap (default)

# Google Gemini
"gemini-3-pro-preview"  # Gemini 3
"gemini-2.5-flash"      # Gemini 2.5

Agent Configuration Schema

{
  "name": "Research Agent",
  "model": "claude-sonnet-4-5-20250929",
  "temperature": 0.7,
  "max_tokens": 8192,
  "system_prompt": "You are a research assistant...",
  "native_tools": ["web_search", "web_fetch", "filesystem"],
  "enable_memory": true,
  "enable_rag": false,
  "timeout_seconds": 300,
  "max_retries": 3
}

Temperature Guidelines

Use Case	Temperature	Rationale
Code generation	0.0 - 0.3	Deterministic, precise
Analysis/Research	0.3 - 0.5	Balanced accuracy
Creative writing	0.7 - 1.0	More variety
Brainstorming	1.0 - 1.5	Maximum creativity

System Prompt Best Practices

Structure

# Role Definition
You are [specific role] specialized in [domain].

# Core Responsibilities
Your main tasks are:
1. [Primary task]
2. [Secondary task]
3. [Supporting task]

# Constraints
- [Limitation 1]
- [Limitation 2]

# Output Format
When responding, always:
- [Format requirement 1]
- [Format requirement 2]

Example: Code Review Agent

You are an expert code reviewer specializing in Python and TypeScript.

Your responsibilities:
1. Identify bugs, security issues, and performance problems
2. Suggest improvements following best practices
3. Ensure code follows project style guidelines

Constraints:
- Focus only on the code provided
- Don't rewrite entire files unless asked
- Prioritize critical issues over style nits

Output format:
- List issues by severity (Critical, Warning, Info)
- Include line numbers for each issue
- Provide specific fix suggestions

Tool Configuration

Native Tools Available in LangConfig

# File System Tools
"filesystem"           # Read, write, list files
"grep"                 # Search file contents

# Web Tools
"web_search"           # Search the internet
"web_fetch"            # Fetch and parse web pages

# Code Execution
"python"               # Execute Python code
"shell"                # Run shell commands (sandboxed)

# Data Tools
"calculator"           # Mathematical operations
"json_parser"          # Parse and query JSON

Tool Selection Guidelines

Agent Purpose	Recommended Tools
Research	web_search, web_fetch, filesystem
Code Assistant	filesystem, python, shell, grep
Data Analysis	python, calculator, filesystem
Content Writer	web_search, filesystem
DevOps	shell, filesystem, web_fetch

Memory Configuration

Short-Term Memory (Conversation)

• Automatically managed by LangGraph checkpointing
• Persists within a workflow execution
• Configurable message window

Long-Term Memory (Cross-Session)

{
  "enable_memory": true,
  "memory_config": {
    "type": "vector",
    "namespace": "agent_memories",
    "top_k": 5
  }
}

RAG Integration

When

enable_rag

is true, agents can access project documents:

{
  "enable_rag": true,
  "rag_config": {
    "similarity_threshold": 0.7,
    "max_documents": 5,
    "rerank": true
  }
}

Agent Patterns

1. Single-Purpose Agent

Best for focused tasks:

{
  "name": "SQL Generator",
  "model": "claude-haiku-4-5-20251015",
  "temperature": 0.2,
  "system_prompt": "You are a SQL expert. Generate only valid SQL queries.",
  "native_tools": []
}

2. Tool-Using Agent

For tasks requiring external data:

{
  "name": "Research Agent",
  "model": "claude-sonnet-4-5-20250929",
  "temperature": 0.5,
  "system_prompt": "Research topics thoroughly using available tools.",
  "native_tools": ["web_search", "web_fetch", "filesystem"]
}

3. Code Agent

For development tasks:

{
  "name": "Code Assistant",
  "model": "claude-sonnet-4-5-20250929",
  "temperature": 0.3,
  "system_prompt": "Help with coding tasks. Write clean, tested code.",
  "native_tools": ["filesystem", "python", "shell", "grep"]
}

Debugging Agent Issues

Common Problems

1. Agent loops infinitely

   • Add stopping criteria to system prompt
   • Set

     max_retries

     and

     recursion_limit

   • Check if tools are returning useful results

2. Agent doesn't use tools

   • Verify tools are in

     native_tools

     list
   • Add explicit tool instructions to system prompt
   • Check tool permissions

3. Responses are inconsistent

   • Lower temperature for more determinism
   • Be more specific in system prompt
   • Use structured output format

4. Agent is too slow

   • Use faster model (haiku instead of opus)
   • Reduce

     max_tokens

   • Simplify system prompt

Examples

User asks: "Create an agent for researching companies"

Response approach:

1. Choose appropriate model (sonnet for balanced capability)
2. Set moderate temperature (0.5 for factual research)
3. Enable web_search and web_fetch tools
4. Write focused system prompt for company research
5. Enable memory for multi-turn research sessions
6. Set reasonable timeouts and retry limits