# Langchain Agents > Expert guidance for building LangChain agents with proper tool binding, memory, and configuration. Use when creating agents, configuring models, or setting up tool integrations in LangConfig. Use when this capability is needed. - Skill: `tomevault-io/langchain-agents-2` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/langchain-agents-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/langchain-agents-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/langchain-agents-2 --- ## 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) ```python # 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 ```json { "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 ```python # 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) ```json { "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: ```json { "enable_rag": true, "rag_config": { "similarity_threshold": 0.7, "max_documents": 5, "rerank": true } } ``` ### Agent Patterns #### 1. Single-Purpose Agent Best for focused tasks: ```json { "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: ```json { "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: ```json { "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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/langconfig) — claim your Tome and manage your conversions.