ConfigGenerator

LangChain Config Generator

Generate LangChain config templates for Python, JSON, RunnableConfig, callbacks, tools, agents, prompts, retries, MCP, and LangGraph workflows.

Output:A ready-to-use configuration file for LangChain Config with best practices applied.

The specific model version (e.g., gpt-4o, claude-3-5-sonnet-20241022, llama3.1).

0.0 = analytical. 1.0 = creative. (Max 2.0)

Maximum tokens to generate in the response.

- Stream responses token-by-token.

The model used to generate document embeddings.

How large each document chunk should be before embedding.

Number of tokens/chars to overlap between chunks to maintain context.

- Bind tools/functions to the LLM (required for Agents).
- Keep track of conversation history.
- Enable LangSmith observability and tracing.

What is this tool?

Short answer: Use a LangChain Config Generator when you need config templates for RunnableConfig, callbacks, tools, prompts, agents, retries, and LangGraph workflows.

This tool helps you scaffold production-grade RAG applications, autonomous agents, and tool-calling interfaces. It handles the boilerplate of connecting LLMs, Vector Stores, and standardizing configuration JSON or Python dictionary inputs.

Note: LangChain imports and config APIs can differ by package version. Always verify against the official LangChain documentation for your installed version.

How to Use This Tool

  1. Select Language: Choose TypeScript or Python for your LangChain environment.
  2. Choose Architecture: Pick RAG Application, AI Agent, Tool Calling App, or Simple Chat.
  3. Configure Options: Set thread_id support, callback tracing, and model parameters.
  4. Generate files: The tool outputs `langchain.config.json`, `langchain_config.py`, `runnable_config.py`, or similar boilerplate files.

Best Practices

  • Use `RunnableConfig` to pass runtime parameters (like user IDs or session tracking metadata) cleanly through complex chains.
  • Enable LangSmith tracing by setting `LANGCHAIN_TRACING_V2=true` in your environment during development.
  • Use smaller chunk sizes (e.g., 500-1000 tokens) for better RAG precision.
  • Implement retries with exponential backoff on your LLM calls to handle rate limits.

Common Mistakes

  • Hardcoding configurations directly into the chain definition instead of using `configurable` fields.
  • Retrieving too many documents (high Top K), exceeding context windows and ballooning API costs.
  • Failing to sanitize user input before passing it into a prompt template.
  • Leaking API keys in frontend code instead of running LangChain strictly on the backend.

Security Notes

  • Never execute code directly from LLM output without a secure sandbox (e.g. Docker, WebAssembly).
  • Apply the Principle of Least Privilege to any Tools you give your AI Agent (e.g., grant read-only DB access).
  • Monitor LangSmith or your observability platform for Prompt Injection attempts from malicious users.

How We Keep Your Configs Safe & Valid

Built-in Error Checking

Every file is checked against official rules. We catch missing fields and bad syntax. YAML indentation errors are flagged right away. Kubernetes, Terraform, and Docker specs are all covered. API versions and labels are verified too. You get valid output every time you generate.

100% Private & Local

All tools run in your browser only. Your API keys never leave your machine. We do not use any tracking scripts. No data is sent to any server. Passwords and secrets stay on your device. Crypto operations use the Web Crypto API. Your privacy is fully protected at all times.

Secure Settings by Default

Configs use safe defaults out of the box. Containers run as non-root users. Root filesystems are set to read-only. Dangerous Linux capabilities are dropped. Network policies limit pod-to-pod traffic. TLS 1.3 is enabled for web servers. Security headers are added where needed.

Ready for CI/CD & Git

Output files are ready for your Git repo. Use them with ArgoCD, Flux, or GitHub Actions. Files use clear formatting and comments. Code review is easy for your team. Indentation and key order are consistent. Test in staging before going to production. Every file is clean and well-structured.

Infrastructure as Code

Store configs in Git alongside your code. Terraform modules include typed variables. Backend configs support remote state locking. Outputs work across multiple modules. Ansible playbooks use clear task steps. Chef and Puppet configs are also supported. Every file works with version control tools.

Monitoring & Tracing

Set up Prometheus with auto-discovery rules. Create Grafana dashboards with template variables. Add alerting rules with severity labels. Use OpenTelemetry for trace collection. Forward logs to Loki or Elasticsearch. Connect to Jaeger or Tempo for tracing. Monitor metrics, logs, and traces together.

Container & Docker Safety

Dockerfiles use multi-stage builds for small images. Base images are pinned to exact versions. Dev files are excluded from final images. Health checks are added for orchestrator use. Containers switch to non-root users. Docker Compose uses named volumes and networks. Resource limits are set in deploy configs.

Multiple Output Formats

Export as YAML, JSON, HCL, or TOML. Kubernetes uses YAML with proper separators. Terraform uses HCL with correct escaping. JSON output has consistent indentation. Copy to clipboard with one click. Preview output with syntax highlighting. Line numbers help you review quickly.

Related Tools

OpenAI Environment GeneratorOllama Docker GeneratorMCP Server Config GeneratorVector Database Config Generator

Frequently Asked Questions

What is a LangChain Config Generator?

A LangChain Config Generator helps developers quickly create configuration files and boilerplate code for LangChain applications, such as RunnableConfig settings, agent definitions, tool setups, and prompt configurations.

What is LangChain RunnableConfig?

RunnableConfig is a configuration object passed to LangChain Runnables (like models, chains, or agents) that controls runtime behavior, such as tags, metadata, callbacks, and thread IDs for conversation memory.

How do I configure LangChain callbacks?

You can configure callbacks by passing a `callbacks` array in your `RunnableConfig`. This is useful for streaming tokens to a UI, logging to LangSmith, or tracking custom metrics during execution.

What is thread_id in LangChain config?

`thread_id` is typically passed inside the `configurable` field of a RunnableConfig. It tells LangChain's memory systems (like LangGraph's checkpointer) which conversation history to load and update.

How do configurable fields work in LangChain?

Configurable fields allow you to define parameters (like which LLM model to use, or search depth) that can be overridden at runtime without changing the underlying chain's code, using the `.with_config()` method.

Can I generate LangChain config for Python?

Yes. This generator creates LangChain configuration snippets tailored for both Python and TypeScript, depending on your selected stack.

Can LangChain work with MCP servers?

Yes, LangChain can integrate with Model Context Protocol (MCP) servers, allowing your agents to access external data sources and tools securely through standard MCP clients.

Can LangChain work with Ollama?

Absolutely. LangChain has built-in support for Ollama (e.g., `ChatOllama` or `OllamaEmbeddings`), making it very easy to build RAG apps using local models.