What is a RunnableConfig, exactly?

RunnableConfig is the standard configuration object LangChain's Runnable interface accepts on every .invoke()/.stream()/.batch() call: it carries tags and metadata for tracing, a list of callback handlers, and execution controls like max_concurrency and recursion_limit. This tool generates the tags/callbacks portion plus a retry and timeout wrapper around it.

Why does the tools list need a description for each tool?

LangChain tools are exposed to the model via their name and description, the model decides which tool to call based on that description, so a vague or missing description directly hurts tool-selection accuracy. The generator requires one for every tool you add.

How is retry different from timeout here?

max_retries controls how many times `.with_retry(stop_after_attempt=n)` will re-run the runnable after a failure (e.g. a transient API error), while the timeout is a hard wall-clock limit passed to `.invoke(..., timeout=n)` that aborts a single attempt if it runs too long. They're independent knobs, not one setting.

Why offer a JSON output option if RunnableConfig is a Python TypedDict?

Teams often need to store or transmit a config's shape outside Python, in a config file, a JS front-end that triggers a LangServe endpoint, or a database record. The JSON option gives you the same tags/callbacks/tools/retry/timeout data as a plain, language-agnostic object.

LangChain RunnableConfig Generator

Overview

Introduction

LangChain chains and agents take a RunnableConfig on nearly every call, but the shape of that dict, plus the retry and timeout wrappers you usually want alongside it, is easy to half-remember and rewrite slightly differently in every project.

This tool generates a consistent RunnableConfig, a bound-tools list, and the surrounding retry/timeout wiring, either as a Python snippet or as plain JSON.

What Is LangChain Config Generator?

A generator for LangChain's RunnableConfig (tags + callbacks), a list of tools with their name and description, and a retry count and timeout applied via `.with_retry()` and an `invoke(..., timeout=...)` call.

In Python mode it outputs a ready-to-paste snippet using `langchain_core.runnables.RunnableConfig` and `StructuredTool.from_function`. In JSON mode it outputs the same data as a plain, portable JSON object.

How LangChain Config Generator Works

You add tags (free-text labels used for tracing/filtering in LangSmith), callback handler names, tool rows (name + description), a max retry count, and a timeout in seconds. The generator validates the numeric fields and requires a description for every tool.

In Python mode, it emits a RunnableConfig dict, a `tools` list built with `StructuredTool.from_function`, and a runnable chain wired with `.with_config()` and `.with_retry()`. In JSON mode, it emits the same fields as a nested JSON object.

When To Use LangChain Config Generator

Use it when wiring a new LangChain chain or agent and you want consistent tags/callbacks for LangSmith tracing plus a sane retry/timeout policy from the start.

It's also handy for documenting a tool-calling agent's bound tools alongside its resilience settings in one artifact you can hand to a teammate or store in a config file.

Often used alongside MCP Server Config Generator, OpenAI Environment Generator and Vector Database Config Generator.

Features

Advantages

Keeps tags, callbacks, tools, retries, and timeout together in one generated artifact instead of scattered across a codebase.
Requires a description for every tool, nudging toward better tool-selection accuracy at generation time rather than after a debugging session.
Supports both a runnable Python snippet and a portable JSON shape for non-Python consumers.

Limitations

Generates a starting snippet, not a full agent definition; you still wire `my_chain` to your actual chain or agent object.
Callback handler names are emitted as-is (e.g. `LangChainTracer()`); the tool doesn't validate that a handler with that name exists in your LangChain version.

Examples

Two tools with LangSmith tracing, Python output

Input

language: python, tags: [prod, support-bot], callbacks: [LangChainTracer], tools: [{name: search, description: Search the web for current information}, {name: lookup_order, description: Look up an order by ID}], maxRetries: 3, timeoutSeconds: 30

Output

config: RunnableConfig = {
    "tags": ["prod", "support-bot"],
    "callbacks": ["LangChainTracer"],
}

tools = [
    StructuredTool.from_function(func=search, name="search", description="Search the web for current information"),
    StructuredTool.from_function(func=lookup_order, name="lookup_order", description="Look up an order by ID"),
]

Tags and callback names are quoted as strings; tool rows become StructuredTool.from_function calls referencing a snake_case function name derived from the tool name.

Best Practices & Notes

Best Practices

Use consistent tags across environments (e.g. `prod`, `staging`) so LangSmith traces can be filtered reliably.
Write tool descriptions the way you'd explain the tool to a new teammate in one sentence, not just its function signature, since the model reads that description too.
Set a timeout comfortably above your slowest expected tool call, but not so high that a hung call blocks a user-facing request indefinitely.

Developer Notes

The Python output targets `langchain_core.runnables.RunnableConfig` (a TypedDict, not a class you instantiate) plus `StructuredTool.from_function`, matching the langchain-core >=0.1 API surface. `with_retry(stop_after_attempt=n)` wraps the runnable in tenacity-based retry logic; the emitted `.invoke(..., timeout=...)` call relies on the runnable's underlying executor honoring the timeout parameter, which most built-in Runnables do.

LangChain Config Generator Use Cases

Scaffolding tags/callbacks/tools/retry/timeout for a new LangChain agent
Documenting an agent's bound tools and resilience settings for a teammate or design doc
Producing a portable JSON representation of a chain's config for a non-Python service

Common Mistakes

Writing a one-word tool description like "search" instead of a full sentence, which gives the model too little signal to choose the right tool.
Setting max_retries high with no timeout, so a slow but not-quite-failing call retries repeatedly without ever bounding total latency.

Tips

Use the JSON output when you need to store the config outside Python, then translate it into a RunnableConfig dict at the call site in your actual chain code.

LangChain Config Generator