> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Build a SQL assistant with on-demand skills
This tutorial shows how to use **progressive disclosure** - a context management technique where the agent loads information on-demand rather than upfront - to implement **skills** (specialized prompt-based instructions). The agent loads skills via tool calls, rather than dynamically changing the system prompt, discovering and loading only the skills it needs for each task.
**Use case:** Imagine building an agent to help write SQL queries across different business verticals in a large enterprise. Your organization might have separate datastores for each vertical, or a single monolithic database with thousands of tables. Either way, loading all schemas upfront would overwhelm the context window. Progressive disclosure solves this by loading only the relevant schema when needed. This architecture also enables different product owners and stakeholders to independently contribute and maintain skills for their specific business verticals.
**What you'll build:** A SQL query assistant with two skills (sales analytics and inventory management). The agent sees lightweight skill descriptions in its system prompt, then loads full database schemas and business logic through tool calls only when relevant to the user's query.
For a complete example of a SQL agent with query execution, error correction, and validation, see our [SQL Agent tutorial](/oss/javascript/langchain/sql-agent). This tutorial focuses on the progressive disclosure pattern which can be applied to any domain.
Progressive disclosure was popularized by Anthropic as a technique for building scalable agent skills systems. This approach uses a three-level architecture (metadata β core content β detailed resources) where agents load information only as needed. For more on this technique, see [Equipping agents for the real world with Agent Skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills).
## How it works
Here's the flow when a user asks for a SQL query:
```mermaid theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#E5F4FF','primaryTextColor':'#030710','primaryBorderColor':'#006DDD','lineColor':'#40668D','secondaryColor':'#F6FFDB','tertiaryColor':'#FDF3FF','tertiaryBorderColor':'#7E65AE','tertiaryTextColor':'#504B5F'}}}%%
flowchart TD
Start([π¬ User: Write SQL query
for high-value customers]) --> SystemPrompt[π Agent sees skill descriptions:
β’ sales_analytics
β’ inventory_management]
SystemPrompt --> Decide{π€ Need sales schema}
Decide --> LoadSkill[π§ load_skill
'sales_analytics']
LoadSkill --> Schema[π Schema loaded:
customers, orders tables
+ business logic]
Schema --> WriteQuery[βοΈ Agent writes SQL query
using schema knowledge]
WriteQuery --> Response([β
Returns valid SQL
following business rules])
%% Styling for light and dark modes
classDef startEnd fill:#F6FFDB,stroke:#6E8900,stroke-width:2px,color:#2E3900
classDef process fill:#FDF3FF,stroke:#7E65AE,stroke-width:2px,color:#504B5F
classDef decision fill:#E5F4FF,stroke:#006DDD,stroke-width:2px,color:#030710
classDef enrichment fill:#EBD0F0,stroke:#885270,stroke-width:2px,color:#441E33
class Start,Response startEnd
class SystemPrompt,LoadSkill,WriteQuery process
class Decide decision
class Schema enrichment
```
**Why progressive disclosure:**
* **Reduces context usage** - load only the 2-3 skills needed for a task, not all available skills
* **Enables team autonomy** - different teams can develop specialized skills independently (similar to other multi-agent architectures)
* **Scales efficiently** - add dozens or hundreds of skills without overwhelming context
* **Simplifies conversation history** - single agent with one conversation thread
**What are skills:** Skills, as popularized by Claude Code, are primarily prompt-based: self-contained units of specialized instructions for specific business tasks. In Claude Code, skills are exposed as directories with files on the file system, discovered through file operations. Skills guide behavior through prompts and can provide information about tool usage or include sample code for a coding agent to execute.
Skills with progressive disclosure can be viewed as a form of [RAG (Retrieval-Augmented Generation)](/oss/javascript/langchain/rag), where each skill is a retrieval unitβthough not necessarily backed by embeddings or keyword search, but by tools for browsing content (like file operations or, in this tutorial, direct lookup).
**Trade-offs:**
* **Latency**: Loading skills on-demand requires additional tool calls, which adds latency to the first request that needs each skill
* **Workflow control**: Basic implementations rely on prompting to guide skill usage - you cannot enforce hard constraints like "always try skill A before skill B" without custom logic
**Implementing your own skills system**
When building your own skills implementation (as we do in this tutorial), the core concept is progressive disclosure - loading information on-demand. Beyond that, you have full flexibility in implementation:
* **Storage**: databases, S3, in-memory data structures, or any backend
* **Discovery**: direct lookup (this tutorial), RAG for large skill collections, file system scanning, or API calls
* **Loading logic**: customize latency characteristics and add logic to search through skill content or rank relevance
* **Side effects**: define what happens when a skill loads, such as exposing tools associated with that skill (covered in section 8)
This flexibility lets you optimize for your specific requirements around performance, storage, and workflow control.
## Setup
### Installation
This tutorial requires the `langchain` package:
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install langchain
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add langchain
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add langchain
```
For more details, see our [Installation guide](/oss/javascript/langchain/install).
### LangSmith
Set up [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langchain-multi-agent-skills-sql-assistant) to inspect what is happening inside your agent. Then set the following environment variables:
```bash bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."
```
```typescript typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_API_KEY = "...";
```
### Select an LLM
Select a chat model from LangChain's suite of integrations:
π Read the [OpenAI chat model integration docs](/oss/javascript/integrations/chat/openai/)
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/openai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm install @langchain/openai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/openai
```
```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
bun add @langchain/openai
```
```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { initChatModel } from "langchain";
process.env.OPENAI_API_KEY = "your-api-key";
const model = await initChatModel("gpt-5.4");
```
```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
model: "gpt-5.4",
apiKey: "your-api-key"
});
```
π Read the [Anthropic chat model integration docs](/oss/javascript/integrations/chat/anthropic/)
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/anthropic
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm install @langchain/anthropic
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/anthropic
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/anthropic
```
```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { initChatModel } from "langchain";
process.env.ANTHROPIC_API_KEY = "your-api-key";
const model = await initChatModel("claude-sonnet-4-6");
```
```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({
model: "claude-sonnet-4-6",
apiKey: "your-api-key"
});
```
π Read the [Azure chat model integration docs](/oss/javascript/integrations/chat/azure/)
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/azure
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm install @langchain/azure
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/azure
```
```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
bun add @langchain/azure
```
```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { initChatModel } from "langchain";
process.env.AZURE_OPENAI_API_KEY = "your-api-key";
process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint";
process.env.OPENAI_API_VERSION = "your-api-version";
const model = await initChatModel("azure_openai:gpt-5.4");
```
```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { AzureChatOpenAI } from "@langchain/openai";
const model = new AzureChatOpenAI({
model: "gpt-5.4",
azureOpenAIApiKey: "your-api-key",
azureOpenAIApiEndpoint: "your-endpoint",
azureOpenAIApiVersion: "your-api-version"
});
```
π Read the [Google GenAI chat model integration docs](/oss/javascript/integrations/chat/google_generative_ai/)
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/google-genai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm install @langchain/google-genai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/google-genai
```
```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
bun add @langchain/google-genai
```
```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { initChatModel } from "langchain";
process.env.GOOGLE_API_KEY = "your-api-key";
const model = await initChatModel("google-genai:gemini-2.5-flash-lite");
```
```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
const model = new ChatGoogleGenerativeAI({
model: "gemini-2.5-flash-lite",
apiKey: "your-api-key"
});
```
π Read the [AWS Bedrock chat model integration docs](/oss/javascript/integrations/chat/bedrock_converse/)
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/aws
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm install @langchain/aws
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/aws
```
```bash bun theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
bun add @langchain/aws
```
```typescript initChatModel theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { initChatModel } from "langchain";
// Follow the steps here to configure your credentials:
// https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
const model = await initChatModel("bedrock:gpt-5.4");
```
```typescript Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatBedrockConverse } from "@langchain/aws";
// Follow the steps here to configure your credentials:
// https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
const model = new ChatBedrockConverse({
model: "gpt-5.4",
region: "us-east-2"
});
```
## 1. Define skills
First, define the structure for skills. Each skill has a name, a brief description (shown in the system prompt), and full content (loaded on-demand):
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { z } from "zod";
// A skill that can be progressively disclosed to the agent
const SkillSchema = z.object({ // [!code highlight]
name: z.string(), // Unique identifier for the skill
description: z.string(), // 1-2 sentence description to show in system prompt
content: z.string(), // Full skill content with detailed instructions
});
type Skill = z.infer;
```
Now define example skills for a SQL query assistant. The skills are designed to be **lightweight in description** (shown to the agent upfront) but **detailed in content** (loaded only when needed):
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { context } from "langchain";
const SKILLS: Skill[] = [
{
name: "sales_analytics",
description:
"Database schema and business logic for sales data analysis including customers, orders, and revenue.",
content: context`
# Sales Analytics Schema
## Tables
### customers
- customer_id (PRIMARY KEY)
- name
- email
- signup_date
- status (active/inactive)
- customer_tier (bronze/silver/gold/platinum)
### orders
- order_id (PRIMARY KEY)
- customer_id (FOREIGN KEY -> customers)
- order_date
- status (pending/completed/cancelled/refunded)
- total_amount
- sales_region (north/south/east/west)
### order_items
- item_id (PRIMARY KEY)
- order_id (FOREIGN KEY -> orders)
- product_id
- quantity
- unit_price
- discount_percent
## Business Logic
**Active customers**:
status = 'active' AND signup_date <= CURRENT_DATE - INTERVAL '90 days'
**Revenue calculation**:
Only count orders with status = 'completed'.
Use total_amount from orders table, which already accounts for discounts.
**Customer lifetime value (CLV)**:
Sum of all completed order amounts for a customer.
**High-value orders**:
Orders with total_amount > 1000
## Example Query
-- Get top 10 customers by revenue in the last quarter
SELECT
c.customer_id,
c.name,
c.customer_tier,
SUM(o.total_amount) as total_revenue
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.status = 'completed'
AND o.order_date >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY c.customer_id, c.name, c.customer_tier
ORDER BY total_revenue DESC
LIMIT 10;`,
},
{
name: "inventory_management",
description:
"Database schema and business logic for inventory tracking including products, warehouses, and stock levels.",
content: context`
# Inventory Management Schema
## Tables
### products
- product_id (PRIMARY KEY)
- product_name
- sku
- category
- unit_cost
- reorder_point (minimum stock level before reordering)
- discontinued (boolean)
### warehouses
- warehouse_id (PRIMARY KEY)
- warehouse_name
- location
- capacity
### inventory
- inventory_id (PRIMARY KEY)
- product_id (FOREIGN KEY -> products)
- warehouse_id (FOREIGN KEY -> warehouses)
- quantity_on_hand
- last_updated
### stock_movements
- movement_id (PRIMARY KEY)
- product_id (FOREIGN KEY -> products)
- warehouse_id (FOREIGN KEY -> warehouses)
- movement_type (inbound/outbound/transfer/adjustment)
- quantity (positive for inbound, negative for outbound)
- movement_date
- reference_number
## Business Logic
**Available stock**:
quantity_on_hand from inventory table where quantity_on_hand > 0
**Products needing reorder**:
Products where total quantity_on_hand across all warehouses is less
than or equal to the product's reorder_point
**Active products only**:
Exclude products where discontinued = true unless specifically analyzing discontinued items
**Stock valuation**:
quantity_on_hand * unit_cost for each product
## Example Query
-- Find products below reorder point across all warehouses
SELECT
p.product_id,
p.product_name,
p.reorder_point,
SUM(i.quantity_on_hand) as total_stock,
p.unit_cost,
(p.reorder_point - SUM(i.quantity_on_hand)) as units_to_reorder
FROM products p
JOIN inventory i ON p.product_id = i.product_id
WHERE p.discontinued = false
GROUP BY p.product_id, p.product_name, p.reorder_point, p.unit_cost
HAVING SUM(i.quantity_on_hand) <= p.reorder_point
ORDER BY units_to_reorder DESC;`,
},
];
```
## 2. Create skill loading tool
Create a tool to load full skill content on-demand:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool } from "langchain";
import { z } from "zod";
const loadSkill = tool( // [!code highlight]
async ({ skillName }) => {
// Find and return the requested skill
const skill = SKILLS.find((s) => s.name === skillName);
if (skill) {
return `Loaded skill: ${skillName}\n\n${skill.content}`; // [!code highlight]
}
// Skill not found
const available = SKILLS.map((s) => s.name).join(", ");
return `Skill '${skillName}' not found. Available skills: ${available}`;
},
{
name: "load_skill",
description: `Load the full content of a skill into the agent's context.
Use this when you need detailed information about how to handle a specific
type of request. This will provide you with comprehensive instructions,
policies, and guidelines for the skill area.`,
schema: z.object({
skillName: z.string().describe("The name of the skill to load"),
}),
}
);
```
The `load_skill` tool returns the full skill content as a string, which becomes part of the conversation as a ToolMessage. For more details on creating and using tools, see the [Tools guide](/oss/javascript/langchain/tools).
## 3. Build skill middleware
Create custom middleware that injects skill descriptions into the system prompt. This middleware makes skills discoverable without loading their full content upfront.
This guide demonstrates creating custom middleware. For a comprehensive guide on middleware concepts and patterns, see the [custom middleware documentation](/oss/javascript/langchain/middleware/custom).
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createMiddleware } from "langchain";
// Build skills prompt from the SKILLS list
const skillsPrompt = SKILLS.map(
(skill) => `- **${skill.name}**: ${skill.description}`
).join("\n");
const skillMiddleware = createMiddleware({ // [!code highlight]
name: "skillMiddleware",
tools: [loadSkill], // [!code highlight]
wrapModelCall: async (request, handler) => {
// Build the skills addendum
const skillsAddendum = // [!code highlight]
`\n\n## Available Skills\n\n${skillsPrompt}\n\n` + // [!code highlight]
"Use the load_skill tool when you need detailed information " + // [!code highlight]
"about handling a specific type of request."; // [!code highlight]
// Append to system prompt
const newSystemPrompt = request.systemPrompt + skillsAddendum;
return handler({
...request,
systemPrompt: newSystemPrompt,
});
},
});
```
The middleware appends skill descriptions to the system prompt, making the agent aware of available skills without loading their full content. The `load_skill` tool is registered as a class variable, making it available to the agent.
**Production consideration**: This tutorial loads the skill list in `__init__` for simplicity. In a production system, you may want to load skills in the `before_agent` hook instead, allowing them to be refreshed periodically to reflect up-to-date changes (e.g., when new skills are added or existing ones are modified). See the [before\_agent hook documentation](/oss/javascript/langchain/middleware/custom#node-style-hooks) for details.
## 4. Create the agent with skill support
Now create the agent with the skill middleware and a checkpointer for state persistence:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
// Create the agent with skill support
const agent = createAgent({
model,
systemPrompt:
"You are a SQL query assistant that helps users " +
"write queries against business databases.",
middleware: [skillMiddleware], // [!code highlight]
checkpointer: new MemorySaver(),
});
```
The agent now has access to skill descriptions in its system prompt and can call `load_skill` to retrieve full skill content when needed. The checkpointer maintains conversation history across turns.
## 5. Test progressive disclosure
Test the agent with a question that requires skill-specific knowledge:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { v4 as uuidv4 } from "uuid";
// Configuration for this conversation thread
const threadId = uuidv4();
const config = { configurable: { thread_id: threadId } };
// Ask for a SQL query
const result = await agent.invoke( // [!code highlight]
{
messages: [
{
role: "user",
content:
"Write a SQL query to find all customers " +
"who made orders over $1000 in the last month",
},
],
},
config
);
// Print the conversation
for (const message of result.messages) {
console.log(`${message._getType()}: ${message.content}`);
}
```
Expected output:
```
================================ Human Message =================================
Write a SQL query to find all customers who made orders over $1000 in the last month
================================== Ai Message ==================================
Tool Calls:
load_skill (call_abc123)
Call ID: call_abc123
Args:
skill_name: sales_analytics
================================= Tool Message =================================
Name: load_skill
Loaded skill: sales_analytics
# Sales Analytics Schema
## Tables
### customers
- customer_id (PRIMARY KEY)
- name
- email
- signup_date
- status (active/inactive)
- customer_tier (bronze/silver/gold/platinum)
### orders
- order_id (PRIMARY KEY)
- customer_id (FOREIGN KEY -> customers)
- order_date
- status (pending/completed/cancelled/refunded)
- total_amount
- sales_region (north/south/east/west)
[... rest of schema ...]
## Business Logic
**High-value orders**: Orders with `total_amount > 1000`
**Revenue calculation**: Only count orders with `status = 'completed'`
================================== Ai Message ==================================
Here's a SQL query to find all customers who made orders over $1000 in the last month:
\`\`\`sql
SELECT DISTINCT
c.customer_id,
c.name,
c.email,
c.customer_tier
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.total_amount > 1000
AND o.status = 'completed'
AND o.order_date >= CURRENT_DATE - INTERVAL '1 month'
ORDER BY c.customer_id;
\`\`\`
This query:
- Joins customers with their orders
- Filters for high-value orders (>$1000) using the total_amount field
- Only includes completed orders (as per the business logic)
- Restricts to orders from the last month
- Returns distinct customers to avoid duplicates if they made multiple qualifying orders
```
The agent saw the lightweight skill description in its system prompt, recognized the question required sales database knowledge, called `load_skill("sales_analytics")` to get the full schema and business logic, and then used that information to write a correct query following the database conventions.
## 6. Advanced: Add constraints with custom state
You can add constraints to enforce that certain tools are only available after specific skills have been loaded. This requires tracking which skills have been loaded in custom agent state.
### Define custom state
First, extend the agent state to track loaded skills:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { StateSchema } from "@langchain/langgraph";
import { z } from "zod";
const CustomState = new StateSchema({
skillsLoaded: z.array(z.string()).optional(), // Track which skills have been loaded // [!code highlight]
});
```
### Update load\_skill to modify state
Modify the `load_skill` tool to update state when a skill is loaded:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { tool, ToolMessage, type ToolRuntime } from "langchain";
import { Command } from "@langchain/langgraph"; // [!code highlight]
import { z } from "zod";
const loadSkill = tool( // [!code highlight]
async ({ skillName }, runtime: ToolRuntime) => {
// Find and return the requested skill
const skill = SKILLS.find((s) => s.name === skillName);
if (skill) {
const skillContent = `Loaded skill: ${skillName}\n\n${skill.content}`;
// Update state to track loaded skill
return new Command({ // [!code highlight]
update: { // [!code highlight]
messages: [ // [!code highlight]
new ToolMessage({ // [!code highlight]
content: skillContent, // [!code highlight]
tool_call_id: runtime.toolCallId, // [!code highlight]
}), // [!code highlight]
], // [!code highlight]
skillsLoaded: [skillName], // [!code highlight]
}, // [!code highlight]
}); // [!code highlight]
}
// Skill not found
const available = SKILLS.map((s) => s.name).join(", ");
return new Command({
update: {
messages: [
new ToolMessage({
content: `Skill '${skillName}' not found. Available skills: ${available}`,
tool_call_id: runtime.toolCallId,
}),
],
},
});
},
{
name: "load_skill",
description: `Load the full content of a skill into the agent's context.`,
schema: z.object({
skillName: z.string().describe("The name of the skill to load"),
}),
}
);
```
### Create constrained tool
Create a tool that's only usable after a specific skill has been loaded:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const writeSqlQuery = tool( // [!code highlight]
async ({ query, vertical }, runtime: ToolRuntime) => {
// Check if the required skill has been loaded
const skillsLoaded = runtime.state.skillsLoaded ?? []; // [!code highlight]
if (!skillsLoaded.includes(vertical)) { // [!code highlight]
return ( // [!code highlight]
`Error: You must load the '${vertical}' skill first ` + // [!code highlight]
`to understand the database schema before writing queries. ` + // [!code highlight]
`Use load_skill('${vertical}') to load the schema.` // [!code highlight]
); // [!code highlight]
}
// Validate and format the query
return (
`SQL Query for ${vertical}:\n\n` +
`\`\`\`sql\n${query}\n\`\`\`\n\n` +
`β Query validated against ${vertical} schema\n` +
`Ready to execute against the database.`
);
},
{
name: "write_sql_query",
description: `Write and validate a SQL query for a specific business vertical.
This tool helps format and validate SQL queries. You must load the
appropriate skill first to understand the database schema.`,
schema: z.object({
query: z.string().describe("The SQL query to write"),
vertical: z.string().describe("The business vertical (sales_analytics or inventory_management)"),
}),
}
);
```
### Update middleware and agent
Update the middleware to use the custom state schema:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const skillMiddleware = createMiddleware({ // [!code highlight]
name: "skillMiddleware",
stateSchema: CustomState, // [!code highlight]
tools: [loadSkill, writeSqlQuery], // [!code highlight]
// ... rest of the middleware implementation stays the same
});
```
Create the agent with the middleware that registers the constrained tool:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const agent = createAgent({
model,
systemPrompt:
"You are a SQL query assistant that helps users " +
"write queries against business databases.",
middleware: [skillMiddleware], // [!code highlight]
checkpointer: new MemorySaver(),
});
```
Now if the agent tries to use `write_sql_query` before loading the required skill, it will receive an error message prompting it to load the appropriate skill (e.g., `sales_analytics` or `inventory_management`) first. This ensures the agent has the necessary schema knowledge before attempting to validate queries.
## Complete example
Here's a complete, runnable implementation combining all the pieces from this tutorial:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import {
tool,
createAgent,
createMiddleware,
ToolMessage,
context,
type ToolRuntime,
} from "langchain";
import { MemorySaver, Command } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";
import { v4 as uuidv4 } from "uuid";
import { z } from "zod";
// A skill that can be progressively disclosed to the agent
const SkillSchema = z.object({
name: z.string(), // Unique identifier for the skill
description: z.string(), // 1-2 sentence description to show in system prompt
content: z.string(), // Full skill content with detailed instructions
});
type Skill = z.infer;
const SKILLS: Skill[] = [
{
name: "sales_analytics",
description:
"Database schema and business logic for sales data analysis including customers, orders, and revenue.",
content: context`
# Sales Analytics Schema
## Tables
### customers
- customer_id (PRIMARY KEY)
- name
- email
- signup_date
- status (active/inactive)
- customer_tier (bronze/silver/gold/platinum)
### orders
- order_id (PRIMARY KEY)
- customer_id (FOREIGN KEY -> customers)
- order_date
- status (pending/completed/cancelled/refunded)
- total_amount
- sales_region (north/south/east/west)
### order_items
- item_id (PRIMARY KEY)
- order_id (FOREIGN KEY -> orders)
- product_id
- quantity
- unit_price
- discount_percent
## Business Logic
**Active customers**: status = 'active' AND signup_date <= CURRENT_DATE - INTERVAL '90 days'
**Revenue calculation**:
Only count orders with status = 'completed'. Use total_amount from orders table,
which already accounts for discounts.
**Customer lifetime value (CLV)**:
Sum of all completed order amounts for a customer.
**High-value orders**:
Orders with total_amount > 1000
## Example Query
-- Get top 10 customers by revenue in the last quarter
SELECT
c.customer_id,
c.name,
c.customer_tier,
SUM(o.total_amount) as total_revenue
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.status = 'completed'
AND o.order_date >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY c.customer_id, c.name, c.customer_tier
ORDER BY total_revenue DESC
LIMIT 10;`,
},
{
name: "inventory_management",
description:
"Database schema and business logic for inventory tracking including products, warehouses, and stock levels.",
content: context`
# Inventory Management Schema
## Tables
### products
- product_id (PRIMARY KEY)
- product_name
- sku
- category
- unit_cost
- reorder_point (minimum stock level before reordering)
- discontinued (boolean)
### warehouses
- warehouse_id (PRIMARY KEY)
- warehouse_name
- location
- capacity
### inventory
- inventory_id (PRIMARY KEY)
- product_id (FOREIGN KEY -> products)
- warehouse_id (FOREIGN KEY -> warehouses)
- quantity_on_hand
- last_updated
### stock_movements
- movement_id (PRIMARY KEY)
- product_id (FOREIGN KEY -> products)
- warehouse_id (FOREIGN KEY -> warehouses)
- movement_type (inbound/outbound/transfer/adjustment)
- quantity (positive for inbound, negative for outbound)
- movement_date
- reference_number
## Business Logic
**Available stock**:
quantity_on_hand from inventory table where quantity_on_hand > 0
**Products needing reorder**:
Products where total quantity_on_hand across all warehouses is
less than or equal to the product's reorder_point
**Active products only**:
Exclude products where discontinued = true unless specifically
analyzing discontinued items
**Stock valuation**:
quantity_on_hand * unit_cost for each product
## Example Query
-- Find products below reorder point across all warehouses
SELECT
p.product_id,
p.product_name,
p.reorder_point,
SUM(i.quantity_on_hand) as total_stock,
p.unit_cost,
(p.reorder_point - SUM(i.quantity_on_hand)) as units_to_reorder
FROM products p
JOIN inventory i ON p.product_id = i.product_id
WHERE p.discontinued = false
GROUP BY p.product_id, p.product_name, p.reorder_point, p.unit_cost
HAVING SUM(i.quantity_on_hand) <= p.reorder_point
ORDER BY units_to_reorder DESC;`,
},
];
// const loadSkill = tool(
// async ({ skillName }) => {
// // Find and return the requested skill
// const skill = SKILLS.find((s) => s.name === skillName);
// if (skill) {
// return `Loaded skill: ${skillName}\n\n${skill.content}`;
// }
// // Skill not found
// const available = SKILLS.map((s) => s.name).join(", ");
// return `Skill '${skillName}' not found. Available skills: ${available}`;
// },
// {
// name: "load_skill",
// description: `Load the full content of a skill into the agent's context.
// Use this when you need detailed information about how to handle a specific
// type of request. This will provide you with comprehensive instructions,
// policies, and guidelines for the skill area.`,
// schema: z.object({
// skillName: z.string().describe("The name of the skill to load"),
// }),
// }
// );
// Build skills prompt from the SKILLS list
const skillsPrompt = SKILLS.map(
(skill) => `- **${skill.name}**: ${skill.description}`
).join("\n");
const skillMiddleware = createMiddleware({
name: "skillMiddleware",
tools: [loadSkill],
wrapModelCall: async (request, handler) => {
// Build the skills addendum
const skillsAddendum =
`\n\n## Available Skills\n\n${skillsPrompt}\n\n` +
"Use the load_skill tool when you need detailed information " +
"about handling a specific type of request.";
// Append to system prompt
const newSystemPrompt = request.systemPrompt + skillsAddendum;
return handler({
...request,
systemPrompt: newSystemPrompt,
});
},
});
const model = new ChatOpenAI({
model: "gpt-5.4-mini",
temperature: 0,
});
// Create the agent with skill support
const agent = createAgent({
model,
systemPrompt:
"You are a SQL query assistant that helps users " +
"write queries against business databases.",
middleware: [skillMiddleware],
checkpointer: new MemorySaver(),
});
// Configuration for this conversation thread
const threadId = uuidv4();
const config = { configurable: { thread_id: threadId } };
// Ask for a SQL query
const result = await agent.invoke(
{
messages: [
{
role: "user",
content:
"Write a SQL query to find all customers " +
"who made orders over $1000 in the last month",
},
],
},
config
);
// Print the conversation
for (const message of result.messages) {
console.log(`${message.type}: ${message.content}`);
}
```
This complete example includes:
* Skill definitions with full database schemas
* The `load_skill` tool for on-demand loading
* `SkillMiddleware` that injects skill descriptions into the system prompt
* Agent creation with middleware and checkpointer
* Example usage showing how the agent loads skills and writes SQL queries
To run this, you'll need to:
1. Install required packages: `pip install langchain langchain-openai langgraph`
2. Set your API key (e.g., `export OPENAI_API_KEY=...`)
3. Replace the model initialization with your preferred LLM provider
## Implementation variations
This tutorial implemented skills as in-memory Python dictionaries loaded through tool calls. However, there are several ways to implement progressive disclosure with skills:
**Storage backends:**
* **In-memory** (this tutorial): Skills defined as Python data structures, fast access, no I/O overhead
* **File system** (Claude Code approach): Skills as directories with files, discovered via file operations like `read_file`
* **Remote storage**: Skills in S3, databases, Notion, or APIs, fetched on-demand
**Skill discovery** (how the agent learns which skills exist):
* **System prompt listing**: Skill descriptions in system prompt (used in this tutorial)
* **File-based**: Discover skills by scanning directories (Claude Code approach)
* **Registry-based**: Query a skill registry service or API for available skills
* **Dynamic lookup**: List available skills via a tool call
**Progressive disclosure strategies** (how skill content is loaded):
* **Single load**: Load entire skill content in one tool call (used in this tutorial)
* **Paginated**: Load skill content in multiple pages/chunks for large skills
* **Search-based**: Search within a specific skill's content for relevant sections (e.g., using grep/read operations on skill files)
* **Hierarchical**: Load skill overview first, then drill into specific subsections
**Size considerations** (uncalibrated mental model - optimize for your system):
* **Small skills** (\< 1K tokens / \~750 words): Can be included directly in system prompt and cached with prompt caching for cost savings and faster responses
* **Medium skills** (1-10K tokens / \~750-7.5K words): Benefit from on-demand loading to avoid context overhead (this tutorial)
* **Large skills** (> 10K tokens / \~7.5K words, or > 5-10% of context window): Should use progressive disclosure techniques like pagination, search-based loading, or hierarchical exploration to avoid consuming excessive context
The choice depends on your requirements: in-memory is fastest but requires redeployment for skill updates, while file-based or remote storage enables dynamic skill management without code changes.
## Progressive disclosure and context engineering
Progressive disclosure is fundamentally a **[context engineering](/oss/javascript/langchain/context-engineering) technique** - you're managing what information is available to the agent and when. This tutorial focused on loading database schemas, but the same principles apply to other types of context.
### Combining with few-shot prompting
For the SQL query use case, you could extend progressive disclosure to dynamically load **few-shot examples** that match the user's query:
**Example approach:**
1. User asks: "Find customers who haven't ordered in 6 months"
2. Agent loads `sales_analytics` schema (as shown in this tutorial)
3. Agent also loads 2-3 relevant example queries (via semantic search or tag-based lookup):
* Query for finding inactive customers
* Query with date-based filtering
* Query joining customers and orders tables
4. Agent writes query using both schema knowledge AND example patterns
This combination of progressive disclosure (loading schemas on-demand) and dynamic few-shot prompting (loading relevant examples) creates a powerful context engineering pattern that scales to large knowledge bases while providing high-quality, grounded outputs.
## Next steps
* Learn about [middleware](/oss/javascript/langchain/middleware) for more dynamic agent behaviors
* Explore [context engineering](/oss/javascript/langchain/context-engineering) techniques for managing agent context
* Explore the [handoffs pattern](/oss/javascript/langchain/multi-agent/handoffs-customer-support) for sequential workflows
* Read the [subagents pattern](/oss/javascript/langchain/multi-agent/subagents-personal-assistant) for parallel task routing
* See [multi-agent patterns](/oss/javascript/langchain/multi-agent) for other approaches to specialized agents
* Use [LangSmith](https://smith.langchain.com?utm_source=docs\&utm_medium=cta\&utm_campaign=langsmith-signup\&utm_content=oss-langchain-multi-agent-skills-sql-assistant) to debug and monitor skill loading
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/multi-agent/skills-sql-assistant.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).