Connect Semgrep to AI Agents: Automate SCA and SCM Syncs

You want to connect Semgrep to an AI agent so your internal platform can independently triage security findings, trigger automated pull requests for vulnerabilities, sync Source Control Management (SCM) repositories, and generate Software Bill of Materials (SBOM) reports on demand. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to manually code dozens of custom endpoints or navigate Semgrep's complex underlying schemas.

Giving a Large Language Model (LLM) read and write access to your Semgrep instance is a massive engineering undertaking. You either spend months building, hosting, and maintaining a custom connector that understands the exact difference between a Semgrep deployment ID and a deployment slug, or you use a managed infrastructure layer that handles the boilerplate for you. If your team uses ChatGPT, check out our guide on connecting Semgrep to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Semgrep to Claude. For developers building custom autonomous workflows, you need a programmatic way to fetch these tools and bind them natively to your agent framework.

This guide breaks down exactly how to fetch AI-ready tools for Semgrep, bind them natively to an LLM using LangChain (or any framework like LangGraph, CrewAI, or Vercel AI SDK), and execute complex DevSecOps workflows. For a deeper look at the architecture behind this approach, refer to our research on architecting AI agents and the SaaS integration bottleneck.

The Engineering Reality of Custom Semgrep Connectors

Building AI agents is the easy part. Connecting them to external SaaS APIs safely and reliably is where the real engineering friction lives. Giving an LLM access to external security data sounds simple in a Jupyter notebook prototype. You write a basic Python request function and wrap it in a @tool decorator. In production, this approach collapses entirely, especially with an ecosystem as robust and complex as Semgrep.

If you decide to integrate Semgrep yourself, you own the entire API lifecycle. Semgrep's API introduces several highly specific integration challenges that fundamentally break standard LLM assumptions and lead to systemic agent hallucination.

The Deployment Identifier Trap

Semgrep's architecture requires nearly every operation to be scoped to a specific deployment. However, the API routing is aggressively strict about the type of identifier it expects. Certain endpoints demand the integer deployment_id (e.g., getting team details or managing ignores), while other endpoints demand the string-based deployment_slug (e.g., fetching project findings or triggering bulk triages).

When you hand-code tools for an LLM, the model will inevitably confuse these two parameters. It will try to pass my-company-sec into an endpoint requiring an integer 12345, causing a hard 400 Bad Request. You then have to write fragile prompt engineering instructions to beg the model to remember which string goes to which path parameter.

The Protobuf-to-JSON Schema Bleed

Unlike simple CRUD APIs, the Semgrep REST API is often heavily influenced by upstream Protocol Buffer schemas. This creates highly nested, non-intuitive JSON payloads. For instance, when interacting with Slack channel mappings or deployment products, the actual configuration data is buried inside an attributes object, and the field names are strictly typed enums derived from the proto definitions.

LLMs are notoriously bad at guessing deeply nested schema structures. If an agent tries to update a notification rule without the precise protobuf-derived JSON shape, the payload is rejected. Teaching an agent these unwritten structural rules via system prompts consumes massive amounts of context window and degrades reasoning performance.

Asynchronous Task Polling Execution

The most powerful actions in Semgrep - triggering automated AI fixes for vulnerabilities or generating full SBOM exports - do not happen synchronously. When you call these endpoints, Semgrep returns a 202 Accepted alongside a task_token_jwt.

LLMs operate synchronously by default. If an agent fires an SBOM request, it expects the SBOM in the immediate HTTP response. When it gets a JWT instead, an untrained agent will either hallucinate the contents of the report or crash. Your integration layer must possess the tools and state awareness to recognize the async task, pause execution, and poll the task status endpoint until the true data payload resolves.

Why a Unified Proxy Tool Layer Matters for Agent Safety

Before you write a single line of integration code, you must decide what layer your agent actually talks to. Direct API tools (exposing the raw Semgrep API directly to the model) push all of the vendor's architectural quirks straight into the LLM's context window.

A unified proxy tool layer collapses these complexities. Every resource on an integration maps into standard REST methods (List, Get, Create, Update, Delete) or custom logical methods (Trigger Fix). Truto handles the pagination, standardizes the query parameter processing, and provides the LLM with a strictly typed JSON schema for every single interaction.

This gives you concrete safety wins:

1. Eliminated Schema Hallucination: The LLM chooses from perfectly defined JSON schemas. Invalid arguments (like confusing a slug for an ID) are rejected by the framework before the network request is even constructed.
2. Normalized Context: The agent interacts with list_all_semgrep_deployment_findings instead of trying to manually assemble complex query strings for /api/v1/deployments/:slug/findings.
3. Framework Agnosticism: Because Truto exposes standard JSON representations, these tools work natively with LangChain, LangGraph, CrewAI, and any other orchestration layer. You are not locked into a proprietary plugin architecture.

Hero Tools for Semgrep

Truto exposes dozens of tools for the Semgrep integration, but when building autonomous agents, a few high-leverage operations form the backbone of your DevSecOps workflows. Here are the core tools you will use to build your system.

List All Semgrep Deployments (list_all_semgrep_deployments)

This is the critical prerequisite tool. Before an agent can do anything, it needs to know what deployments the authenticated token has access to, gathering the id and slug needed for all subsequent operations.

Usage Note: This tool should always be the first step in your agent's reasoning loop if it does not already hold the deployment context.

"Fetch my available Semgrep deployments so we can get the necessary ID and slug to start analyzing our repositories."

List Deployment Findings (list_all_semgrep_deployment_findings)

This tool retrieves code, supply chain, and AI-powered scan findings for a specific deployment. It handles the pagination cursor under the hood, returning a structured list of vulnerabilities including severity, confidence, rule message, and the exact line of code.

Usage Note: This requires the deployment_slug, not the integer ID. The agent will use this to audit current security posture.

"Pull the latest high-severity security findings for the 'production-web' deployment slug."

Create an Issue Fix Job (create_a_semgrep_issue_fix_job)

This is arguably the most powerful tool in the arsenal. It triggers an automated SAST fix job for a given issue in Semgrep, kicking off an AI-powered workflow that analyzes the vulnerability, generates a code fix, and automatically opens a pull request in the source repository.

Usage Note: This is an asynchronous operation. The agent must pass the deployment_id and issue_id.

"I see a SQL injection vulnerability on issue ID 84930. Trigger an automated issue fix job to generate a PR for this finding."

Bulk Update Findings (semgrep_deployment_findings_bulk_update)

Agents excel at mass triage. This tool allows the agent to apply triage updates (like marking as false positive, or ignoring) to matching findings in a single operation across the deployment.

Usage Note: The agent must construct the exact filter logic in the JSON payload, which is safeguarded by the Truto-provided JSON schema.

"Take all the findings from the deprecated 'legacy-auth' repository and bulk update their triage state to ignored."

Refresh Repositories Async (create_a_semgrep_repos_refresh_async)

Source control drifts quickly. This tool schedules an asynchronous job in Semgrep to sync all projects in a deployment to their source control manager (like GitHub or GitLab), ensuring the scanner has the latest code.

Usage Note: Returns a 202 Accepted. The agent knows the sync is queued and can proceed to other tasks while Semgrep processes the repositories.

"We just onboarded a new GitHub org. Trigger an async repository refresh for our deployment to pull in the new projects."

Generate SBOM Async (create_a_semgrep_deployment_sbom_async)

Compliance requirements often demand a Software Bill of Materials. This tool starts an asynchronous job to generate the SBOM.

Usage Note: This endpoint returns a task_token_jwt. The agent will need to take this token and use a secondary task-polling tool to retrieve the actual SBOM data once the background job finishes.

"Generate an SBOM for our deployment. Take the task token you receive and poll the tasks endpoint until the SBOM is ready for me to review."

For the complete inventory of available proxy tools and their exact JSON schemas, visit the Semgrep Integration Page.

Workflows in Action

When you give an LLM access to these tools, it stops being a chatbot and becomes an autonomous security operator. Here is how specific DevSecOps personas use these tools in real-world scenarios.

1. The Automated Vulnerability Triage and PR Generator

Security engineers spend hours manually reviewing alerts and trying to write patches for common vulnerabilities. An agent can completely automate the initial response phase.

"Review the latest high-severity findings for the 'core-api' deployment. If you find any SQL injection or Cross-Site Scripting (XSS) vulnerabilities that have high confidence, trigger an automated AI fix job for them immediately."

Agent Execution Steps:

1. The agent calls list_all_semgrep_deployments to find the slug and integer ID for 'core-api'.
2. It calls list_all_semgrep_deployment_findings using the slug, filtering for high severity and high confidence.
3. It iterates through the results, identifying SQLi and XSS issues.
4. For each identified issue, it calls create_a_semgrep_issue_fix_job using the deployment ID and issue ID.

Result: The user is informed that the agent found 4 critical vulnerabilities and has successfully queued 4 automated pull requests in GitHub to resolve them, without any human intervention.

2. The On-Demand SBOM Auditor

During a compliance audit, a compliance officer needs immediate visibility into the supply chain.

"Generate a fresh SBOM for our main deployment. Once it is ready, retrieve it and summarize the top 3 most prevalent open-source licenses we are using."

Agent Execution Steps:

1. The agent calls create_a_semgrep_deployment_sbom_async and receives a task_token_jwt.
2. Knowing this is asynchronous, the agent enters a short loop, calling the list_all_semgrep_tasks tool with the JWT.
3. Once the status returns as completed, it reads the resulting SBOM payload.
4. The LLM analyzes the JSON natively to aggregate and count the licenses.

Result: The compliance officer receives a clean markdown summary of the top 3 licenses, backed by a freshly generated SBOM, handling the asynchronous polling complexity completely out of sight.

3. The Cross-Repo CI/CD Provisioner

Platform engineers need to ensure that every repository in the organization is actually being scanned. When new repos are added, they must be provisioned with the right secrets and GitHub Actions.

"Trigger a sync of our source control repositories to make sure Semgrep sees the latest projects. Then, bulk provision the Semgrep CI GitHub Actions across all repositories that don't have it yet."

Agent Execution Steps:

1. The agent calls create_a_semgrep_repos_refresh_async to force Semgrep to look at the SCM.
2. Once synced, it calls create_a_semgrep_repos_provision, passing the deployment ID and a filter object defining which repositories should receive the semgrep-ci GitHub action injection.

Result: The platform engineer ensures total CI/CD scan coverage across the entire engineering org with a single sentence.

Building Multi-Step Workflows

To build these autonomous systems, you must bind Truto's dynamically generated tool schemas directly to your LLM framework. Truto provides an endpoint (/integrated-account/:id/tools) that returns all enabled methods as JSON schemas perfectly formatted for function calling.

Below is a conceptual architecture using TypeScript and LangChain.

Handling API Rate Limits Safely

Before diving into the agent loop, we must address a critical architectural requirement: rate limits.

Factual Note on Rate Limits: Truto does not retry, throttle, or apply backoff on rate limit errors automatically. When the upstream Semgrep API returns an HTTP 429 (Too Many Requests), Truto passes that error directly back to the caller. However, Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification.

Your agent orchestration framework is fully responsible for catching these errors, reading the ratelimit-reset header, and implementing the appropriate retry or backoff logic.

The Architecture Flow

sequenceDiagram
    participant App as Your AI App
    participant LC as Agent (LangChain)
    participant Truto as Truto Tool API
    participant Semgrep as Semgrep API
    
    App->>Truto: GET /integrated-account/<id>/tools
    Truto-->>App: Return JSON Tool Schemas
    App->>LC: .bindTools(schemas)
    App->>LC: User Prompt: "Triage my findings"
    
    loop Agent Reasoning
        LC->>Truto: Execute Tool (e.g., bulk_update)
        Truto->>Semgrep: POST /api/v1/deployments/triages
        
        alt HTTP 429 Too Many Requests
            Semgrep-->>Truto: 429 Error
            Truto-->>LC: 429 Error + ratelimit-reset header
            Note over LC,Truto: App catches error, waits for reset, retries
            LC->>Truto: Retry Tool Execution
        else Success
            Semgrep-->>Truto: 200 OK
            Truto-->>LC: JSON Response Payload
        end
    end
    
    LC-->>App: Final Natural Language Answer

Implementation Example

Using the truto-langchainjs-toolset, you can initialize the tools and wrap your invocation in a safety loop that respects Semgrep's rate limit boundaries.

import { ChatOpenAI } from "@langchain/openai";
import { TrutoToolManager } from "truto-langchainjs-toolset";
import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents";
import { ChatPromptTemplate } from "@langchain/core/prompts";
 
async function runSemgrepAgent(prompt: string) {
  // 1. Initialize the Truto Tool Manager with your tenant and token
  const toolManager = new TrutoToolManager({
    trutoUrl: "https://api.truto.one",
    accessToken: process.env.TRUTO_API_KEY,
    accountId: process.env.SEMGREP_INTEGRATION_ID
  });
 
  // 2. Fetch the dynamically generated tools for Semgrep
  const tools = await toolManager.getTools();
 
  // 3. Initialize the LLM and bind the tools natively
  const llm = new ChatOpenAI({
    modelName: "gpt-4-turbo-preview",
    temperature: 0,
  }).bindTools(tools);
 
  // 4. Create the agent execution environment
  const promptTemplate = ChatPromptTemplate.fromMessages([
    ["system", "You are an elite DevSecOps automation agent. Use your Semgrep tools to manage security findings and deployments."],
    ["user", "{input}"],
    ["placeholder", "{agent_scratchpad}"],
  ]);
 
  const agent = await createOpenAIToolsAgent({
    llm,
    tools,
    prompt: promptTemplate,
  });
 
  const executor = new AgentExecutor({ agent, tools });
 
  // 5. Execute with Rate Limit Protection
  try {
    const result = await executor.invoke({ input: prompt });
    console.log(result.output);
  } catch (error) {
    if (error.response && error.response.status === 429) {
      // Read the normalized headers provided by Truto
      const resetTime = error.response.headers['ratelimit-reset'];
      console.warn(`Rate limit hit. Must backoff until timestamp: ${resetTime}`);
      // Implement your application-specific wait/retry logic here
    } else {
      console.error("Agent execution failed:", error);
    }
  }
}
 
// Run the workflow
runSemgrepAgent("Find my available deployments and list the high severity findings.");

In this setup, the complexity of Semgrep's underlying REST architecture, Protobuf mappings, and precise payload requirements are completely abstracted away from your LLM. The agent simply receives clean JSON instructions, decides which tool to call, and your application handles the deterministic execution and rate limit safety.

Architecting for Scale

Building AI agents that interact with external security products requires more than just clever prompts; it requires robust, stable infrastructure that protects the model from schema hallucinations and unpredictable API behaviors. By routing your agent's reasoning through Truto's unified proxy tools, you guarantee that every interaction with Semgrep is validated, structurally sound, and scalable.

Stop spending weeks writing custom integration wrappers and prompt-engineering your way out of 400 Bad Request errors. Connect your agent, fetch the tools, and let your system autonomously handle the complexities of modern DevSecOps.