---
title: "LangGraph"
description: "11 posts about LangGraph, summarised, each linking to the original."
---

# LangGraph
> 11 posts about LangGraph, summarised, each linking to the original.

## Articles

### [Evaluating AI agents: moving past the vibe check](https://yomu.fyi/post/evaluating-ai-agents-moving-past-the-vibe-check.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Hugo Selbie, Dani Zamora, Jesse Carah
- Published: Oct 15, 2026
- Type: Problem & solution

Manual vibe checks for AI agents miss silent failures like retry loops, token waste, and hallucinated parameters while plausible final answers mask them. The post outlines a structured evaluation pipeline using the open-source agent-eval toolkit, starting with critical user journeys (CUJs) that define success in concrete, testable clauses. Synthetic user simulations (via ADK) generate multi-turn scenarios for initial trace collection, which developers grade with a ‘red pen’ before automating checks. Four layers of verification are recommended: unit tests, deterministic metrics, LLM-as-a-judge with boolean rubrics (avoiding unreliable 1-to-5 scales), and integration tests, all standardized on OpenInference/OpenTelemetry traces. The rollout progresses from Day 0 manual review through Day 1 automated checks to Day 2 CI/CD gates and shared dashboards, with real production traces fed back into the golden dataset.


### [How to Encrypt LangGraph Checkpoints With MongoDB CSFLE](https://yomu.fyi/post/how-to-encrypt-langgraph-checkpoints-with-mongodb-csfle.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Oct 14, 2026
- Type: Tutorial

LangGraph checkpoints persist agent state, including user messages and tool results, as plaintext BSON in MongoDB by default, leaving sensitive data accessible to anyone who can read the database. MongoDB Client-Side Field Level Encryption (CSFLE) encrypts data on the client before transmission, ensuring the database engine never sees plaintext values and protecting against privileged database users or data breaches. This post demonstrates implementing a custom CipherProtocol for LangGraph’s EncryptedSerializer that uses explicit CSFLE with randomized encryption, configured with AWS KMS and a data encryption key stored in a key vault collection for envelope encryption. The encrypted payload replaces the default Base64 encoding, while only thread IDs, checkpoint IDs, and namespaces remain unencrypted to support runtime lookups without exposing sensitive content. This approach aligns with enterprise security and compliance requirements for customer-facing AI agents, preserving resumable sessions and human-in-the-loop workflows while preventing database administrators or attackers from reading the state.


### [Add NeMo Guardrails to a LangGraph agent on OpenShift AI](https://yomu.fyi/post/add-nemo-guardrails-to-a-langgraph-agent-on-openshift-ai.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Tarun Etikala
- Published: Oct 5, 2026
- Type: Tutorial

LangGraph agents can call tools and follow prompts but lack safety guardrails, risking unsafe responses like explaining fraud. NeMo Guardrails acts as a transparent proxy between the agent and LLM, inspecting every request and response without requiring agent code changes. On Red Hat OpenShift AI, the guardrails service is deployed via a NemoGuardrails custom resource managed by the TrustyAI Operator, and the agent’s BASE\_URL points to this service instead of the model endpoint. The example demonstrates three rail types: regex filtering for jailbreak patterns, content safety classification against S1-S13 categories, and topic safety to keep the agent within a banking domain, with optional tracing using MLflow and OpenTelemetry. Rails execute sequentially, with the first failure short-circuiting the chain, and the separation lets safety policy be owned by guardrails while the agent handles reasoning and tools.


### [AiChemy: Next](https://yomu.fyi/post/aichemy-next.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Yen Low, Sean Zhang
- Published: Sep 18, 2026
- Type: Tutorial

AiChemy is a multi-agent assistant for cross-disciplinary research that combines external MCP servers including OpenTargets, PubChem, and PubMed with Databricks-managed chemical libraries. It can identify disease targets and drug candidates, retrieve chemical and pharmacokinetic properties, assess safety and toxicity, and attach findings to verifiable data sources. Its guided workflow covers disease-to-target-to-drug discovery and literature validation, while a vector search over a 250,000-molecule ZINC15 index uses 1024-bit ECFP embeddings to find compounds similar to Elacestrant. To build a custom supervisor, users connect public MCP servers, transform structured drug data into a Genie space, and index unstructured chemical data for similarity search. They can assemble it with no-code Agent Bricks or deploy a Langgraph supervisor with Lakebase, MLflow AgentServer, and a React interface; invocations are logged and traced through MLflow using OpenTelemetry standards.


### [I got frustrated with AWS, so we built our own sandbox](https://yomu.fyi/post/i-got-frustrated-with-aws-so-we-built-our-own-sandbox.md)
- Company: [Boxcompute](https://yomu.fyi/company/boxcompute.md)
- Author: Farhan Helmy
- Published: Sep 16, 2026
- Type: Explainer

At a healthcare startup, an agent workflow built with LangGraph was constrained by context length and by the limited tools exposed to it for data analysis. Changing one node gave the agent access to Amazon Bedrock AgentCore Code Interpreter, allowing it to use pandas and other available data-science libraries in a sandbox. It could inspect files and outputs, write and run code, and adjust its analysis iteratively while keeping working data and intermediate artifacts in the workspace rather than sending every row through the conversation. Although the experiment demonstrated the usefulness of this controlled autonomy, regional availability forced the author to use a US region from Malaysia, motivating BoxCompute, an infrastructure project intended to provide agent workspaces with control over execution, resources, output collection, and cleanup.


### [Agent platform (Part 1): How we help Grab build and run AI agents at scale](https://yomu.fyi/post/agent-platform-part-1-how-we-help-grab-build-and-run-ai-agents-at-scal.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kendrick Tan
- Published: Jul 24, 2026
- Type: Explainer

Grab’s AI support bot grew from an internal GPT-powered Level-0 service into the foundation for LLM-Kit, an internal framework now used by more than 500 services, alongside over 50 registered remote MCP servers and a company-wide LLM gateway handling billions of tokens monthly. The bot used a single-agent reasoning loop and a tool plane spanning Glean, Kibana, GitLab, Slack, and HTTP plugins, but scaling exposed missing evaluations, fragmented observability, provider-switching friction, and costly production plumbing. LLM-Kit addresses those problems with generated FastAPI services, LangGraph agent templates, OpenTelemetry tracing, configuration and secret-management integrations, evaluation routes, remote MCP tool discovery, and gRPC contracts, clients, health checks, and channel selection. The post concludes that framework concerns became platform concerns at 500 agents, leading to a gateway, remote MCP framework, and evals platform described as the series continues.


### [Building Production-Scale Visual AI for Claims and Returns at Swiggy Instamart](https://yomu.fyi/post/building-production-scale-visual-ai-for-claims-and-returns-at-swiggy-i.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Harshit Juneja
- Published: Jun 16, 2026
- Type: Problem & solution

Swiggy Instamart built two vision-language-model systems to handle more than 40,000 daily claims involving wrong, expired, poor-quality, damaged, and packaging-related items, while reducing reliance on human verification. The Image Validation Model (IVM) compares customer uploads with catalog images, routes each category through a specialised LangGraph StateGraph handler, and combines its structured verdict with fraud, confidence, and business-rule signals. Reverse Quality Check (RQC) compares delivery executives’ pickup photos with the original claim images, allowing matching returns to proceed and prompting retries when they do not. The architecture keeps visual assessment probabilistic but makes routing, aggregation, escalation, and financial decisions deterministic, with traces for category, outputs, confidence, cost, and latency. After more than six months in production, the systems are associated with a 6% customer-satisfaction increase, 20,000 fewer weekly Image Validation tickets, 98.5% RQC accuracy, and over 5,000 daily RQC tickets resolved without human intervention.


### [From Rigid Rules to Empathetic Agents: Rebuilding Swiggy’s Order Cancellation Experience with LLMs](https://yomu.fyi/post/from-rigid-rules-to-empathetic-agents-rebuilding-swiggy-s-order-cancel.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Aishwary Yadav
- Published: May 29, 2026
- Type: Problem & solution

Swiggy rebuilt its “I Want To Cancel” (IWTC) support journey because a roughly 150K-line deterministic decision tree treated cancellation keywords alike, repeated canned deterrence, and could leave users stuck without handoff. The replacement is a stateful, multi-turn framework orchestrated with LangGraph: an intent layer identifies needs such as delay help, coupon support, direct cancellation, or human access, while live order signals and deterministic policy checks determine the next action. Full graph state is checkpointed in Redis by order- and session-level thread IDs, allowing conversations to resume across turns. Swiggy evaluated intent reliability using more than 100k historical chats and a pre-deployment suite of about 500 representative queries, then operated the system with reporting and alerts for behavioral and service metrics. The conclusion favors a hybrid design in which LLMs handle language while deterministic systems anchor truth and business decisions.


### [Explaining Every Rupee: How We Built Reliable LLM Support Bots for Delivery Partners](https://yomu.fyi/post/explaining-every-rupee-how-we-built-reliable-llm-support-bots-for-deli.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Yash Khandelwal
- Published: May 29, 2026
- Type: Problem & solution

Swiggy faced close to 9 lakh monthly payout- and incentive-related tickets, where delivery executives needed accurate explanations of earnings, deductions, missed incentives, and credited amounts. The system separates financial truth from language generation: deterministic preprocessing and internal APIs resolve incentive eligibility, payout breakdowns, deductions, and conditions before an LLM explains the result. Two multilingual bots—the Daily Incentive Bot and Payout Bot—use a LangGraph StateGraph with LLM, tool, and validator nodes, Redis checkpointing for multi-turn context, and post-processing translation into the executive’s preferred language. Azure Translator preserve-list controls help keep business-critical terminology consistent, while structured escalation sends out-of-scope cases and context to human agents. Moving retrieval from Databricks SQL queries to an aggregated internal API reduced latency from 8–10 seconds to milliseconds; the bots now resolve about 90% end to end, cut resolution from roughly 20 minutes to seconds, and improved CSAT by 40%.


### [From firefighting to building: How AI agents restored our team’s core productivity](https://yomu.fyi/post/from-firefighting-to-building-how-ai-agents-restored-our-team-s-core-p.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sneh Agrawal
- Published: Mar 19, 2026
- Type: Problem & solution

Grab’s Analytics Data Warehouse team supports more than 1,000 monthly users and 15,000 tables, but repetitive questions about definitions, lineage, quality, and basic enhancements consumed about 40% of engineering time. It deployed a multi-agent AI system that routes Slack requests into enhancement or investigation pathways, using FastAPI and LangGraph for stateful, cyclical collaboration, Redis for caching and sessions, and PostgreSQL for persistent memory. Specialized agents query Trino, Hive, or Delta Lake, inspect GitLab code, check incidents and pipeline health, or prepare semi-automated code changes, while human reviewers approve production modifications. The system autonomously resolves most standard inquiries and some common enhancements, reducing resolution time by an order of magnitude and bringing simple answers to users within minutes. The resulting capacity shift reclaimed several FTEs worth of engineering bandwidth and moved hundreds of hours from reactive support to roadmap work.


### [How Etsy Uses LLMs to Improve Search Relevance](https://yomu.fyi/post/how-etsy-uses-llms-to-improve-search-relevance.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Yuqing Zhang
- Published: Jan 16, 2026
- Type: Explainer

Etsy’s Search Relevance team addresses a limitation of engagement-based search signals: popular listings can attract clicks even when they are not the best match for a query. It introduces semantic relevance, measured through a framework combining human-curated golden labels, an LLM annotator, cascaded models, and production integrations, with categories for relevant, partially relevant, and irrelevant query-listing pairs. The data pipeline uses sampled search-log pairs, dual human annotation, an evolving guideline, few-shot chain-of-thought prompting with o3 in LangGraph, and self-consistency sampling; a Qwen 3 VL 4B teacher then labels data at scale before distilling into a BERT-based two-tower student. The student supports real-time use with less than 10ms additional latency, while the framework evaluates and enhances search relevance alongside engagement signals.