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- LangGraph provides structured, reliable agent orchestration as an Agent SDK, while h2oGPTe contributes deep research, synthesis, and reasoning capabilities that can be invoked as a function or node inside LangGraph workflows.
- h2oGPTe is model agnostic, compliance ready, and API first, and is fully compatible with industry standard protocols including MCP, the OpenAI API, OpenAPI, and REST.
- The commercial loan renewal agent example runs three parallel retrieval-augmented generation nodes, RAG-Policy, RAG-Entity, and RAG-Market, before pausing for human in the loop review of each stream.
- The commercial loan renewal agent synthesizes a draft credit memo covering summary, context, financials, risk, recommendations, covenants, pricing, and sensitivities, supported by inline citations, once all RAG streams are accepted.
- The commercial loan renewal workflow logs all retrievals, decisions, edits, and metrics for governance and auditability after the memo receives final human approval.
For the LangGraph Community
We’re hearing from customers who are already bringing H2O.ai and LangGraph together in their technology stacks. LangGraph provides the backbone for structured, reliable workflows, while h2oGPTe contributes deep research, synthesis, and reasoning capabilities.
But this isn’t just about LangGraph. h2oGPTe is designed to strengthen any ecosystem through APIs. By combining deterministic workflows with agentic reasoning and coding, organizations can unlock a more powerful and effective approach to enterprise AI.
Distinct Tools for a Stronger AI Ecosystem
Enterprise AI isn’t about finding a single tool that does it all. Different challenges demand different approaches. The most effective environments bring together tools that each address their own areas of strength.
At H2O.ai, we see h2oGPTe, our enterprise research agent, and LangGraph, a widely used orchestration framework, as two such tools. They serve different roles but work side by side — and, more importantly, h2oGPTe strengthens any framework through APIs. Together, they give organizations both structure and advanced reasoning capabilities.
LangGraph: Agent SDK
LangGraph is an Agent SDK — a lego box for building agent networks. Developers define how agents interact, including routing and orchestration logic, which makes LangGraph especially suited for structured, reliable, and repeatable processes. It can support both simple and dynamic agent behaviors, but the design choices rest with the builder.
It’s a solid choice for builders when consistency and control are essential.
h2oGPTe: Deep Research & Agentic Reasoning and Coding
Unlike an SDK, h2oGPTe comes as a ready-to-use dynamic research agent — capable of reasoning, coding, tool use, and synthesis out of the box. Developers don’t need to build the agent framework themselves; they can drop h2oGPTe directly into workflows through APIs or even as a node in LangGraph.
h2oGPTe is built for open-ended, complex research problems:
- Retrieval, synthesis, and summarization across structured and unstructured data
- Generating and running code dynamically
- Producing clear answers from complex information
h2oGPTe is model-agnostic, compliance-ready, and proven at scale. It emphasizes governance and flexibility, making it well-suited for situations where the process isn’t defined in advance.
And because it’s API-first, h2oGPTe complements any framework. It can be invoked as a function or node within LangGraph workflows and is fully compatible with industry-standard protocols like MCP, OpenAI API, OpenAPI, and REST.
Example Use Case: Commercial Loan Renewal Agent
Automates loan renewal reviews by ingesting borrower alerts, running parallel RAG retrievals (policy, entity, market), enabling human review and memo synthesis, and tracking approvals with full audit trails.
Structure
The commercial loan renewal agent is structured as a multi-agent workflow using LangGraph, with h2oGPTe powering retrieval and synthesis for each evidence stream. The process follows these steps:
- Ingest: The workflow is triggered when a borrower is flagged for review (e.g., renewal, limit change). The ingest step sets up the workflow state with borrower and sector details, and formulates queries for policy, entity, and market evidence.
- Parallel RAG Retrieval: Three retrieval nodes run in parallel:
- RAG-Policy: Retrieves relevant credit policy, sector appetite, methodologies, and exemplars
- RAG-Entity: Pulls borrower financials, exposures, compliance history, and relationship data
- RAG-Market: Gathers sector benchmarks, macro trends, peer comparisons, and market news
- Human-in-the-Loop (HITL) Review: Each RAG output is paused for human review. Reviewers can accept, request rework, or trigger a rerun of any stream before proceeding.
- Synthesis: When all RAG streams are accepted, the workflow synthesizes a draft credit memo covering summary, context, financials, risk, recommendations, covenants, pricing, and sensitivities, supported by inline citations.
- Final Approval (HITL): The completed memo is presented for final human approval. If changes are needed, synthesis can be rerun with edits tracked.
- Output & Audit: Once approved, the memo is pushed to the credit management system. The workflow logs all retrievals, decisions, edits, and metrics for governance and auditability.
Would you like to learn more? Drop us a note!