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You can build an agentic GraphRAG system on TigerGraph by combining graph queries, document-based retrieval, and an LLM-driven decision layer that selects an appropriate retrieval method for each question. “GraphProbe AI” is the project name used in this guide, not a separately identified official TigerGraph product. TigerGraph’s official GraphRAG project documents the underlying approach: a graph-powered assistant, a knowledge-graph builder, and an Agentic engine that can choose among retrieval methods.
What you are building
A GraphRAG system retrieves information from both structured relationships and unstructured documents. TigerGraph GraphRAG combines a TigerGraph database, vector retrieval, and generative AI. Its repository describes two services: a natural-language assistant for graph-powered question answering and a knowledge-graph builder for documents and graphs. Users can interact through a chat interface or APIs.
In this article, “GraphProbe AI” refers to an application you build around those capabilities. The official README does not identify a separate product by that name, so treat the repository’s documented features as the reference implementation—not as proof that a custom build has been released, endorsed, or independently benchmarked.
How the agent chooses a retrieval method
An agentic system does not have to run the same retrieval steps for every question. It can inspect the question and decide whether the answer is best supported by graph structure, document content, or a combination. TigerGraph GraphRAG’s README describes an Agentic engine that can select structural graph queries, vector search, or community search; it can also use external MCP tools and cite the chunks and queries it used.
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Use graph queries for explicit relationships
Questions about entities and their connections are candidates for structural graph queries. For example, a question asking which components depend on a named service is naturally expressed as a relationship lookup—provided those entities and dependency edges exist in your graph. A graph query can follow those modeled links rather than relying only on text that happens to mention both components.
Use vector search for relevant document passages
Questions about explanations, policies, or other material stored in documents may be better answered by finding semantically relevant passages. Vector retrieval compares an encoded question with document chunks. It can surface relevant text even when the wording differs, but it does not by itself establish that two entities have a particular relationship.
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Use community search for broader graph context
Community search is another method listed for the Agentic engine. It can be useful when a question calls for a broader view of related groups in a graph rather than a single direct edge or passage. The repository description establishes that the method is available; it does not establish that it is best for every community-level question.
Combine evidence when the question needs both
A question such as “Which services are affected by this policy, and what does the policy say about the impact?” needs both relationship evidence and explanatory text. A useful agent plan is to retrieve the relevant graph relationships, find the supporting document passages, and answer with those evidence sources kept distinct. The system should not treat a semantically similar passage as proof of a graph relationship, or an existing edge as proof of a document’s full explanation.
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The README describes these as available approaches, not as a guarantee that the agent will always choose correctly or produce a more accurate answer than a fixed pipeline. Evaluate routing and answer quality against questions from your own data.
Agentic and Classic modes compared
| Mode | Retrieval control | What the README describes | Trade-off to consider |
|---|---|---|---|
| Agentic | The engine selects a retrieval approach for the question. | Structural graph queries, vector search, community search, external MCP tools, and citations for chunks and queries used. | Flexible routing means you should inspect the selected tools and evidence when validating behavior. The README does not establish superior accuracy. |
| Classic | A more predictable question-answering route. | The Classic engine remains available alongside Agentic mode. | A more controlled route may be easier to reason about, but the README does not provide a comparative accuracy evaluation. |
Choose the mode based on the behavior you need to inspect and control, then compare them on the same representative questions. Do not assume that the agentic option is inherently better simply because it can choose among more retrieval methods.
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Build the system in stages
- Define the questions and evidence. List the questions users need answered and identify which answers depend on graph relationships, document passages, or both. This determines what information must be represented in the graph and what documents must be indexed.
- Prepare a small, representative data sample. Include examples of the entities, relationships, and document types that matter. Keep the initial corpus small: the project README warns that rebuilding embeddings and graph structures from raw data can incur provider costs.
- Set up TigerGraph and the application environment. The README lists TigerGraph DB 4.2 or later and Docker with the Docker Compose plugin or Kubernetes as prerequisites. It documents an integrated Docker deployment as well as using a pre-installed or separate TigerGraph instance. Its from-scratch Python demonstration requires Python 3.11 or later. These are version-sensitive requirements; check the repository’s current README before following its setup instructions.
- Configure your LLM services. The project requires you to configure your own provider credentials. The README names OpenAI, Azure, Google Cloud/Vertex AI, AWS Bedrock, Ollama, Hugging Face, and Groq in its configuration guidance. Embeddings, knowledge-graph generation, and chat can use separately configured models, so decide which service handles each task and keep credentials in the appropriate deployment configuration.
- Build the graph and document index. Use the knowledge-graph builder for your documents and graphs, then make sure the resulting entity and relationship structure reflects the questions you intend to answer. Index document chunks for vector retrieval. Start with the sample rather than rebuilding the full corpus, and track model usage as you refine extraction and indexing.
- Configure and test retrieval behavior. Try graph-focused, document-focused, and mixed questions. For each, inspect whether the selected route matches the evidence needed and whether the returned answer cites the relevant chunks and queries. Where a route is unsuitable, adjust your data, configuration, or mode choice rather than assuming a fluent answer is a supported one.
- Expose the assistant through the interface your users need. The project describes access through a chat interface or APIs. Connect the chosen interface to the assistant and test the complete path—from user question through retrieval to cited response—with realistic questions before broad use.
Choose a deployment route
| Route | What it means | Operational consideration |
|---|---|---|
| Integrated Docker deployment | Run the documented integrated deployment using Docker Compose with its plugin. | Convenient for bringing the documented components up together. You still configure your LLM provider and credentials. |
| Kubernetes | Deploy using the documented Kubernetes option. | Fits a Kubernetes-based operating environment, but the README provides no universal production sizing recommendation. |
| Separate or pre-installed TigerGraph | Use the application with a TigerGraph instance managed separately or already installed. | Separates database operation from the application deployment; plan for connectivity and configuration between them. |
The repository documents these routes but does not establish which is right for every production environment. Choose based on how your team operates TigerGraph and application services, and verify requirements against the current project documentation.
Plan for model costs and licensing
The project provides no standard cost estimate. The README cautions that rebuilding embeddings and graph structures from raw data can cost money; actual usage depends on the provider, model, and corpus. Keep the first indexing run small, monitor provider usage, and avoid repeatedly rebuilding a large corpus while tuning extraction or chunking.
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The repository describes the software as AGPL-3.0 and says it is provided as-is without warranties or guarantees. Review the current license and support terms in the repository before adopting it, particularly if you plan to modify or distribute the software. The README’s release history includes v2.0.2 dated August 28, 2026; release and licensing details may change.
Validate answers before relying on them
- Check that the agent chose a retrieval method suited to the question, rather than judging only whether the prose sounds plausible.
- Inspect cited chunks and graph queries to confirm that the answer is supported by the retrieved evidence.
- Test questions with missing entities, ambiguous names, and conflicting documents, as well as straightforward questions.
- Compare Agentic and Classic behavior on the same cases if you need to decide which mode to expose.
- Keep graph evidence and document evidence distinguishable in the answer, especially when a conclusion depends on both.
These checks are particularly important because the project README describes capabilities and workflows, not independent evaluations of accuracy, speed, or scale.
Quick Recap
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