Frequently Asked Questions About Better Stack Understand-Anything Codebase Mapping
21 answers covering everything from basics to advanced usage.
// Basics
What exactly is the 'meaning layer' that Understand-Anything adds?
The meaning layer is the explanation of what code actually does in business terms — which flow a file belongs to, where a request starts, what downstream services depend on it, and what breaks if you change it. Traditional tools show structure (imports, file trees, dependency arrows). Understand-Anything's multi-agent LLM processing interprets that structure into domains, flows, and business concepts, producing the 'from files to meaning' transformation.
What is the difference between static analysis and multi-agent LLM processing in Understand-Anything?
Static analysis extracts the raw structure — files, imports, function calls, class hierarchies, and module boundaries. Multi-agent LLM processing then interprets that structure to identify business domains, explain what flows exist, label components with meaningful descriptions, and surface behavioral relationships that pure structural analysis cannot detect. The combination is what produces a knowledge graph rather than just a dependency diagram.
Does Understand-Anything work with microservices or only monoliths?
It works with both. For monoliths, it maps internal module boundaries, domain layers, and cross-cutting flows. For microservices, it maps individual services and their relationships, though you may need to scan each service separately or point it at a mono-repo containing all services. The tool is especially valuable for microservices architectures where understanding which service owns which flow is non-obvious.
What is grep-and-guess and why is it a problem?
Grep-and-guess is the default developer behavior when facing an unfamiliar codebase: jumping between files with search commands, reading random code paths, and asking an AI to explain fragments without system context. It is slow, error-prone, and produces incomplete mental models that lead to risky changes. Understand-Anything replaces this with structured, graph-driven exploration that shows the full picture before you touch anything.
// How To
Can I scope an Understand-Anything scan to only part of a monorepo?
Yes. You can point the tool at a specific subdirectory rather than the entire repository, and you can specify a focus area when running the scan. For very large monorepos, scoping is recommended both to manage token costs and to get more relevant results. Once the full scan exists, you can also scope the Guided Tour to a specific domain like payments or auth without rescanning.
How do I use the three safety questions before making changes?
Before touching any code, use the knowledge graph to explicitly answer: (1) What does this code depend on? (2) What flow does it belong to? (3) What might break if it changes? Navigate to the relevant module in the dashboard, check its dependency connections, trace its flow membership, and review the diff impact view. If you cannot confidently answer all three, you are not ready to make the change safely.
How do I feed Understand-Anything output to Cursor or Copilot?
Extract structured architecture knowledge from the graph — domain maps, flow descriptions, dependency chains, and module explanations. Paste these into your AI agent's context window or attach them as reference files. In Cursor, you can include graph outputs in the project-level context. The key is providing structured architecture knowledge rather than raw file dumps. This gives the agent system-level awareness instead of fragment-level guessing.
How do I onboard a new developer using Understand-Anything?
Replace the traditional onboarding of 'read these 12 wiki pages and ask around' with: open the knowledge graph, take the Guided Tour scoped to their team's domain, then let them ask better questions based on actual system understanding. The graph eliminates the fog that makes the first weeks unproductive. New developers arrive at their first PR with genuine architectural context rather than educated guesses.
Can multiple team members share the same Understand-Anything graph?
The graph is generated per scan and lives within your development environment. To share it, you can run the scan on a shared machine, export the structured architecture knowledge, or have each team member run their own scan against the same repo. For team-wide adoption, running a single scan and sharing the dashboard access is most token-efficient. The onboarding workflow explicitly recommends handing the graph to new team members.
// Troubleshooting
What if the Understand-Anything scan runs out of tokens partway through?
If you exhaust your rate limit mid-scan, the scan will fail or produce incomplete results. This is why auditing your token budget is Step 1 in the workflow. For large repos, consider scoping to a subdirectory first, upgrading your API plan before starting, or running the scan during off-peak hours if your plan has rolling rate limits. Do not attempt a second scan immediately — wait for your rate limit to reset.
The knowledge graph looks cool but I'm not sure what to do with it — how do I get value?
Start with the Guided Tour, not the visual graph. The tour walks you through entry points, validation, logic, database interactions, and error handling in sequence. This is what converts the graph from a pretty diagram into actionable understanding. After the tour, use the three safety questions before any change, and extract structured context for your AI agent. If you skip the tour and only browse the visual graph, you will miss most of the value.
Why is my Understand-Anything dashboard not loading after the scan?
Ensure you reloaded plugins after installation — this is a common miss. Run the 'dashboard' command explicitly after the scan completes. If the scan was interrupted or ran out of tokens, the dashboard may not have data to display. Check your terminal output for errors during the scan phase. On very large repos, the dashboard may take additional time to render the full graph.
Is the Understand-Anything graph accurate or does the LLM hallucinate?
The graph is grounded in static analysis — real file structures, real imports, real function calls. The LLM layer adds interpretation (domain labels, flow explanations, business concept identification), which can occasionally be imprecise. This is why the methodology's fifth principle is 'Good Judgment Still Required.' Use the graph to understand where everything is going, then verify critical paths by reading the actual code before touching production.
What happens if I skip the Guided Tour and just browse the graph?
You get a visually impressive but superficially useful diagram. The Guided Tour is what converts the graph from a 'cool picture' into actionable flow understanding by walking you through entry point → validation → logic → database → external APIs → error handling in sequence. Without it, you see the pieces but miss how the machine works. Skipping the tour is listed as one of the top pitfalls of using the tool.
// Comparisons
How does Understand-Anything compare to just using ChatGPT or Claude to explain code?
Pasting files into ChatGPT or Claude gives you explanations of fragments — individual files or functions without system context. Understand-Anything scans the entire repository, builds a structural model via static analysis, then uses multi-agent LLM processing to interpret meaning across the whole codebase. The result is a connected knowledge graph where you can trace flows across services, see impact zones, and navigate architecture layers. It is the difference between asking about one puzzle piece versus seeing the completed puzzle.
How does Understand-Anything compare to tools like Sourcegraph or CodeScene?
Sourcegraph excels at code search and cross-reference navigation. CodeScene focuses on behavioral code analysis and technical debt hotspots. Understand-Anything occupies a different niche: it builds a meaning-layer knowledge graph using LLM processing, producing domain maps, flow explanations, and interactive guided tours. Sourcegraph shows you where things are; CodeScene shows you where problems accumulate; Understand-Anything shows you how the system behaves and what flows connect the pieces.
Is Understand-Anything better than manually drawing architecture diagrams?
Manual architecture diagrams are valuable but they go stale quickly and reflect the drawer's mental model, which may be incomplete or outdated. Understand-Anything generates the map directly from the current codebase, so it reflects what actually exists rather than what someone remembers. The tradeoff is token cost and the fact that the LLM layer may occasionally misinterpret intent. Ideally, use the generated graph as a starting point and refine with team knowledge.
// Advanced
Can I use Understand-Anything on a knowledge base or documentation repo, not just code?
Yes. The tool accepts a knowledge base directory as input, not just source code repositories. It can map relationships between documentation files, surface topical domains, and create a navigable graph of how concepts connect. The meaning-layer extraction works on any text-heavy corpus, though the Guided Tour and flow-tracing features are most powerful when applied to actual codebases with executable logic.
How often should I re-run Understand-Anything on a codebase?
Re-run after significant architectural changes — new services added, major refactors completed, or domain boundaries shifted. For active development, a monthly or quarterly rescan keeps the graph current. For onboarding a new team member, always run against the latest main branch. Be mindful of token costs for each rescan. The graph is a snapshot; it does not auto-update as code changes.
What is diff impact and how do I use it for safe refactoring?
Diff impact is the graph feature that surfaces what other modules, services, or flows would be affected by a proposed change. Before refactoring, navigate to the target module in the dashboard and inspect its diff impact connections. This reveals downstream consumers, dependent flows, and batch jobs that your change could break — dependencies that may not be obvious from reading the code alone. It is critical for scoping and sequencing refactors safely.
Can I export the Understand-Anything knowledge graph for use outside the dashboard?
The primary interaction surface is the dashboard, but you can extract structured architecture knowledge — domain maps, flow descriptions, dependency data — and use it as context for AI agents, documentation, or team presentations. The export format depends on your environment, but the key output to capture is the meaning-layer data, not just the visual graph. This structured data is what makes the graph valuable beyond the dashboard itself.