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To estimate what a code change may affect, an AI agent can use Tree-sitter to parse files, query grammar-defined declarations and references, resolve those references into a dependency graph, then traverse the graph backward from changed symbols. The result is an estimate of likely impact—not a complete or exact account of every runtime dependency. Tree-sitter supplies syntax structure; cross-file and semantic resolution are separate parts of the system.
No attributable project account or repository establishes how a particular team built the system named in the original headline. The design below is therefore a reproducible architecture, not a claim of first-hand implementation or measured results.
What a blast-radius analyzer needs to determine
The practical question is: “What breaks if I change this function?” A useful analyzer must identify the changed entity, find code that depends on it, and show how each relationship was established. The graph should help an agent prioritize investigation, not certify that a change is safe.
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Tree-sitter is a parser generator and incremental parsing library. It produces concrete syntax trees (CSTs) and can update trees as source changes. Its stated goals include broad language applicability, resilience when source contains syntax errors, and an embeddable runtime. Those properties make it useful for repository analysis, but a CST is not itself a repository-wide dependency graph.
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Build the analysis in six stages
1. Parse files with an explicit language and version
Select a Tree-sitter grammar for every supported language, parse each file, and retain the file identity and parser and grammar versions alongside the result. A node has source positions, parent and child relationships, and may be traversed through named nodes and grammar fields. These details let the analyzer map syntax back to exact source ranges and locate the construct containing an edit. The official Tree-sitter Introduction and Basic Parsing guide document these parser and tree properties.
Do not assume every file parses cleanly. Tree-sitter can represent syntax problems using ERROR and MISSING nodes. Preserve that information: a partially parsed file may still yield useful captures, but its analysis should not be presented with the same confidence as a clean parse.
2. Extract definitions and references with grammar-aware queries
Write queries for each supported grammar to capture declarations and reference sites. Store a normalized name, entity kind, role, file, and source range for each capture. Tree-sitter’s Code Navigation Systems guide demonstrates captures such as @definition.function, @reference.call, and @name, as well as the tree-sitter tags approach for identifying definitions and references.
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Capture patterns are language-specific: a function declaration, method call, import, or class may have different grammar nodes and fields in different languages. Treat queries as versioned analyzer inputs, not universal patterns. Test each query against small fixture files that include expected definitions, references, and relevant syntax edge cases; the navigation guide also describes testing tag queries.
3. Resolve references to candidate definitions
A capture says that syntax resembles a reference; it does not establish which declaration that reference means. Resolution should be an explicit component that uses available evidence, such as imports, lexical scope, qualified names, receiver or type information, and project metadata. Depending on the language and construct, the result may be one definition, several candidates, or no resolved target.
Call-site resolution is a central challenge in call-graph construction. Dynamic dispatch, reflection, framework conventions, generated code, incomplete type information, and conditional imports can all complicate it. Record the resolution method and confidence or uncertainty rather than silently turning a guess into a definite edge. The ACER paper by Andrew Chen, Yanfu Yan, and Denys Poshyvanyk (2023) discusses the precision trade-off in call-graph construction and the richer information available from compiler-based intermediate representations, which require compilation.
4. Store a directed, provenance-aware graph
Represent code entities as nodes and resolved relationships as typed, directed edges. For a call graph, a convenient convention is caller → callee; then finding callers affected by a changed callee means traversing incoming call edges. Imports, inheritance, and other relations can use their own edge types and rules. Nodes may represent symbols, files, or modules, depending on the analysis question.
| Edge kind | Example relationship | Use in impact analysis |
|---|---|---|
| Call | A function or method calls another symbol | Find direct callers and callers further up the call chain |
| Import | A file or module imports another module | Find code that may depend on an imported module |
| Inheritance | A class extends or implements another type | Find subclasses or implementations affected by a base-type change |
These are relationship types an implementation may support, not relationships Tree-sitter resolves automatically. Attach provenance to each edge: the source range, query or rule, resolution method, and uncertainty. An agent can then distinguish a directly captured call with a unique target from an approximate relationship inferred through a broader rule.
5. Map the diff to symbols and traverse dependents
For each changed range, use its file and byte positions to find the enclosing symbol or symbols. If an edit falls outside a recognized symbol, retain the file or module as the affected entity rather than discarding the change. Starting from the changed entity, traverse reverse dependency edges to collect possible callers or dependents.
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- Identify changed ranges. Read the diff and retain each changed file and source range.
- Map ranges to graph nodes. Find the smallest enclosing symbol, while preserving file-level changes that have no suitable symbol node.
- Traverse incoming edges. Follow only relevant edge types and record the path from each dependent to the changed node.
- Report direct and transitive impact separately. Include traversal depth, edge kinds, provenance, and unresolved or uncertain links.
- Give the agent evidence to inspect. Return symbol names and source locations so it can open the relevant code rather than treating reachability as proof of a defect.
This traversal is a proposed implementation pattern based on Tree-sitter’s source locations and the graph architectures described in Codebase-Memory; it is not a measured behavior for a particular product. A direct caller is one graph edge away. A transitive dependent is reachable through multiple edges, and the longer path generally requires more care before interpreting it as likely impact.
6. Keep the graph fresh and make gaps visible
When a file changes, reparse it and refresh its extracted entities and incident edges; incremental parsing can reduce repeated work. The implementation must still ensure that removed or renamed definitions do not leave stale graph links. Track the versions and identities used to build graph data so the agent can tell which repository state it represents.
- Surface files with parse errors or missing syntax nodes.
- Test query captures with fixtures and detect changes when grammars or query rules are updated.
- Mark unsupported languages and unresolved references instead of treating missing edges as evidence that no dependency exists.
- Refresh affected graph data after edits and invalidate links that depended on changed definitions.
- Keep source ranges and resolution provenance available for review.
What the impact estimate can—and cannot—say
A syntax-level graph estimates likely impact for the constructs and relationships the analyzer can extract and resolve. It can miss relationships that depend on runtime behavior, compiler type information, build configuration, generated sources, or framework-specific conventions. Conversely, broad or uncertain resolution can include possible dependents that are not reached at runtime. A “no dependents found” result therefore means no dependents were found within the analyzer’s coverage, not that the change has no effect.
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Compare implementations on language and grammar coverage, reference-resolution precision, handling of dynamic dispatch and generated code, incremental refresh cost, graph freshness, and the context or tool-call cost imposed on the agent. Tree-sitter’s broad language applicability helps with parsing coverage; it does not eliminate the semantic-resolution work. The ACER paper’s comparison of AST-based analysis with richer intermediate representations is useful context for that trade-off, not an AI-agent performance benchmark.
What published agent results do—and do not—show
The 2026 Codebase-Memory preprint by Martin Vogel, Falk Meyer-Eschenbach, Severin Kohler, Elias Grünewald, and Felix Balzer describes persistent graph construction and impact analysis as part of a code-agent architecture. Across 31 real-world repositories, its authors report 83% answer quality versus 92% for a file-exploration agent, alongside ten times fewer tokens and 2.1 times fewer tool calls. These are the authors’ reported results for that system and evaluation; they are not independently verified here and should not be generalized to other analyzers.
A 2022 USENIX Security Symposium paper provides an example of using Tree-sitter node locations and types to classify edits in a research study. That is evidence of a syntax-analysis application, not a direct evaluation of AI-agent blast-radius accuracy.
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