An AI agent can estimate what a code change may affect by parsing a repository with Tree-sitter, extracting definitions and references, resolving those references into a dependency graph, then tracing backward from the changed symbols. Tree-sitter supplies syntax structure—not a complete, semantically resolved dependency graph—so the result is a reasoned estimate whose accuracy depends on what the analyzer can recognize and resolve.
No first-party account or repository substantiates the specific “we built” implementation implied by the original headline. This article instead lays out a reproducible architecture, distinguishing Tree-sitter’s documented capabilities from the additional analysis an agent system must provide.
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What blast-radius analysis needs to establish
When a function changes, the practical question is not just which lines differ. It is which callers, modules, tests, or other code entities may depend on the changed behavior. A useful analyzer represents those relationships explicitly, then follows them from the edited code to likely dependents.
Tree-sitter is a parser generator and incremental parsing library. It produces concrete syntax trees for supported languages and is designed to work across languages, tolerate syntax errors, and be embedded in other software. Its trees expose node types, source positions, parent-child relationships, and named-node traversal. Those features make it possible to locate and classify code constructs, but they do not by themselves determine every symbol’s meaning or target.
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How to build the analysis pipeline
1. Parse files and retain their identity
Choose a Tree-sitter grammar for each language in scope, parse the repository’s files, and keep the file path and parser and grammar versions alongside each result. Source positions let later stages connect a changed range in a diff to the relevant syntax nodes and enclosing definitions.
For an edited file, incremental parsing can update a syntax tree as the source changes rather than treating every edit as an unrelated full parse. The analyzer still needs to track which files and graph relationships have become stale; parsing alone does not refresh a repository-wide dependency model.
2. Extract definitions and references with grammar-aware queries
Write queries for the grammar-specific syntax that represents declarations and uses. Captures can label entities by role—for example, Tree-sitter’s navigation guide uses names such as @definition.function, @reference.call, and @name. A capture should retain at least the entity’s name, role, file, and source range so the system can explain where its evidence came from.
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The exact patterns vary by language grammar. A query that recognizes a call-shaped expression can identify a possible reference; it cannot necessarily establish which declaration that call reaches. Likewise, Tree-sitter’s tagging support can classify definitions and references, while repository-wide linking requires additional resolution logic.
3. Resolve references into candidate targets
Resolution is the step that turns syntax observations into relationships. Depending on the language and project, the resolver may use qualified names, imports, lexical scope, receiver and type information, and project metadata. It should allow for zero, one, or several candidate definitions rather than silently treating every matching name as a certain link.
Call-graph construction is especially sensitive to this step: dynamic dispatch and incomplete type information can make a call site’s target uncertain. A syntax-level analyzer may therefore miss a real relationship or report several possible targets. Build-system, compiler, framework, or runtime knowledge can supply information that the syntax tree does not contain.
4. Store relationships as a directed graph
Represent code entities as nodes—such as symbols, files, or modules—and supported relationships as typed, directed edges. Examples include calls, imports, and inheritance. Keep provenance with each edge: the source file and range, the query or rule that found it, and the resolution method. Record uncertainty as well, so the agent can distinguish a directly observed relationship from an approximation.
This graph is a separate analysis layer built on extracted and resolved facts; it is not an output that Tree-sitter automatically supplies. A Tree-sitter-based code-graph architecture and impact-analysis approach is described in Vogel and colleagues’ 2026 Codebase-Memory preprint.
5. Map the diff to symbols and traverse dependents
- Read the diff’s changed file paths and source ranges.
- Use the parsed trees to find the enclosing definitions for those ranges. If an edit is outside a recognized definition, retain the file or module as the affected entity rather than assigning it to an unrelated symbol.
- Start from the changed entities and follow reverse dependency edges: for example, from a changed function to functions that call it, then to callers of those callers.
- Return the reachable entities with edge kinds and traversal depth. Separate direct dependents from transitive ones, and surface uncertain links rather than presenting them as confirmed impacts.
This traversal is a practical design pattern, not a measured behavior of a named implementation. The output should describe likely impact among the constructs and relationships the analyzer can resolve—not promise an exhaustive list of everything that could break.
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6. Keep extraction and graph state trustworthy
Tree-sitter can represent malformed or incomplete syntax with ERROR and MISSING nodes. Check for these during extraction, since a query may fail to capture a construct when the source is incomplete or the grammar does not match what the analyzer expects. Test queries against representative fixtures and use tag-query tests to catch changes in capture behavior.
When files change, refresh their parsed results and the edges derived from them, then expose unresolved references and unsupported grammars in the agent’s output. Otherwise, a graph can appear precise while quietly omitting relationships or retaining stale ones.
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A useful impact report is inspectable: it identifies the changed symbol, lists direct and transitive dependents separately, names the relationship type along each path, and points to source ranges that support the links. It should also state where analysis is incomplete—for example, when a language grammar is unsupported, a reference remains unresolved, or a target is only one of several candidates.
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These details help a developer decide whether to inspect a caller, run a test, or supply more context. A bare list of file names hides both the reasoning and the confidence behind the estimate.
Where this approach is useful—and where it falls short
Tree-sitter’s concrete syntax trees and query system provide a practical foundation for multi-language structural analysis, including repositories that may contain syntax errors. A graph layered on that structure can make likely dependents easier for an agent to find than searching text alone.
But a syntax-level graph is only as complete as its language coverage, extraction rules, and reference resolution. Dynamic dispatch, generated code, framework conventions, and relationships that depend on build or runtime information can all exceed what syntax captures establish. The ACER paper by Andrew Chen, Yanfu Yan, and Denys Poshyvanyk (2023) discusses the call-resolution challenge and contrasts AST-based approaches with richer intermediate representations that can carry more information but require compilation.
When comparing implementations, examine grammar coverage, reference-resolution precision, handling of dynamic dispatch and generated code, refresh cost and graph freshness, and the context or tool-call cost imposed on the agent. A graph may be fast and broadly available while leaving some relationships unresolved; a compiler-backed representation may provide richer semantics while requiring a more demanding setup.
How to interpret published performance figures
The 2026 Codebase-Memory preprint by Martin Vogel, Falk Meyer-Eschenbach, Severin Kohler, Elias Grünewald, and Felix Balzer reports an evaluation 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. Those are results reported for that system and evaluation, not a general performance guarantee for Tree-sitter-based agents or blast-radius analysis.
The ACER paper is relevant to call-graph resolution trade-offs, but its reviewed abstract does not provide a directly transferable benchmark for AI-agent impact analysis. Neither paper establishes the performance of the particular implementation implied by the original headline.
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