Trae codebase context with a persistent project graph

What is Trae codebase context?

Trae codebase context is the combination of repository files, project rules, open tasks, codebase indexing, tools, and any external context available to the agent. Trae can find code through its own search and indexing, while a Graphify graph adds a durable structural map of dependencies, calls, communities, and documentation links. That division matters because better local retrieval does not automatically create an inspectable artifact that another session, branch, or engineer can reuse.

• Source files remain authoritative for implementation behavior.
• AGENTS.md explains commands, conventions, and when to query the graph.
• graph.json provides reusable project relationships for later Trae sessions.
• Keep graph artifacts in the repository when several assistant sessions need one structural source.

How do you install Graphify for Trae?

Run graphify trae install after installing the graphifyy package. The command writes Graphify guidance into the repository's AGENTS.md so Trae can discover the workflow from project instructions rather than relying on a prompt that disappears after the session. It updates the Graphify section and preserves unrelated project rules, but it does not build the graph or replace Trae's own configuration.

• Install Python 3.10 or newer and graphifyy before configuring Trae.
• Run graphify trae install from the target repository root.
• Review the AGENTS.md change and commit the project policy with the code.
• Build graphify-out artifacts before relying on graph queries.
• Re-run the installer after upgrading Graphify when its guidance changes.

What does Graphify add beyond Trae codebase indexing?

Trae indexing helps the agent locate relevant code. Graphify adds an inspectable relationship artifact that can survive across sessions and connect code with documents, rationale, and subsystem clusters. This matters when the answer depends on how modules relate rather than where a string appears, especially when many files contain the same class name or API concept. Use the graph as a candidate map, then let source evidence decide which structural path is real.

• Trace reverse dependencies before changing a shared interface.
• Find paths between controllers, services, data access, and tests.
• Use communities and high-connectivity nodes to understand architecture.
• Link implementation to design notes and product constraints.
• Use the report for a project-wide briefing before broad refactors.

When should Trae query the graph instead of searching files?

Use the graph first for architecture, dependency, call-path, migration, and blast-radius questions. Use Trae's normal file search for exact strings, environment values, recent edits, and small local fixes where the relationship map adds no useful information. A practical default is graph query, targeted source reads, then validation, so Trae spends context on files the structural evidence says matter.

• Graph first for cross-module refactors and service boundaries.
• Search first for exact errors, configuration keys, and unfamiliar text.
• Open every cited source file before editing.
• Run focused tests after the assistant proposes a change.
• Use graph results to rule files out as well as identify files to read.

How do you keep Trae's graph context fresh?

Refresh the graph after interface changes, file moves, dependency upgrades, or large refactors. Graphify update re-extracts code without the semantic LLM pass, while a full extract is appropriate when important docs, PDFs, diagrams, or screenshots changed. In branch-heavy workflows, record which commit produced the graph and rebuild after merges that change module boundaries.

• Run graphify update . after code-only structural changes.
• Run a full extraction when multimodal project context changed.
• Use watch or repository hooks when active development needs continuous refreshes.
• Record the graph revision in AGENTS.md when teams work across branches.
• Never silently reuse graph.json from a different commit.

How do you verify graph-based Trae answers?

Require Trae to cite source files and tests before accepting an architectural claim. Graph edges can be extracted, inferred, or ambiguous, so a returned path is a review candidate rather than proof of runtime behavior. A low-confidence edge is a prompt to inspect more source, not evidence that a dependency is safe to change.

• Check the exact symbols and paths in current source.
• Confirm that graph.json matches the active commit.
• Run type checks and tests selected from affected paths.
• Review security and product intent separately from graph evidence.
• Capture corrected graph conclusions when the team uses graph memory feedback.