Native context
Continue
Agent mode can inspect files, search code, use Git, apply rules, and call configured tools.
Continue codebase context combines file exploration, code search, Git history, project rules, and tools available to Agent mode. Graphify adds a persistent structural graph so Continue can reuse dependencies, call paths, communities, and documentation relationships instead of rediscovering the same repository shape in every session. The integration is explicit: install Graphify, build graphify-out artifacts, add Continue rules, and expose Graphify MCP only when structured tools are needed.
Published Updated
Graphify stores inspectable artifacts so codebase knowledge can be reviewed by humans and reused by AI coding assistants.

Native context
Agent mode can inspect files, search code, use Git, apply rules, and call configured tools.
Persistent layer
Graph artifacts preserve dependencies, communities, docs, and rationale across sessions.
Integration
Use .continue/rules for policy and a project MCP block for structured graph queries.
Continue codebase context is the project information available to Continue's Chat, Edit, and Agent modes. Agent mode can explore files with tools, include user-selected context, follow project rules, use Git history, and call MCP tools configured for the workspace. The model sees only the context and tool results the host selects or exposes.
Graphify builds reusable graph artifacts from code and project sources. Continue can consult GRAPH_REPORT.md for a concise architecture briefing, read graph.json when appropriate, or query Graphify through MCP for paths, affected nodes, and explanations. The graph supplies a durable map; Continue still opens the cited source before editing.
Install graphifyy, build the repository graph, create a Continue rule that explains the workflow, and optionally register a local Graphify MCP server. Graphify does not currently document a native Continue installer, so use the documented rules and MCP surfaces instead of inventing a one-command integration.
The rule should describe when to query the graph, which artifacts are authoritative, how to verify results, and when to refresh the graph. It should not copy the entire architecture into prompt text or instruct Continue to trust inferred edges without opening source.
Use MCP when Continue needs a small structured answer such as a path, explanation, or affected-node list. Read a report or graph.json directly when the task needs artifact inspection without a running server. Use ordinary Continue file tools for exact text, current source, and compiler diagnostics.
Keep graph data inside the same trust boundary as the repository, expose only read-only tools where possible, and refresh after structural changes. Review generated project configuration before committing it, especially if the rules or MCP settings contain local paths or credentials.
Build the graph once, add a minimal project rule, and use Continue normally for current files and tests. Introduce MCP after the team confirms the graph is fresh and the rule produces bounded queries. Review the first week's accepted and rejected graph answers before expanding to more repositories.
The highest-risk failures are stale graph data, ambiguous symbol resolution, MCP payloads that consume too much context, and project rules that tell Continue to trust graph summaries over source. Monitor these directly rather than measuring only page views or tool-call counts.
Continue should not infer that an edge is a runtime call, that an old recommendation still matches the branch, or that a graph summary replaces compiler diagnostics and tests. Graph confidence describes how a relationship was derived; it does not certify behavior, compatibility, security, or performance.
Ask Continue to name the graph edge, source files, symbols, confidence, and tests supporting its conclusion. Open those files, confirm the graph revision, and run the focused checks. A graph path identifies a candidate relationship; it does not prove that the current branch compiles or behaves correctly.
pip install graphifyy
graphify extract .
graphify . --mcpname: Graphify
version: 0.0.1
schema: v1
mcpServers:
- name: Graphify
type: stdio
command: graphify
args:
- .
- --mcp---
name: Graphify codebase context
alwaysApply: true
---
Use graphify-out/GRAPH_REPORT.md before broad architecture exploration.
Use Graphify for cross-module paths and reverse dependencies.
Open every cited source file and run focused tests before editing.No native Continue installer is documented in the current Graphify repository. Use project rules and the documented local MCP server instead.
Continue's older @Codebase provider is deprecated. Current guidance favors Agent mode tools, project rules, and configured MCP servers.
No. Use Graphify for structural relationships and project context, and Continue's native tools for exact strings, current files, diagnostics, and recent edits.
Teams can commit selected graph artifacts when policy and repository size allow. Exclude sensitive or generated content and confirm the graph matches the committed source revision.
Continue through the Graphify topic cluster with pages that explain assistant context, architecture review, documentation, and graph-based codebase understanding.
AGENTS.md vs a codebase knowledge graph
Compare AGENTS.md instructions with a codebase knowledge graph, see what each layer stores, and learn how to combine them for persistent AI coding context.
MCP codebase knowledge graph for AI coding assistants
Use an MCP codebase knowledge graph to give Codex, Claude, and other agents structured queries for dependencies, architecture, and project context.
How to install Graphify and build a code graph
Install Graphify, generate your first code knowledge graph, query it from an AI assistant, and keep the graph fresh with update, watch, and hook workflows.
Start from the use cases page or upload a project ZIP to build a graph that your team can inspect and reuse.