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Build a queryable code graph, validate edit scope, and log reasoning.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-context-graph" yet — see the docs or source repo.
Scan the current project, build a file and symbol knowledge graph, and find which files and functions call UserService.login; before I modify it, list the impacted scope and potential risks.
A call graph for the login method, related files, affected modules, and pre-edit risk notes.
Only allow edits to code directly related to the login flow in src/auth/ and src/api/auth.ts. If changes outside this scope are needed, stop first, explain why, and wait for my approval.
The agent edits only within the allowed scope; if out-of-scope changes are required, it explains why and suggests next steps without writing files.
Output the append-only reasoning log for this code change, showing in time order which symbols you queried, why you changed these files, and what each edit was meant to fix.
An auditable change log that clearly records the query process, decision rationale, and purpose of each modification.
Enable AI coding agents to communicate, share state, and coordinate work in real time.
Query code structure and cross-language relationships via MCP with auditable access logs.
Build a project knowledge graph for code search, traversal, and Q&A.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Coordinate multiple AI agents on software projects with shared tasks and context.
Index codebases into Neo4j for analysis, dependency mapping, and impact assessment.