Synaptic
Active·★ 31·AGPL-3.0·Updated 2026-10-18
Synaptic transforms codebases into a persistent, queryable knowledge graph to enhance understanding and facilitate advanced development tasks. It uses this graph to answer structural and architectural queries, trace impact, plan refactors, and integrate with AI coding assistants.
#code analysis#knowledge graph#static analysis#multi-language#refactoring#impact analysis#developer tools#AI assistant integration
01
Features
01Multi-language Code Graph Generation: Extracts symbols and relationships across 30+ programming languages using tree-sitter to build a comprehensive code knowledge graph.
02Advanced Code Querying & Analysis: Supports structural and architectural queries (SYNQL), reverse impact analysis ("what depends on this?"), and time-travel diffing across Git history.
03Safe Refactoring & Change Forecasting: Generates confidence-scored execution plans for refactors (rename, move, extract) and forecasts the blast radius, at-risk tests, and potential cycles of a change before it's made.
04AI Assistant Integration via MCP Server: Exposes 28 read-only tools over an MCP server (stdio or HTTP) for AI coding assistants to leverage the graph for tasks like subgraph search, impact prediction, and structural analysis.
02
Compatibility
Rust
Runtime
Verified via docs
Linux
OS
Verified via docs
macOS
OS
Verified via docs
Windows
OS
Verified via docs
Multi-language (30+)
Codebase
Verified via docs
AI Assistants (MCP)
Integration
Verified via docs
03
Quick start
1
$ cargo install --path bin/synaptic
04
Use cases
↳Understanding Large Codebases: Quickly gain structural clarity, identify "god nodes" or import cycles, and understand surprising cross-module connections in large monorepos or federated repositories.
↳Pre-commit Impact Assessment: Before making a change, forecast its blast radius, identify at-risk tests, and detect potential new cycles to prevent regressions.
↳Automated Code Auditing: Audit SQL code for performance and security issues (e.g., SQL injection, missing indexes, RLS gaps) directly from the graph.
↳AI-powered Developer Workflow: Integrate with AI coding assistants to enable smarter, graph-aware responses for tasks like context retrieval, refactoring plans, and architectural queries, reducing token costs significantly.
05
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