codesynapse
An MCP server delivering architecture-level code knowledge to AI assistants.
Open SourceCoding & Dev
Visit website Free to browse · updated 2026-06-25
What is codesynapse?
The tool extracts abstract syntax trees across dozens of languages and constructs nodes for code elements along with edges representing calls, inheritance, and containment. Multiple module graphs merge into a unified structure that enables queries about cross-module impacts and hierarchies without requiring external services or specialized hardware. Hybrid retrieval combines precise lexical matching with dense vector similarity to surface relevant results even when terminology varies. All processing stays on the local machine using an embedded database, ensuring no data leaves the user's control and eliminating latency from remote APIs. By supplying full graph context at the start of each session, the server allows AI coding environments to move beyond isolated file analysis toward informed decisions on refactoring, dependency tracing, and system-wide changes.
Key features
Structural knowledge graph from source code (classes, functions, files)
32 MCP tools with hybrid BM25 + dense vector search
Multi-module graph merging into global graph
Fully local operation with no cloud APIs or GPU
Tree-sitter AST extraction for 30+ languages
Blast radius and cross-module hierarchy analysis
Model2Vec embeddings with Sled embedded DB
What you can use codesynapse for
Architecture-aware code assistance
Supplies AI coding assistants with a structural knowledge graph of classes, functions, and files so they can reason about hierarchies, call chains, and module ownership instead of treating each file in isolation.
Change impact analysis
Enables blast radius and cross-module hierarchy queries that reveal which parts of a codebase would be affected by a proposed edit or refactoring.
Multi-module codebase exploration
Merges graphs from separate modules into one global graph and supports hybrid BM25 plus dense vector search for concept-level questions across the entire project.
How to use codesynapse
- 1Clone the codesynapse repository
- 2Build the Rust binaries with Cargo
- 3Edit codesynapse.toml to point at your source folders
- 4Start the MCP server and connect it to Claude Code or Cursor
- 5Load the generated graph context at the start of each session
codesynapse pricing
Pricing model: Open Source. Plan details are indicative — check the site for current prices.
Open Source
Free
- MIT license
- Fully local
- No cloud API needed
- 32 MCP tools
Editor's verdict
Pros
- +Zero cloud dependencies and fully local
- +32 MCP tools for architecture-level context
- +Hybrid search combining BM25 and dense vectors
Cons
- –Early-stage project (single commit)
- –Requires local Rust binary installation
- –Graph regeneration gated on mtime changes
Our take: codesynapse is a solid coding & dev choice. It's valued for zero cloud dependencies and fully local and 32 mcp tools for architecture-level context. The main trade-off is early-stage project (single commit). A good pick if you want capable AI without a high upfront cost.
Frequently asked questions
No. It runs entirely locally using Tree-sitter for AST extraction, Model2Vec embeddings, and an embedded Sled database.
Summary
codesynapse is a solid coding & dev choice. It's valued for zero cloud dependencies and fully local and 32 mcp tools for architecture-level context. The main trade-off is early-stage project (single commit). A good pick if you want capable AI without a high upfront cost.
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