Codebadger

The interesting design decision here is that Codebadger does not try to be the assistant. It is infrastructure: an MCP server that turns Joern's Code Property Graph into something an LLM can query, so the model reasons over function call chains and data flow instead of guessing from embeddings. That is a meaningfully different architecture from vector-search-over-repo tools, and it shows up in the kinds of questions that become answerable — "which code paths handle this error without logging it", "what data reaches this SQL statement", "where are the circular dependencies between these modules". Where it fits: teams on large, multi-language codebases where the cost of a wrong edit is high, and security or architecture work that depends on following data rather than reading files in order. Self-hosted container deployment also means the codebase stays inside your network or CI runner, which matters for regulated environments. Where it does not: you cannot treat it as a plug-and-play assistant. You need Docker (or an equivalent container runtime), and you need to accept the CPG generation step — it is the resource-intensive part, and query latency after that depends on how the graph was built. Joern is a serious piece of program-analysis software, and Codebadger inherits both its reach across languages and its learning curve. Beginners looking for simple search or regex-level answers will find the setup tax hard to justify, and anyone hoping for a hosted dashboard will need to supply their own infrastructure. The honest framing: this is a power tool for engineers who already think in graphs, not a shortcut for people who don't.