Forensic-deepdive vs Stagewise

Forensic-deepdive and Stagewise are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Forensic-deepdive

The tool analyzes a codebase across nine languages, builds an embedded graph at `/.deepdive/graph.lbug`, and exposes it over an MCP server so coding agents get structured answers about symbols, imports, call chains, endpoints, and git authorship — not raw file dumps. Five durable Markdown artifacts serve as the human-readable projection of that same graph, so your team gets onboarding docs and mental-model documentation without a separate documentation pass. The graph nodes cover Files, Symbols, Modules, Commits, Authors, Endpoints, and DbTables, which means cross-stack call flow tracing and co-change pattern analysis are first-class queries. The project is Apache-2.0 and self-hosted, with no hosted offering described — your codebase never leaves your infrastructure. The graph must be rebuilt or updated as the codebase changes; the freshness burden falls on the team.

Stagewise

Open-source agentic IDE with embedded frontend coding agent that runs in your browser on localhost.

Attribute	Forensic-deepdive	Stagewise
Pricing	Free	Paid
Price	—	$20/mo
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Python	macOS, Linux, Windows (browser-based + desktop app)
Released	—	2024
Pros	Persistent embedded graph at `/.deepdive/graph.lbug` stores structural relationships across files, symbols, imports, call chains, and git history, so coding agents query pre-computed architecture instead of reparsing source on every session — which means context windows go to reasoning, not reconstruction. MCP server exposes the graph directly to AI coding agents, so tools like Claude's agent loop can ask structured questions about endpoints, authorship, or call flows and get answers grounded in the actual codebase rather than probabilistic recall. Nine-language polyglot analysis means a single graph covers mixed-stack repositories — teams running Python services alongside TypeScript frontends and Go infrastructure get cross-language call tracing without splitting the analysis. Five auto-generated Markdown artifacts produce human-readable documentation as a by-product of graph construction, so onboarding docs and architectural mental models stay in sync with the codebase without a separate writing pass. Apache-2.0 license and self-hosted-only design mean the graph — and every piece of codebase structure it encodes — stays on your infrastructure, which matters for teams whose source cannot leave a private environment.	Eliminates context switching between browser and editor Works with existing production codebases without refactoring Compatible with all major frontend frameworks Bring-your-own-key support for AI providers High cache-hit rates (87.6% average) for cost efficiency
Cons	The graph captures codebase state at analysis time and does not update itself; on a codebase with frequent commits, agents query stale structural data between runs — teams that need accurate context on active branches wire a graph-rebuild step into CI, which adds pipeline complexity and rebuild time proportional to repo size. Zero community forks and zero stars at the time of scraping means bug reports, edge-case language support, and parser correctness issues have no community surface — teams that hit a parsing failure in their stack have no forum thread to find and must open an issue against a single-maintainer repo, with no documented SLA. Teams that need agents to not just query structure but act on it — planning refactors, executing multi-file edits, managing PRs autonomously — will find forensic-deepdive provides context supply only; the execution layer is absent by design, and those teams reach for a full agent platform (Devin, SWE-agent, or similar) where the context graph is one component inside a broader task loop.	AGPL-3.0 license restricts commercial use without licensing Primarily focused on frontend development, not full-stack Requires local development environment setup

Bottom line

Forensic-deepdive is free while Stagewise is paid. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Forensic-deepdive and Stagewise?

Forensic-deepdive is Free and open source, while Stagewise is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Forensic-deepdive better than Stagewise?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.