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Forensic-deepdive
Pricing
- Model
- Free
Summary
AI coding agents hallucinate architecture because they have no persistent, structured picture of your codebase — every session starts from scratch, and every context window is a best guess. forensic-deepdive exists to fix that: a persistent code knowledge graph that your agents can actually query.
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.
Bottom line: Pick this when your AI coding agents are wasting context budget on structure they should already know — but plan for a graph-refresh discipline in CI, because stale graph data is worse than no graph at all.
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Pros
Sign in to edit- 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.
Cons
Sign in to edit- 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.
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About
- Platforms
- Python
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-26T08:16:05.544Z
Best For
Who it's for
- AI coding agents using MCP
- Large polyglot codebases needing persistent graphs
- Teams maintaining durable human-readable code docs
What it does well
- Supplying structured codebase context to AI coding agents
- Generating onboarding and mental-model documentation
- Tracing cross-stack call flows and endpoint handlers
- Analyzing git history, authorship, and co-change patterns
Integrations
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Frequently Asked Questions
- Is Forensic-deepdive free?
- Yes — Forensic-deepdive is fully free to use. There is no paid tier.
- Is Forensic-deepdive open source?
- Yes. Forensic-deepdive is open source.
- Does Forensic-deepdive have an API?
- Yes. Forensic-deepdive exposes a developer API. See the official documentation at https://github.com/dhevenddra/forensic-deepdive for details.
- Can I self-host Forensic-deepdive?
- Yes. Forensic-deepdive supports self-hosting on your own infrastructure.
- What platforms does Forensic-deepdive support?
- Forensic-deepdive is available on: Python.
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Curated lists that include this category
AI coding agents regularly fail not because the model is wrong but because the context they receive is wrong — file snippets without import resolution, symbol names without call-chain provenance, endpoints with no trace back to handlers. forensic-deepdive addresses this by parsing a codebase across nine languages, building a persistent embedded graph of its structural and historical relationships, and serving that graph through an MCP server that coding agents can query directly. The core artifacts are nodes for Files, Symbols, Modules, Commits, Authors, Endpoints, and DbTables, connected by edges for DEFINES, MEMBER_OF, IMPORTS, CALLS, EXTENDS, IMPLEMENTS, and TOUCHED_BY_COMMIT — a vocabulary precise enough for agents to answer questions like ‘what calls this endpoint’ or ‘who last changed this module and what else changed with it’.
The differentiating design choice is persistence. Most context-supply approaches reparse on every session; forensic-deepdive writes the graph to `/.deepdive/graph.lbug` and keeps it there. Queries return structured, pre-computed answers rather than ad-hoc file scans, which means agent sessions start with a complete architectural picture rather than rebuilding one from whatever fits in the context window. The five Markdown artifacts — the human-readable projection of the same graph — mean onboarding documentation is a by-product of the analysis, not a separate authoring task.
The tool fits teams running AI coding agents (via MCP) against large polyglot repositories where structural context is the bottleneck. It also fits teams that want durable, auto-generated code documentation without a dedicated docs workflow. Where it breaks: the graph reflects the state of the codebase at the time of the last analysis run, so rapidly changing repos produce stale structural data unless graph refresh is wired into CI. The vendor describes no hosted offering and no managed refresh service — operational discipline for graph freshness is entirely the team’s responsibility.