Inspiration

Software teams lose a ton of time to context switching. Important decisions happen in meetings, technical reality lives in the codebase, blockers stay buried in chat or in people’s heads, and turning all of that into actual engineering work is way more manual than it should be. We wanted to build something that closes that gap.

RepoGhost was inspired by the idea of an AI sidecar that sits next to a developer’s workflow and understands both sides of the problem: what the team is saying and what the repo actually contains. Instead of just being another chatbot, we wanted it to feel like a project-aware assistant that can turn messy context into something actionable.

What it does

RepoGhost is an always-on-top desktop AI assistant for developers. It takes in a permitted project folder and optional meeting notes or transcript input, then uses a staged multi-agent workflow to understand the codebase, interpret the discussion, and generate structured engineering output.

It helps with things like:

identifying likely relevant files and modules
extracting decisions, blockers, and action items from meetings
mapping vague discussion back to concrete code areas
generating task breakdowns and ticket-ready items
preparing handoff notes and standup summaries
surfacing risks, dependencies, and next steps

The goal is to reduce the friction between team discussion and actual execution.

How we built it

We designed RepoGhost as a desktop-first experience with a compact widget and an expanded workspace view so it feels lightweight but still powerful. The product is structured around one user-facing assistant backed by a multi-agent workflow.

The system is organized in stages:

Stage 1: Parallel ingestion Agents analyze the repo structure, parse meeting input, search for relevant code, retrieve prior context, and inspect docs.

Stage 2: Parallel reasoning A second layer of agents resolves vague discussion into technical meaning, detects blockers, estimates impact, and identifies testing gaps.

Stage 3: Parallel output generation The final layer turns everything into practical outputs like action plans, tickets, handoff notes, standup summaries, and review scope.

We focused on making the workflow visible so the product clearly feels agentic instead of looking like a single prompt-response system.

Challenges we ran into

One of the biggest challenges was making the system feel genuinely multi-agent instead of just pretending to be. It is easy to say “agentic,” but much harder to design clear stages, dependencies, and outputs that actually justify that label.

Another challenge was balancing ambition with hackathon reality. The full vision for RepoGhost is large, so we had to be intentional about what needed to exist now versus what could be designed for future expansion. We also had to think carefully about how to make repo understanding, meeting understanding, and structured output generation work together in a clean pipeline.

UI and product direction were also important challenges. We did not want another generic web dashboard. We wanted something that felt like a real developer tool: compact, always available, and fast to interact with.

Accomplishments that we're proud of

We are proud that RepoGhost feels like a real product instead of just a concept. The idea is clear, the workflow is structured, and the use case is grounded in an actual pain point developers deal with constantly.

We are also proud of the multi-agent design itself. RepoGhost is not just answering questions. It is breaking work into specialized roles, merging context, and producing outputs developers can actually use.

Another thing we are proud of is the product framing. Turning the experience into an always-on-top desktop assistant gives it a much stronger identity and makes it feel more practical than a standard chatbot interface.

What we learned

We learned that agentic systems need strong orchestration to be useful. Having multiple agents is not enough by itself. The real value comes from how their outputs are staged, merged, and turned into something coherent.

We also learned that developer tools need to be immediately legible. If the product cannot explain itself quickly, it becomes harder to trust. That pushed us to think more clearly about visible workflows, structured outputs, and UI design that communicates what the system is doing.

On the product side, we learned that there is a lot of value in connecting human conversation to technical implementation. That gap is bigger than it seems, and closing it unlocks a lot of speed.

What's next for repoGhost

Next, we want to make RepoGhost more persistent and context-aware over time. That means stronger project memory, better indexing, better ownership inference, and deeper repo understanding.

We also want to expand the workflow into more development modes, like onboarding support, bug triage, PR review prep, and architecture drift detection. On the product side, we want to keep refining the desktop experience so it feels even more like a true sidecar for engineers.

Long term, the vision is for RepoGhost to become a real project brain for development teams: something that continuously understands repo context, team decisions, and current work, and helps convert all of that into execution-ready output.