Author here. Quick context on the benchmarks:

We used RARV (Retrieve, Analyze, Reason, Validate) pattern with multi-agent collaboration. Each problem gets worked by specialized agents, reviewed by 3 parallel reviewers (code, business logic, security), and only merged after consensus.

The 99.67% isn't cherry-picked. Full run against standard SWE-Bench dataset. Happy to share methodology if anyone wants to reproduce.

On the swarm architecture for those curious:

Engineering (8 types): frontend, backend, database, mobile, API, QA, perf, infra Operations (8 types): devops, SRE, security, monitoring, incident, release, cost, compliance Business (8 types): marketing, sales, finance, legal, support, HR, investor, partnerships Data (3 types): ML, data eng, analytics Product (3 types): PM, design, tech writer Growth (4 types): growth hacker, community, success, lifecycle Review (3 types): code, business, security

Agents don't step on each other. Frontend agent never thinks about database schemas. QA agent never writes deployment scripts. Domain isolation is key.

For the skeptics (fair): FireLater repo has full git history. You can see the commits. No human intervention in the implementation phase.

I reviewed outputs and approved deployments. But architecture decisions, code, tests, docs - all Loki Mode.

It's not perfect. Some rough edges. But it works and enterprises can self-host it today.

vs single-agent coding assistants: They tap out around 50% on SWE-Bench. No specialization. No parallel review. No self-healing.

vs other multi-agent frameworks: Most focus on chat or simple task delegation. Loki Mode runs full SDLC - from PRD to deployed product with monitoring and business ops.

vs hiring a team: Obviously humans are better for ambiguous problems. But for well-defined PRDs, this removes the "I'll get to it this weekend" bottleneck.

Last time someone raised concerns about web crawling for competitive research. Valid point.

New version has configurable research modes. You can disable external crawling entirely and run fully offline if needed. Feedback heard.