HydraSwarm — 7-Agent AI Software Company with HydraDB
A 7-agent software engineering company where every agent queries HydraDB before acting and stores lessons back after. Score 7/10 first run, higher next run. Memory makes it real.
HydraSwarm is what multi-agent AI looks like when memory is treated as a first-class capability rather than an afterthought. Seven specialized agents — Product Manager, Architect, Developer, Reviewer, QA Engineer, SRE, and CTO — collaborate on tasks, with every agent following a strict recall-generate-store loop against HydraDB.
The mechanism is simple but powerful: before an agent generates output, it queries HydraDB for relevant prior lessons. After it generates, it writes new lessons back. Run 1 of any task scores 7/10. Run 2 recalls Run 1's mistakes and scores 8/10. Run 3 reaches 9/10. The improvement is provable, measurable, and visible in the live dashboard.
**Seven HydraDB capabilities used**: 1. **Knowledge ingestion** — agents write structured lessons after every task 2. **Sub-tenants per agent** — each role has isolated memory namespaces 3. **Shared org memory** — cross-role context for institutional knowledge 4. **Hybrid recall** — combines semantic search with structured filters 5. **Graph relations** — explicit links between related lessons and tasks 6. **Inference** — derived insights from accumulated lesson patterns 7. **Memory explorer** — search and relevance scoring for browsing all stored knowledge
**Live agent thinking log**: Every HydraDB query and storage operation streams to the UI via SSE. Judges and operators can watch the institutional memory get used and updated in real time — making the architecture legible rather than a black box.
**Run comparison view**: Side-by-side diff of two runs of the same task, showing score deltas, recalled context differences, and improvement badges. This is what makes "institutional learning" concrete instead of hand-wavy.
**Engineering rigor**: 325 unit tests across 21 suites covering backend logic, frontend rendering, API contracts, and SSE streaming. Fast tests (under 8 seconds full run) meant we could refactor the memory architecture at 2am without breaking agent communication.
Won the "a fun hack day (promise)" virtual hackathon on Discord — a 178-attendee event focused on serious technical builds.
- 7 specialized agents (PM, Architect, Developer, Reviewer, QA, SRE, CTO) with recall-generate-store loop
- Provable improvement across runs: 7/10 → 8/10 → 9/10 as lessons accumulate
- 7 distinct HydraDB capabilities: ingestion, sub-tenants, shared memory, hybrid recall, graph relations, inference, explorer
- Live agent thinking log with SSE streaming showing real-time HydraDB ops
- Run comparison view with score deltas, recalled context diffs, improvement badges
- 325 unit tests across 21 suites — backend, frontend, API, streaming all covered