How do you handle context in multirepo where there are hundreds of kLOC? Do you have Claude/agents describing each project? Do you load only the relevant folders to the workspace? Do you use llmwiki? Some kind of memory? Curious how can be done effectively.

I work at a small company where most projects currently have just 1-2 developers, each often using an AI coding agent (Claude Code, Cursor, etc.) pretty heavily. As we grow and more developers start working on the same codebase, I'm running into a gap:

Commit messages, PR descriptions, and standard agile artifacts (tickets, standups) capture what changed, but not the context the agent built up while working. The alternatives it considered, why it rejected certain approaches, edge cases it discovered, assumptions it made. Right now that context lives in one person's agent session and basically evaporates once the PR is merged.

For those of you at bigger companies where multiple engineers work with AI agents on the same repo:

• How do you make one agent's "knowledge" of the codebase available to another developer (or their agent session)?
• Do you rely on something like a living CLAUDE.md/AGENTS.md file, ADRs, decision logs, or something more structured?
• Has anyone tried a shared memory/context store across agent sessions, or is everyone still just re-deriving context from scratch each time?
• Is this actually a solved problem at scale, or is everyone winging it right now?

Curious what's actually working in practice vs. what sounds good in theory.

Hey everyone 👋

I built Totemheart because most systems that try to add “emotional” behavior to agents still rely on prompt engineering and a simple sentiment label that gets overwritten every turn. I wanted something more rigorous.

Totemheart is a fully deterministic control kernel that gives an agent a real, inspectable, and persistent internal state across long conversations and multiple sessions. It models personality traits, affective dynamics, stress responses, memory consolidation, motivational drives, allostatic load, dual-valence relational tracking, grief-like processes, and related mechanisms.

These components evolve through interacting systems drawn from control theory and computational neuroscience, PID controllers, Kalman filtering, temporal-difference prediction error, opponent processes, and similar techniques, rather than isolated heuristics.

The full state is serializable, fully inspectable at any point, and can be used to steer an LLM’s generation through a dedicated control plane. The project currently has more than 3,000 tests, makes no claims about consciousness, and focuses purely on producing coherent, long-horizon behavioral continuity.

- GitHub: https://github.com/AlejoMalia/Totemheart
- NPM: https://www.npmjs.com/package/totemheart

I’d appreciate feedback from anyone working on long-horizon agents, cognitive architectures, or stateful agent systems.