Hive

Single-thread chat breaks the moment a task needs side effects — file edits, API calls, database lookups, deployment hooks — or when multiple domains need different temperaments (a cautious reviewer vs an aggressive refactorer). Naive multi-agent setups devolve into agents talking past each other or duplicating work because nobody owns state.

I also kept running into the “GasTown-shaped” problem: teams want pipeline semantics — queues, stages, approvals, replay — without giving up the flexibility of LLM reasoning inside each stage. Hive is where I explore how close we can get with a hobbyist’s resources but a production-minded mindset.

At the center is a workflow definition layer: nodes for agents, tools, branches, and merge points; edges carry typed payloads so downstream steps do not guess JSON shapes. Tooling is first-class: each tool has a schema, timeouts, idempotency hints where possible, and structured errors that can route to recovery agents instead of silent failure.

Runs are observable: structured logs, step timings, token and cost estimates, and replay from a checkpoint when something flaky happens mid-graph. Human gates are explicit nodes, not something you remember to ask for in a prompt.

Implementation leans on TypeScript for orchestration glue, with LLM providers behind narrow interfaces so models can be swapped or mixed per node without rewriting the graph runtime.

Hive is where I prototype the automations I actually want for my own repos: triage, doc generation, multi-file refactors with review passes, and “do the boring 80% then hand me the diff.” It is not a no-code toy; it is closer to a personal CI for knowledge work.

The direction is more shared presets (think GasTown-style community recipes), hardened sandboxes for tool execution, and clearer guarantees around secrets so orchestration stays safe as graphs get bigger.