Multi Bot Agentic
Description
Deterministic multi-provider AI-agent orchestrator — ODA loops, GPT/Claude/Gemini/Kimi adapters, safety controls, event log
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
multi-bot-agentic
`multi-bot-agentic` is a standalone AI-agent engineering showcase: a deterministic agent coordinator with explicit **Observe -> Decide -> Act** loops, durable event logs, rationale traces, provider adapters, and bounded safety controls.
It is built as a portfolio-quality recreation of the `multi-bot` product idea without depending on private infrastructure. The default path runs fully offline with a deterministic fake provider. Real adapters are included for GPT-5.5/OpenAI-compatible models, Claude Sonnet 4.6 via Claude Code CLI, Gemini 3.x, and Kimi K2/Moonshot.

Why It Exists
Most agent demos let the LLM decide everything. This repo takes the production-minded path:
- The LLM is an input source, not the control plane.
- A deterministic decision engine chooses actions.
- Every decision has a rationale trace.
- Every lifecycle transition is persisted.
- Every external integration goes through an adapter.
- Safety controls bound scope, runtime, tools, and cancellation.
Use Cases: Issues This Solves
1. "My agent did something, but I cannot explain why."
LLM-first agents often skip straight from prompt to action. When something goes wrong, the transcript may show what the model said, but not which control rule allowed the action.
`multi-bot-agentic` writes every decision as a durable event with a `RationaleTrace`: rule id, observations used, rejected actions, and explanation. You can replay the run later and inspect exactly why the engine chose `call_llm`, `call_tool`, `finish`, or `cancel`.
multi-bot-agentic replay --event-log data/runs.sqlite --event-type decision --format text
2. "I want to use AI agents, but I do not want the model directly executing tools."
Many agent frameworks let the model choose and invoke tools directly. That is convenient, but risky for production workflows where tool access should be explicit, bounded, and auditable.
This repo
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