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Tighten README and cap CI agent-semantic replay at 10 sessions.
Focus the README on N:M Worker reuse and semantic policy; keep functional e2e as the local default. Co-authored-by: Cursor <cursoragent@cursor.com>
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.github/workflows/e2e-kind.yaml

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E2E_SUITE: agent-semantic
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ACTORDOCK_API: http://127.0.0.1:18080
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EVAL_OUT_DIR: docs/eval/results
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AGENT_SEMANTIC_LIMIT: "0"
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AGENT_SEMANTIC_LIMIT: "10"
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AGENT_SEMANTIC_INFLIGHT: "8"
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AGENT_SEMANTIC_MIN_WORKERS: "2"
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AGENT_SEMANTIC_SPEED: "60"

README.md

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# actordock
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# Actordock
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Hundreds of agents, a handful of sandbox Pods—plus research on **how to prioritize and allocate** those sandboxes under scarce Workers.
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> **IMPORTANT:** Experimental research project. Not production-ready.
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Actordock multiplexes idle agent sandboxes onto a warm Worker pool (gVisor suspend/resume) and treats scheduling policy as a first-class, measurable concern.
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Actordock studies **sandbox multiplexing under scarce Workers**:
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## Quickstart (Kind)
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- **Worker reuse** — many sandboxes share a small Worker pool via Pause / Suspend / Resume (N:M), not one Pod per sandbox.
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- **Agent-semantic policy plugin** — on the same reuse hooks, decide who yields the slot using agent phase/lock and session score, not arrival order alone.
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## Components
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- **controlplane** — scheduling / Place·Suspend·Resume, signals, and policy
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- **worker** — gVisor runtime + local snapshots + upload/download
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- **policy** — pluggable strategies (including `semantic-score`)
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- **snapshotstore** — rustfs / object-store snapshots
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## Run
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```bash
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./hack/kind-up.sh # start cluster + deploy
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./hack/verify-local.sh # e2e/functional (default); E2E_SUITE=agent-semantic for policy replay
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./hack/kind-up.sh
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./hack/verify-local.sh # functional e2e (default)
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```
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Design & research docs: [docs/](./docs/). E2E: [e2e/](./e2e/).
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DeepSeek agent demo (sandbox `run_code` + semantic traces): [demos/agent-llm-multiplex/](./demos/agent-llm-multiplex/).

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