What this is

In the oh-my-* tradition: pi gives you a minimal, extensible agent runtime — this
repo gives you the whole configured outfit on top of it. omd is the bundled
terminal agent; every part below mounts as a pi extension and works with any
OpenAI-compatible backend.

• A memory that consolidates itself — runtime signals distill into facts, facts
  that recur across sessions get promoted, confident facts ground behavior next time.
  A TTL sweep keeps it bounded; every write passes a validation gate.
• A DAG execution engine (the flagship — details below) — a conductor turns tasks
  into typed node graphs that run concurrently, get verified by a cross-model skeptic,
  and escalate only on failure.
• Gates enforced in code, not prompts — plan mode blocks writes until the plan is
  aligned; an edit→verify gate blocks side-effect commands until typecheck/tests pass;
  dangerous commands hit a fail-closed classifier.
• An MCP router — every MCP server collapses behind one on-demand menu; large tool
  outputs offload to a searchable sandbox.
• A 17-skill bundle — 12 harness skills (session rituals, memory, verification,
  review) + 5 DAG skills, .claude/skills compatible, with a self-improvement flywheel
  that mines recurring wins into new skill proposals.

The model is the engine. This is the rest of the car.

The DAG engine

A single agent chat does everything serially in one context window; ad-hoc parallel
calls lose structure. The engine turns a task into a typed DAG of small nodes:

task ──▶ conductor (one LLM call) ──▶ plan { nodes, deps }
                                          │
                            ready-set concurrent scheduling
              ┌───────────┬───────────┼───────────┬───────────┐
          command       inproc      agent        map       primitive
          (CLI, no    (single-shot (tool-using (runtime   (tournament/router/
           LLM)        LLM call)   sub-agent)   fan-out)   race/escalation/saga)
              └───────────┴───────────┼───────────┴───────────┘
                               cross-model verifier
                                          │
                          pass ◀──────────┴──────────▶ fail → escalate & re-plan

• One node, one fault boundary — a failed node becomes a [failed] input
  downstream; the rest keeps running. Nodes start the moment their own deps settle.
• map nodes — when the work-list is unknown at plan time, a lister discovers it
  at runtime and a template stamps out one child per item, with stable resumable ids.
• A typed primitive menu — the conductor selects schema-validated control flow
  (parallel, pipeline, tournament, router, race, escalation, saga with
  reverse compensation), each unit-capped and fail-closed.
• Trust is external — plans are Zod-validated; a cross-model skeptic judges results
  (defaults to fail when uncertain); escalation to a stronger model happens only on
  failure, and only if you configured one.
• Recorded and resumable — plans, per-node results and token usage land in SQLite;
  artifact-hashed checkpoints let a rerun skip done nodes; planToMermaid() draws any
  run; an ε-greedy bandit learns per node-kind which configured model is worth it.

Quick start

git clone https://github.com/AbyssCN/oh-my-dag.git oh-my-dag
cd oh-my-dag
bun install

# any supported / OpenAI-compatible backend — no baked default
export OMD_RUNTIME_PROVIDER=deepseek
export OMD_RUNTIME_MODEL=deepseek-v4-pro
export DEEPSEEK_API_KEY=sk-...

bun run omd             # interactive terminal agent
bun run omd -p "..."    # one-shot, non-interactive

Or bun run init for an interactive wizard. bun link puts omd / oh-my-dag
on your PATH. Config lives in .env — copy .env.example.
Legacy XIHE_* env names are still accepted. Requires Bun ≥ 1.3.

In the TUI

From the shell (no TUI needed)

Each script has --help; the matching skill in skills/ documents the discipline.

Status — what's real today

Honesty over marketing. As of v0.2:

Design rules

• Reliability lives outside the model. Validated plans, code-enforced gates,
  verifiers that default to fail, memory writes behind a validation gate.
• Wide over deep. Parallelism and fault isolation both come from width.
• Pay for quality only on failure. Cheap model works; escalation is opt-in and
  triggered by a failed verify, not by vibes.

License

MIT — see LICENSE.

这是什么

oh-my-* 传统：pi 提供极简可扩展的 agent 运行时，这个仓库给它配上整套装备。
omd 是内置终端 agent；以下每一层都以 pi extension 挂载，任意 OpenAI 兼容后端可用。

• 自我整理的记忆——运行时信号蒸馏成事实，跨 session 复现的事实升级为 confident，
  confident 事实落地为下次的行为；TTL 清扫保持有界，每次写入过校验闸。
• DAG 执行引擎（旗舰）——conductor 把任务变成有类型的节点图并发执行：五类执行器
  （command / inproc / agent / map 运行时展开 / primitive 原语菜单含 saga 补偿回滚）、
  ready-set 就绪即跑调度、节点级故障边界、跨模型校验失败才升级、checkpoint 断点续跑、
  SQLite 落库 + Mermaid 画图、bandit 模型路由。
• 代码级闸门而非 prompt 劝告——计划模式阻断写操作直到方案对齐；写后必验闸拦住未验证
  改动后的副作用命令；危险命令过 fail-closed 分类器。
• MCP 路由——所有 MCP server 收敛到一个按需菜单，大输出卸载到可搜索沙箱。
• 17 个技能——12 个 harness 技能（session 仪式/记忆/验证/审查）+ 5 个 DAG 技能，
  兼容 .claude/skills，配自进化飞轮（复现的成功模式挖掘成新技能提案）。

模型是引擎，这里是整辆车的其余部分。

快速开始

见上方英文 Quick start：bun install 后设 OMD_RUNTIME_PROVIDER/MODEL + 后端 key，
bun run omd 启动（旧 XIHE_* 环境变量仍兼容）。TUI 内 /cg /audit /iterate 等
命令、shell 侧 bun run dag-research|dag-council|dag-fanout|dag-review|dag-build。

现状（诚实版）

上方 Status 表为准：DAG 引擎全链路（五类执行器、原语菜单、校验升级、不动点迭代、
断点续跑、bandit 路由）、记忆层、计划模式、写后必验闸、MCP 路由、17 技能包——
已交付；计划模板重放、跨进程持久化工作流存储——在路线图上。

设计准则

可靠性在模型之外；宽优于深；只为失败付费。