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Own it, approve it, audit it.","maintainers":[{"name":"jackchen_me","email":"chenkaijie02@gmail.com"}],"readme":"<h1 align=\"center\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/open-multi-agent/open-multi-agent/main/.github/brand/logo-mark-dark.svg\">\n    <source media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/open-multi-agent/open-multi-agent/main/.github/brand/logo-mark-light.svg\">\n    <img alt=\"\" src=\"https://raw.githubusercontent.com/open-multi-agent/open-multi-agent/main/.github/brand/logo-mark-light.svg\" width=\"72\">\n  </picture>\n  <br>OMA\n</h1>\n\n<p align=\"center\">\n  <strong>Agents your organization can own, approve, and audit.</strong><br/>\n  OMA (Open Multi-Agent) is a self-hosted TypeScript agent runtime: consequential actions wait for durable, tamper-evident approvals, and every run leaves a record you can verify offline, byte for byte.\n</p>\n\n<p align=\"center\">\n  <a href=\"https://www.npmjs.com/package/@open-multi-agent/core\"><img src=\"https://img.shields.io/npm/v/@open-multi-agent/core\" alt=\"npm version\"></a>\n  <a href=\"https://nodejs.org/\"><img src=\"https://img.shields.io/node/v/@open-multi-agent/core\" alt=\"Node.js version\"></a>\n  <a href=\"https://github.com/open-multi-agent/open-multi-agent/actions/workflows/ci.yml\"><img src=\"https://github.com/open-multi-agent/open-multi-agent/actions/workflows/ci.yml/badge.svg\" alt=\"CI\"></a>\n  <a href=\"https://github.com/open-multi-agent/open-multi-agent/actions/workflows/supply-chain-audit.yml\"><img src=\"https://github.com/open-multi-agent/open-multi-agent/actions/workflows/supply-chain-audit.yml/badge.svg\" alt=\"Supply chain audit\"></a>\n  <a href=\"./LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-green\" alt=\"MIT License\"></a>\n  <a href=\"https://codecov.io/gh/open-multi-agent/open-multi-agent\"><img src=\"https://codecov.io/gh/open-multi-agent/open-multi-agent/graph/badge.svg\" alt=\"codecov\"></a>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://open-multi-agent.com/?utm_source=npm&utm_medium=package_readme\">Website</a> ·\n  <a href=\"https://open-multi-agent.com/getting-started/introduction/?utm_source=npm&utm_medium=package_readme\">Docs</a> ·\n  <a href=\"https://www.npmjs.com/package/@open-multi-agent/core\">npm</a> ·\n  <a href=\"https://github.com/open-multi-agent/open-multi-agent/discussions\">Discussions</a>\n</p>\n\n<p align=\"center\">\n  <strong>English</strong> · <a href=\"./README_zh.md\">中文</a>\n</p>\n\n<br />\n\n`@open-multi-agent/core` is the OMA orchestration runtime for TypeScript backends. Give it one agent, an explicit task graph, or a **dynamic workflow** that the coordinator generates from a goal at runtime.\n\nThe runtime schedules dependencies, runs independent work in parallel, shares context across agents, and returns an inspectable result. For product positioning and known users, see the [project overview](https://github.com/open-multi-agent/open-multi-agent#readme).\n\n## Contents\n\n[Quick Start](#quick-start) · [Execution Modes](#execution-modes) · [Scheduling](#scheduling) · [Capabilities](#capabilities) · [Architecture](#architecture) · [Examples](#examples) · [Providers](#providers) · [Production](#production) · [Documentation](#documentation)\n\n## Quick Start\n\nRequires Node.js 20 or newer. For production, use a currently maintained\nNode.js LTS release. Node.js 20 is upstream-EOL and retained only as a\nmigration compatibility window; OMA will remove it in the next major release,\nno earlier than 2026-10-31. Scaffold and run a starter in one command:\n\n```bash\nnpm create oma-app@latest my-oma\n```\n\nIn an interactive terminal, the scaffolder selects a starter and Cloud/Ollama runtime, installs dependencies, then runs a deterministic demo and produces an offline dashboard. The demo uses scripted model responses, needs no API key, and makes no model request; OMA orchestration still runs locally for real. Pass `--no-install` to generate files only, or `--no-run` to install without starting the demo.\n\nTo add OMA to an existing backend:\n\n```bash\nnpm install @open-multi-agent/core\n```\n\n```typescript\nimport { OpenMultiAgent, type AgentConfig } from '@open-multi-agent/core'\n\nconst model = process.env.OMA_MODEL ?? 'gpt-5.4'\n\nconst agents: AgentConfig[] = [\n  { name: 'researcher', model, systemPrompt: 'Find the relevant facts.' },\n  { name: 'analyst', model, systemPrompt: 'Compare evidence and identify tradeoffs.' },\n]\n\nconst orchestrator = new OpenMultiAgent({\n  defaultProvider: 'openai',\n  defaultModel: model,\n})\n\nconst team = orchestrator.createTeam('research-team', {\n  name: 'research-team',\n  agents,\n  sharedMemory: true,\n})\n\nconst result = await orchestrator.runTeam(team, 'Compare three approaches and recommend one.')\nconsole.log(result.agentResults.get('coordinator')?.output)\n```\n\n<details>\n<summary>Pause consequential tool calls for approval</summary>\n\n```typescript\nimport { FileStore, OpenMultiAgent } from '@open-multi-agent/core'\n\n// Your keys and your endpoint: a hosted provider, or a local server through baseURL.\nconst oma = new OpenMultiAgent({\n  defaultProvider: 'openai',\n  defaultModel: 'gpt-5.4',\n  // Consequential tool calls (file writes, shell) pause for a human decision.\n  onToolCall: ({ consequential }) => (consequential ? { action: 'suspend' } : { action: 'allow' }),\n})\n\nconst team = oma.createTeam('ops', {\n  name: 'ops',\n  agents: [{ name: 'operator', systemPrompt: 'Reconcile overdue invoices.', toolPreset: 'readwrite' }],\n})\n\n// The coordinator plans the task DAG from the goal; the checkpoint store keeps the run durable.\nconst result = await oma.runTeam(team, 'Find overdue invoices and draft the reminders.', {\n  checkpoint: { store: new FileStore('./.oma/run.json') },\n})\n\n// result.status?.code === 'suspended' until a reviewer decides result.pendingApprovals,\n// each bound to a hash of exactly what the reviewer was shown.\n```\n\n</details>\n\nSet `OPENAI_API_KEY` for this example. For other hosted or local models, see [Providers](#providers).\n\n## Execution Modes\n\n| Mode | Method | When to use | Example |\n|------|--------|-------------|---------|\n| Single agent | `runAgent()` | One agent, one prompt | [`basics/single-agent`](examples/basics/single-agent.ts) |\n| Auto-orchestrated team | `runTeam()` | Give a goal, let the coordinator plan and execute | [`basics/team-collaboration`](examples/basics/team-collaboration.ts) |\n| Explicit pipeline | `runTasks()` | You define the task graph and assignments | [`basics/task-pipeline`](examples/basics/task-pipeline.ts) |\n\nUse `planOnly` to inspect a generated task graph before execution, then `createPlanArtifact()` and `runFromPlan()` to replay it. `runConsensus()` adds a proposer→judge verification loop when one answer needs extra scrutiny.\n\n### Structured single-agent input\n\n`Agent.run()`, `Agent.stream()`, and `OpenMultiAgent.runAgent()` keep the string form above and also accept a complete `LLMMessage[]`, for caller-owned conversation history or blocks such as base64 images. Structured input is validated and defensively copied, and process and ACP backends stay string-only: they reject structured arguments rather than discarding history or images. See [structured agent input](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/structured-input.md) for copy, hook, and external-backend semantics, or run [`basics/structured-input`](examples/basics/structured-input.ts).\n\n### Execution routing\n\n`runTeam()` uses the deterministic router by default and makes no extra model call. `executionRouting: { strategy: 'hybrid' }` keeps deterministic Team decisions and sends only Single candidates to a one-call, no-tool `TaskProfiler`; results then expose `routingDecision` and `semanticRoutingAssessment`. The Profiler falls back to the Coordinator adapter and then the orchestrator's default provider, so it can make a provider call even when every worker has its own adapter. See [execution routing](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/execution-routing.md) for that provider boundary and the full policy precedence; [model routing](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/model-routing.md) selects models inside the chosen topology.\n\n### Declared governance roles\n\nWhen an application must enforce named independent roles, declare that governance intent instead of relying on wording in the goal:\n\n```typescript\nconst governed = await orchestrator.runTeam(team, 'Review the evidence and assess the risk.', {\n  governanceIntent: 'required',\n  requiredRoles: ['researcher', 'analyst'],\n  requiredOrder: ['researcher', 'analyst'],\n})\n\nif (governed.governanceConclusion !== 'satisfied') {\n  throw new Error('Required governance was not satisfied by the executed topology.')\n}\n```\n\nThe topology comes only from these structured fields, so equivalent goals in different languages produce the same roles and order. `governanceConclusion` comes from the structured execution receipt rather than from role names or approval wording in the model answer, so governance-sensitive applications must check it separately from `success`. See [declared governance roles](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/tool-configuration.md#declared-governance-roles-in-runteam).\n\n## Scheduling\n\nSet `schedulingStrategy` on `OpenMultiAgent` to choose how unassigned tasks are\nmapped to agents. The setting applies to coordinator-generated `runTeam()`\nplans and explicit or restored task queues. Tasks with an explicit `assignee`\nkeep that assignment.\n\nTask DAG execution is event-driven: a downstream task starts as soon as its\ndependencies are satisfied, without waiting for unrelated tasks from the same\nready set, and dependency outputs reach dependents as task-scoped results and\nvalidated structured handoffs.\n\n```typescript\nconst orchestrator = new OpenMultiAgent({\n  schedulingStrategy: 'composite',\n  schedulingWeights: { fit: 0.7, load: 0.3 },\n})\n```\n\n| Strategy | Assignment behavior | Recommended when |\n|----------|---------------------|------------------|\n| `dependency-first` (default) | Assigns tasks that unblock the most downstream work first, rotating eligible agents | The task graph has meaningful dependencies |\n| `round-robin` | Distributes tasks in queue order across eligible agents | Agents are interchangeable |\n| `least-busy` | Chooses the eligible agent with the fewest active or newly assigned tasks | Task duration varies and load balance matters |\n| `capability-match` | Filters explicit task requirements, then prefers declared capability tags before legacy keyword affinity | Tasks or agents declare differentiated requirements/capabilities |\n| `composite` | Ranks tasks by blocked dependents, then maximizes fit and available capacity across eligible agents | Criticality, capability fit, and current load should influence one decision |\n\nAgents may declare `description`, `capabilities`, `costTier`, and `latencyClass`, and tasks may add hard `requires` constraints; every strategy fails before worker execution when they cannot be satisfied. Weight semantics, load normalization, `strictAssignees`, and the `NO_ELIGIBLE_AGENT` and `INVALID_ASSIGNEE` failure modes are covered in [task scheduling and dispatch](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/task-scheduling.md).\n\n## Capabilities\n\n| Capability | What you get |\n|------------|--------------|\n| **Dynamic orchestration** | Runtime goal decomposition, dependency-aware scheduling, parallel branches, configurable assignment, task-scoped results and handoffs, opt-in team context for workers (`revealCoordinator`), and final synthesis. |\n| **Models and reasoning** | Mix built-in, OpenAI-compatible, AI SDK, or local models; map one `thinking` config to each provider's reasoning setting, route phases separately, and preserve reasoning only when explicitly enabled. |\n| **Tools and handoffs** | Built-in tools are default-deny; custom tools, MCP, and guarded `delegate_to_agent` handoffs are opt-in, and consequential tools on undeclared runs are flagged for confirmation. |\n| **Controlled outputs** | Send text or structured single-agent input, stream per agent, validate results with Zod, approve or durably suspend plans, task rounds, dispatches, and tool calls, rewrite messages/prompts or post-process results with `beforeRun` / `afterRun`, and cancel with `AbortSignal`. |\n| **Evaluation** | Version EvalSets, run reference scorers, gate CI with offline reports, persist results, or sample production runs on a best-effort path. |\n| **Memory and recovery** | Shared memory is pluggable; checkpoints resume interrupted runs without repeating completed tasks. |\n| **Observability** | Stable run identity, traces, execution receipts, redaction, TraceStore, and the offline DAG/Waterfall Viewer are available without a hosted service. |\n| **External agents** | ACP and process backends let coding CLIs participate while OMA keeps scheduling, memory, and budgets; the per-call tool gate, filesystem sandbox, and LLM egress policy do not cover them. |\n\n## Architecture\n\n```text\ngoal or explicit tasks\n         |\n         v\nCoordinator -> Task DAG -> Scheduler -> AgentPool\n                    |                       |-- LLM adapters\n                    |                       `-- tools / external backends\n                    |\n                    |-- SharedMemory / checkpoints\n                    |-- TraceRecord -> TraceStore / Run Viewer / OTel\n                    `-- results -> evaluation (offline / sampled, observe-only)\n```\n\nThe coordinator plans once by default; the scheduler owns execution order. Applications can opt into append-only adaptive recovery when task outcomes need to revise the unstarted part of the graph. Agents share results through memory, while checkpoints and traces form separate recovery and observability paths. Evaluation observes completed results and never changes them. Detailed contracts live in the linked subsystem guides below.\n\n## Examples\n\nStart with one example that matches the behavior you need:\n\n| Goal | Example |\n|---|---|\n| Send image blocks and caller-owned history | [`basics/structured-input`](examples/basics/structured-input.ts) |\n| See coordinator planning | [`basics/team-collaboration`](examples/basics/team-collaboration.ts) |\n| Build an explicit DAG | [`cookbook/contract-review-dag`](examples/cookbook/contract-review-dag.ts) |\n| Observe event-driven DAG dispatch | [`patterns/event-driven-dag`](examples/patterns/event-driven-dag.ts) |\n| Validate structured output | [`patterns/structured-output`](examples/patterns/structured-output.ts) |\n| Delegate between agents | [`patterns/agent-handoff`](examples/patterns/agent-handoff.ts) |\n| Replay a frozen plan | [`patterns/plan-replay`](examples/patterns/plan-replay.ts) |\n| Suspend and resume an approval | [`patterns/durable-approval`](examples/patterns/durable-approval.ts) |\n| Embed OMA in a backend | [`integrations/express-customer-support`](examples/integrations/express-customer-support/) |\n| Export an offline trace viewer | [`integrations/observability-v2/run-viewer`](examples/integrations/observability-v2/run-viewer.ts) |\n\nThe [example index](examples/README.md) lists every runnable example across basics, cookbook workflows, patterns, providers, and integrations.\n\n## Providers\n\nChange `provider`, `model`, and credentials; the agent shape stays the same.\n\n| Route | Use |\n|---|---|\n| Built in | Anthropic, OpenAI, Azure OpenAI, Copilot, Grok, DeepSeek, Doubao, Hunyuan, MiniMax, MiMo, Qiniu |\n| Optional peers | Gemini (`@google/genai`) and Bedrock (`@aws-sdk/client-bedrock-runtime`) |\n| OpenAI-compatible | Set `provider: 'openai'` + `baseURL` for Ollama, vLLM, LM Studio, OpenRouter, Groq, Mistral, Kimi, Qwen, or Zhipu |\n| AI SDK | Use `AISdkAdapter` with `ai` and your selected `@ai-sdk/*` provider (AI SDK 7 needs Node.js 22+) |\n\nOptional integrations load only when used: core directly installs only `@anthropic-ai/sdk`, `openai`, and `zod`; other SDKs are lazy-loading opt-in peers, and OpenTelemetry lives entirely in `@open-multi-agent/otel`. Dependency changes are weighed on demonstrated value plus security, size, maintenance, and compatibility cost, not a fixed count.\n\nImage generation and editing use a separate interface: `runImage()` tries an ordered chain of image models with per-model retries, fallback, output validation, and a record for every provider call, and ships native adapters for the OpenAI Images API, OpenRouter, Seedream on Volcengine Ark, and Black Forest Labs. See [image generation](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/image-generation.md).\n\nSee [Providers](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/providers.md), [framework-owned LLM egress policy](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/egress-policy.md), [Self-hosting and data residency](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/self-hosting.md), and [Tool configuration](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/tool-configuration.md) for credentials, models, the AI SDK bridge, reasoning settings, MCP, local endpoints, self-hosted deployment, and the exact network-enforcement boundary.\n\n**Provider sponsors**\n\nPaid sponsors supporting `open-multi-agent`. Sponsorship does not affect technical decisions or model recommendations.\n\n- **[Atlas Cloud](https://www.atlascloud.ai/console/coding-plan)**: Full-modal AI inference platform giving one API for video, image, and LLM across 300+ curated models. $5 credit vouchers for OMA users, first come first served. See the [Atlas Cloud setup guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/providers-atlascloud.md).\n\n## Production\n\n| Goal | Configure |\n|---|---|\n| Bound work | `maxTurns`, `timeoutMs`, `callTimeoutMs`, `contextStrategy`, [`loopDetection`](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/budgets-and-limits.md) |\n| Control spend | `maxTokenBudget`; `maxCostBudget` + application-owned `estimateCost` |\n| Limit tools | `tools` / `toolPreset`, `cwd` / `defaultCwd`, tool-output caps |\n| Recover | Task retries, checkpointing, `restore()`, and opt-in adaptive plan repair |\n| Own a run across workers | Opt-in [`runStore`](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/run-store.md): one execution lease per run, fenced checkpoint writes, durable lifecycle |\n| Review work | `planOnly`, inline approval callbacks, or [durable approval gates](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/durable-approvals.md); your application owns the approval surface and transport |\n| Observe | Trace sinks, TraceStore, execution receipts, Run Viewer, or the optional OTel adapter |\n\nBudget checks run at turn and task boundaries, so a run can overshoot by up to one model turn; they are not a cent-exact stop. `estimateCost` receives each call's token usage plus the agent, effective `model`, `provider`, phase, and `taskId`, and your application owns the price table. [Budgets and limits](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/budgets-and-limits.md) covers every ceiling, where it is checked, and what happens when one trips.\n\nBuilt-in tools are default-deny, and every model-visible tool result is sent to\nyour model provider, so grant read and exec access deliberately. Tools may keep\napplication-owned data separate while returning text, image, or file content\nthrough `modelOutput`; see the [tool configuration guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/tool-configuration.md#rich-image-and-file-results).\nFilesystem tools stay within the configured `cwd`; granted `bash` is not\nsandboxed. Its execution target can be replaced through a\n[`ShellExecutor`](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/sandbox-and-shell.md#shell-executors),\nwhile the default `LocalShellExecutor` preserves host execution and is not a\nsecurity boundary. Secrets are redacted from traces, shell output, and Viewer\npayloads by default, but result messages and checkpoints have their own\npersistence boundary.\n\n### Observability\n\nCore already provides run identity, trace sinks, execution receipts, queryable in-memory/file stores, and an offline Run Viewer. These cover local debugging, audit artifacts, and post-run analysis without OpenTelemetry.\n\n[`@open-multi-agent/otel`](https://github.com/open-multi-agent/open-multi-agent/blob/main/packages/otel/README.md) is an **optional integration** for teams that already operate a centralized OpenTelemetry stack. It converts OMA traces into standard OTel spans so multi-agent runs can join company-wide monitoring, alerting, and incident workflows. The application owns the provider and its lifecycle; telemetry failures never change the run result.\n\nSee the [observability guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/observability.md) and the [migration guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/observability-migration.md).\n\n<p align=\"center\">\n  <img src=\"https://raw.githubusercontent.com/open-multi-agent/open-multi-agent/main/.github/brand/demo-dashboard-hero.gif\" alt=\"OMA Run Viewer replaying a real multi-agent run: task DAG and span waterfall views with per-task status, assignee, tokens, and tool calls\" width=\"960\" height=\"540\" loading=\"lazy\">\n</p>\n<p align=\"center\"><em>The offline Run Viewer replaying a real run from the trace store: task DAG, span waterfall, and per-task evidence, with no hosted service involved.</em></p>\n\n### Run store and execution leases\n\nA checkpoint says what a run can resume from; it does not say who is allowed to resume it, so two workers can load the same snapshot and both advance it. The opt-in `runStore` adds the missing authority: one authoritative record per run holding its lifecycle status, an execution lease, and a monotonic fencing token. A worker acquires the lease before it dispatches anything, every checkpoint write is fenced with its token, and a run taken over by another worker stops instead of overwriting the new owner's state. Suspended runs stop depending on a live process, and an operator can cancel or resume one from outside the worker. Off by default, unchanged behavior when off, and documented in the [run store guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/run-store.md).\n\n### Run journal\n\nWhen a long run goes wrong, the record usually missing is what each agent actually saw at the moment it was asked. The opt-in run journal keeps it: every message and tool result as an appended event, plus the exact block a context strategy put in place of the turns it dropped, so a finished run can be read back instead of reconstructed by guesswork. `verifyRun()` then checks offline that every block the model saw is reproducible from the log rather than trusting the log's own account of itself, which establishes order and lineage rather than tamper-evidence, and `restore()` can resume from the last appended event instead of the last snapshot. It is off by default, costs nothing when off, and is documented in the [run journal guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/run-journal.md).\n\n## Documentation\n\n| Area | Guides |\n|---|---|\n| Build agents | [Providers](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/providers.md), [structured input](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/structured-input.md), [tools](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/tool-configuration.md), [sandbox and shell](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/sandbox-and-shell.md), [MCP](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/mcp.md), [context](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/context-management.md) |\n| Run reliably | [Evaluation](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/evaluation.md), [evaluation in CI](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/evaluation-ci.md), [checkpoint & resume](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/checkpoint.md), [run store and leases](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/run-store.md), [durable approvals](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/durable-approvals.md), [adaptive recovery](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/adaptive-recovery.md), [execution routing](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/execution-routing.md), [model routing](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/model-routing.md), [consensus](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/consensus.md), [errors](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/errors.md) |\n| Control workflows | [Coordinator](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/coordinator.md), [plan preview & replay](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/plan-replay.md), [shared memory](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/shared-memory.md), [hooks and callbacks](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/hooks-and-callbacks.md), [streaming](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/streaming.md), [budgets and limits](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/budgets-and-limits.md), [external agents](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/external-agents.md) |\n| Operate | [Observability](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/observability.md), [Run Viewer](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/run-viewer.md), [CLI](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/cli.md), [production checklist](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/production-checklist.md), [glossary](https://github.com/open-multi-agent/open-multi-agent/blob/main/docs/glossary.md), [production examples](examples/production/README.md) |\n\n## Contributing\n\nIssues and PRs are welcome. For production examples, follow the [acceptance criteria](examples/production/README.md); for code changes, see the [contribution guide](https://github.com/open-multi-agent/open-multi-agent/blob/main/.github/CONTRIBUTING.md).\n\n## Contributors\n\n<a href=\"https://github.com/open-multi-agent/open-multi-agent/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=open-multi-agent/open-multi-agent&max=100\" />\n</a>\n\nPer-contributor credits by area are in [CONTRIBUTORS.md](https://github.com/open-multi-agent/open-multi-agent/blob/main/CONTRIBUTORS.md).\n\n## License\n\nMIT\n\nMaintained by [YuanASI (Shenzhen YuanASI Technology Co., Ltd.)](https://yuanasi.com/en?utm_source=npm&utm_medium=package_readme&utm_campaign=open_multi_agent).\n","readmeFilename":"README.md"}