@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.

The 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.

Contents

Quick Start · Execution Modes · Scheduling · Capabilities · Architecture · Examples · Providers · Production · Documentation

Quick Start

Requires Node.js 20 or newer. For production, use a currently maintained Node.js LTS release. Scaffold and run a starter in one command:

npm create oma-app@latest my-oma

In 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.

To add OMA to an existing backend:

npm install @open-multi-agent/core

import { OpenMultiAgent, type AgentConfig } from '@open-multi-agent/core'

const model = process.env.OMA_MODEL ?? 'gpt-5.4'

const agents: AgentConfig[] = [
  { name: 'researcher', model, systemPrompt: 'Find the relevant facts.' },
  { name: 'analyst', model, systemPrompt: 'Compare evidence and identify tradeoffs.' },
]

const orchestrator = new OpenMultiAgent({
  defaultProvider: 'openai',
  defaultModel: model,
})

const team = orchestrator.createTeam('research-team', {
  name: 'research-team',
  agents,
  sharedMemory: true,
})

const result = await orchestrator.runTeam(team, 'Compare three approaches and recommend one.')
console.log(result.agentResults.get('coordinator')?.output)

Set OPENAI_API_KEY for this example. For other hosted or local models, see Providers.

Execution Modes

| Mode | Method | When to use | Example | |------|--------|-------------|---------| | Single agent | runAgent() | One agent, one prompt | basics/single-agent | | Auto-orchestrated team | runTeam() | Give a goal, let the coordinator plan and execute | basics/team-collaboration | | Explicit pipeline | runTasks() | You define the task graph and assignments | basics/task-pipeline |

Use 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.

Structured single-agent input

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 for copy, hook, and external-backend semantics, or run basics/structured-input.

Execution routing

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 for that provider boundary and the full policy precedence; model routing selects models inside the chosen topology.

Declared governance roles

When an application must enforce named independent roles, declare that governance intent instead of relying on wording in the goal:

const governed = await orchestrator.runTeam(team, 'Review the evidence and assess the risk.', {
  governanceIntent: 'required',
  requiredRoles: ['researcher', 'analyst'],
  requiredOrder: ['researcher', 'analyst'],
})

if (governed.governanceConclusion !== 'satisfied') {
  throw new Error('Required governance was not satisfied by the executed topology.')
}

The 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.

Scheduling

Set schedulingStrategy on OpenMultiAgent to choose how unassigned tasks are mapped to agents. The setting applies to coordinator-generated runTeam() plans and explicit or restored task queues. Tasks with an explicit assignee keep that assignment.

Task DAG execution is event-driven: a downstream task starts as soon as its dependencies are satisfied, without waiting for unrelated tasks from the same ready set, and dependency outputs reach dependents as task-scoped results and validated structured handoffs.

const orchestrator = new OpenMultiAgent({
  schedulingStrategy: 'composite',
  schedulingWeights: { fit: 0.7, load: 0.3 },
})

| Strategy | Assignment behavior | Recommended when | |----------|---------------------|------------------| | dependency-first (default) | Assigns tasks that unblock the most downstream work first, rotating eligible agents | The task graph has meaningful dependencies | | round-robin | Distributes tasks in queue order across eligible agents | Agents are interchangeable | | least-busy | Chooses the eligible agent with the fewest active or newly assigned tasks | Task duration varies and load balance matters | | capability-match | Filters explicit task requirements, then prefers declared capability tags before legacy keyword affinity | Tasks or agents declare differentiated requirements/capabilities | | 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 |

Agents 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.

Capabilities

| Capability | What you get | |------------|--------------| | 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. | | 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. | | 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. | | 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. | | Evaluation | Version EvalSets, run reference scorers, gate CI with offline reports, persist results, or sample production runs on a best-effort path. | | Memory and recovery | Shared memory is pluggable; checkpoints resume interrupted runs without repeating completed tasks. | | Observability | Stable run identity, traces, execution receipts, redaction, TraceStore, and the offline DAG/Waterfall Viewer are available without a hosted service. | | External agents | ACP and process backends let coding CLIs participate while OMA keeps scheduling, memory, and budgets. |

Architecture

goal or explicit tasks
         |
         v
Coordinator -> Task DAG -> Scheduler -> AgentPool
                    |                       |-- LLM adapters
                    |                       `-- tools / external backends
                    |
                    |-- SharedMemory / checkpoints
                    |-- TraceRecord -> TraceStore / Run Viewer / OTel
                    `-- results -> evaluation (offline / sampled, observe-only)

The 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.

Examples

Start with one example that matches the behavior you need:

| Goal | Example | |---|---| | Send image blocks and caller-owned history | basics/structured-input | | See coordinator planning | basics/team-collaboration | | Build an explicit DAG | cookbook/contract-review-dag | | Observe event-driven DAG dispatch | patterns/event-driven-dag | | Validate structured output | patterns/structured-output | | Delegate between agents | patterns/agent-handoff | | Replay a frozen plan | patterns/plan-replay | | Suspend and resume an approval | patterns/durable-approval | | Embed OMA in a backend | integrations/express-customer-support | | Export an offline trace viewer | integrations/observability-v2/run-viewer |

The example index lists 50+ runnable examples across basics, cookbook workflows, patterns, providers, and integrations.

Providers

Change provider, model, and credentials; the agent shape stays the same.

| Route | Use | |---|---| | Built in | Anthropic, OpenAI, Azure OpenAI, Copilot, Grok, DeepSeek, Doubao, Hunyuan, MiniMax, MiMo, Qiniu | | Optional peers | Gemini (@google/genai) and Bedrock (@aws-sdk/client-bedrock-runtime) | | OpenAI-compatible | Set provider: 'openai' + baseURL for Ollama, vLLM, LM Studio, OpenRouter, Groq, Mistral, Kimi, Qwen, or Zhipu | | AI SDK | Use AISdkAdapter with ai and your selected @ai-sdk/* provider (AI SDK 7 needs Node.js 22+) |

Optional 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.

See Providers, framework-owned LLM egress policy, and Tool configuration for credentials, models, the AI SDK bridge, reasoning settings, MCP, local endpoints, and the exact network-enforcement boundary.

Provider sponsors

Paid sponsors supporting open-multi-agent. Sponsorship does not affect technical decisions or model recommendations.

• Atlas Cloud: 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.

Production

| Goal | Configure | |---|---| | Bound work | maxTurns, timeoutMs, callTimeoutMs, contextStrategy, loopDetection | | Control spend | maxTokenBudget; maxCostBudget + application-owned estimateCost | | Limit tools | tools / toolPreset, cwd / defaultCwd, tool-output caps | | Recover | Task retries, checkpointing, restore(), and opt-in adaptive plan repair | | Review work | planOnly, inline approval callbacks, or durable approval gates | | Observe | Trace sinks, TraceStore, execution receipts, Run Viewer, or the optional OTel adapter |

Budget 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.

Built-in tools are default-deny, and every model-visible tool result is sent to your model provider, so grant read and exec access deliberately. Tools may keep application-owned data separate while returning text, image, or file content through modelOutput; see the tool configuration guide. Filesystem tools stay within the configured cwd; granted bash is not sandboxed. Its execution target can be replaced through a ShellExecutor, while the default LocalShellExecutor preserves host execution and is not a security boundary. Secrets are redacted from traces, shell output, and Viewer payloads by default, but result messages and checkpoints have their own persistence boundary.

Observability

Core 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.

@open-multi-agent/otel is an optional enterprise 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.

See the observability guide, migration guide, and performance guidance.

Run journal

When 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, 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.

Documentation

| Area | Guides | |---|---| | Build agents | Providers, structured input, tools, context | | Run reliably | Evaluation, checkpoint & resume, durable approvals, adaptive recovery, execution routing, model routing, consensus | | Control workflows | Plan preview & replay, shared memory, external agents | | Operate | Observability, CLI, production examples |

Contributing

Issues and PRs are welcome. For production examples, follow the acceptance criteria; for code changes, see the contribution guide.

Contributors

Per-contributor credits by area are in CONTRIBUTORS.md.

License

MIT