Open Multi Agent
Open-source TypeScript multi-agent framework for goal-driven and explicit task DAG orchestration
A strong pick for TypeScript teams that need controlled multi-agent orchestration without vendor lock-in. The goal-to-DAG workflow, default-deny tools, and offline evidence via Run Viewer are genuinely useful, and it's free. Skip it if you're not on TypeScript or need a managed cloud service—LangGraph (Python) or LangSmith (managed observability) may fit better. The durable approval and recovery features in v1.15.0 are clear differentiators for production trust.
Verified 6d ago · liveness 76/100 · cite: rightaichoice.com/tools/open-multi-agent
- TypeScript backend teams building production multi-agent systems
- Projects needing mixed-model orchestration without vendor lock-in
- Teams that require goal-driven task decomposition and explicit DAG control
- Use cases demanding fine-grained safety: approvals, consensus, budget limits
- Non-TypeScript ecosystems (no Python or other language support)
- Teams seeking a no-code agent builder
- Use cases needing real-time agent handoffs
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Skip Open Multi-Agent if you're not working in TypeScript/Node.js, need a no-code builder or a fully managed cloud service with UI dashboards, or require real-time agent handoffs—none of those are supported.
Goal-to-DAG decomposition consumes tokens at runtime, which can add up for simple repetitive tasks where a hand-coded graph would be cheaper.
Open Multi-Agent is free under MIT license, with no signup or usage limits. This undercuts managed orchestration services like CrewAI or AutoGen cloud tiers, which can charge per-seat or per-usage. For teams with engineering capacity to run their own infrastructure, it's a zero-cost alternative to LangGraph (free but Python) or LangSmith (paid observability).
In short
Open Multi Agent — Open-source TypeScript multi-agent framework for goal-driven and explicit task DAG orchestration. Best for TypeScript backend teams building production multi-agent systems, Projects needing mixed-model orchestration without vendor lock-in, Teams that require goal-driven task decomposition and explicit DAG control. Free to use.
What's new in Open Multi Agent
Checked 4 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
v1.17.0 released with opt-in run event journaling and checkpoint v5
v1.17.0 adds an opt-in append-only run journal, verifyRun() for reproducible runs, and checkpoint schema v5; JSON Schema required fields now derive from Zod optionality.
Incident Postmortems: Parallel Investigation, Serial Judgment
runTasks() DAG starts log, deployment, and blast-radius agents in parallel, keeping results unmerged in taskResults for analyst review.
Competitive Monitoring: Isolate Each Source Before Comparing Them
One agent per source extracts structured claims; aggregator reads validated records, preserving contradictions between vendor posts, threads, and articles.
Support Tickets: A Fixed Pipeline and a Routed Agent Team
Tutorial: keep high-volume support on a typed classify→draft→QA graph; escalations get a coordinator-built team with refunds behind a runtime-enforced confirmation gate.
Translation Drift: Route the Back-Translation to Another Model
Translate with one provider, back-translate with another via model-routing; reviewer returns structured drift findings; reviewer measured with a versioned EvalSet.
What people actually say about Open Multi Agent — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
44 mentions across 5 sources (Hacker News, YouTube, Product Hunt, GitHub, Lemmy) · researched Aug 3, 2026.
- +TypeScript-native with only 3 runtime dependencies — lightweight and portable.
- +Goal-driven coordinator auto-generates the task DAG, cutting boilerplate significantly.
- +Supports 13+ providers and any OpenAI-compatible endpoint — true mix-and-match.
- +Production guardrails: plan inspection, approval rounds, consensus passes, loop detection.
- +Opt-in tools/MCP servers give fine-grained control over agent capabilities.
- −Documentation is still catching up — advanced features lack clear guides.
- −OpenAI-compatible providers (DeepSeek, Mistral) may have tool-calling glitches.
- −Learning curve for runConsensus, approval rounds, and loop detection is steep.
- −Visual debugging tools are missing — tracing DAG execution is text-heavy.
- −Small ecosystem: few third-party integrations, mostly Vercel AI SDK.
- • No paid tiers, but self-hosting requires infrastructure (e.g., Docker) if you run local models.
- • Time investment for learning and integration is the real cost.
Viability Score
How well maintained and how widely used is Open Multi Agent? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Goal-driven coordination (runTeam) with automatic task DAG decomposition
- Explicit task graph execution (runTasks) with full control
- Single-agent execution (runAgent)
- Parallel execution of independent DAG nodes
- Mixed model providers: Anthropic, OpenAI, Gemini, Bedrock, Azure OpenAI, DeepSeek, Ollama, Gemma, and more
- OpenAI-compatible endpoint support
- MCP (Model Context Protocol) integration with opt-in tool access
- Streaming of tokens and node-state transitions
- Structured output with schema validation
- Cross-provider reasoning (thinking configs)
- Plan inspection before execution
- Approval rounds for tool calls (onToolCall)
- Consensus pass (runConsensus) for output validation
- Loop detection to halt repeating agents
- Budgets, retries, timeouts, and checkpoints at task boundaries
About Open Multi Agent
Open Multi-Agent (OMA) is an MIT-licensed, open-source TypeScript framework for building multi-agent systems that run entirely in your own environment. You can start from a goal and let the coordinator decompose it into a task DAG, or define the graph explicitly with runTasks() for full control over execution order. In goal mode, the framework routes work to the most capable agents, executes independent nodes in parallel, and synthesizes results—all while keeping policy at the boundaries that matter. Built for TypeScript backend and AI platform teams, OMA lets you mix models from providers like Anthropic, OpenAI, Gemini, Bedrock, Azure OpenAI, DeepSeek, and Ollama, plus any OpenAI-compatible endpoint. It supports the Model Context Protocol (MCP) and native tools under a default-deny permission model—no tool runs without your approval. Streaming, structured output, and cross-provider reasoning are built in. Reliability features include plan inspection before execution, approval rounds for tool calls, consensus passes (runConsensus), loop detection, budgets, retries, timeouts, and checkpoints. Recent releases added durable approval gates with checkpointed review state, mid-turn recovery that preserves runner messages and pending tool calls, and structured message history support in Agent.run(), stream(), and prompt(). Also added: adaptive plan recovery via PlanPatch, pluggable execution routing with a built-in DeterministicRouter, and an opt-in EgressPolicy to restrict LLM network requests. Every run produces evidence—task DAGs, token and cost rollups, tool calls, and status—viewable in an offline Run Viewer that never sends data to a hosted service. With over 6,800 GitHub stars and 55 contributors, OMA is a community-driven alternative to managed cloud orchestration. It's not for teams wanting a no-code builder or non-TypeScript ecosystems, but for those who value control, privacy, and observability without vendor lock-in, it's a strong fit.
Behind the Verdict
Open Multi-Agent hits a specific sweet spot: TypeScript teams building production multi-agent systems who want code-level control and privacy. It's not for everyone, and knowing who it's for is half the buying decision. If you live in the Node/TypeScript ecosystem and need to orchestrate agents from multiple providers—OpenAI, Anthropic, Gemini, DeepSeek, local Ollama—without clouds gatekeeping your data, OMA gives you a free, MIT-licensed foundation. The goal-to-DAG flow (runTeam) is the hook: type an outcome, and the framework decomposes it into a task graph you can inspect before execution. That plan-inspection step is a real trust win for teams that need to audit what agents will do. You also get explicit DAG control via runTasks(), so you can hard-code the topology when you know the flow and don't want the coordinator guessing. When to pick it: you're on TypeScript, you need mixed-model orchestration, and you care about data staying in your environment. The default-deny tool policy and approval gates (onToolCall) are serious governance features—your agents can't accidentally run a destructive bash command without an explicit yes. The event-driven scheduler and per-task retries with timeouts make it robust for production. The offline Run Viewer is a differentiator: you get full evidence—DAGs, token costs, tool calls—without shipping anything to a hosted service. Watch out for: it's TypeScript-only. If your stack is Python, LangGraph is the natural alternative, but you lose the provider-agnostic, MIT-licensed simplicity OMA offers. There's also no managed cloud UI—you're expected to run and monitor it yourself, though you can wire in OpenTelemetry for traces. For teams needing no-code builders, this isn't it. But if you're comfortable writing a bit of TypeScript and
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Real-world workflow fit
Concrete scenarios for the personas Open Multi Agent actually fits — and what changes day-one when you adopt it.
You need to add multi-agent orchestration to an existing Next.js app via a Vercel AI SDK integration. You install @open-multi-agent/core, define a runTeam() goal, and route to agents via the Vercel AI SDK.
Outcome: Within an hour you have a working multi-agent service that executes on your API routes, with streaming and structured output.
You're building a contract review system that needs parallel clause extraction, compliance checks, and summaries. You use runTasks() to define the DAG explicitly, executing nodes in parallel.
Outcome: The system processes contracts faster with parallel execution, and approval gates ensure each tool call is reviewed before it runs.
You need to automate incident postmortems. You set up a runTasks() DAG that starts log, deployment, and blast-radius agents in parallel, keeping results unmerged until the analyst reviews validated payloads.
Outcome: You get a structured postmortem report with evidence, traceability, and parallel investigation, reducing manual analysis time.
Use Cases
- Build adaptive customer support that routes to specialist agents based on the goal
- Run contract review with parallel clause extraction, compliance check, and summary
- Automate incident postmortems with parallel investigations feeding root-cause analysis
- Create a fully local multi-agent team using Ollama and Gemma at zero API cost
- Add multi-agent orchestration to an existing Vercel AI SDK app via a Next.js API route
- Decompose a complex research goal into a task DAG with mixed-model agents
- Run parallel meeting summarization with specialist agents for actions, sentiment, topics
- Conduct security vulnerability review with parallel attack surface, data security, and supply-chain analysis
Models Under the Hood
as of 2026-08-27
Limitations
- Open Multi-Agent is a TypeScript framework requiring Node.js 20 or newer as of v1.14.0.
- There is no managed cloud offering; you run and maintain it yourself.
- Goal-to-DAG decomposition incurs token costs at runtime, which can be less efficient than hand-coded graphs for simple repetitive workflows.
- The bundled openai dependency moved to v6 in v1.14.0, which may cause a nested duplicate if your project also uses the openai package directly.
as of 2026-08-20
Verification history
We have re-verified Open Multi Agent 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Open Multi Agent tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
MIT License
$0/mo
Ideal for
Individual developers, startups, and enterprises that want a free, self-hosted multi-agent framework with full source code access and no usage limits.
What this tier adds
This is the single free tier: you get the complete framework, all features, and community support via GitHub—no upsell or paid features.
Where the pricing makes sense
The company stage and team size where Open Multi Agent's pricing actually pencils out — and where peers do it cheaper.
Open Multi-Agent is free under MIT license, with no signup or usage limits. This undercuts managed orchestration services like CrewAI or AutoGen cloud tiers, which can charge per-seat or per-usage. For teams with engineering capacity to run their own infrastructure, it's a zero-cost alternative to LangGraph (free but Python) or LangSmith (paid observability).
Setup time & first value
How long it actually takes to get something useful out of Open Multi Agent — broken out by persona, not the marketing-page minute.
For a TypeScript developer, you can get started in about 15 minutes: run `npm create oma-app@latest my-oma`, then follow the README to set up your first goal-driven run (read-only key optional). For a production deployment with budgets, approvals, and durable checkpoints, budget a few hours to configure storage and policies.
Switching to or from Open Multi Agent
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangGraph (Python): You'll need to rewrite your agents and orchestrator in TypeScript. OMA's goal-mode can help plan the DAG, but you'll port the graph structure manually.
- ↗To LangGraph (Python): You'd need to rewrite your agents in Python and adapt the orchestration logic; OMA's explicit DAG can guide the structure.
- ↗To a managed service like CrewAI: You'd host your agents there, but you'd lose the offline evidence and default-deny tools unless you replicate them.
Integrations
Resources & Guides
Tutorials & Learning
Tools that pair well with Open Multi Agent
Common stack mates teams adopt alongside Open Multi Agent, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Open Multi Agent vs Presto Voice
Presto Voice and Open Multi-Agent serve completely different needs. Presto is a specialized, enterprise-scale drive-thru voice AI for QSR chains seeking revenue lift and operational efficiency—backed by new adoptions like Dairy Queen. Open Multi-Agent is a free, open-source TypeScript framework for developers building multi-agent systems with any LLM, offering cutting-edge goal decomposition and local execution. Choose Presto if you run a chain of drive-thrus; choose Open Multi-Agent if you build software agents.
Open Multi Agent vs Locus Robotics
These tools are not direct competitors. Locus Robotics is a physical warehouse automation solution for high-volume eCommerce and 3PL operations, requiring a RaaS subscription. Open Multi-Agent is a free open-source TypeScript framework for orchestrating AI agents with goal-driven DAG generation. Choose Locus if you need to move physical goods faster; choose OMA if you need to coordinate LLM-based software agents.
Open Multi Agent vs Truleo
Choose Truleo if you're a law enforcement agency needing to unify siloed data for investigations. Choose Open Multi-Agent if you're a TypeScript developer building a multi-agent system and want free, flexible orchestration. These tools serve entirely different markets—no direct overlap.
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Google's open-source ADK framework for production-grade AI agents in Python, TypeScript, Go, Java, and Kotlin.
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