open-multi-agent is a multi-agent orchestration framework for TypeScript backends. Give it a goal; a coordinator agent decomposes it into a task DAG, parallelizes independents, and synthesizes the result. Drops into any Node.js backend.

Your engineers describe the goal, not the graph.

Graph-first frameworks make you enumerate every node and edge up front. OMA runs a dynamic workflow: the task DAG is built at runtime, so it adapts to the goal instead of being hand-wired for one. The coordinator emits that plan as data for a deterministic scheduler to execute, so the plan is inspectable and replayable.

@open-multi-agent/core keeps a lightweight core. The orchestration engine plus the mainstream model providers (Anthropic, OpenAI, and any OpenAI-compatible endpoint) work out of the box; additional providers (Gemini, Bedrock), MCP, and the Vercel AI SDK bridge are opt-in peer dependencies you install only when you use them.

Contents

Quick Start · Three Ways to Run · Features · Orchestration Controls · Ecosystem · Examples · How Is This Different? · Architecture · Supported Providers · Production Checklist · Documentation · Contributing

Quick Start

Requires Node.js >= 18 (the optional Vercel AI SDK 7 bridge needs Node.js >= 22).

The fastest way to see a multi-agent run — scaffold a project and start it in one command:

npm create oma-app@latest

The first run shows the coordinator decompose one goal into a multi-agent DAG, then opens a dashboard of the run. To add the library to an existing project instead:

npm install @open-multi-agent/core

Migrating from @jackchen_me/open-multi-agent? That package is deprecated; install @open-multi-agent/core instead.

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

// Works with any OpenAI-compatible provider. Set OPENAI_API_KEY for OpenAI, or
// set OPENAI_BASE_URL + OMA_MODEL for Groq, DeepSeek, Ollama, etc.
const model = process.env.OMA_MODEL ?? 'gpt-5.4'

// Built-in tools are opt-in (default-deny): each agent gets only the tools it
// lists in `tools` (or a `toolPreset`). List neither and the agent gets none.
const agents: AgentConfig[] = [
  { name: 'architect', model, systemPrompt: 'Design clean API contracts.', tools: ['file_write'] },
  { name: 'developer', model, systemPrompt: 'Implement runnable TypeScript.', tools: ['bash', 'file_read', 'file_write', 'file_edit'] },
  { name: 'reviewer', model, systemPrompt: 'Review correctness and security.', tools: ['file_read', 'grep'] },
]

const orchestrator = new OpenMultiAgent({
  defaultProvider: 'openai',
  defaultModel: model,
  defaultBaseURL: process.env.OPENAI_BASE_URL, // unset = OpenAI
  onProgress: (event) => console.log(event.type, event.task ?? event.agent ?? ''),
})

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

// Built-in filesystem tools default to a `<cwd>/.agent-workspace` sandbox.
const result = await orchestrator.runTeam(
  team,
  `Create a REST API for a todo list in ${process.cwd()}/.agent-workspace/todo-api/`,
)

console.log(result.success, result.totalTokenUsage.output_tokens)

Run an example locally

git clone https://github.com/open-multi-agent/open-multi-agent && cd open-multi-agent
npm install
export OPENAI_API_KEY=sk-...
npx tsx packages/core/examples/basics/team-collaboration.ts

Three agents collaborate on a REST API while onProgress streams the coordinator's task DAG:

agent_start coordinator
task_start design-api
task_complete design-api
task_start implement-handlers
task_start scaffold-tests         // independent tasks run in parallel
task_complete scaffold-tests
task_complete implement-handlers
task_start review-code            // unblocked after implementation
task_complete review-code
agent_complete coordinator        // synthesizes final result
Success: true
Tokens: 12847 output tokens

Local models via Ollama need no API key, see providers/ollama. For hosted providers (OPENAI_API_KEY, GEMINI_API_KEY, etc.), see Supported Providers.

Three Ways to Run

| 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 |

For answers that need scrutiny, runConsensus() runs a proposer→judge verification loop (with an opt-in per-task verify hook). See Consensus.

Preview the coordinator's task DAG without executing it, or pin that plan and replay the same graph later without another coordinator call:

// Decompose once and review the plan
const preview = await orchestrator.runTeam(team, goal, { planOnly: true })

// Turn it into a diffable, version-controllable artifact (plain JSON)
const plan = orchestrator.createPlanArtifact(preview)

// Later: replay the exact graph (same task ids, deps, assignees), no coordinator
const result = await orchestrator.runFromPlan(team, plan)

Route orchestration phases to different models with an opt-in modelRouting policy: a flagship model plans, a cheap model runs the leaf tasks. Match by phase, agent, task role/priority, or leaf status; first match wins, and omitting it leaves model selection unchanged. See Model routing.

Features

| Capability | What you get | |------------|--------------| | Goal-driven coordinator | One runTeam(team, goal) call decomposes the goal into a task DAG, parallelizes independents, and synthesizes the result. Unassigned tasks are auto-scheduled — dependency-first (default), round-robin, least-busy, or capability-match. | | Mix providers in one team | 13 built-in providers plus any OpenAI-compatible endpoint (Ollama, vLLM, LM Studio, OpenRouter, Groq), mixed freely in one team. Local servers that emit tool calls as plain text are recovered by a fallback parser. (full list · setup) | | Extended thinking / reasoning | One thinking config maps to Anthropic thinking, Gemini thinkingConfig, and OpenAI reasoning_effort; reasoning is streamed as events, with opt-in preservation across a provider switch. (cross-provider-reasoning) | | Tools + MCP | 6 built-in (bash, file_*, grep, glob), all opt-in (default-deny — grant via tools / toolPreset), plus delegate_to_agent handoff (cycle + depth guards), custom tools via defineTool() + Zod, stdio MCP servers via connectMCPTools(). (tool config) | | Streaming + structured output | Token-by-token streaming on every adapter (per-agent during team runs via onAgentStream); Zod-validated final answer with auto-retry on parse failure. (structured-output) | | Human-in-the-loop | Gate execution with onPlanReady (approve the plan before any agent runs) and onApproval (approve between task rounds), or inspect first with planOnly. | | Pin and replay plans | Serialize a planOnly decomposition with createPlanArtifact, then runFromPlan replays the exact task graph without re-invoking the coordinator. (patterns/plan-replay) | | Lifecycle hooks + cancellation | beforeRun rewrites the prompt, afterRun post-processes or rejects the result; pass an AbortSignal to cancel a run in flight. | | Configurable coordinator | Override the coordinator's model, provider, adapter, system prompt, or tools via runTeam(team, goal, { coordinator }). | | External coding agents (ACP) | Swap an agent's LLM loop for an external coding CLI over the Agent Client Protocol: set backend: { kind: 'acp', … } and the subprocess runs its own turns, while the pool, scheduler, queue, shared memory, and budget stay backend-agnostic. (external agents) | | Observability | onProgress events, onTrace spans, post-run HTML dashboard rendering the executed task DAG, plus a run-level metrics rollup on TeamRunResult.metrics (tokens, retries, error/failure counts, task-duration stats). API keys and tokens are redacted from traces, bash output, and the dashboard. (observability guide) | | Pluggable shared memory | Default in-process KV; swap in Redis / Postgres / your own backend by implementing MemoryStore. (shared memory) | | Checkpoint & resume | Opt-in per-run checkpointing over any MemoryStore: snapshot on each completed task, then restore() skips finished tasks to continue after a crash or restart. The bundled zero-dependency FileStore makes checkpoints durable out of the box; best-effort saves never take the run down. (checkpoint & resume) | | Sandboxed filesystem workspace | Built-in filesystem tools are sandboxed to <cwd>/.agent-workspace by default; agents sharing the default configuration share this root. For per-agent isolation, set AgentConfig.cwd; for a different shared root, set OrchestratorConfig.defaultCwd; pass null to disable. (sandbox config) |

Production controls (context strategies, task retry with backoff, loop detection, tool output truncation/compression) are covered in the Production Checklist.

Orchestration Controls

Fine-grained control over a runTeam run. All optional; defaults keep behavior unchanged.

Inject team context. Prepend the goal, roster, and this worker's role to every worker prompt — helps workers stay aligned and makes multi-step runs easier to debug. Off by default; worker prompts stay byte-identical when omitted.

await orchestrator.runTeam(team, goal, { revealCoordinator: true })

Approve before running. Inspect the coordinator's plan before any agent executes, and again between task rounds. These live on the orchestrator. Returning false aborts; remaining tasks are marked skipped.

const orchestrator = new OpenMultiAgent({
  onPlanReady: async (tasks) => tasks.length <= 10,        // gate the whole plan
  onApproval:  async (completed, next) => next.length > 0, // gate each round
})

Cap estimated cost. Keep the price table in your app and provide an estimator. OMA passes the effective model, provider, phase, and taskId (when available) so you can price per model. The cap is checked at the same turn/task boundaries as maxTokenBudget, so a run can overshoot by up to one model turn; it is not a cent-exact stop.

const prices = {
  'gpt-5.4-mini': { input: 0.75, output: 4.5 }, // USD per 1M tokens
}

const orchestrator = new OpenMultiAgent({
  maxCostBudget: 0.25,
  estimateCost: (usage, { model }) => {
    const price = prices[model] ?? { input: 0, output: 0 }
    return (usage.input_tokens / 1_000_000) * price.input
      + (usage.output_tokens / 1_000_000) * price.output
  },
})

Cancel a run. Pass an AbortSignal; aborting stops the run in flight.

const controller = new AbortController()
const run = orchestrator.runTeam(team, goal, { abortSignal: controller.signal })
// controller.abort() from elsewhere to cancel

Configure the coordinator. Give the planner its own model, adapter, or extra instructions without touching the worker agents.

await orchestrator.runTeam(team, goal, {
  coordinator: { model: 'claude-opus-4-6', instructions: 'Prefer fewer, larger tasks.' },
})

Fan-out without dependencies. For MapReduce-style parallelism, use AgentPool.runParallel() directly. See patterns/fan-out-aggregate.

Shell & CI. Use the JSON-first oma binary. See docs/cli.md.

Ecosystem

open-multi-agent launched 2026-04-01 under MIT. Known users and integrations to date:

Built with OMA

• temodar-agent (~60 stars). WordPress security analysis platform by Ali Sünbül. Uses our built-in tools (bash, file_*, grep) directly inside a Docker runtime. Confirmed production use.
• Mark Galyan runs OMA fully offline on local quantized models, using the Coordinator and context compaction to keep autonomous agent loops alive under tight VRAM limits. Contributor since the framework's first month, across compaction, sampling, and tool-call parsing.
• PR-Copilot. AI pull-request review assistant by kidoom. Runs an OMA review team (coordinator + scoped reviewer agents), defines repo-context tools with defineTool, and adds a custom ContextStrategy for token-aware PR-diff compression. Public code on @open-multi-agent/core.
• StuFlow by znc15. Terminal AI coding assistant on OMA's orchestration core: builds a team and drives it through runAgent / runTasks / runTeam with a custom RunTeamOptions coordinator, paired with DeepSeek. Public code on @open-multi-agent/core.

Using open-multi-agent in production or a side project? Open a discussion and we will list it here.

Integrations

• Engram — "Git for AI memory." Syncs knowledge across agents instantly and flags conflicts. (repo)
• @agentsonar/oma — Sidecar detecting cross-run delegation cycles, repetition, and rate bursts.
• CodingScaffold — Agentic-coding scaffold that lists OMA as an optional orchestration backend, with a runTeam workflow template.

Built an integration? See the integration guide for how to submit a reference or vendor example and get your product listed.

Featured partner

For products and platforms with a deep open-multi-agent integration. See the Featured partner program for terms and how to apply.

Examples

examples/ is organized by category: basics, cookbook, patterns, providers, and integrations. See examples/README.md for the full index. (production/ is open for contributions — see the acceptance criteria.)

Real-world workflows (cookbook/)

End-to-end scenarios you can run today. Each one is a complete, opinionated workflow.

• contract-review-dag: four-task DAG for contract review with parallel branches and step-level retry on failure.
• meeting-summarizer: three specialised agents fan out on a transcript, an aggregator merges them into one Markdown report with action items and sentiment.
• competitive-monitoring: three parallel source agents extract claims from feeds; an aggregator cross-checks them and flags contradictions.
• translation-backtranslation: translate EN to target with one provider, back-translate with another, flag semantic drift.
• incident-postmortem-dag: three independent root tasks fan out at t=0, then a root-cause hypothesizer and postmortem writer synthesize them into one document.
• personalized-interview-simulator: a stateful interviewer (Agent.prompt() across turns) plus a transcript-reading observer, with readline human input and a Zod-validated debrief.

Patterns and integrations

• basics/team-collaboration: runTeam() coordinator pattern.
• patterns/structured-output: any agent returns Zod-validated JSON.
• patterns/multi-perspective-code-review: a generator feeds security, performance, and style reviewers running in parallel, then a synthesizer returns Zod-validated findings.
• patterns/cross-provider-reasoning: preserve a reasoning model's thought stream across a provider switch via preserveReasoningAsText.
• patterns/cost-tiered-pipeline: assign a different model per stage and estimate per-model USD cost from onTrace token counts.
• patterns/fan-out-aggregate: MapReduce-style fan-out via AgentPool.runParallel().
• patterns/agent-handoff: synchronous sub-agent delegation via delegate_to_agent.
• patterns/plan-replay: decompose a goal once with planOnly, serialize it with createPlanArtifact, then replay the same DAG via runFromPlan without re-running the coordinator.
• integrations/trace-observability: onTrace spans for LLM calls, tools, and tasks.
• integrations/mcp-github: expose an MCP server's tools to an agent via connectMCPTools().
• Provider examples: scripts under examples/providers/ covering hosted providers, OpenAI-compatible endpoints, and local models.

Full applications

Clone-and-run apps with their own package.json, not npx tsx scripts. Each embeds OMA in a real backend.

• integrations/express-customer-support: Express REST API. runTasks() behind POST /tickets with per-agent Zod schemas, swappable provider env vars, and HTTP error mapping. Runs on one DeepSeek key (npm install && npm start).
• integrations/with-vercel-ai-sdk: Next.js app. OMA runTeam() plus AI SDK useChat streaming (npm install && npm run dev).

Run any script with npx tsx packages/core/examples/<path>.ts; the full applications above use their own npm scripts.

How is this different from X?

Most TypeScript teams picking a multi-agent layer are really choosing between OMA, LangGraph JS, and Mastra. The mechanism is what differs.

vs. LangGraph JS. LangGraph has you design a declarative graph (nodes, edges, conditional routing) up front, then compiles it into an invokable; OMA's Coordinator decomposes the goal into a task DAG at runtime and auto-parallelizes independents. Both checkpoint and resume, though LangGraph's persistence ecosystem runs deeper. Reach for OMA when the plan should adapt to the goal instead of being wired ahead of time.

vs. Mastra. Both are TypeScript-native; the difference is who drives orchestration. Mastra has you wire the workflow by hand. OMA is goal-driven: hand its Coordinator a goal and it builds the task DAG at runtime. runTeam(team, goal) in one call.

vs. CrewAI. CrewAI is the established multi-agent option in Python. OMA brings goal-driven decomposition to TypeScript backends with a lean runtime (three core dependencies, plus opt-in peers you install only when you use them) and direct Node.js embedding, with no separate Python service to stand up alongside your stack.

vs. Vercel AI SDK. AI SDK is the LLM-call layer (provider abstraction, streaming, tool calls, and structured outputs), not a multi-agent orchestrator. Use it alone for single-agent calls; reach for OMA the moment you need a coordinated team. OMA even ships an optional AI SDK bridge.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│  OpenMultiAgent (Orchestrator)                                  │
│                                                                 │
│  createTeam()  runTeam()  runTasks()  runAgent()  getStatus()   │
└──────────────────────┬──────────────────────────────────────────┘
                       │
            ┌──────────▼──────────┐
            │  Team               │
            │  - AgentConfig[]    │
            │  - MessageBus       │
            │  - TaskQueue        │
            │  - SharedMemory     │
            └──────────┬──────────┘
                       │
         ┌─────────────┴─────────────┐
         │                           │
┌────────▼──────────┐    ┌───────────▼───────────┐
│  AgentPool        │    │  TaskQueue             │
│  - Semaphore      │    │  - dependency graph    │
│  - runParallel()  │    │  - auto unblock        │
└────────┬──────────┘    │  - cascade failure     │
         │               └───────────────────────┘
┌────────▼──────────┐
│  Agent            │
│  - run()          │    ┌────────────────────────┐
│  - prompt()       │───►│  LLMAdapter            │
│  - stream()       │    │  - 13 built-in         │
└────────┬──────────┘    │    providers           │
         │               │  - OpenAI-compatible   │
         │               │  - AI SDK bridge       │
         │               └────────────────────────┘
┌────────▼──────────┐
│  AgentRunner      │    ┌──────────────────────┐
│  - conversation   │───►│  ToolRegistry        │
│    loop           │    │  - defineTool()      │
│  - tool dispatch  │    │  - 6 built-in tools  │
└───────────────────┘    │  + delegate (opt-in) │
                         └──────────────────────┘

Supported Providers

Change provider, model, and set the env var. The agent config shape stays the same.

const agent: AgentConfig = {
  name: 'my-agent',
  provider: 'anthropic',
  model: 'claude-sonnet-4-6',
  systemPrompt: 'You are a helpful assistant.',
}

| Kind | How to configure | Services | |------|------------------|----------| | Built-in, no extra install | Set provider to anthropic, openai, azure-openai, copilot, grok, deepseek, doubao, hunyuan, minimax, mimo, or qiniu; the bundled @anthropic-ai/sdk / openai SDK supplies the endpoint. | Anthropic, OpenAI, Azure OpenAI, GitHub Copilot, xAI Grok, DeepSeek, Doubao (Volcengine), Hunyuan (Tencent MaaS), MiniMax, MiMo, Qiniu | | Built-in, needs a peer install | Set provider: 'gemini' after npm i @google/genai, or provider: 'bedrock' after npm i @aws-sdk/client-bedrock-runtime. | Google Gemini, AWS Bedrock | | OpenAI-compatible endpoints | Set provider: 'openai' plus baseURL and, when needed, apiKey. No extra install. | Ollama, vLLM, LM Studio, llama.cpp server, OpenRouter, Groq, Mistral, Moonshot (Kimi), Qwen, Zhipu | | Vercel AI SDK | Import AISdkAdapter from @open-multi-agent/core/ai-sdk; install optional peer ai plus an @ai-sdk/* provider. | Any AI SDK provider (60+ models and hosts) |

See docs/providers.md for env vars, model examples, local tool-calling, timeouts, and troubleshooting.

Dependencies

Installing @open-multi-agent/core pulls in three runtime dependencies: @anthropic-ai/sdk, openai, and zod. That is the entire core: Anthropic, OpenAI, and every OpenAI-compatible endpoint run on these three alone.

Everything else is an opt-in peer dependency you install only when you reach for it. Each loads lazily, so a project that never uses one never installs it.

| Capability | Install | Trigger | |------------|---------|---------| | Gemini provider | npm i @google/genai | provider: 'gemini' | | Bedrock provider | npm i @aws-sdk/client-bedrock-runtime | provider: 'bedrock' | | MCP tools | npm i @modelcontextprotocol/sdk | connectMCPTools() | | Vercel AI SDK bridge | npm i ai @ai-sdk/<provider> | new AISdkAdapter(...) |

Vercel AI SDK (optional)

With the bridge peers installed (see the table above), pass adapter: new AISdkAdapter(model) on AgentConfig to route that agent through the AI SDK instead of the built-in provider factory. provider, apiKey, baseURL, and region are ignored when adapter is set. Mixed teams work as usual: only agents with adapter use the AI SDK.

import { openai } from '@ai-sdk/openai'
import { AISdkAdapter } from '@open-multi-agent/core/ai-sdk'
import { OpenMultiAgent } from '@open-multi-agent/core'

const oma = new OpenMultiAgent()
await oma.runAgent(
  {
    name: 'researcher',
    model: 'gpt-4o',
    adapter: new AISdkAdapter(openai('gpt-4o')),
    systemPrompt: 'You are a researcher.',
  },
  'What are the latest AI trends?',
)

The coordinator accepts the same hook via runTeam(team, goal, { coordinator: { adapter: new AISdkAdapter(...) } }).

Production Checklist

Before going live, wire up the controls that protect token spend, recover from failure, and let you debug.

| Concern | Knob | Where it lives | |---------|------|----------------| | Bound the conversation | maxTurns per agent + contextStrategy (sliding-window / summarize / compact / custom) | AgentConfig | | Bound wall-clock time | timeoutMs per agent (aborts a run that hangs, common with local models) | AgentConfig | | Bound a single LLM call | callTimeoutMs per agent (aborts one stalled adapter.chat(), uniform across providers) | AgentConfig | | Cap tool output | maxToolOutputChars (or per-tool maxOutputChars) + compressToolResults: true | AgentConfig and defineTool() | | Recover from failure | Per-task maxRetries, retryDelayMs, retryBackoff (exponential multiplier) | Task config used via runTasks() | | Survive a crash or restart | checkpoint (pass a runId, or a durable MemoryStore like the bundled FileStore) + restore() to resume, skipping completed tasks | OrchestratorConfig / run options | | Hard-cap token spend | maxTokenBudget on the orchestrator | OrchestratorConfig | | Cap estimated cost | maxCostBudget + estimateCost; you own the per-model price table, and checks happen at turn/task boundaries rather than cent-exact mid-call stops | OrchestratorConfig | | Catch stuck agents | loopDetection with onLoopDetected: 'terminate' (or a custom handler) | AgentConfig | | Trace and audit | onTrace to your tracing backend; persist renderTeamRunDashboard(result) | OrchestratorConfig | | Redact secrets | Automatic — API keys, tokens, and Authorization headers stripped from traces, bash output, and dashboard payloads | built-in (on by default) | | Grant tools deliberately | Built-in tools are opt-in (default-deny): an agent gets only what it lists in tools / toolPreset; list neither and it gets none. bash stays unsandboxed once granted, and every tool result is sent to your model provider — so grant read/exec access on purpose. defaultToolPreset restores the old "all tools" behavior in one line | AgentConfig / OrchestratorConfig | | Bound filesystem reach | cwd / defaultCwd (default .agent-workspace subdir; widen with process.cwd(), disable with null) | AgentConfig / OrchestratorConfig |

Documentation

• Providers — env vars, model examples, local tool-calling, timeouts, troubleshooting.
• Tool configuration — tool presets, custom tools, the filesystem sandbox, and MCP.
• Observability — onProgress events, onTrace spans, and the post-run dashboard.
• Shared memory — the default store and custom MemoryStore backends.
• Checkpoint & resume — opt-in per-run snapshot/resume over any MemoryStore; survive crashes and restarts.
• Context management — sliding window, summarization, compaction, and custom compressors.
• CLI — the JSON-first oma binary for shell and CI.
• Consensus — the runConsensus proposer→judge primitive, the per-task verify hook, and the budget invariant.
• Model routing — the opt-in modelRouting policy: match by phase / agent / role / priority / leaf, first match wins.
• Plan preview & replay — preview the coordinator's task DAG with planOnly, freeze it with createPlanArtifact, then runFromPlan replays the exact graph without re-invoking the coordinator.

Contributing

Issues, feature requests, and PRs are welcome. Some areas where contributions would be especially valuable:

• Production examples. Real-world end-to-end workflows. See examples/production/README.md for the acceptance criteria and submission format.
• Documentation. Guides, tutorials, and API docs.
• Translations. Help translate this README into other languages. Open a PR.

Contributors

Framework features

• @ibrahimkzmv (token budget, context strategy, dependency-scoped context, tool presets, glob, MCP integration, configurable coordinator, CLI, dashboard rendering, trace event types)
• @apollo-mg (context compaction fix, sampling parameters)
• @tizerluo (onPlanReady, onAgentStream)
• @CodingBangboo (planOnly mode)
• @Xin-Mai (output schema validation)
• @JasonOA888 (AbortSignal support)
• @EchoOfZion (coordinator skip for simple goals)
• voidborne-d (OpenAI mixed-content fix, text-tool-extractor depth fix)
• @NamelessNATM (agent delegation base implementation)
• @MyPrototypeWhat (reasoning blocks, reasoning_effort, sampling parity, trace input/output)
• @SiMinus (streaming reasoning events)
• @matthewYang08 (OpenAI reasoning-to-text fallback)
• @dvirarad (OpenAI-family adapter hardening)
• @cat0825 (model routing policy, plan replay, structured shared-memory handoff)
• @mvanhorn (checkpoint & resume)
• @lesbass (run-level metrics rollup on TeamRunResult)
• @tlysanhuo (trace span parent linkage)
• @LambIessz (orchestrator cost budget, MessageBus persistence in checkpoints)
• @Bobuyoucrypto (Windows bash timeout process-tree kill)

Provider integrations

• @ibrahimkzmv (Gemini)
• @hkalex (DeepSeek, MiniMax)
• @marceloceccon (Grok)
• @Klarline (Azure OpenAI)
• @Deathwing (GitHub Copilot)
• @JackChiang233 (Qiniu)
• @CodingBangboo (AWS Bedrock)
• @kidoom (MiMo, Doubao)
• @KaitlynFeng (Hunyuan)
• @octo-patch (MiniMax-M3 model upgrade)

Examples & cookbook

• @mvanhorn (research aggregation, code review, meeting summarizer, Groq example, Mistral example)
• @Kinoo0 (code review upgrade)
• @Optimisttt (research aggregation upgrade)
• @Agentscreator (Engram memory integration)
• @fault-segment (contract-review DAG)
• @HuXiangyu123 (cost-tiered example)
• @zouhh22333-beep (translation/backtranslation)
• @pei-pei45 (competitive monitoring)
• @mmjwxbc (interview simulator)
• @binghuaren96 (incident postmortem DAG)
• @DaiMao-UT (paper replication triage)
• @oooooowoooooo (rare disease information triage)
• @CodingBangboo (Express customer support pipeline)
• @nuthalapativarun (Doubao and Zhipu provider examples)
• @goodneamtakenbydogs (Moonshot and Qwen provider examples)
• @suans4746-del (narrative puzzle hint arbitration)
• @gregkonush (Bilig WorkPaper MCP integration)

Docs & tests

• @tmchow (llama.cpp docs)
• @kenrogers (OpenRouter docs)
• @jadegold55 (LLM adapter test coverage)
• @btroops (DeepSeek tool-calling tests)
• @nuthalapativarun (context-management docs)
• @Oxygen56 (errors.ts tests, provider docs for Grok/DeepSeek/Doubao)
• @RheagalFire (LiteLLM gateway docs)

License

MIT