SYSTEM NOMINAL
|UPTIME 99.97%|8 RUNNING · 2 QUEUED · 2 IDLE2026-05-30 06:52:26 UTC

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Wire LLMs, APIs, and custom logic into autonomous agents that execute multi-step workflows while you sleep. No Zapier duct-tape. No Python glue scripts. Just agents that run.

Agents Deployed

0

across 342 orgs · updated 05:10:54

Tasks Completed · Last 24h

0

+12.4% vs prior day · p99 latency 3.2s

Avg Execution Time

1.4s

median across all agent types · SLA 5s

Agent Status Grid
8 Running2 Queued2 Idle
Agent IDWorkflowModelTasksLatencyStatus
agt-001invoice-parserGPT-4o2,8470.9s
RUNNING
agt-002crm-enricherClaude 3.55,1021.2s
RUNNING
agt-003lead-qualifierGPT-4o1,2931.1s
RUNNING
agt-004contract-reviewerClaude 3.5——
QUEUED
agt-005data-normalizerGemini 1.58,8410.7s
RUNNING
agt-006support-routerGPT-4o-mini3,2170.4s
RUNNING
agt-007report-generatorGPT-4o——
IDLE
agt-008email-drafterClaude 3.5——
QUEUED
agt-009code-reviewerGPT-4o4,1292.1s
RUNNING
agt-010ticket-triageGPT-4o-mini9,9040.3s
RUNNING
agt-011forecast-agentGemini 1.5——
IDLE
agt-012doc-indexertext-embed-33,3660.6s
RUNNING
Showing 12 of 1,247 agents · Auto-refresh 2sLast sync: 06:52:26 UTC
Spec Sheet

Every column you were
about to check manually.

Technical evaluation criteria, side by side. Click any row to expand rate limits, webhook schemas, and retry policies.

SpecificationOrchestrate✓ZapierLangChainn8n
Setup
Time to first agent
< 5 minutes
15–30 min
2–4 hours
30–60 min

Quick-start via CLI or API

Orchestrate ships a single CLI command that scaffolds your first agent with model binding, retry policy, and webhook endpoint pre-configured.

$ npm install -g @orchestrate/cli
$ orchestrate init --model gpt-4o --name invoice-parser
✓ Agent scaffolded in ./agents/invoice-parser
✓ Webhook endpoint: https://run.orchestrate.io/agt-001
✓ Retry policy: 3x exponential backoff
$ orchestrate deploy
→ Agent live in 4.2s
Infrastructure required
None (managed)
None (managed)
Full stack (self-managed)
Self-host or cloud
Models
Supported LLMs
40+ models
OpenAI only
All (code required)
12 via nodes

Model registry — 40+ providers

Orchestrate maintains a live model registry covering OpenAI, Anthropic, Google Gemini, Mistral, Cohere, and 35+ open-source models via Ollama and HuggingFace endpoints. Model routing can be rule-based (cost, latency, capability) or AI-selected per task.

// model_config.yaml
model:
  primary: gpt-4o
  fallback: claude-3-5-sonnet
  routing:
    cost_ceiling: $0.02/task
    latency_sla: 3s
    auto_failover: true
Logic
Branching depth
Unlimited (graph)
2 levels (Paths)
Unlimited (code)
5 levels

DAG-based execution graph

Orchestrate models workflows as directed acyclic graphs (DAGs). Each node can branch to N downstream nodes based on output conditions, model confidence scores, or arbitrary custom logic. Cycles are supported for retry loops.

// workflow.json (excerpt)
{
  "nodes": {
    "classify": { "model": "gpt-4o-mini", "next": ["approve", "escalate", "reject"] },
    "approve": { "action": "crm.updateStatus", "next": ["notify"] },
    "escalate": { "action": "slack.alert", "condition": "confidence < 0.7" }
  }
}
Parallel execution
Yes
No
Yes
Yes
Reliability
Error recovery
Auto-retry + fallback model
Manual replay only
Custom code required
Workflow-level retry

Three-layer error recovery

Layer 1: Per-step retry with exponential backoff (configurable). Layer 2: Fallback model routing when primary LLM fails or exceeds latency SLA. Layer 3: Dead-letter queue with human escalation webhook for unrecoverable failures.

retry_policy:
  max_attempts: 3
  backoff: exponential
  initial_delay: 500ms
  max_delay: 30s
  on_exhausted:
    action: dead_letter_queue
    notify: ops@yourco.com
    include: [trace_id, inputs, error]
Uptime SLA
99.9% (contractual)
99.9% (best-effort)
Self-managed
99.5% (cloud)
Observability
Audit logging
Full trace + inputs/outputs
Basic task history
Custom (LangSmith)
Execution logs only

Immutable execution traces

Every agent execution produces a cryptographically signed trace log including: model inputs/outputs, tool calls, latency breakdown per step, token counts, cost attribution, and operator identity. Logs are queryable via API and exportable to S3, Datadog, or Splunk.

GET /v1/traces/agt-001/exec-2026022705104
{
  "trace_id": "tr_8xK2mN9pQ",
  "agent": "invoice-parser",
  "duration_ms": 1243,
  "steps": [
    { "step": "extract", "model": "gpt-4o", "tokens": 847, "cost": 0.0042 },
    { "step": "validate", "tool": "schema_check", "passed": true },
    { "step": "post", "action": "crm.create", "status": 201 }
  ],
  "signed": "sha256:a3f9..."
}
Compliance
SOC 2 Type II
Yes
Yes
No
No
Data residency (EU/US)
Yes
No
No
Yes
Cost
Pricing model
Per execution (flat)
Per task (step × records)
Infrastructure cost
Per workflow run

Flat per-execution pricing — no task multiplication

Zapier bills per task, meaning a 10-step workflow processing 100 records = 1,000 tasks billed. Orchestrate bills per agent execution regardless of internal step count or record volume. A 50-step workflow processing 10,000 records = 1 execution billed.

Rate limits
10,000 exec/min (Enterprise)
100 tasks/min
API-dependent
500 exec/min (cloud)

Rate limit specifications

Orchestrate rate limits are enforced at the organization level, not per-workflow. Burst allowance is 3× sustained rate for up to 60 seconds. Limits scale automatically with plan tier and can be raised on request with 24h SLA.

// Rate limit headers on every response
X-RateLimit-Limit: 10000
X-RateLimit-Remaining: 9847
X-RateLimit-Reset: 1740632400
X-Burst-Remaining: 29847
Retry-After: (only on 429)
Data sourced from public documentation · Last verified Feb 2026Download full spec PDF →
Execution Engine

Graph-based orchestration.
Branching logic that doesn't hit a ceiling.

Live Execution · invoice-parser
RUNNING
⚡TriggerWebhook · Schedule · API call
🧠ClassifyGPT-4o · confidence score
⑃BranchRoute by confidence threshold
🔗EnrichCRM lookup · data append
✓PostWrite to Salesforce · Slack alert
Step: Branchexec time: 0.79s
→model: gpt-4o
→tokens_in: 847
→tokens_out: 213
→confidence: 0.94
→next_branch: auto_approve
workflow.ts
1import { Agent, workflow } from '@orchestrate/sdk';
2
3const invoiceAgent = new Agent({
4 model: 'gpt-4o',
5 fallback: 'claude-3-5-sonnet',
6 retry: { max: 3, backoff: 'exponential' },
7});
8
9export const invoiceWorkflow = workflow({
10 trigger: { type: 'webhook', path: '/invoices' },
11 steps: [
12 {
13 id: 'extract',
14 agent: invoiceAgent,
15 prompt: 'Extract line items and total from invoice',
16 output: 'structured',
17 },
18 {
19 id: 'validate',
20 tool: 'schema.validate',
21 schema: InvoiceSchema,
22 on_fail: 'dead_letter',
23 },
24 {
25 id: 'route',
26 branch: [
27 { if: 'total > 10000', next: 'human_review' },
28 { if: 'confidence < 0.8', next: 'human_review' },
29 { else: 'auto_approve' },
30 ],
31 },
32 ],
33});

Avg First-Year ROI

147%

Based on 342 production deployments

Ops Cost Reduction

30%

Analyst estimate, automated workflows

Hours Saved / Week

12.5h

Per operator using agent automation

Connects to
OpenAIAnthropicGeminiSalesforceHubSpotSlackGitHubPostgresStripeNotionJiraLinearZapierTwilioSendGridOpenAIAnthropicGeminiSalesforceHubSpotSlackGitHubPostgresStripeNotionJiraLinearZapierTwilioSendGrid
Field Reports

Engineering teams who
stopped building and started shipping.

"We replaced four Zapier zaps, two Python cron scripts, and a shared spreadsheet with one Orchestrate workflow. It's been running for six weeks without a single page."
Marcus Okonkwo profile photo

Marcus Okonkwo

VP Engineering · Meridian Fintech

6 wks

zero incidents

"The spec comparison sold my CTO in one meeting. We didn't have to build a deck — we just sent the link. Closed the eval in 9 days."
Priya Venkataraman profile photo

Priya Venkataraman

Engineering Lead · Stackwise Labs

9 days

eval to contract

"LangChain gave us flexibility but no guardrails. Orchestrate gave us both. The audit logs alone justify the cost — our compliance team went from skeptical to enthusiastic."
Devon Hargrove profile photo

Devon Hargrove

CTO · Elevate Health Systems

SOC 2

audit passed

"We were 3 weeks into building our own orchestration layer when we found Orchestrate. We stopped immediately. The branching logic handles cases we hadn't even designed for yet."
Fatima Al-Rashidi profile photo

Fatima Al-Rashidi

Head of Automation

50%+

of companies expected to adopt AI orchestration platforms by 2025

Gartner Analysis

85%

of enterprises report improved operational efficiency after adopting AI agent orchestration

Industry Report 2025

50%+

of companies expected to adopt AI orchestration platforms by 2025

Gartner Analysis

23%

CAGR — AI orchestration market 2023–2028

Market Research

300%

long-term ROI reported by teams with connected multi-step automation

McKinsey Ops Study

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