Currently in beta — free during preview

Orchestrate agents.
Automate workflows.

InferWeave is an enterprise-grade platform for designing, deploying, and managing multi-agent workflows. Coordinate teams of AI agents to automate complex business processes with built-in human oversight and full audit trails.

MIT Licensed · Changelog · Launching Q3 2026

quickstart.py
from inferweave import Agent, Workflow

# Define specialized agents with roles and tools
researcher = Agent(
    name="researcher",
    role="Gather and summarize market data",
    tools=["web_search", "doc_reader"],
    memory=True
)

analyst = Agent(
    name="analyst",
    role="Evaluate findings and produce report",
    tools=["calculator", "chart_gen"],
    memory=True
)

# Build the workflow with handoffs
workflow = Workflow(
    name="market-analysis",
    steps=[researcher, analyst],
    handoff="sequential",
    human_approval=True  # gate before final output
)

result = workflow.run(input="Q2 competitor landscape")
CLI pip install inferweave && inferweave init click to copy

Built for enterprise agent workflows

A complete platform for multi-agent orchestration. Define agent roles, design task dependencies, enforce human oversight, and monitor everything in production.

Visual workflow designer

Design multi-step agent workflows in a drag-and-drop canvas or in code. Define task dependencies, conditional branches, parallel execution lanes, and convergence points. Export to workflow.yaml for version control.

Agent role management

Create specialized agents with distinct roles, tool permissions, and memory scopes. Assign agents to tasks based on capability matching. Manage agent configurations centrally with agents.yaml.

Human-in-the-loop checkpoints

Insert approval gates at any point in a workflow. Reviewers receive structured summaries and can approve, reject, or redirect tasks. Configurable escalation policies and SLA timers keep processes moving.

Audit trails & enterprise SSO

Every agent action, tool invocation, handoff, and human decision is logged with full provenance. Integrate with your identity provider via SAML/OIDC for SSO. Role-based access control across all workflows and agents.

Three steps to production

Go from idea to deployed multi-agent workflow in minutes. Configuration is declarative and version-controlled.

1

Define your agents

Create agents in agents.yaml with roles, tool access, memory settings, and guardrails. Each agent is a composable building block for your workflows.

2

Design the workflow

Use the visual designer or write workflow.yaml to define task sequences, handoff protocols, parallel branches, and human approval gates.

3

Deploy and monitor

Run inferweave deploy to push your workflow. Monitor agent activity, task completion rates, and bottlenecks from the real-time dashboard.

workflow.yaml
# workflow.yaml
name: customer-onboarding
version: 1

agents:
  intake:
    role: Extract and validate customer data
    tools: [ocr, crm_lookup, validator]
  compliance:
    role: Run KYC/AML checks against regulations
    tools: [kyc_api, sanctions_db]
  provisioner:
    role: Set up accounts and send welcome kit
    tools: [account_api, email_sender]

steps:
  - agent: intake
    output: customer_profile
  - agent: compliance
    input: customer_profile
    human_approval: true
    sla_minutes: 30
  - agent: provisioner
    input: approved_profile

on_failure: escalate_to_human

Real workflow patterns

Two common patterns: defining agents with tools and memory in Python, and configuring human approval gates for sensitive workflows.

Agent with tools and persistent memory
agents.py
from inferweave import Agent, Tool, Memory

# Custom tool for the agent to use
crm_tool = Tool(
    name="crm_lookup",
    description="Search CRM by customer ID or name",
    endpoint="https://internal.corp/api/crm",
    auth="${CRM_TOKEN}"
)

support_agent = Agent(
    name="support-tier-1",
    role="Handle incoming support tickets, "
          "classify urgency, gather context",
    tools=[crm_tool, "knowledge_base"],
    memory=Memory(
        backend="redis",
        ttl_hours=48,
        scope="per_customer"
    ),
    guardrails=["no_pii_in_logs", "max_actions_10"]
)

# Register and deploy
support_agent.register()
Human approval gate configuration
approval_flow.yaml
# Multi-step procurement approval workflow
name: procurement-approval
version: 2
trigger: purchase_request_submitted

steps:
  - name: extract_details
    agent: doc_parser
    action: Parse PO and extract line items

  - name: vendor_check
    agent: compliance_checker
    action: Verify vendor against approved list
    depends_on: extract_details

  - name: manager_review
    type: human_gate
    assignee: ${requestor.manager}
    sla_minutes: 120
    escalate_to: vp_operations
    depends_on: vendor_check
    show_fields:
      - total_amount
      - vendor_name
      - compliance_status

  - name: execute_order
    agent: procurement_agent
    action: Submit PO to vendor system
    depends_on: manager_review

Simple, transparent pricing

You pay for InferWeave platform usage. Bring your own model provider keys — no markup on underlying LLM costs.

Free
$0 / mo
For prototyping and personal projects
  • Up to 5 agents
  • 3 active workflows
  • 1,000 workflow runs / month
  • Community Discord support
  • 7-day execution log retention
Get Started
Enterprise
Custom
For organizations with advanced requirements
  • Unlimited workflow runs
  • Dedicated infrastructure
  • Enterprise SSO (SAML / OIDC)
  • Role-based access control
  • Unlimited audit trail retention
  • On-prem deployment option
  • Dedicated support engineer
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