AI Workflow Infrastructure
The infra layer for
cost-efficient AI ops
Build multi-agent workflows in Python. Deploy in one command. Automatically optimized by a critic engine that cuts costs by 76%.
How it works
From idea to production in four steps.
Define your agent in Python or YAML. Our critic engine handles the rest.
# support_agent.py
from fibonacci import Workflow, LLMNode, ToolNode
wf = Workflow("Customer Support Agent")
ingest = ToolNode(
id="ingest_ticket",
tool="zendesk_read",
params={"ticket_id": "{{input.id}}"}
)
classify = LLMNode(
id="classify",
instruction="""Classify this ticket:
{{ingest_ticket}}
Categories: billing, technical, general"""
)Define your workflow in code
Write your agent logic in Python or YAML. Chain LLM nodes, tool integrations, and conditional logic into a workflow graph — just like writing any other script.
One command to production
Ship your workflow with a single command. Fibonacci handles orchestration, retry logic, rate limits, and secrets management — zero infra work.
AI optimizes your workflow
Our multi-agent critic engine iteratively reviews your workflow graph. It downgrades models where quality holds, adds caching for repeated calls, and injects retry logic — cutting costs by up to 76% automatically.
Watch it run in real time
Trigger workflows via API, schedule, or event. Monitor every node execution in real time with full observability — latency, cost, token usage, and step-level logs.
Get Started
One command. That's it.
Install the SDK and start building workflows in under a minute.
Enterprise Security
Your keys. Our vault.
Encrypted keychain storage with automatic API key redaction and audit logging.
Composable Integrations
100+ tools. One SDK.
Connect to every tool in your stack without managing a single webhook.
