LLMs plan.
Ploston executes.
Stop paying for LLMs to orchestrate your tools. Define deterministic YAML workflows. Let agents call them as MCP tools. Predictable, testable, reproducible.
LLM-driven orchestration is fundamentally broken
Letting an LLM decide every tool call at runtime introduces compounding costs, non-determinism, and opacity at exactly the layer that needs to be reliable.
Token cost explosion
Every tool call returns a result to the LLM, which re-reads context before calling the next tool. A 5-step workflow can consume 10,000+ tokens where 600 would suffice.
Non-deterministic behavior
The same request produces different tool sequences. You can't unit test it, you can't replay it, and you can't guarantee compliance. That's not production software — it's a roulette wheel.
Zero auditability
When an agent fails mid-workflow, debugging means deciphering opaque LLM reasoning. No trace. No replay. No way to tell ops, legal, or security exactly what happened.
Every step bounces through the LLM
The LLM acts as planner and executor — receiving tool results, deciding the next call, and re-reading the full context at each step. Costs and latency compound with every action.
Steps 2→3→4→5→6→7 repeat for every tool in the chain — each costing more tokens than the last.
The difference is dramatic
Same task: extract links from BBC and create a reminder for each. With Ploston, the LLM makes one call instead of twelve — and the result is identical every time.
Built for production. Loved by developers.
75–90% Token Savings
Multi-step workflows execute inside Ploston with zero LLM roundtrips. The agent calls once; Ploston handles every downstream step. Token costs collapse.
Deterministic by Design
Same inputs always produce the same outputs. No reasoning variance, no hallucinated steps, no surprises in production. Write tests. Build with confidence.
YAML Workflows + GitOps
Workflows are code. Version control, code review, CI/CD pipelines, rollback. Your existing engineering culture just works — no visual builder lock-in required.
Self-Hosted & Air-Gapped
Runs entirely in your infrastructure. Your data never leaves your environment. Supports Docker, Kubernetes, and air-gapped deployments.
Fully Testable Agents
Unit test workflows like any other code. Replay executions offline. Simulate edge cases. For the first time, agent behavior is verifiable before it ships.
Open MCP Ecosystem
Ploston proxies calls to any MCP server — filesystem, GitHub, Slack, databases, and the entire growing open ecosystem. Workflows become MCP tools themselves.
Three steps to deterministic agents
Define a YAML workflow
Write your multi-step tool sequence as a declarative YAML file. Steps, inputs, outputs, conditions — all version-controlled alongside your code.
Ploston exposes it as an MCP tool
Ploston registers the workflow as a named MCP tool. Your agent — Claude, GPT, any MCP client — discovers it automatically alongside native tools.
Agent calls once. Ploston handles the rest.
The LLM issues a single tool call. Ploston executes every step deterministically, handles retries, collects results, and returns one clean output.
Step 1 → Your workflow definition
# workflows/bbc-link-reminders.yaml
name: bbc-link-reminders
version: "1.0"
description: Extract links and create reminders
inputs:
- source_url
- keyword
steps:
- id: crawl
tool: firecrawl_scrape
params:
url: "{{ inputs.source_url }}"
- id: filter
depends_on: [crawl]
code: |
links = context.steps["crawl"].output.get("links", [])
keyword = context.inputs["keyword"].lower()
result = [l for l in links if keyword in l.get("title", "").lower()]
- id: remind
depends_on: [filter]
code: |
links = context.steps["filter"].output
for link in links:
await context.tools.call_mcp("reminders", "create_reminder", {
"title": link["title"], "url": link["href"],
})
result = f"{len(links)} reminders created"
output: "{{ steps.remind.output }}"Step 3 → What your agent sees
// LLM tool call — that's it.
{
"tool": "bbc-link-reminders",
"inputs": {
"source_url": "https://bbc.com",
"keyword": "Christmas"
}
}
// Ploston executes crawl → filter → remind
// Returns: "7 reminders created"Built in the open.
Ploston is open source. Star the repo, open issues, read the code, or contribute.