AI Agents as ManifestsDefine the Graph. Any Language Runs It.
Designed for What Comes After Hello World
From OpenAPI tool generation to distributed execution and cost tracking, Liman designed each one around real production pain points.
OpenAPI → Tools Generation
Automatically generate LLM tools from OpenAPI specifications. Transform any API into agent tools without writing MCP servers.
Service Account Authorization
Built-in authorization with service accounts. State isolation, credential provisioning, and minimal permissions with least privilege principles.
Dynamic Prompt Localization
Multi-language support with automatic system prompt generation. Increase function calling accuracy across languages.
OTel & FinOps Out-of-Box
Built-in OpenTelemetry integration with cost tracking. Monitor performance, token usage, and financial metrics automatically.
Condition Expression Language
Custom DSL for intelligent flow control between nodes. Express complex routing decisions declaratively without repetitive conditional logic.
Distributed Edges
Connect nodes via MCP, A2A, HTTP, WebSocket, or shared memory. Build distributed agents across AWS Lambda and processes.
Plugin Ecosystem
Extensible plugin system with built-in and custom plugins. Auto-context stitching, evaluation agents, and anomaly detection.
Kustomize Overlays
Layer configurations using Kustomize-like overlays. Perfect for multi-environment deployments and language variants.
See How It Works
Define your agent in YAML. Run it with the SDK.
kind: LLMNode
name: assistant
prompts:
system:
en: |
You are a helpful assistant.
Always be polite and provide clear answers.from liman import Agent
from langchain_openai import ChatOpenAI
agent = Agent(
"./specs",
start_node="assistant",
llm=ChatOpenAI(model="gpt-5.4-mini"),
)
response = await agent.step("Hello!")
print(response)Stay Updated
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Participate in specification development and share experiences with other developers