Agentor
CelestoAI214 ดาว
Open source version of Claude Managed Agents. Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent.
- ทรานสปอร์ต
- stdio
- สถานะ
- โลคัล
Power your agent
การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิด| Feature |
|---|
| Description |
|---|
| Docs |
|---|
| 🚀 MCP & tool security | The only full FastAPI compatible MCP Server with decorator API | Link |
| 🦾 Agent-to-agent | Multi-agent communication | Link |
| 📊 Observability | Agent tracing and monitoring | Link |
| 💾 Durable runs | Persist, resume, and fork agent runs | Durable runs docs |
| 🔍 Tool Search API | Reduced tool context bloat | Link |
The recommended method of installing agentor is with pip from PyPI.
pip install agentor
This installs the v0.1.0 line, built on Agentor's own agent engine — durable, forkable runs included.
Tools with heavy or vendor-specific dependencies ship as extras, so the base install stays small:
pip install "agentor[google]" # GmailTool, CalendarTool
pip install "agentor[all]" # every optional tool
Available extras: google, exa, git, github, slack, postgres, scrapegraph, all.
You can also install the latest bleeding edge version (could be unstable) of agentor, should you feel motivated enough, as follows:
pip install git+https://github.com/celestoai/agentor@main
Build an Agent, connect external tools or MCP Server and serve as an API in just a few lines of code:
from agentor.tools import GetWeatherTool
from agentor import Agentor
agent = Agentor(
name="Weather Agent",
model="gpt-5-mini", # Use any LLM provider - gemini/gemini-2.5-pro or anthropic/claude-3.5
tools=[GetWeatherTool()]
)
result = agent.run("What is the weather in London?") # Run the Agent
print(result)
# Serve Agent with a single line of code
agent.serve()
Point base_url at any provider that speaks OpenAI's /chat/completions — OpenRouter,
Groq, Together, Fireworks, DeepSeek, vLLM, Ollama, or Anthropic's and Gemini's compatible
endpoints — with no extra dependency:
agent = Agentor(
name="Assistant",
model="openrouter/auto",
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
)
Run the following command to query the Agent server:
curl -X 'POST' \
'http://localhost:8000/chat' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"input": "What is the weather in London?"
}'
agent.serve() gives you an ordinary ASGI app, so host it wherever you already
run Python services.
Give the agent a store and every run becomes an append-only event log that survives process death. Resume an interrupted run, or fork any persisted run — even a completed one — into a new, independent run that keeps the full trace, the model's reasoning included when the provider returns it:
from agentor import Agentor
from agentor.engine.store import FileStore
agent = Agentor(name="Assistant", model="gpt-5-mini", store=FileStore("runs"))
result = agent.run("Draft a launch plan") # persisted under result.run_id
# Interrupted mid-run? agent.resume(result.run_id) picks up where it left off.
fork = agent.fork(result.run_id, "Make it punchier") # fork.run_id is a new run; parent untouched
fork and resume have async twins, afork and aresume.
Tracing is off unless you ask for it. A trace carries prompts, tool arguments, tool results, and the model's reasoning when a provider returns it — so nothing leaves your process by default.
Turn it on for an agent:
agent = Agentor(name="Assistant", enable_tracing=True) # needs CELESTO_API_KEY
Or decide per run:
agent.run("public question")
agent.run("contains customer data", tracing=False) # this run sends nothing
agent.run("debug this", tracing=True) # trace just this one
tracing= is accepted by run, arun, chat and stream_chat. View traces at
celesto.ai/observe.
Skills are folders of instructions, scripts, and resources that Claude loads dynamically to improve performance on specialized tasks.
Agent Skills help agents pull just the right context from simple Markdown files. The agent first sees only a skill’s name and short description. When the task matches, it loads the rest of SKILL.md, follows the steps, and can call a shell environment to run the commands the skill points to.
SKILL.md when relevantSkill layout example:
example-skill/
├── SKILL.md # required instructions + metadata
├── scripts/ # optional helpers the agent can call
├── assets/ # optional templates/resources
└── references/ # optional docs or checklists
Using a skill to create a GIF:
from agentor.tools import ShellTool
from agentor import Agentor
agent = Agentor(
name="Assistant",
model="gemini/gemini-3-flash-preview",
instructions="Your job is to create GIFs. Lean on the shell tool and any available skills.",
skills=[".skills/slack-gif-creator"],
tools=[ShellTool()],
)
async for chunk in await agent.chat("produce a cat gif", stream=True):
print(chunk)
Bootstrap an Agent directly from a markdown file with metadata for name, tools, model, and temperature:
---
name: WeatherBot
tools: [get_weather]
model: gpt-4o-mini
temperature: 0.3
---
You are a concise weather assistant.
Load it with:
from agentor import Agentor
agent = Agentor.from_md("agent.md")
result = agent.run("Weather in Paris?")
Agentor enables you to build a custom MCP Server using LiteMCP. You can run it inside a FastAPI application or as a standalone MCP server.
Agentor uses the official MCP Python SDK 2.3 or newer within the 2.x line. FastMCP is not an Agentor dependency; the LiteMCP decorator API is unchanged.
from agentor.mcp import LiteMCP, get_token
mcp = LiteMCP(name="my-server", version="1.0.0")
@mcp.tool(description="Get weather for a given location")
def get_weather(location: str) -> str:
# *********** Control authentication ***********
token = get_token()
if token != "SOME_SECRET":
return "Not authorized"
return f"Weather in {location}: Sunny, 72°F"
mcp.serve()
Key Difference: LiteMCP is a native ASGI app that integrates directly with FastAPI using standard patterns. FastMCP requires mounting as a sub-application, diverging from standard FastAPI primitives.
Use MCPServer for a remote server that supports Streamable HTTP. Agentor
discovers its tools, including paginated results, and closes the connection
after the run:
from agentor import Agentor
from agentor.mcp import MCPServer
agent = Agentor(
name="Assistant",
model="gpt-5-mini",
tools=[MCPServer(url="https://your-server.example/mcp")],
)
Hosts that supply their own HTTP transport can pass http_client_factory to
MCPServer. The factory receives headers and timeout keyword arguments and
must return a new httpx2.AsyncClient. Agentor owns that client's lifecycle,
closing it when connection setup fails or the MCP connection closes.
The Parallel Search example connects an agent to web search and page extraction without a Parallel API key. It also includes a tools-only mode that runs without model credentials:
uv run python examples/parallel_search.py --tools-only "Agentor durable agent runs"
The A2A Protocol defines standard specifications for agent communication and message formatting, enabling seamless interoperability between different AI agents.
Key Features:
/.well-known/agent-card.json describing agent capabilities, skills, and endpointsAgentor makes it easy to serve any agent as an A2A protocol.
from agentor import Agentor
agent = Agentor(
name="Weather Agent",
model="gpt-5-mini",
tools=["get_weather"],
)
# Serve agent with A2A protocol enabled automatically
agent.serve(port=8000)
# Agent card available at: http://localhost:8000/.well-known/agent-card.json
Any agent served with agent.serve() automatically becomes A2A-compatible with standardized endpoints for message sending, streaming, and task management.
We'd love your help making Agentor even better! Please read our Contributing Guidelines and Code of Conduct.
Apache 2.0 License - see LICENSE for details.
Open source version of Claude Managed Agents. Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Agentor ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-26 Open source version of Claude Managed Agents. Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent.