deep-agent-deployment

deep-agent-deployment is a skill for Claude Code, Codex from bdiasti/maestro-bundle-cli. It costs 43 tokens per session (1,239 once invoked), scanned A, original, MIT.

A deployment guide for Deep Agents as web APIs, streaming services, command-line tools, or containerized applications. It covers serving an agent and connecting it to persistent conversation storage.

In plain words
What is it for?
Use it to create a FastAPI REST endpoint, WebSocket streaming, Docker deployment, LangGraph Platform deployment, or a CLI interface.
Why use it?
It explains how to move an agent from local code into a usable service or command-line program.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is COPY src/ ./src/.

Good fit Use it to create a FastAPI REST endpoint, WebSocket streaming, Docker deployment, LangGraph Platform deployment, or a CLI interface.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/bdiasti/maestro-bundle-cli
agentmods
npx agentmods add skills/bdiasti/maestro-bundle-cli/deep-agent-deployment

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for deep-agent-deployment

README.md
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Your own site
<a href="https://agentmods.dev/skills/bdiasti/maestro-bundle-cli/deep-agent-deployment"><img src="https://agentmods.dev/badge/skills/bdiasti/maestro-bundle-cli/deep-agent-deployment/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/bdiasti/maestro-bundle-cli/deep-agent-deployment"><img src="https://agentmods.dev/badge/skills/bdiasti/maestro-bundle-cli/deep-agent-deployment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,239 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.01239
Opus 5.5 $0.00017 $0.00496
Sonnet 5.5 $0.00009 $0.00248
Haiku 4.5 $0.00004 $0.00124

Measured 2mo ago against content hash c65ae3ab57dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

deep-agent-deployment scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -X POST http://localhost:8000/api/chat \
templates/bundle-ai-agents-deep/skills/deep-agent-deployment/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Agent Deployment

Deploy your Deep Agent as a REST API, WebSocket server, or CLI tool for production use.

When to Use

  • When serving the agent as an API endpoint
  • When deploying to LangGraph Platform
  • When creating a Docker container for the agent
  • When building a CLI interface

Available Operations

  1. Serve as FastAPI REST API
  2. Add WebSocket streaming
  3. Deploy with Docker
  4. Deploy to LangGraph Platform
  5. Create CLI interface

Multi-Step Workflow

Step 1: FastAPI REST API

# api/server.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from deepagents import create_deep_agent
from langgraph.checkpoint.postgres import PostgresSaver

app = FastAPI(title="Deep Agent API")

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    checkpointer=PostgresSaver(conn_string=os.environ["DATABASE_URL"])
)

class ChatRequest(BaseModel):
    message: str
    thread_id: str

class ChatResponse(BaseModel):
    response: str
    thread_id: str

@app.post("/api/chat", response_model=ChatResponse)
async def chat(req: ChatRequest):
    config = {"configurable": {"thread_id": req.thread_id}}
    result = agent.invoke(
        {"messages": [{"role": "user", "content": req.message}]},
        config=config
    )
    return ChatResponse(
        response=result["messages"][-1].content,
        thread_id=req.thread_id
    )

Step 2: WebSocket Streaming

# api/websocket.py
from fastapi import WebSocket

@app.websocket("/ws/chat/{thread_id}")
async def ws_chat(websocket: WebSocket, thread_id: str):
    await websocket.accept()
    config = {"configurable": {"thread_id": thread_id}}

    while True:
        message = await websocket.receive_text()

        async for event in agent.astream_events(
            {"messages": [{"role": "user", "content": message}]},
            config=config,
            version="v2"
        ):
            if event["event"] == "on_chat_model_stream":
                chunk = event["data"]["chunk"].content
                if chunk:
                    await websocket.send_text(chunk)

        await websocket.send_text("[DONE]")

Read the full file on GitHub · 197 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 197 lines · 43 tokens per session scan A c65ae3ab57dd

Subscribe to this mod's changes

deep-agent-deployment is a skill published in the GitHub repository bdiasti/maestro-bundle-cli (22 stars, last pushed 7mo ago), licensed MIT. It adds 43 tokens to every session and 1,239 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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