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.
git clone --depth 1 https://github.com/bdiasti/maestro-bundle-clinpx agentmods add skills/bdiasti/maestro-bundle-cli/deep-agent-deploymentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bdiasti/maestro-bundle-cli/deep-agent-deployment)<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.
<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>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.
| Model | Per session | Once 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 |
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 \ 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
- Serve as FastAPI REST API
- Add WebSocket streaming
- Deploy with Docker
- Deploy to LangGraph Platform
- 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]")
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.
- 2mo ago First seen · 197 lines · 43 tokens per session scan A c65ae3ab57dd
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.
deep-agent-deployment compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| deep-agent-deploymentbdiasti/maestro-bundle-cli | Skill | 22 | 7mo ago | MIT | A | 43 | Claude Code, Codex |
| apify-actor-developmentsickn33/agentic-awesome-skills | Skill | 50k | today | MIT | A | 27 | Claude Code |
| dynamo-recipe-runnerNVIDIA/skills | Skill | 4.0k | today | Apache-2.0 | A | 49 | Claude Code, Codex |
| gcp-essentialsericrisco/rsc-harness | Skill | 598 | today | MIT | A | 91 | Claude Code, Codex |
| vss-setup-video-analytics-apiNVIDIA-AI-Blueprints/video-search-and-summarization | Skill | 2.0k | today | Apache-2.0 | B | 52 | Claude Code, Codex |
| deployoliver-kriska/claude-elixir-phoenix | Skill | 574 | 7d ago | MIT | A | 46 | Claude Code |
| enterpriseww-w-ai/bkit-claude-code | Skill | 604 | 13d ago | Apache-2.0 | A | 106 | Claude Code |