Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Agentic-Insights/foundry --skill aws-agentcore-langgraphgit clone --depth 1 https://github.com/Agentic-Insights/foundryWrote 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/agentic-insights/foundry/aws-agentcore-langgraph)<a href="https://agentmods.dev/skills/agentic-insights/foundry/aws-agentcore-langgraph"><img src="https://agentmods.dev/badge/skills/agentic-insights/foundry/aws-agentcore-langgraph/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/agentic-insights/foundry/aws-agentcore-langgraph"><img src="https://agentmods.dev/badge/skills/agentic-insights/foundry/aws-agentcore-langgraph.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 | $0.00092 | $0.01317 |
| Opus 5.5 | $0.00037 | $0.00527 |
| Sonnet 5.5 | $0.00018 | $0.00263 |
| Haiku 4.5 | $0.00009 | $0.00132 |
Grade A, and why
aws-agentcore-langgraph scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS AgentCore + LangGraph
Multi-agent systems on AWS Bedrock AgentCore with LangGraph orchestration. Source: https://github.com/aws/bedrock-agentcore-starter-toolkit
Install
pip install bedrock-agentcore bedrock-agentcore-starter-toolkit langgraph
uv tool install bedrock-agentcore-starter-toolkit # installs agentcore CLI
Quick Start
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition # routing + tool execution
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from typing import Annotated
from typing_extensions import TypedDict
class State(TypedDict):
messages: Annotated[list, add_messages]
builder = StateGraph(State)
builder.add_node("agent", agent_node)
builder.add_node("tools", ToolNode(tools)) # prebuilt tool executor
builder.add_conditional_edges("agent", tools_condition) # routes to tools or END
builder.add_edge(START, "agent")
graph = builder.compile()
app = BedrockAgentCoreApp() # Wraps as HTTP service on port 8080 (/invocations, /ping)
@app.entrypoint
def invoke(payload, context):
result = graph.invoke({"messages": [("user", payload.get("prompt", ""))]})
return {"result": result["messages"][-1].content}
app.run()
CLI Commands
| Command | Purpose |
|---|---|
agentcore configure -e agent.py --region us-east-1 |
Setup |
agentcore configure -e agent.py --region us-east-1 --name my_agent --non-interactive |
Scripted setup |
agentcore launch --deployment-type container |
Deploy (container mode) |
agentcore launch --disable-memory |
Deploy without memory subsystem |
agentcore dev |
Hot-reload local dev server |
agentcore invoke '{"prompt": "Hello"}' |
Test |
agentcore destroy |
Cleanup |
Core Patterns
Multi-Agent Orchestration
- Orchestrator delegates to specialists (customer service, e-commerce, healthcare, financial, etc.)
- Specialists: inline functions or separate deployed agents; all share
session_idfor context
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/agentcore-cli.md 2.6 KB
- references/agentcore-gateway.md 3.0 KB
- references/agentcore-memory.md 2.7 KB
- references/agentcore-runtime.md 2.8 KB
- references/langgraph-patterns.md 2.9 KB
- references/reference-architecture-advertising-agents-use-case.pdf 172 KB
- scripts/agent-details.sh 713 B runs code
- scripts/list-all.sh 1.7 KB runs code
- scripts/memory-details.sh 622 B runs code
- scripts/tail-logs.sh 610 B runs code
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.
- 1mo ago First seen · 120 lines · 92 tokens per session scan A bdb51b9ec18a
aws-agentcore-langgraph is a skill published in the GitHub repository Agentic-Insights/foundry (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 92 tokens to every session and 1,317 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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