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 agentmods add agents/langchain-ai/langgraphjs/modelsgit clone --depth 1 https://github.com/langchain-ai/langgraphjsWhat 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 | $0.00000 | $0.00473 |
| Opus 5 | $0.00000 | $0.00236 |
| Sonnet 5 | $0.00000 | $0.00095 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
models 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 2d 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.
What it actually says
Models
This page describes how to configure the chat model used by an agent.
Tool calling support
To enable tool-calling agents, the underlying LLM must support tool calling.
Compatible models can be found in the LangChain integrations directory.
Using initChatModel
The initChatModel utility simplifies model initialization with configurable parameters:
import { initChatModel } from "langchain/chat_models/universal";
const llm = await initChatModel(
"anthropic:claude-3-7-sonnet-latest",
{
temperature: 0,
maxTokens: 2048
}
);
Refer to the API reference for advanced options.
Using provider-specific LLMs
If a model provider is not available via initChatModel, you can instantiate the provider's model class directly. The model must implement the BaseChatModel interface and support tool calling:
import { ChatAnthropic } from "@langchain/anthropic";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
// highlight-next-line
const llm = new ChatAnthropic({
modelName: "claude-3-7-sonnet-latest",
temperature: 0,
maxTokens: 2048
});
const agent = createReactAgent({
// highlight-next-line
llm,
// other parameters
});
!!! note "Illustrative example"
The example above uses `ChatAnthropic`, which is already supported by `initChatModel`. This pattern is shown to illustrate how to manually instantiate a model not available through `initChatModel`.
Additional resources
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.
- 2d ago First seen · 58 lines · 0 tokens per session scan A 6c1668ae9723
models is an agent published in the GitHub repository langchain-ai/langgraphjs (3,242 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 473 tokens. 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 2026-08-30.
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