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/contextgit 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 | $0.02472 |
| Opus 5 | $0.00000 | $0.01236 |
| Sonnet 5 | $0.00000 | $0.00494 |
| Haiku 4.5 | $0.00000 | $0.00247 |
Grade A, and why
context 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 yesterday.
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 — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Agents often require more than a list of messages to function effectively. They need context.
Context includes any data outside the message list that can shape agent behavior or tool execution. This can be:
- Information passed at runtime, like a
user_idor API credentials. - Internal state updated during a multi-step reasoning process.
- Persistent memory or facts from previous interactions.
LangGraph provides three primary ways to supply context:
| Type | Description | Mutable? | Lifetime |
|---|---|---|---|
| Config | data passed at the start of a run | ❌ | per run |
| State | dynamic data that can change during execution | ✅ | per run or conversation |
| Long-term Memory (Store) | data that can be shared between conversations | ✅ | across conversations |
You can use context to:
- Adjust the system prompt the model sees
- Feed tools with necessary inputs
- Track facts during an ongoing conversation
Providing Runtime Context
Use this when you need to inject data into an agent at runtime.
Config (static context)
Config is for immutable data like user metadata or API keys. Use when you have values that don't change mid-run.
Specify configuration using a key called "configurable" which is reserved for this purpose:
await agent.invoke(
{ messages: "hi!" },
// highlight-next-line
{ configurable: { userId: "user_123" } }
)
State (mutable context)
State acts as short-term memory during a run. It holds dynamic data that can evolve during execution, such as values derived from tools or LLM outputs.
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.
- yesterday First seen · 330 lines · 0 tokens per session scan A f39874ccac2d
context is an agent published in the GitHub repository langchain-ai/langgraphjs (3,242 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,472 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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