Smarter, faster agents. Needlepath sends your model only what matters.

Copy, paste, run.

One key, one call before your model call, and a dashboard to watch it. The code below is the same as the docs; swap in your records and your prompt.

1. 1
   Sign up and get an API key

   Sign up for public beta today. The key is minted in the console and lives in your environment as NEEDLEPATH_API_KEY.

2. 2
   Send your records and your prompt, get the context back, call your model

   PythonTypeScriptLangChainLiteLLMShell

   pip install needlepathCopy

   import os
   from needlepath import NeedlepathClient, ContextRecord, TaskSpec
   from openai import OpenAI
   
   client = NeedlepathClient(api_key=os.environ["NEEDLEPATH_API_KEY"], operating_point="np-2026-08-r4")
   
   # One record per item the agent holds: purchase orders, the supplier contract, the conversation so far.
   records = [ContextRecord(text=po.text, kind="external_data", id=po.id, title=po.title) for po in purchase_orders]
   prompt = "Which Corvane Freight purchase orders are still open past their delivery date?"
   
   result = client.select(records=records, task=TaskSpec(prompt=prompt), max_context_tokens=4000)
   context = result.rendered_context if result.applied else "\n\n".join(r.text for r in records)
   
   # Your model call, unchanged, with the context ahead of the prompt.
   answer = OpenAI().chat.completions.create(
       model="your-model", messages=[{"role": "user", "content": f"{context}\n\n{prompt}"}]
   )

3. 3
   Watch it work

   Requests, tokens metered and tokens saved, per key, in the console. When Needlepath stands aside, the whole set goes through unchanged.

Full clients, the LlamaIndex integration and the rules every integration follows: docs.nextmoca.com/integrations.

The rest of the agent stack, built to the same standard.