# I published pip install ajah-sdk and npm install ajah-sdk — here's what they do

> Source: <https://dev.to/vignesh_reddy_53e403f62d2/i-published-pip-install-ajah-sdk-and-npm-install-ajah-sdk-heres-what-they-do-3m9h>
> Published: 2026-06-18 18:14:54+00:00

After two weeks of building Ajah — an

open-source self-hosted LLM observability

gateway — today I hit a milestone that

actually matters for developer adoption.

pip install ajah-sdk

npm install ajah-sdk

Both are live. Both work. Here's what

they do and why I built them.

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THE PROBLEM THEY SOLVE

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Ajah is a gateway proxy that sits between

your app and any LLM provider. It scores

every response for hallucination risk,

verifies RAG outputs, detects narrative

drift across sessions, attributes costs

per feature, and masks PII before storage.

Before the SDKs, using Ajah required:

Now it's one import.

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PYTHON SDK

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pip install ajah-sdk

from ajah import AjahClient

client = AjahClient(

gateway_url="[http://localhost:8080](http://localhost:8080)",

api_key="your-groq-key",

feature_name="my-app",

user_id="user-123",

)

response = client.chat(

model="llama-3.3-70b-versatile",

messages=[{"role": "user",

"content": "Hello"}],

)

Every call through the SDK automatically

injects the Ajah observability headers:

X-Feature-Name, X-User-ID, X-Session-ID,

X-Agent-Step

These headers drive the entire Ajah

pipeline — cost attribution, quality

scoring, PII detection, session tracing.

Session tracking for multi-turn agents:

with client.session() as session:

plan = session.chat(

model="llama-3.3-70b-versatile",

messages=[{"role": "user",

"content": "Plan research"}],

step_name="step-1-planner",

)

research = session.chat(

model="llama-3.3-70b-versatile",

messages=[{"role": "user",

"content": "Execute plan"}],

step_name="step-2-researcher",

)

print(f"View session: {session.dashboard_url}")

AjahSession automatically increments step

numbers, maintains the session ID across

turns, and gives you a direct URL to the

visual step tree in the Ajah dashboard.

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NODE.JS SDK (TYPESCRIPT)

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npm install ajah-sdk

import { AjahClient } from 'ajah-sdk'

const client = new AjahClient({

gatewayUrl: '[http://localhost:8080](http://localhost:8080)',

apiKey: process.env.GROQ_API_KEY!,

featureName: 'my-app',

userId: 'user-123',

})

const response = await client.chat({

model: 'llama-3.3-70b-versatile',

messages: [{ role: 'user',

content: 'Hello' }],

})

Full TypeScript types included.

AjahSession works the same way:

const session = client.session()

const r1 = await session.chat({

model: 'llama-3.3-70b-versatile',

messages: [...],

stepName: 'step-1-planner',

})

console.log(session.dashboardUrl)

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WHAT RUNS BEHIND THE SDK

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Every call through the SDK goes through

the Ajah gateway which runs:

Hallucination scoring — sentence

transformers evaluate every response

for factual grounding. Async. Zero

latency added.

Claim density detection — flags responses

that make many specific claims on

low-context prompts.

Linguistic hedge detection — flags

overconfident responses on complex

medical, legal, or financial questions.

Narrative drift detection — compares

claims across session turns. Flags

when a model reverses position.

Cost attribution — USD cost per call,

tracked by feature and model.

PII masking — emails, phones, SSNs,

credit cards masked before storage.

RAG verification — if you pass source

documents, responses are verified

against them. Contradictions flagged.

Prometheus metrics — all signals exposed

at /metrics for Grafana integration.

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SELF-HOSTED

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The SDK points at your own running Ajah

instance. No data goes through my servers.

git clone [https://github.com/VigneshReddy-afk/ajah](https://github.com/VigneshReddy-afk/ajah)

cd ajah

docker-compose up -d

Then use the SDK pointing at localhost:8080.

MIT license. Free forever.

→ pip install ajah-sdk

→ npm install ajah-sdk

→ github.com/VigneshReddy-afk/ajah

→ useajah.com