MagnoApi - i built a Memory Context API that gives memory to llms

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MagnoAPI — Memory for AI Apps

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Live API — Free to start

Your AI app<br>remembers<br>everything.

MagnoAPI gives any AI app persistent memory across conversations.<br>Two API calls. Works with Claude, GPT, Gemini — any LLM.<br>No infrastructure to manage.

- Recommended by Surya S.B.,<br>Programmer and developer<br>★★★★★

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API endpoints

1K<br>Free calls/month

Avg response time

LLMs supported

// how developers use it

Drop two calls around your LLM. Done.

Every request to your AI app follows this exact flow. MagnoAPI sits invisibly between your app and any LLM, giving it memory without changing how you call the LLM.

User<br>Sends a message to your app<br>"What Python framework should I use?"

① MagnoAPI Search<br>POST /memory/search<br>Fetches relevant past context for this user

② Your LLM Call<br>Claude / GPT / Gemini<br>Prompt = past memories + user message. LLM now "remembers".

③ MagnoAPI Store<br>POST /memory/store<br>Saves this conversation for next time

Python<br>JavaScript<br>cURL

# ① Before calling your LLM — fetch relevant memories<br>memories = requests.post(<br>"https://magno-memory-api-production.up.railway.app/memory/search",<br>headers={"X-API-Key": "magno_sk_your_key"},<br>json={"user_id": "user_123", "query": user_message, "top_k": 3}<br>).json()["results"]

# Build enriched prompt<br>context = "\n".join([m["text"] for m in memories])<br>prompt = f"Past context:\n{context}\n\nUser: {user_message}"

# Call your LLM exactly as you normally would<br>response = anthropic.messages.create(model="claude-sonnet-4-20250514",<br>messages=[{"role":"user", "content": prompt}])

# ② After LLM responds — store this exchange<br>requests.post(<br>"https://magno-memory-api-production.up.railway.app/memory/store",<br>headers={"X-API-Key": "magno_sk_your_key"},<br>json={"user_id": "user_123", "text": f"User asked: {user_message}"}

// ① Before calling your LLM — fetch relevant memories<br>const res = await fetch('https://magno-memory-api-production.up.railway.app/memory/search', {<br>method: 'POST',<br>headers: { 'Content-Type': 'application/json', 'X-API-Key': 'magno_sk_your_key' },<br>body: JSON.stringify({ user_id: 'user_123', query: userMessage, top_k: 3 })<br>});<br>const memories = (await res.json()).results;

// Build enriched prompt<br>const context = memories.map(m => m.text).join('\n');<br>const prompt = `Past context:\n${context}\n\nUser: ${userMessage}`;

// ② After LLM responds — store this exchange<br>await fetch('https://magno-memory-api-production.up.railway.app/memory/store', {<br>method: 'POST',<br>headers: { 'Content-Type': 'application/json', 'X-API-Key': 'magno_sk_your_key' },<br>body: JSON.stringify({ user_id: 'user_123', text: `User asked: ${userMessage}` })<br>});

# ① Search memories before your LLM call<br>curl -X POST \<br>https://magno-memory-api-production.up.railway.app/memory/search \<br>-H 'X-API-Key: magno_sk_your_key' \<br>-H 'Content-Type: application/json' \<br>-d '{"user_id":"user_123","query":"your message","top_k":3}'

# ② Store memory after your LLM responds<br>curl -X POST \<br>https://magno-memory-api-production.up.railway.app/memory/store \<br>-H 'X-API-Key: magno_sk_your_key' \<br>-H 'Content-Type: application/json' \<br>-d '{"user_id":"user_123","text":"User asked about frameworks"}'

// the problem

Every AI conversation starts from zero. That ends now.

✕ Without MagnoAPI

✕User explains their preferences every single session

✕AI forgets everything the moment the chat ends

✕Developers spend weeks building custom memory from scratch

✕No context, no continuity, no personalization

✓ With MagnoAPI

✓AI remembers preferences, history, and context automatically

✓Memories persist across every session forever

✓Two lines of code — drop into any existing app

✓Semantic search finds the most relevant memories instantly

// getting started

Three steps. Seriously, that's it.

01

Get your API key

Enter your email below and receive a unique magno_sk_ key instantly. No credit card. No setup. Free forever up to 1,000 calls/month.

02

Search before each LLM call

Call POST /memory/search with the user's message. Get back the most relevant past memories. Inject them into your prompt.

03

Store after each response

Call POST /memory/store after your LLM responds. The conversation is saved as a vector....

memory memories context post store json

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