Metadata-Version: 2.4
Name: ontbo
Version: 1.3.5
Summary: Python client for the Ontbo API. Go-to https://www.ontbo.com/.
Project-URL: Homepage, https://github.com/Ontbo/ontbo-context-api
Project-URL: Issues, https://github.com/Ontbo/ontbo-context-api/issues
Author-email: Stéphane Aubry <contact@ontbo.com>
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License-File: LICENSE
Requires-Dist: requests
Description-Content-Type: text/markdown

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
</head>
<body>
  <img src="./static/banner.png"></img>

  <h1>🛸 ONTBO - Cognitive Context API for AI Agents</h1>

  <p align="center"><strong>Make AIs understand your users.</strong></p>
  <p align="center">👉 Cognitive Context API for your AI Agents</p>
  <p align="center">🌐 Homepage:<a href="https://www.ontbo.com">https://www.ontbo.com</a></p>
  <p align="center">🤖 Developper hub:<a href="https://api.ontbo.com">https://api.ontbo.com</a></p>
  <p align="center">📄Datasheet: <a href="./static/datasheet.pdf">here</a></p>
  <p align="center">Enjoying ONTBO? ⭐️ us to support the project!</p>

  <div>
<h2>What is Ontbo?</h2>

Ontbo is a user context layer for you AI systems. It ingests conversations history and maintains a live user profile.
Your agent queries for relevant information about the user for highly relevant answers.

<h2>🚀 Get started (seriously, 5 min)</h2>

1️⃣ Create your account and fetch your api key→ <a href="https://api.ontbo.com">https://api.ontbo.com</a>

2️⃣ Install the lib
```
pip install ontbo
```

3️⃣ Your first application in a few lines of code.
```py
from ontbo import Ontbo, SceneMessage

ontbo = Ontbo(token="<YOU_API_KEY_HERE>")
profile = ontbo.create_profile("alice_test")
scene = profile.create_scene("scene_test")

scene.add_messages(
  [SceneMessage(content="Hello, my name is Alice!")], 
  update_now=True)

print(profile.query_facts("What the user's name?"))
```

  <p>💡 Not using Python? See directly our Web API reference → <a href="https://api.ontbo.com/api/tests/docs">https://api.ontbo.com/api/tests/docs</a></p>
  </div>

  <div>
    <h2>⚡ Research Insights - Why you Win ?</h2>
    <table>
      <tr>
        <th><i>Metric</i></th>
        <th><i>ONTBO</i></th>
        <th><i>SoTA</i></th>
      </tr>
      <tr>
        <td><i>Recall context</i></td>
        <td><b>🔥 91%</b></td>
        <td>70%</td>
      </tr>
      <tr>
        <td><i>Token cost</i></td>
        <td><b>💸 -95%</b></td>
        <td>Full-data retrieval</td>
      </tr>
      <tr>
        <td><i>Latency</i></td>
        <td><b>⚡ 40 ms (P50)</b></td>
        <td>&gt;200 ms</td>
      </tr>
      <tr>
        <td><i>Model creation speed</i></td>
        <td><b>🚀 +200%</b></td>
        <td>Baseline</td>
      </tr>
    </table>
    <p>🔄 <b>4 retrieval modes</b>: best-performance, chain-of-thought, balanced, low-latency<br>
    ✔️ pick precision/latency/cost tradeoffs per request</p>
  </div>

  <div>
    <h2>📖 Introduction</h2>
    <p>If your assistants hallucinate, token bills spike, and user context lives in 12 different silos – <strong>ONTBO fixes that.</strong></p>
    <p>Plug a <b>cognitive layer</b> in front of your LLMs/Agents and stop duct-taping prompt engineering.<br>
    Your agents become sharper, faster, and cheaper.</p>
    <p><strong>Memory stores. Context understands.</strong></p>
  </div>



  <div>
    <h2>🧩 Main Features</h2>
    <ul>
      <li><strong>Context Layer</strong> → multi-agent, reasoning-based retrieval that packs only the facts that matter</li>
      <li><strong>Lifecycle Management</strong> → facts are versioned, updated, and conflict-resolved automatically</li>
      <li><strong>Autonomous CoT Orchestration</strong> → self-directed reasoning → fewer tokens, faster replies, higher accuracy</li>
      <li><strong>Data Governance</strong> → white-box by design — full traceability, provenance, and user-level control baked in</li>
    </ul>
  </div>

  <div>
    <h2>🎯 Use Cases</h2>
    <ul>
      <li><strong>Personal AI</strong> → not memory. Awareness that adapts to you in real time</li>
      <li><strong>Customer Support</strong> → not logs. Clear user story that resolves issues on the first touch</li>
      <li><strong>Healthcare</strong> → not raw data. Connect all the dots for reliable patient profiles → safer adaptive care</li>
      <li><strong>Developer &amp; Creator Copilots</strong> → not autocomplete. Repo + workflow intelligence that guides the next move</li>
    </ul>
    <p>🤖 <a href="https://api.ontbo.com">https://api.ontbo.com</a></p>
  </div>

  <div>
    <h2>🏗 Strategic Benefits (Product &amp; Infra)</h2>
    <ul>
      <li><strong>Product</strong> → sharper, context-aware responses → higher CSAT &amp; conversions</li>
      <li><strong>Infra</strong> → big LLM token savings &amp; lower inference load → measurable cost reductions</li>
      <li><strong>Moat</strong> → fine-grained, context-rich personalization with traceable provenance</li>
      <li><strong>Ops</strong> → faster model creation &amp; iteration (+200%), predictable scaling, fewer cold starts</li>
    </ul>
  </div>

  <div>
    <h2>📚 Documentation &amp; Support</h2>
    <ul>
      <li><strong>API reference docs:</strong> <a href="https://api.ontbo.com/api/tests/docs">https://api.ontbo.com/api/tests/docs</a></li>
      <li><strong>Community:</strong> <a href="https://discord.com/invite/N8h4ZBJb">Discord</a> · <a href="https://x.com/ONTBO_AI">X</a></li>
      <li><strong>Contact:</strong> <a href="mailto:contact@ontbo.com">contact@ontbo.com</a></li>
    </ul>
  </div>

  <div>
    <h2>🤝 License &amp; Contributions</h2>
    <ul>
      <li><strong>License: </strong><a href="./LICENSE">Apache 2.0</a></li>
      <li><strong>Contributions:</strong> PRs welcome. Open issues for bugs/feature requests</li>
      <li><strong>Security:</strong> responsible disclosure at contact@ontbo.com</li>
    </ul>
  </div>

  <br><p>✨ Let’s build something people didn’t think was possible.<br>
  🔥 Go break things (and tell us what you build).</p>

</body>
</html>






