ontbo 1.3.1

Python client for the Ontbo API. Go-to https://www.ontbo.com/.

🛸 ONTBO - Cognitive Context API for AI Agents

Make AIs understand your users.

👉 Cognitive Context API for your AI Agents

🌐 Homepage:https://www.ontbo.com

🤖 Developper hub:https://api.ontbo.com

📄Datasheet: here

Enjoying ONTBO? ⭐️ us to support the project!

What is Ontbo?

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.

🚀 Get started (seriously, 5 min)

1️⃣ Create your account and fetch your api key→ https://api.ontbo.com

2️⃣ Install the lib

pip install ontbo

3️⃣ Your first application in a few lines of code.

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?"))

💡 Not using Python? See directly our Web API reference → https://api.ontbo.com/api/tests/docs

⚡ Research Insights - Why you Win ?

Metric	ONTBO	SoTA
Recall context	🔥 91%	70%
Token cost	💸 -95%	Full-data retrieval
Latency	⚡ 40 ms (P50)	>200 ms
Model creation speed	🚀 +200%	Baseline

🔄 4 retrieval modes: best-performance, chain-of-thought, balanced, low-latency
✔️ pick precision/latency/cost tradeoffs per request

📖 Introduction

If your assistants hallucinate, token bills spike, and user context lives in 12 different silos – ONTBO fixes that.

Plug a cognitive layer in front of your LLMs/Agents and stop duct-taping prompt engineering.
Your agents become sharper, faster, and cheaper.

Memory stores. Context understands.

🧩 Main Features

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

🎯 Use Cases

• Personal AI → not memory. Awareness that adapts to you in real time
• Customer Support → not logs. Clear user story that resolves issues on the first touch
• Healthcare → not raw data. Connect all the dots for reliable patient profiles → safer adaptive care
• Developer & Creator Copilots → not autocomplete. Repo + workflow intelligence that guides the next move

🤖 https://api.ontbo.com

🏗 Strategic Benefits (Product & Infra)

• Product → sharper, context-aware responses → higher CSAT & conversions
• Infra → big LLM token savings & lower inference load → measurable cost reductions
• Moat → fine-grained, context-rich personalization with traceable provenance
• Ops → faster model creation & iteration (+200%), predictable scaling, fewer cold starts

📚 Documentation & Support

• API reference docs: https://api.ontbo.com/api/tests/docs
• Community: Discord · X
• Contact: [e-mail]

🤝 License & Contributions

• License: Apache 2.0
• Contributions: PRs welcome. Open issues for bugs/feature requests
• Security: responsible disclosure at [e-mail]

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