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Show HN: Nous – give GTM agents one context graph across your tools

https://github.com/NousC/opennous
9•sethiaakash04•1h ago
We built this because deploying more agents did not make our GTM better, it made it worse. Every agent started from scratch each session and took six tool calls for the same prospect across CRM, inbox, and notetaker, often with conflicting results. Since nothing carried over between sessions, agents never became more reliable over time.

Nous is a context layer beneath your agents that turns each touchpoint into a structured observation linked to the right person and company. It derives claims, which are beliefs about a fact with their confidence and freshness. The graph runs on your own data, improves with each agent outcome, allowing one agent to read the whole account in a single call instead of using six tools.

Comments

sethiaakash04•1h ago
It's open source under the AGPL license, so you can self-host it. Happy to discuss identity resolution, the epistemic model (observations to entities to claims), and answer questions.
bennetglinder1•1h ago
how is this different from just giving my agent access to my CRM?
sethiaakash04•1h ago
CRMs are built for humans to update records. Nous is built for AI agents to read and write context. It stores observations, derives claims, tracks confidence and freshness, and gives agents one API instead of making them coordinate multiple tools.
bennetglinder1•1h ago
so I would still need a CRM but agents would use the context graph?
sethiaakash04•57m ago
Exactly, CRM is for your business records and the context graph is for aligning your agent!
1209vikas•1h ago
How do you handle conflicting facts from two sources when you are extracting the observations?
sethiaakash04•1h ago
The most recent observation is used as the claim. If there are conflicting observations, each one lowers the confidence score, but the newest still takes priority. The only exception is that CRM fields follow their own priority order. The human-entered data comes first, followed by enriched data, then inferred data. For all other cases, the newest observation is used.
binnaniaditya1•1h ago
How are you solving identity resolution? What happens when two people share a name at the same company?
sethiaakash04•54m ago
It's a waterfall sequence of hard identifiers such as email, LinkedIn URL, domain, and CRM ID and it only accepts exact matches and does not use fuzzy search. This approach works for most situations. If there is still no match, the system creates a new entity instead of making an incorrect guess.

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