N71
Summary
AI agents are only as useful as the context they carry — and when that context lives in siloed tools with no shared memory, every new session starts from zero. N71 is built to fix that: a managed memory layer that connects your systems, surfaces relevant context proactively, and exposes it to external agents via MCP.
N71 positions itself as organizational memory infrastructure — ingesting data from connected sources, generating what it calls 'Thoughts' (synthesized context), and making those available to AI agents or human team members with governed access controls. The vendor publishes benchmark results claiming a 0.574 overall score on a 100-episode memory suite, against a reported best-published score of 0.42 — and notably highlights cascade updating, where a changed fact propagates to dependent facts, and absence detection, knowing what it no longer knows. That's a meaningful architectural claim: most memory systems give you retrieval; N71 claims it gives you coherent, self-correcting knowledge. The catch is that this is a fully managed, cloud-only service with no self-hosting option, which creates a hard ceiling for teams in air-gapped or strict data-residency environments. Regulated industries get a 'governed deployment' path, the vendor states, but the architecture details are thin on the public page.
Bottom line: N71 is the right bet when your agents need persistent, cross-tool context that updates itself — and the wrong one when your legal team requires data to never leave your own infrastructure.
Pricing Plans
SubscriptionIndividual
For one person and their agents
- 17 source types
- MCP access
- Proactive feed
- Full provenance
Team
Five seats included, $39 per additional seat
- Shared workspace
- Entity-level permissions
- Team intelligence
- Admin controls and audit trail
Managed
Annual commitment, dedicated engineering
- Everything in Team
- Dedicated deployment engineering
- Per-organization ontology
- Priority support and reviews
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Cascade fact updating propagates changes across dependent knowledge automatically, so agents don't act on contradicted information after your source data changes.
- MCP-native agent connectivity means any MCP-compatible agent can consume organizational memory without custom integration code, which means you're not building a bespoke bridge every time you add an agent.
- Permission-controlled shared workspaces let teams expose the same memory layer across roles with audit trails, so you get shared context without losing access governance — the thing that usually forces teams to duplicate context manually.
- Proactive context delivery surfaces relevant Thoughts without requiring the agent to know what to query, which means agents operate on context they didn't know to ask for — the failure mode that makes reactive retrieval systems miss obvious facts.
- Vendor-published benchmarks with downloadable episodes and judges give you a validation path for the memory quality claims — which is more than most memory systems offer before you commit to a production integration.
Cons
Sign in to edit- No self-hosted or on-premises deployment exists: any team in an air-gapped environment, or any legal team that has ruled out third-party data processing for a given dataset, cannot use N71 at all — the architecture requires sending data to a managed service, full stop. Those teams evaluate self-hosted alternatives.
- The benchmark methodology is vendor-published: the 100-episode suite, judges, and results come from N71's own production pipeline. Until an independent party reproduces the study, the cascade and absence scores are claims, not third-party verified facts — which matters when you're betting agent reliability on memory quality at scale.
- The 'governed deployment' and 'sovereign AI infrastructure' positioning for regulated environments is asserted on the vendor page but not architecturally described in public documentation. Teams in regulated industries who need specifics on data handling, residency, and audit log formats will spend procurement time filling that gap before they can sign off on a production deployment.
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About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-01T20:16:47.847Z
Best For
Who it's for
- Individuals needing persistent cross-tool context
- Organizations seeking managed or sovereign AI infrastructure
What it does well
- Maintaining personal organizational memory across connected sources
- Sharing federated insights within teams via permissions
- Providing context to external AI agents through MCP
- Running governed deployments in regulated environments
Integrations
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Frequently Asked Questions
- Is N71 free?
- N71 is a paid tool. No permanent free tier is offered.
- Is N71 open source?
- No — N71 is a closed-source tool. Source code is not publicly available.
- Does N71 have an API?
- Yes. N71 exposes a developer API. See the official documentation at https://n71.ai for details.
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Curated lists that include this category
Most AI memory layers are retrieval stores: you put context in, you get context out, and if the underlying facts change, nobody tells the agent. N71 takes a different approach. The vendor describes it as a ‘company brain’ that connects to your existing systems, synthesizes context into persistent ‘Thoughts,’ and delivers those proactively to agents or team members rather than waiting to be queried. The core workflow is connection → synthesis → delivery: link your sources, let N71 map relationships and surface insights, then expose that memory to AI agents through an MCP interface or share it across teams via permission-controlled workspaces.
The differentiation N71 leads with is cascade coherence. The benchmark results the vendor publishes show a 0.628 score on cascade updating — the ability to propagate a changed fact to all dependent facts — versus a reported 0.01 for competing systems. Absence detection, knowing what the system no longer knows, scores 0.42 against 0.01. If those numbers hold in production, they represent a qualitative gap: agents operating on N71 memory should receive fewer stale or contradicted facts than agents operating on conventional vector stores.
N71 fits teams where persistent, cross-tool context is the bottleneck — individuals who need AI that remembers across sessions and tools, or teams running agents that need to act on shared organizational knowledge without each session starting cold. It breaks for organizations that cannot send data to a third-party managed service. There is no self-hosted deployment path; the vendor mentions ‘sovereign AI infrastructure’ as a use case but the public page does not describe a self-hosted option. Teams with hard data-residency requirements will hit that wall before they see the memory quality benefits.
External agent connectivity runs through MCP, which means any agent that supports the Model Context Protocol can consume N71’s memory layer without custom integration work. Team-level sharing uses a permissions model with audit trails — a feature the vendor specifically calls out for regulated environment use cases.