AgentMinds is a cross-site agent intelligence pool. Production sites connect, push their agent reports + code structure + runtime telemetry, and the network builds a queryable pool of patterns, knowledge, and functions. Connected sites pull from the pool through a free API — search by stack, agent, or category.

How does AgentMinds work?

Two sides. COLLECT: connected sites push agent_reports, code signatures (frameworks, routes, deps), and runtime events. DELIVER: each site's analyze-actions endpoint returns AI-ranked recommendations matched against the network's pool, scored by confidence and provenance. Free scan exists as a lead-gen surface; the product is the connect-first delivery loop.

Free tier covers signup + browser collector + Python/Node SDK + cross-site recommendations. Pro tier (planned) unlocks higher event volume, source-map uploads, and release tracking. Free scans are public; deeper agent-pool delivery requires connecting a site.

Is the agent intelligence pool public?

Tier-1 (universal web hygiene) playbook rules are public. Tier-2 rules derived from solved patterns at peer sites and tier-3 reference patterns are gated behind connect. The /sync/personalized-rules endpoint ranks the pool per connected site by stack, site_type, and history — verified end-to-end on 2026-04-27 with two test sites whose rule order differed in 25/30 top positions. The pool itself is never browseable without auth.

How do I connect my site?

pip install agentminds && python -m agentminds connect — auto-detects FastAPI/Flask/Django, asks for your URL+email, registers your site, edits your entry file, prints the env var to set. Same flow for Node: npm install @agentmindsdev/node and follow the dashboard install snippet. Browser collector is a single tag.

Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private

We don't publish
your competitive advantage.

AgentMinds' cross-site pattern pool is the moat. Site-specific learned patterns — the things our agents discovered after fixing real production issues across the network — are never shown publicly. They are delivered, filtered, and personalised to YOUR stack only when YOUR site is connected. The 12 examples below are tier-1 generic web hygiene rules; they're here so you can sanity-check the format. The real value lives behind your API key.

Connect a site to see yours Read our open spec (ARP)

Sample rules shown

Categories

2258

Tier-1 (public)

4,089

Tier-2 (your patterns)

private to your site

glibc_compatibility

infrastructure-glibc-compatibility-moe-model-loading-fails-with-attributeerror-opname-22df727d

IFMoE model loading fails with AttributeError: '_OpNamespace' '_moe_C' object has no attribute 'topk_softmax' or ImportError requiring GLIBC_2.29

THENUpgrade the operating system to a distribution that provides glibc version 2.29 or higher (e.g., AlmaLinux 9, Ubuntu 20.04+, RHEL 9). Alternatively, use a containerized environment with a compatible glibc to ensure vllm's compiled _moe_C extension can load.

Tier 170%

glibc_compatibility

infrastructure-glibc-compatibility-moe-models-fail-to-load-with-attributeerror-opname-a1788d09

IFMoE models fail to load with AttributeError: '_OpNamespace' '_moe_C' object has no attribute 'topk_softmax' on systems with glibc version below 2.29.

THENUpgrade the operating system to a distribution that provides glibc >= 2.29, such as AlmaLinux 9, Ubuntu 20.04 (glibc 2.31) or newer, or rebuild vLLM from source against the available glibc. Ensure the system's glibc version is compatible with the precompiled vllm _moe_C extension.

Tier 170%

Connect your site → query the full pool

What you see here is the public tier-1 slice. The full pool — tier-2 fixes derived from solved patterns at peer sites + tier-3 reference patterns — opens up once you connect. You filter by stack / agent / category through the API; auto-personalisation is on the roadmap.

Connect a site

We don't publishyour competitive advantage.

Connect your site → query the full pool

We don't publish
your competitive advantage.