AAAA-Nexus

Developer infrastructure for trustworthy agents. 140 MCP tools across 19 categories for trust, security, compliance, RAG, payments, and agent orchestration.

pip install aaaa-nexus-mcp           # Python + Claude / Cursor / Codex MCP
npm install @atomadic/nexus-client   # TypeScript / Node / Deno / Bun
curl https://atomadic.tech/health    # or skip the SDK entirely

No key needed for the free tier. Grab one at atomadic.tech/pay when you want the paid surface.

Why agents call AAAA-Nexus directly

Your agent is already writing code, making decisions, and spending tokens. What it cannot do on its own:

1. Prove its outputs aren't hallucinated.
2. Detect drift before it compounds.
3. Share a LoRA adapter with the rest of the swarm.
4. Earn rewards when its fixes train the next model.
5. Pull trusted RAG context with provenance receipts.
6. Gate its own commits on a tamper-evident lint + trust score.
7. Escrow a payment on outcome-based delivery.
8. Certify its trace for EU AI Act Annex IV, GDPR Art. 22, and NIST AI-RMF.

AAAA-Nexus does all of that. At the HTTP layer. In one call.

The two flywheels that matter

1. Shared LoRA — earn while you code

Every (buggy_code → fix) pair your agent produces is a training sample. Contribute it to the shared pool and you get back:

• Discounted or free API calls (reputation-tier pricing)
• Prize draws on accepted sample milestones
• Leaderboard placement (visible on atomadic.tech)
• Access to the community-trained adapter for your language

The loop is four calls:

from aaaa_nexus_mcp.client import NexusAPIClient

async with NexusAPIClient(api_key="an_...") as c:
    # 1. Capture locally (free, PII-scrubbed, hash-chained)
    await c.post("/v1/lora/capture", {"bad": bad, "good": good, "language": "python"})

    # 2. Contribute the buffered batch (micro-rebate per accepted sample)
    await c.post("/v1/lora/contribute", {"min_quality": 0.6})

    # 3. Pull the current community adapter
    adapter = await c.get("/v1/lora/adapter/python")

    # 4. Claim your reputation reward
    await c.post("/v1/lora/reward/claim", {"agent_id": "my-agent-001"})

See examples/lora_flywheel.py for the end-to-end script.

2. Trusted RAG cycle — provenance or it didn't happen

Ordinary RAG pulls whatever the vector index returns. Trusted RAG gates each chunk on:

• Source domain allowlist
• Freshness window
• Hallucination oracle verdict on the retrieved text
• Drift score against the live knowledge graph
• Tamper-evident receipt (SHA-256 chained to the lineage vault)

ctx = await c.post("/v1/rag/augment", {
    "query": "latest EU AI Act Annex IV requirements",
    "max_results": 5,
    "freshness_hours": 168,
    "source_policy": "trusted_only",
    "required_domains": ["europa.eu", "nist.gov"],
})
# ctx["results"][i] includes: text, source_url, trust_score, receipt_hash

Every response ships with a _guard block: {hallucination, drift, guarded_at}. You never have to run those checks yourself.

See examples/trusted_rag.py for the full cycle including certificate emission.

Key capabilities

... View full README on GitHub