veroq 2.1.1

VeroQ Python SDK — the verified intelligence layer for AI agents.

veroq

Verified AI. One function call. Stop shipping hallucinations.

Official Python SDK for VeroQ. shield() any LLM output — every claim fact-checked with evidence chains, confidence scores, and verification receipts.

Migrating from polaris-news? Drop-in replacement — just change your import.

Prompt Shield — One Line to Verify Any LLM

from veroq import shield

result = shield("NVIDIA reported $22B in Q4 revenue")
print(result.trust_score)      # 0.73
print(result.is_trusted)       # False — claims contradicted
print(result.corrections)      # [{"claim": "...", "correction": "actual revenue was $68B"}]
print(result.verified_text)    # text with corrections inline
print(result.receipt_ids)      # ["vr_abc123"] — permanent proof

Works with any LLM. One function. Every claim fact-checked.

Installation

pip install veroq

Quick Start

from veroq import VeroqClient

client = VeroqClient()  # uses VEROQ_API_KEY env var

# Ask anything — routes to 41 intents automatically
answer = client.ask("How is NVDA doing?")
print(answer["summary"])
print(answer["trade_signal"])  # { action: "hold", score: 55 }

# Verify any claim — evidence chains + confidence breakdown
result = client.verify("NVIDIA beat Q4 earnings by 20%")
print(result["verdict"])          # "supported"
print(result["confidence"])       # 0.92
print(result["evidence_chain"])   # [{ source: "Reuters", ... }]

# Stream in real-time
for event in client.ask_stream("AAPL price and technicals"):
    if event["type"] == "summary_token":
        print(event["data"]["token"], end="", flush=True)

Multi-Agent Workflows

# Verified Swarm — 5 agents with automatic verification
swarm = client.create_verified_swarm(
    "Analyze NVDA for a long position",
    roles=["planner", "researcher", "verifier", "critic", "synthesizer"],
    escalation_threshold=75,
    credit_budget=30,
)
print(swarm["synthesis"]["summary"])
print(swarm["budget"])                # { spent: 12, remaining: 18 }
print(swarm["verification_summary"])  # { avg_confidence: 82 }

# Domain-specific runtime
legal = client.create_runtime(
    "GDPR data retention requirements",
    vertical="legal",
    cost_mode="premium",
)

External Tool Calls

result = client.call_external_tool("alphavantage", "get_quote", {"symbol": "NVDA"})
# Permission engine → rate limiter → cache → execution → audit

Self-Improvement Feedback

client.submit_feedback(
    session_id=swarm["session_id"],
    query="NVDA analysis",
    reason="data_gap",
    detail="Missing Q4 insider trading data",
)

Enterprise Features

client.configure_enterprise({
    "enterprise_id": "acme-capital",
    "escalation_threshold": 80,
    "escalation_pauses": True,
    "session_id": "trading-session-001",
})

lineage = client.get_decision_lineage("ask", {"question": "Should I buy NVDA?"})
print(lineage["decision"])     # "review" — high-stakes detected

trail = client.get_audit_trail(session_id="trading-session-001")

Why VeroQ?

	What you get
Trust	Evidence chains and confidence breakdowns on every response
Safety	Permission engine, decision lineage, human-in-the-loop escalation
Cost control	3 cost modes, per-step budgets, credit transparency
Continuous improvement	Feedback loop with web search fallback fills data gaps over time
Multi-domain	Finance (flagship), legal, research, compliance, custom verticals

Cached Shield — High-Volume Pipelines

from veroq import CachedShield

cached = CachedShield(max_cache=1000, ttl_seconds=3600)
result = cached("NVIDIA reported $22B in Q4 revenue")   # API call
result = cached("NVIDIA reported $22B in Q4 revenue")   # instant, 0 credits
print(cached.stats())  # {'hits': 1, 'misses': 1, 'hit_rate': 0.5, 'size': 1}

Agent Monitoring

# Set up autonomous monitoring
client.agent_auto_monitor("my-bot", trust_threshold=0.7, check_interval_hours=6)

# Manual health check
health = client.agent_health_check("my-bot")
print(health["health"]["status"])        # "healthy" or "degraded"
print(health["health"]["trust_trend"])   # "improving" / "declining" / "stable"

All Methods

Method	Description
shield(text)	Verify any LLM output (module-level)
CachedShield(...)	Local LRU cache for high-volume shield calls
ask(question)	Ask any financial question
ask_stream(question)	Stream via SSE
verify(claim)	Fact-check with evidence chain
verify_output(text)	Extract + verify claims from any text
create_verified_swarm(query, ...)	Multi-agent verified pipeline
create_runtime(query, ...)	Domain-specific runtime
call_external_tool(server_id, tool, params)	Secure external tool proxy
submit_feedback(...)	Self-improvement feedback
memory_store(agent_id, key, value)	Store agent memory
memory_recall(agent_id)	Recall agent memories
memory_list(agent_id)	List all agent memories
agent_auto_monitor(agent_id)	Configure autonomous monitoring
agent_health_check(agent_id)	Trigger health check
watch(tickers)	Real-time SSE verification stream
configure_enterprise(config)	Enterprise governance
get_decision_lineage(tool, input, output)	Decision audit
get_audit_trail(session_id?)	Audit trail
feed() / brief(id) / search(query)	Intelligence briefs
stream(categories?)	Stream briefs via SSE

Error Handling

from veroq import AuthenticationError, RateLimitError, NotFoundError

try:
    client.ask("NVDA analysis")
except RateLimitError as e:
    print(f"Retry after: {e.retry_after}s")

Backward Compatibility

PolarisClient aliased to VeroqClient. Both VEROQ_API_KEY and POLARIS_API_KEY supported.

Links

• API Reference | MCP Server | TypeScript SDK | Enterprise