groundcheck 0.3.0

Trust-weighted hallucination detection for AI agents. Verify LLM outputs against multiple sources with contradiction awareness. Zero dependencies. Sub-2ms.

GroundCheck

Trust-weighted hallucination detection for AI agents. Zero dependencies. Sub-2ms.

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The Problem

Your AI agent says "you work at Amazon." Memory says "Microsoft." Most systems won't catch this — they just return the most similar embedding and hope for the best. GroundCheck catches it in <2ms with zero dependencies.

Install

pip install groundcheck

10-Second Demo

from groundcheck import GroundCheck, Memory

verifier = GroundCheck()

memories = [
    Memory(id="m1", text="User works at Microsoft", trust=0.9),
    Memory(id="m2", text="User lives in Seattle", trust=0.8),
]

result = verifier.verify("You work at Amazon and live in Seattle", memories)

print(result.passed)          # False
print(result.hallucinations)  # ["Amazon"]
print(result.corrected)       # "You work at Microsoft and live in Seattle"
print(result.confidence)      # 0.65

What Makes This Different

Other systems treat verification as a binary "is this grounded?" check against a single source. GroundCheck is different:

	Other systems	GroundCheck
Sources	Single string or premise/hypothesis pair	Multiple memories with per-source trust scores
Trust	All sources treated equally	Trust-weighted — high-trust memories override low-trust
Contradictions	Not detected	Cross-memory conflict detection with resolution
Correction	Flag only — no fix	Auto-rewrites hallucinations with grounded facts
Temporal	No awareness	most_recent vs most_trusted resolution
Dependencies	Often torch, transformers, etc.	Zero (stdlib only)
Latency	500ms – 3,000ms+	1.17ms mean
Extra LLM calls	Some require 3-5 per check	Zero

How It Works

Generated text + Retrieved memories (with trust scores)
    → Extract fact claims (universal: any domain, any structure)
    → Detect contradictions across memories (dynamic slot tracking)
    → Build grounding map (fuzzy match claims to memories)
    → Check disclosure requirements (trust-weighted)
    → Calculate confidence score
    → Generate corrections (strict mode)
    → VerificationReport

Trust-Weighted Verification

GroundCheck doesn't treat all sources equally. Each memory has a trust score:

memories = [
    Memory(id="m1", text="User is named Alice", trust=0.9),   # High trust
    Memory(id="m2", text="User is named Bob", trust=0.3),     # Low trust
]

result = verifier.verify("Your name is Bob", memories)
print(result.requires_disclosure)  # True — trust gap > 0.3
print(result.contradiction_details[0].most_trusted_value)  # "alice"
print(result.contradiction_details[0].most_recent_value)   # depends on timestamps

Verification Modes

• strict — generates corrected text, replaces hallucinations with grounded facts
• permissive — detects and reports, doesn't rewrite

result = verifier.verify("You live in Paris", memories, mode="strict")
print(result.corrected)  # Rewritten with grounded facts

result = verifier.verify("You live in Paris", memories, mode="permissive")
print(result.corrected)  # None — permissive doesn't rewrite

Universal Fact Extraction

GroundCheck v0.2 extracts facts from any domain — not just personal profiles. Nine pattern families cover:

Pattern	Example
Copular (X is Y)	"The server is running Ubuntu 22.04"
Possessive (X has Y)	"Python has garbage collection"
Non-copular verbs	"Tesla manufactures electric vehicles"
Clause splitting	"Bob is 30, lives in NYC, and works at Google"
Decisions & plans	"We chose Postgres" / "They decided to use Rust"
Requirements	"The app requires Node 18+"
Prescriptive	"Always use HTTPS for API calls"
Numeric	"The latency is 3.5ms" / "Revenue: $4.2 billion"
Named slots	name, employer, location, age, hobby, etc.

35+ known exclusive slots with mutual exclusivity knowledge (a person has one employer but many hobbies). All extracted slots are tracked for contradictions — including dynamically discovered ones.

Neural Mode (Optional)

For paraphrase handling and semantic matching, install the neural extras:

pip install groundcheck[neural]

# Explicit control (v0.3.0+)
verifier = GroundCheck(neural=True)   # Enable paraphrase matching (default)
verifier = GroundCheck(neural=False)  # Zero-dep, sub-2ms regex only

# Catches paraphrases regex can't:
memories = [Memory(id="m1", text="User works at Google")]
result = verifier.verify("Employed by Google", memories)   # ✓ passes
result = verifier.verify("I live in New York City",        # ✓ matches "NYC"
         [Memory(id="m2", text="User lives in NYC")])

Models are loaded lazily on first use — no startup cost until you need them.

Five matching strategies fire in order: exact → normalization → fuzzy → synonym → embedding. NLI-based contradiction refinement filters false positives for dynamically-discovered slots.

Mode	Paraphrase Accuracy	Latency
Regex-only (default)	70%	1.17ms
Neural	85-90%	~15ms

API Reference

GroundCheck

• GroundCheck(neural=True) — constructor. neural=True enables semantic matching (requires groundcheck[neural]), neural=False for zero-dependency mode.
• verify(generated_text, retrieved_memories, mode="strict") → VerificationReport
• extract_claims(text) → Dict[str, ExtractedFact]
• find_support(claim, memories) → match info

extract_fact_slots(text) (standalone function)

Universal fact extractor — works on any domain text, not just personal facts. Returns Dict[str, ExtractedFact] with dynamically discovered slot names.

VerificationReport

• passed: bool — did verification pass?
• corrected: Optional[str] — rewritten text (strict mode)
• hallucinations: List[str] — hallucinated values
• grounding_map: Dict — claim → supporting memory
• confidence: float — trust-weighted confidence (0.0-1.0)
• contradiction_details: List[ContradictionDetail] — full conflict info
• requires_disclosure: bool — must the response acknowledge conflicts?

Memory

• id: str — unique identifier
• text: str — memory content
• trust: float — trust score (0.0-1.0, default 1.0)
• timestamp: Optional[int] — when this was stored

ContradictionDetail

• slot: str — which fact slot conflicts
• values: List[str] — conflicting values
• most_trusted_value — value from highest-trust memory
• most_recent_value — value from most recent memory

Performance

Benchmark: 1,000 verifications
Mean latency:  1.17ms
P95 latency:   2.09ms
P99 latency:   3.41ms
Memory: ~2MB RSS
Dependencies: 0

MCP Server (Agent Integration)

GroundCheck ships with an MCP server that gives any AI agent persistent fact memory with contradiction detection. Works with VS Code Copilot, Claude Desktop, Cursor, and any MCP-compatible client.

pip install groundcheck[mcp]

Add to your config (VS Code .vscode/mcp.json, Claude claude_desktop_config.json, etc.):

{
  "servers": {
    "groundcheck": {
      "command": "groundcheck-mcp",
      "args": ["--db", ".groundcheck/memory.db"]
    }
  }
}

Three tools are exposed:

Tool	When to call	What it does
crt_store_fact	User states a fact	Stores with trust score, detects contradictions
crt_check_memory	Before answering about the user	Returns relevant memories with trust scores
crt_verify_output	Before sending a response	Catches hallucinations, auto-corrects, scores confidence

Full MCP setup guide →

Development

git clone https://github.com/blockhead22/GroundCheck.git
cd GroundCheck
python -m venv .venv
.venv\Scripts\activate  # or source .venv/bin/activate
pip install -e ".[dev]"
pytest

License

MIT