Epistemic guard that hooks into transformer hidden states to classify whether the model is recalling training data, using context, or confabulating. Exposes a probe layer trained on Fisher geometry (AUROC 0.9944 validated on Qwen2.5) that fires before token commitment. Blocks file writes and code commits when confidence drops below threshold. The symbolic layer tracks uncertain values across inference steps, so if a variable assignment gets flagged as confabulation risk, downstream operations using it stay blocked until verification. Reach for this when you're letting an agent write code or modify files and need circuit-breaker semantics tied to the model's actual epistemic state rather than parsing output text for hedging language.
AI doesn't remember what it wasn't sure about. Credence does.
pip install credence-guard
credence demo # 30-second smoke test, no API key required
[mcp] adds the FastMCP server for Claude Code. Core package has zero hard dependencies.
You say: "The rate limit is probably around 50 — I haven't confirmed it yet."
Fifteen turns later, Claude writes:
RATE_LIMIT = 50 # no warning. no flag. shipped.
The API rejects every request at 2am. The real limit was 10. Claude forgot you weren't sure.
This isn't hallucination. The model reproduced exactly what it read. What it read had the qualifier stripped — by context compression, fifteen turns back.
Tracks uncertain values the moment you state them. Blocks writes that embed those values until you confirm them.
you say "rate limit is probably 50"
→ observer registers it (before Claude responds)
→ Claude writes: RATE_LIMIT = 50 # ⚠ CREDENCE[unverified]
→ write blocked until you confirm
Every other tool warns. Credence enforces.
# Claude generates this. Credence intercepts before it ships.
class StripeClient:
API_VERSION = "2023-10-16" # ⚠⚠ CREDENCE[stale]: API date versions change on release — verify before shipping
RATE_LIMIT = 100 # ⚠ CREDENCE[unverified]: I think Stripe rate limit is around 100 req/min
TOKEN_EXPIRY = 3600 # ⚠⚠ CREDENCE[stale]: Token/session lifetime values are set by the vendor — verify
MAX_RETRIES = 3
TIMEOUT_MS = 5000
credence: blocked Edit — 2 unverified value(s)
→ I think Stripe rate limit is around 100 req/min | TOKEN_EXPIRY = 3600
Verify first, then retry. Use credence_constraints to see all pending.
After you confirm: "Confirmed — rate limit is 100 req/min per stripe.com/docs" → gate clears.

1. Add to .mcp.json:
{ "mcpServers": { "credence": { "command": "credence-server" } } }
2. Add to .claude/settings.json:
{
"hooks": {
"UserPromptSubmit": [
{ "hooks": [{ "type": "command", "command": "python3 -m credence.observer" }] }
],
"PreToolUse": [
{
"matcher": "Write|Edit|Bash|NotebookEdit",
"hooks": [{ "type": "command", "command": "python3 -m credence.hooks" }]
}
]
}
}
Done. No API key required.
Registry: Credence creates
epistemic_registry.dbin your working directory. Add*.dbto your.gitignore, or setCREDENCE_DB=~/.credence/registry.dbto keep it global.Session tracking: Set
CREDENCE_SESSION_ID=my-projectto keep constraints stable across directory changes and terminal restarts.Event log: The gate writes block/allow events to
~/.credence/events.jsonl(local only, never sent anywhere). SetCREDENCE_NO_LOG=1to disable.Constraint cap: The registry allows up to 500 constraints per session by default. Override with
CREDENCE_MAX_CONSTRAINTS=<n>.
Two layers, neither requires model cooperation:
| Layer | Hook | Role |
|---|---|---|
| Observer | UserPromptSubmit | Passive listener — registers uncertain values before Claude generates anything |
| Gate | PreToolUse | Blocks writes that embed unverified values |
The observer fires before the model processes your message. If you say "I think the rate limit is 50", the registry has that entry before Claude generates a single token.
credence: blocked Edit — 2 unverified value(s)
→ rate limit is probably 50 req/min | token expires in 3600s
Verify first, then retry. Use credence_constraints to see all pending.
Once verified, the gate clears.
46% of uncertainty qualifiers are stripped by Claude Haiku during context compression. Credence blocks 100% of those writes (n=50, bootstrap CI: [0%–0%]).
Validated across 7 open-weight models (Qwen, Mistral, Llama, Phi, Gemma) from 5 organizations: same failure mode, same block rate.
credence demo # smoke test, no API key
credence stats # false-positive rate from real gate usage
credence feedback 1|2|3 # tag last gate block: correct / noise / skip
python3 -m pytest tests/ -q # 829 tests
python3 -m evals.latency_report # P50/P95/P99
Full methodology: docs/TECHNICAL_REPORT.md
credence/ pip-installable package
observer.py passive UserPromptSubmit hook
hooks.py PreToolUse enforcement gate
mcp_server.py 17-tool MCP server
registry.py SQLite constraint store
memory.py cross-session persistence
tests/ 829 tests
evals/ validation studies + multi-model benchmarks
docs/ technical report, architecture, ETP spec
credence_gate/ Rust gate (alternative to Python hooks.py)
experimental/ Phase 2 work — not yet shipped
paper/ Research paper draft + figures
The scientific basis for Credence is documented in paper/ (EQL / EQLR / FCR).
The companion geometry thesis — on confabulation detection and why the detection axis is dissociable from the causal control axis — lives in a separate repo: → Detection Without Control
Lakshmi Chakradhar Vijayarao — GitHub · LinkedIn · X
Apache 2.0 License