stemedb/CLAUDE.md

Episteme (StemeDB)

A probabilistic knowledge graph database that stores Claims, not Facts. Append-only Merkle DAG with read-time resolution via Lenses.

Core Concept: "Git for Truth" - conflicting assertions coexist, resolved at query time through Consensus, Recency, Authority, or custom Lenses.

Find Your Guide

If you need to...	Read this
Get started fast	quickstart.md
Understand what Episteme is	what-is-episteme.md
Understand the technical vision	vision.md
See use cases	use-cases/README.md
Understand architecture	architecture.md
Learn data structures	docs/data-structures.md
Understand governance models	docs/specs/governance-models.md
See the roadmap	roadmap.md
See completed phases	roadmap-archive.md
Build apps on Episteme	docs/app-concepts/index.md
Consumer Health vertical	docs/app-concepts/consumer-health.md
Use Go SDK	ai-lookup/services/sdk.md
Write Rust code	.claude/guides/backend/rust-guidelines.md
Set up local dev	.claude/guides/local/setup.md
Run tests	.claude/guides/local/testing.md
Understand quality checks	.claude/guides/local/quality-checks.md
Learn about simulation	ai-lookup/features/simulation.md
Advance the simulator	arena-roadmap.md
Work on storage/DAG	Load skill: stemedb-core
Implement a Lens	Load skill: stemedb-lens
Work on domain ontology	crates/stemedb-ontology/
Consumer Health UAT	uat/consumer-health/README.md
Verify production readiness	uat/production-readiness/README.md
Plan a milestone	/plan-milestone command
Analyze use case gaps	/analyze-gaps command
Add an API endpoint	.claude/guides/backend/api-endpoints.md
Integrate with AI tools	.claude/guides/integrations/ai-coding-assistant-integration.md
ADK-Go + Episteme	.claude/guides/integrations/adk-go-episteme.md
Distributed architecture	docs/research/distributed-write-path.md
Write UAT reports	.claude/guides/local/uat-reports.md
Phase 6 UAT results	ai-lookup/features/phase6-uat.md
Configure Aphoria hosted mode	.claude/guides/services/aphoria-hosted-mode.md
Aphoria config reference	ai-lookup/features/aphoria-config.md
Work on Admin Dashboard	applications/stemedb-dashboard/ (Next.js + shadcn/ui)
Work on Disputed app	applications/disputed/
Understand repo structure	ai-lookup/repo-structure.md
Aphoria LLM eval	Load skill: aphoria-llm-optimization
General LLM optimization	Load skill: llm-optimization
Install Aphoria	Load skill: aphoria-install
Run Aphoria self-review	Load skill: aphoria-self-review
Author claims from diffs	Load skill: aphoria-claims
Suggest new claims	Load skill: aphoria-suggest

Roadmap Maintenance

Two files, strict separation:

File	Contains	When to modify
roadmap.md	Current + future work only	Add new phases, update task status
roadmap-archive.md	Completed phases (1-7, 8A, MVP)	Move items when phase completes

Rules:

• When a phase completes: Move entire phase section to archive, update status table in both files
• When adding tasks: Add to current phase in roadmap.md with - [ ] checkbox format
• When completing tasks: Change - [ ] to - [x], add brief implementation notes
• Keep roadmap.md under 500 lines — if it grows, archive more aggressively
• Current phase always has "🎯" marker in status table

Task format:

- [ ] **P1.2 Feature Name**: Brief description
    - [ ] Subtask one
    - [ ] Subtask two

Phase completion checklist:

1. All tasks marked [x] in roadmap.md
2. Cut entire phase section, paste into roadmap-archive.md
3. Update status tables in both files
4. Update "Current Focus" in roadmap.md header

Aphoria: What Is a Claim?

A claim is a human-authored statement about what code MUST do and WHY, with provenance and consequences.

Claims vs Observations

Type	What it is	Who creates it	Example
Observation	Grep result: "this code does X"	Extractors (automated)	imports/tokio: true
Claim	Rule: "code MUST do X because Y, or Z breaks"	Humans (via skill)	"Core MUST NOT import tokio because it creates runtime coupling. If tokio appears in core imports, the library becomes async-only and breaks sync users."

Observations are garbage. They're indexed facts with no meaning. Nobody cares that imports/format: true — that's just grep output.

Claims are the product. They encode architectural decisions, safety invariants, and spec compliance with full context: provenance (where the rule came from), invariant (what must stay true), and consequence (what breaks if violated).

Structure of a Claim

[[claim]]
id = "core-no-tokio-001"
concept_path = "stemedb/core/imports/tokio"
predicate = "imported"
value = false
comparison = "absent"  # Code MUST NOT have this
provenance = "Architecture decision by jml 2024-12-15"
invariant = "Core modules MUST remain sync-only"
consequence = "Importing tokio makes core async-only, breaking sync library users"
authority_tier = "expert"
category = "architecture"
evidence = ["ADR-003", "design review notes"]
status = "active"

Aphoria Workflows (Primary Use Cases)

Day-to-day (commit-time claim authoring):

1. Look at the entire diff
2. Use aphoria-claims skill to identify "claimable" patterns (spec constants, ordering changes, boundary violations, derive changes on wire types)
3. Skill does lookups: aphoria claims list to check what exists
4. If alignment needed, skill uses aphoria claims update or supersede
5. Skill crafts and submits new claims via aphoria claims create
6. If needed for audit, create paired extractor

Audit (scan-time claim verification):

1. Direction 1: aphoria scan runs extractors → observations, compares against authored claims → PASS/CONFLICT/MISSING
2. Direction 2: aphoria verify run walks all claims, verifies each one's pattern exists in code → PASS/CONFLICT/MISSING

The skill drives the CLI. The CLI doesn't know about the skill. They connect via skill calling aphoria claims commands in a loop.

Critical Rules

• Append-Only: NEVER mutate existing Assertions. Create new ones.
• Content-Addressed: Assertion ID = BLAKE3 hash of content.
• No Unwrap: NEVER use unwrap() or expect() in production code. CI enforces via clippy::unwrap_used and clippy::expect_used at deny level.
• Defensive Writes: All writes go through WAL with fsync.
• Zero-Copy: Use rkyv for serialization. ALWAYS use stemedb_core::serde::{serialize, deserialize} — NEVER use raw AllocSerializer in production code.
• Instrument Critical Paths: Use #[instrument] on public methods in WAL, storage, ingestion, and lens code. Include meaningful fields (key_len, payload_len, offset, candidates_count, lens).
• Structured Logging: Use tracing (info!, warn!, error!) instead of println!/eprintln!. Clippy enforces via print_stdout/print_stderr at warn level. CLI binaries (e.g., stemedb-sim) may use #![allow()] for user-facing output.
• Document Changes: Update ai-lookup/ when adding new types/concepts. Keep skills in sync with code.
• No Git Operations: NEVER use git stash, git branch, git checkout, or any git operations unless the user explicitly tells you to.
• No GitHub Workflows: We use pre-commit hooks, not GitHub Actions CI.

Quick Reference

# Build
cargo build --workspace

# Test (choose based on need)
cargo test -p stemedb-core        # Fast: single crate (~30s)
cargo test --workspace --lib      # Medium: all unit tests (~3min)
cargo nextest run                 # Full: parallel runner (~5min)
cargo test --workspace            # Legacy: sequential (~15min)

# Lint (must pass before commit)
cargo clippy --workspace -- -D warnings
cargo fmt --check

Port Scheme (181XX)

Offset	Service	Default	Env Var
+0	HTTP API	18180	STEMEDB_BIND_ADDR
+1	Cluster Gateway	18181	STEMEDB_NODE_API_ADDR
+2	Cluster RPC	18182	STEMEDB_NODE_RPC_ADDR
+3	SWIM Gossip	18183	via SwimConfig
+4	Metrics	18184	(reserved)
+5	Admin	18185	(reserved)
+6	Latent Signal	18186	—
+7	Community App	18187	—

Specialized Agents

Domain	Agent	When to use
Product Vision	episteme-product-visionary	Use cases, "why not Postgres?", product-market fit
Pilot Prep	enterprise-skeptic-buyer	Pressure-test demos, find gaps, prepare for tough questions
Aphoria Pitch	aphoria-skeptic-buyer	Pressure-test Aphoria demos, security tool buyer objections
Aphoria Phase 7	declarative-extractor-skeptic	Pressure-test declarative extractors, LLM extraction, pattern learning
Aphoria Phase 9	autonomous-learning-skeptic	Pressure-test autonomous promotion, shadow mode, cross-project learning
General Rust	primary-developer	Feature implementation, refactoring
Code Quality	rust-quality-engineer	Reviews, test coverage, clippy
Storage	storage-engine-architect	WAL, LSM, crash recovery
Graph Engine	rust-graph-engine-architect	Lock-free structures, cache optimization
Defensive	defensive-systems-architect	Rate limiting, circuit breakers, hostile input
Distributed	distributed-systems-engineer	CRDT replication, Raft coordination, Merkle sync, clustering
Lenses	stemedb-lens-architect	Query resolution, ranking algorithms
Planning	stemedb-planner	Milestone planning, roadmap

Architecture Overview

Write Path (Spine):           Read Path (Cortex):
[Agent] -> [Ingestion]        [Agent] <- [Lens Engine]
              |                              |
              v                              |
         [WAL/Fsync]                  [Index Lookup]
              |                              |
              v                              |
         [KV Store] <--------------------+

Crates

Crate	Purpose	Status
stemedb-core	Assertion, LifecycleStage, MaterializedView, types, signing utilities	✅ Implemented
stemedb-wal	Write-ahead log with crash recovery	✅ Implemented
stemedb-storage	KVStore, VoteStore, IndexStore, TrustRankStore, QuarantineStore, SimilarityIndex	✅ Implemented
stemedb-ingest	Ingestion pipeline, signature verification, ContentDefenseLayer	✅ Implemented
stemedb-query	Query engine, Materializer for O(1) MV: reads	✅ Implemented
stemedb-lens	Lenses (Recency, Consensus, Authority, Vote/Trust-aware)	✅ Implemented
stemedb-api	HTTP API with axum + utoipa OpenAPI docs	✅ Implemented
stemedb-sim	Simulation for testing the pipeline	✅ Implemented
stemedb-merkle	BLAKE3 Merkle tree for diff detection	✅ Implemented
stemedb-rpc	gRPC services for node-to-node communication	✅ Implemented
stemedb-sync	Merkle sync, gossip broadcast, anti-entropy	✅ Implemented
stemedb-cluster	Cluster membership (SWIM), sharding, gateway	✅ Implemented
stemedb-ontology	Domain definitions (Pharma), subject builders, medical extractors	✅ Implemented

SDKs

SDK	Purpose	Status
sdk/go/steme	Go HTTP client with Ed25519 signing and fluent builders	✅ Implemented
sdk/go/adk	ADK-Go tools and callbacks for AI agents	✅ Implemented

Latent Signal (latent/)

Python CLI tools for adverse event signal detection. Different rules from Rust crates:

Allowed:

• print() for user-facing CLI output (these are scripts, not libraries)
• except Exception as e: for CLI error handling (log and continue)

Required:

• Environment Variables for URLs: NEVER hardcode localhost URLs without env fallback
  • Use os.getenv("VAR", "http://localhost:...") in Python
  • Use process.env.VAR || 'http://localhost:...' in TypeScript
• StemeDB Integration: New ingestors should use StemeDBClient pattern from adk-agent/, not write to JSONL files