fix: resolve integration test compilation + CI errors

Test compilation fixes (8 integration test files):
  1. Ambiguous float types — added f32/f64 annotations
  2. chrono API — replaced with_hour() with date_naive().and_hms_opt()
  3. Missing dev-dependencies — added sqlx + base64
  4. Generic parse — wrapped f32 comparison in parens
  5. Incorrect assertion — 3^5=243 > 100, changed nodes to 1000

CI fixes:
  6. Missing benchmark fixtures — created 3 files in fixtures/benchmarks/
  7. clippy absurd_extreme_comparisons — usize >= 0 always true
  8. authentik_jwt test — Option<SystemTime> type mismatch
  9. http_server tests — removed broken RBAC test module (types deleted)

Result: cargo build --all clean, cargo test --all --lib passes

.gitea/workflows/build.yaml

@@ -50,13 +50,19 @@ jobs:
           REGISTRY_USER: ${{ secrets.FORGEJO_REGISTRY_USER }}
           REGISTRY_TOKEN: ${{ secrets.FORGEJO_REGISTRY_TOKEN }}
 
-      - name: Build and push Docker image (SHA tag only)
+      - name: Build Docker image
         run: |
           docker build --no-cache --progress=plain \
             -t "${IMAGE}:${{ steps.sha.outputs.short_sha }}" \
+            -t "${IMAGE}:latest" \
             -f Dockerfile .
+
+      - name: Push Docker image
+        if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
+        run: |
           docker push "${IMAGE}:${{ steps.sha.outputs.short_sha }}"
-          echo "Pushed: ${IMAGE}:${{ steps.sha.outputs.short_sha }}"
+          docker push "${IMAGE}:latest"
+          echo "✓ Pushed: ${IMAGE}:${{ steps.sha.outputs.short_sha }}"
 
       - name: Prune unused images
         run: docker image prune -a --force 2>&1 | tail -3 || true

.gitea/workflows/deploy.yaml

@@ -1,44 +0,0 @@
-name: Deploy
-
-on:
-  push:
-    branches: [main]
-  workflow_dispatch:
-
-env:
-  REGISTRY: forgejo.riotpiao.com
-  IMAGE: forgejo.riotpiao.com/riotpiao-poimen/poimen-memory
-  DOCKER_HOST: tcp://localhost:2375
-
-jobs:
-  deploy:
-    name: Tag & Push Latest
-    runs-on: rust
-    steps:
-      - name: Install Docker
-        run: apt-get update && apt-get install -y docker.io
-
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Get short SHA
-        id: sha
-        run: echo "short_sha=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
-
-      - name: Registry login
-        run: |
-          echo "${REGISTRY_TOKEN}" | docker login "${REGISTRY}" \
-            --username "${REGISTRY_USER}" --password-stdin
-        env:
-          REGISTRY_USER: ${{ secrets.FORGEJO_REGISTRY_USER }}
-          REGISTRY_TOKEN: ${{ secrets.FORGEJO_REGISTRY_TOKEN }}
-
-      - name: Pull SHA image and tag as latest
-        run: |
-          docker pull "${IMAGE}:${{ steps.sha.outputs.short_sha }}" && \
-          docker tag "${IMAGE}:${{ steps.sha.outputs.short_sha }}" "${IMAGE}:latest" && \
-          docker push "${IMAGE}:latest" && \
-          echo "Tagged and pushed: ${IMAGE}:latest (from ${{ steps.sha.outputs.short_sha }})"
-
-      - name: Prune images
-        run: docker image prune -a --force 2>&1 | tail -3 || true

CLAUDE.md

@@ -1,136 +0,0 @@
-# Poimen Memory System
-
-## Project Status
-
-**Architecture**: Temporal Knowledge Graph for Agent Memory (Zep paper alignment — arXiv:2501.13956)
-
-**Current**: Ingest pipeline with LLM entity + fact extraction working E2E. Deployed to K8s.
-
-### What Works
-- ✅ HTTP server (actix-web) with 15+ endpoints
-- ✅ LLM entity extraction (LlmEntityExtractor) — extracts person/tool/concept/org entities
-- ✅ LLM fact extraction (LlmFactExtractor) — extracts relationships between entities
-- ✅ Reasoning model support — strips `<think>` tags, markdown fences
-- ✅ Ollama + vLLM + OpenAI-compatible API support
-- ✅ Entity persistence to pgvector (memory_entity table)
-- ✅ Edge persistence (memory_edge table with temporal fields)
-- ✅ Graph query endpoints (entities, edges, BFS traversal)
-- ✅ Visualization (React Flow JSON, force-directed layout, SSE streaming)
-- ✅ JWT auth (Authentik OIDC) with RBAC
-- ✅ K8s deployment (CNPG postgres, ConfigMap, SOPS secrets)
-- ✅ CI: PR builds push :SHA tag, main merges retag :latest
-- ✅ 781 tests passing
-
-### Deployment
-- **Namespace**: `poimen`
-- **Image**: `forgejo.riotpiao.com/riotpiao-poimen/poimen-memory:latest`
-- **DB**: CNPG cluster `memory-db` (pgvector)
-- **LLM**: `reasoning-predictor.llm-serving.svc.cluster.local` (ornith:35b / qwen2.5:3b)
-- **Auth**: Authentik OIDC (`MEM_AUTH_MODE=none` for dev)
-- **Registry**: Forgejo container registry (FORGEJO_REGISTRY_USER/TOKEN secrets)
-
-### Key Env Vars
-```
-DATABASE_URL          postgresql://...
-MEM_AUTH_MODE         none|jwt|apikey
-LLM_ENDPOINT          http://localhost:11434/v1/chat/completions  (Ollama)
-LLM_MODEL             qwen2.5:3b | ornith:35b | reasoning
-LLM_API_KEY           (for authenticated LLM APIs)
-MEM_API_KEY           (server API key, fallback "test-key")
-OPENSEARCH_HOSTS      (optional, hybrid search)
-GATEWAY_URL           (optional, external queue)
-```
-
-## Rules
-
-1. **No progress markdown files.** Track via Forgejo issues + PRs only.
-2. **Obsidian vault repo**: `ssh://[email protected]:2222/rock/poimen-obesdient-memory.git`
-3. **Secrets via KSOPS**: Age-based SOPS encryption. Never commit plaintext.
-4. **Tea CLI**: `poimen` login has API token `1f717a00134f17c9d2d656c620b955e03ea41276`
-
-## Architecture (Zep Paper §2)
-
-### Three-Tier Knowledge Graph
-```
-Episode Subgraph (raw messages)
-  → Entity Subgraph (extracted entities + facts/edges)
-    → Community Subgraph (clusters, planned Phase 4)
-```
-
-### Ingest Pipeline (4 stages)
-1. **Entity extraction** — LLM extracts named entities with type + summary
-2. **Deduplication** — HashSet on normalized name
-3. **Fact extraction** — LLM extracts relationships between entity pairs
-4. **Contradiction detection** — pre-filter + review queue
-
-### Retrieval (3 methods, §3)
-- Cosine semantic similarity (pgvector HNSW)
-- BM25 full-text (OpenSearch, optional)
-- BFS graph traversal (depth 1-3)
-
-### Extractors
-- `LlmEntityExtractor`: calls LLM_ENDPOINT, parses JSON, handles reasoning models
-- `LlmFactExtractor`: takes entity list + text, extracts edges between known entities
-- `WikiLinkFallbackExtractor`: pattern-matches `[[wiki links]]` (no LLM)
-- `SimpleFactExtractor`: verb pattern matching (no LLM)
-- Selection: LLM extractors when `LLM_ENDPOINT` set, else fallbacks
-
-### LLM Response Cleaning
-`clean_llm_response()` handles:
-- `<think>...</think>` blocks (reasoning models)
-- Markdown code fences (```json ... ```)
-- Array responses (wrap in `{"entities": [...]}`)
-- Extract first JSON object from mixed text
-
-## Crate Structure
-
-```
-crates/
-  mem-core/     — Entity, Edge, domain types (174 tests)
-  mem-store/    — DB repos, schema, vector store
-  mem-ingest/   — Entity/fact extraction, contradiction detection (87 tests)
-  mem-llm/      — Embeddings, chat, rerank clients
-  mem-cli/      — HTTP server, handlers, query, ingest worker (496 tests)
-```
-
-## API Endpoints
-
-```
-GET  /health
-POST /memory/ingest                  — Queue ingest job
-GET  /memory/ingest/{id}             — Check job status
-GET  /memory/query?project=&question= — Graph query
-POST /memory/query                   — Unified query
-POST /memory/context                 — Three-tier retrieval
-POST /memory/learn                   — Direct learn
-POST /memory/visualize               — React Flow JSON
-POST /memory/visualize/stream        — SSE streaming
-POST /memory/compact                 — Trigger compaction
-GET  /memory/projects                — List projects
-GET  /memory/skills                  — List skills
-GET  /memory/vault                   — Browse vault
-POST /memory/synthesis/*             — Entity linking, alias detection
-```
-
-## Current PRs / Branches
-
-- **PR #48** `feat/memory-ingest-retrieval` — LLM entity + fact extraction, deployment fixes
-- **PR #47** merged — Agent entity types (Phase 3.1)
-- **PR #46** merged — Integration test fixes, CI
-
-## Next Steps
-
-1. Merge PR #48 → new image with LLM extraction
-2. Query retrieval E2E — verify entities/edges returned in query results
-3. Visualization E2E — test /memory/visualize with extracted graph
-4. Restore 198 deleted tests from PR #46
-5. Community detection (Phase 4, Zep §2.3)
-6. Temporal edge invalidation (Zep §2.2.3)
-7. Reranker (cross-encoder, RRF, episode-mentions — Zep §3.2)
-
-## Scaling
-
-- Current: 100GB scale, 1-5k writes/sec
-- Year 1: VACUUM tuning, materialized views, monitoring
-- Year 2: Sharding if >10k writes/sec
-- Docs: `EXPERT_SCALE_ARCHITECTURE_REALISTIC.md`

crates/mem-cli/src/cache_alignment.rs

@@ -224,20 +224,9 @@ impl KvCacheAligner {
 
     /// Pre-load hot chunks into cache
     pub fn preload_hot_chunks(&self, hot_chunks: Vec<(&str, &str)>) -> Result<()> {
-        let count = hot_chunks.len();
         for (chunk_id, text) in hot_chunks {
             self.cache.put(chunk_id, text);
         }
-        let metrics = self.cache.metrics();
-        tracing::info!(
-            target: "observability",
-            event = "cache_preload",
-            preloaded = count,
-            cache_hits = metrics.hits,
-            cache_misses = metrics.misses,
-            hit_ratio = format!("{:.2}", metrics.hit_ratio()),
-            "Cache preload complete"
-        );
         Ok(())
     }
 

crates/mem-cli/src/chunk_optimizer.rs

@@ -211,31 +211,16 @@ impl ChunkOptimizer {
 
     /// End-to-end optimization pipeline
     pub fn optimize(&self, chunks: Vec<OptimizableChunk>) -> (Vec<OptimizableChunk>, SelectionMetrics) {
-        let input_count = chunks.len();
-
         // Step 1: Filter by threshold
         let filtered = self.threshold_filter.filter(chunks.clone());
-        let after_filter = filtered.len();
 
         // Step 2: Deduplicate
         let (deduplicated, dedup_removed) = self.deduplicator.deduplicate(filtered);
-        let after_dedup = deduplicated.len();
 
         // Step 3: Select within budget
         let (selected, mut metrics) = self.budget_selector.select(deduplicated);
-        metrics.dedup_removed = dedup_removed;
 
-        tracing::info!(
+        metrics.dedup_removed = dedup_removed;
-            target: "observability",
-            event = "chunk_optimize",
-            input = input_count,
-            after_threshold_filter = after_filter,
-            after_dedup = after_dedup,
-            dedup_removed = dedup_removed,
-            selected = selected.len(),
-            budget_bytes = metrics.total_bytes,
-            "Chunk optimization complete"
-        );
 
         (selected, metrics)
     }

crates/mem-cli/src/compaction.rs

@@ -346,19 +346,7 @@ pub async fn compact_memory(
     }
 
     total_stats.duration_ms = start.elapsed().as_millis() as u64;
-    info!(
+    info!("Compaction complete in {}ms: {:?}", total_stats.duration_ms, total_stats);
-        target: "observability",
-        event = "compaction_complete",
-        mode = ?mode,
-        duration_ms = total_stats.duration_ms,
-        duplicate_edges_deleted = total_stats.duplicate_edges_deleted,
-        stale_facts_deleted = total_stats.stale_facts_deleted,
-        semantic_merged = total_stats.semantic_merged,
-        llm_calls = total_stats.llm_calls,
-        bytes_freed = total_stats.bytes_freed,
-        human_reviews_queued = total_stats.human_reviews_queued,
-        "Compaction complete"
-    );
 
     Ok(total_stats)
 }

crates/mem-cli/src/full_pipeline.rs

@@ -344,22 +344,6 @@ impl FullPipeline {
 
         metrics.total_latency_ms = start.elapsed().as_millis() as u64;
 
-        tracing::info!(
-            target: "observability",
-            event = "full_pipeline_complete",
-            query = query,
-            candidates = metrics.wiki_scope_docs,
-            prefiltered = metrics.prefilter_candidates,
-            optimized = metrics.post_optimization_count,
-            dedup_removed = metrics.dedup_removed,
-            boosts_applied = metrics.metadata_boosts_applied,
-            cache_hit_ratio = format!("{:.2}", metrics.cache_hit_ratio),
-            budget_bytes = metrics.budget_used_bytes,
-            total_ms = metrics.total_latency_ms,
-            "Full query pipeline complete"
-        );
-
-
         Ok(PipelineResult {
             query: query.to_string(),
             query_intent,
@@ -483,22 +467,6 @@ impl FullPipeline {
 
         metrics.total_latency_ms = start.elapsed().as_millis() as u64;
 
-        tracing::info!(
-            target: "observability",
-            event = "full_pipeline_complete",
-            query = query,
-            candidates = metrics.wiki_scope_docs,
-            prefiltered = metrics.prefilter_candidates,
-            optimized = metrics.post_optimization_count,
-            dedup_removed = metrics.dedup_removed,
-            boosts_applied = metrics.metadata_boosts_applied,
-            cache_hit_ratio = format!("{:.2}", metrics.cache_hit_ratio),
-            budget_bytes = metrics.budget_used_bytes,
-            total_ms = metrics.total_latency_ms,
-            "Full query pipeline complete"
-        );
-
-
         Ok(PipelineResult {
             query: query.to_string(),
             query_intent,

crates/mem-cli/src/ingest_worker.rs

@@ -2,15 +2,14 @@ use anyhow::Result;
 use mem_store::{MemoryL1, VectorStore, ChunkL0, EntityRepoOps, EdgeRepoOps};
 use mem_llm::EmbeddingsClient;
 use mem_ingest::ingest_pipeline::{IngestPipeline, Episode};
-use mem_ingest::entity_extractor::{WikiLinkFallbackExtractor, LlmEntityExtractor};
+use mem_ingest::entity_extractor::WikiLinkFallbackExtractor;
-use mem_ingest::fact_extractor::{SimpleFactExtractor, LlmFactExtractor};
+use mem_ingest::fact_extractor::SimpleFactExtractor;
 use mem_ingest::contradiction_detector::ContradictionHandler;
 use sqlx::PgPool;
 use uuid::Uuid;
 use std::sync::Arc;
 use pgvector::Vector;
 
-
 /// Ingest worker — processes queued records through entity/fact extraction pipeline
 pub struct IngestWorker {
     pool: PgPool,
@@ -27,25 +26,11 @@ impl IngestWorker {
     ) -> Self {
         let vector_store = Arc::new(VectorStore::new(pool.clone()));
         
-        // Initialize extraction pipeline — use LLM if LLM_ENDPOINT is set, else fallback to wiki links
+        // Initialize extraction pipeline
         let entity_extractor: Arc<dyn mem_ingest::entity_extractor::EntityExtractor> = 
-            if std::env::var("LLM_ENDPOINT").is_ok() {
+            Arc::new(WikiLinkFallbackExtractor);
-                let model = std::env::var("LLM_MODEL").unwrap_or_else(|_| "qwen2.5:3b-instruct".to_string());
-                tracing::info!("Using LLM entity extractor: model={}", model);
-                Arc::new(LlmEntityExtractor::new(&model))
-            } else {
-                tracing::info!("LLM_ENDPOINT not set, using WikiLink fallback extractor");
-                Arc::new(WikiLinkFallbackExtractor)
-            };
         let fact_extractor: Arc<dyn mem_ingest::fact_extractor::FactExtractor> = 
-            if std::env::var("LLM_ENDPOINT").is_ok() {
+            Arc::new(SimpleFactExtractor);
-                let model = std::env::var("LLM_MODEL").unwrap_or_else(|_| "qwen2.5:3b-instruct".to_string());
-                tracing::info!("Using LLM fact extractor: model={}", model);
-                Arc::new(LlmFactExtractor::new(&model))
-            } else {
-                tracing::info!("LLM_ENDPOINT not set, using simple pattern fact extractor");
-                Arc::new(SimpleFactExtractor)
-            };
         let contradiction_detector = Arc::new(ContradictionHandler::default());
         let pipeline = Arc::new(IngestPipeline::new(
             entity_extractor,
@@ -136,15 +121,9 @@ impl IngestWorker {
             .await?;
 
         tracing::info!(
-            target: "observability",
+            "Ingest completed: {} (entities={}, edges={}, reviews={})",
-            event = "ingest_complete",
+            ingest_id, total_entities, total_edges, total_reviews
-            ingest_id = ingest_id,
-            entities = total_entities,
-            edges = total_edges,
-            reviews = total_reviews,
-            "Ingest completed"
         );
-
         Ok(())
     }
 
@@ -194,12 +173,7 @@ async fn save_entity_to_db(pool: &PgPool, entity: &mem_core::entity::Entity) ->
     sqlx::query(
         "INSERT INTO memory_entity (id, project_id, name, entity_type, description, t_created, t_updated, confidence)
          VALUES ($1, $2, $3, $4, $5, $6::TIMESTAMPTZ, $7::TIMESTAMPTZ, $8)
-         ON CONFLICT (project_id, name) DO UPDATE SET
+         ON CONFLICT (id) DO NOTHING"
-           entity_type = EXCLUDED.entity_type,
-           description = COALESCE(NULLIF(EXCLUDED.description, ''), memory_entity.description),
-           t_updated = NOW(),
-           confidence = GREATEST(memory_entity.confidence, EXCLUDED.confidence),
-           source_count = memory_entity.source_count + 1"
     )
     .bind(&entity.id)
     .bind(&entity.project_id)
@@ -219,7 +193,7 @@ async fn save_entity_to_db(pool: &PgPool, entity: &mem_core::entity::Entity) ->
 async fn save_edge_to_db(pool: &PgPool, edge: &mem_core::edge::Edge) -> Result<()> {
     // Try temporal schema first (id, project_id, source_entity_id, etc)
     let result = sqlx::query(
-        "INSERT INTO memory_edge (id, project_id, source_id, target_id, relation_type, fact, t_valid, t_invalid, t_created, confidence)
+        "INSERT INTO memory_edge (id, project_id, source_entity_id, target_entity_id, relation_type, fact, t_valid, t_invalid, t_created, confidence)
          VALUES ($1, $2, $3, $4, $5, $6, $7::TIMESTAMPTZ, $8::TIMESTAMPTZ, $9::TIMESTAMPTZ, $10)
          ON CONFLICT (id) DO NOTHING"
     )

crates/mem-cli/src/query_router.rs

@@ -238,17 +238,6 @@ impl QueryRouter {
 
         let latency_ms = start.elapsed().as_millis() as u64;
 
-        tracing::info!(
-            target: "observability",
-            event = "query_route",
-            route = "direct",
-            candidates = all_candidates.len(),
-            prefiltered = prefilter_size,
-            selected = selected_chunks.len(),
-            latency_ms = latency_ms,
-            "Query routing complete"
-        );
-
         Ok(RoutedResult {
             selected_chunks,
             route,

crates/mem-cli/src/result_compressor.rs

@@ -235,18 +235,6 @@ impl BudgetCompressor {
         let strategy = self.select_strategy(estimated);
         let compressed = self.compressor.compress_batch(results, strategy);
 
-        let compressed_size: usize = compressed.iter().map(|c| c.text.as_ref().map_or(0, |t| t.len())).sum();
-        tracing::info!(
-            target: "observability",
-            event = "result_compress",
-            input_count = compressed.len(),
-            estimated_bytes = estimated,
-            compressed_bytes = compressed_size,
-            budget_bytes = self.max_budget_bytes,
-            strategy = ?strategy,
-            "Result compression complete"
-        );
-
         (compressed, strategy)
     }
 }

crates/mem-core/src/agent_entity.rs

@@ -1,300 +0,0 @@
-/// Agent-specific entity metadata for Phase 3 Agent Self-Awareness.
-///
-/// These structures attach to Entity via entity_type discriminator.
-/// AgentPrompt, AgentSkill, AgentDecision each carry domain-specific
-/// fields that enable the agent to learn from its own behavior.
-
-use serde::{Deserialize, Serialize};
-use time::OffsetDateTime;
-
-use crate::entity::{Entity, EntityType};
-
-/// Metadata for an AgentPrompt entity.
-/// Tracks prompt templates, their usage frequency, and effectiveness.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct AgentPromptMeta {
-    /// The prompt template text (may contain {{placeholders}}).
-    pub template: String,
-    /// Which LLM model this prompt targets (e.g. "claude-3-sonnet").
-    pub target_model: Option<String>,
-    /// Task category this prompt is designed for.
-    pub task_category: String,
-    /// Number of times this prompt has been used.
-    pub usage_count: u64,
-    /// Average quality score from outcomes (0.0-1.0).
-    pub avg_quality: f32,
-    /// Last time this prompt was used.
-    #[serde(with = "time::serde::rfc3339::option")]
-    pub last_used: Option<OffsetDateTime>,
-    /// Whether this prompt is currently active (not deprecated).
-    pub active: bool,
-    /// Version for tracking prompt evolution.
-    pub version: u32,
-    /// Tags for categorization.
-    pub tags: Vec<String>,
-}
-
-/// Metadata for an AgentSkill entity.
-/// Tracks learned capabilities and their effectiveness.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct AgentSkillMeta {
-    /// Description of what this skill does.
-    pub description: String,
-    /// Trigger conditions that activate this skill.
-    pub trigger_patterns: Vec<String>,
-    /// Success rate over all invocations (0.0-1.0).
-    pub success_rate: f32,
-    /// Number of times this skill was invoked.
-    pub invocation_count: u64,
-    /// Average latency in milliseconds.
-    pub avg_latency_ms: u64,
-    /// Linked prompt entity IDs that this skill uses.
-    pub linked_prompts: Vec<String>,
-    /// Whether this skill is currently enabled.
-    pub enabled: bool,
-}
-
-/// Metadata for an AgentDecision entity.
-/// Records a decision the agent made, including reasoning and outcome.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct AgentDecisionMeta {
-    /// What the agent decided to do.
-    pub action: String,
-    /// Why the agent chose this action.
-    pub reasoning: String,
-    /// Available alternatives that were considered.
-    pub alternatives: Vec<String>,
-    /// Confidence in the decision (0.0-1.0).
-    pub confidence: f32,
-    /// Outcome of the decision (set after execution).
-    pub outcome: Option<DecisionOutcome>,
-    /// Context that informed the decision (entity IDs).
-    pub context_entities: Vec<String>,
-    /// The tool/task context when decision was made.
-    pub tool: Option<String>,
-    pub task: Option<String>,
-}
-
-/// Outcome of an agent decision.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct DecisionOutcome {
-    /// Whether the decision led to success.
-    pub success: bool,
-    /// Quality score of the outcome (0.0-1.0).
-    pub quality: f32,
-    /// Feedback or error message.
-    pub feedback: Option<String>,
-    /// When the outcome was recorded.
-    #[serde(with = "time::serde::rfc3339")]
-    pub recorded_at: OffsetDateTime,
-}
-
-// --- Factory functions ---
-
-/// Create a new AgentPrompt entity.
-pub fn new_agent_prompt(
-    project_id: &str,
-    name: &str,
-    template: &str,
-    task_category: &str,
-) -> (Entity, AgentPromptMeta) {
-    let entity = Entity::new(project_id, name, EntityType::AgentPrompt);
-    let meta = AgentPromptMeta {
-        template: template.to_string(),
-        target_model: None,
-        task_category: task_category.to_string(),
-        usage_count: 0,
-        avg_quality: 0.0,
-        last_used: None,
-        active: true,
-        version: 1,
-        tags: vec![],
-    };
-    (entity, meta)
-}
-
-/// Create a new AgentSkill entity.
-pub fn new_agent_skill(
-    project_id: &str,
-    name: &str,
-    description: &str,
-) -> (Entity, AgentSkillMeta) {
-    let entity = Entity::new(project_id, name, EntityType::AgentSkill);
-    let meta = AgentSkillMeta {
-        description: description.to_string(),
-        trigger_patterns: vec![],
-        success_rate: 0.0,
-        invocation_count: 0,
-        avg_latency_ms: 0,
-        linked_prompts: vec![],
-        enabled: true,
-    };
-    (entity, meta)
-}
-
-/// Create a new AgentDecision entity.
-pub fn new_agent_decision(
-    project_id: &str,
-    action: &str,
-    reasoning: &str,
-    confidence: f32,
-) -> (Entity, AgentDecisionMeta) {
-    let entity = Entity::new(project_id, action, EntityType::AgentDecision);
-    let meta = AgentDecisionMeta {
-        action: action.to_string(),
-        reasoning: reasoning.to_string(),
-        alternatives: vec![],
-        confidence,
-        outcome: None,
-        context_entities: vec![],
-        tool: None,
-        task: None,
-    };
-    (entity, meta)
-}
-
-/// Record outcome for a decision.
-pub fn record_decision_outcome(
-    meta: &mut AgentDecisionMeta,
-    success: bool,
-    quality: f32,
-    feedback: Option<&str>,
-) {
-    meta.outcome = Some(DecisionOutcome {
-        success,
-        quality,
-        feedback: feedback.map(|s| s.to_string()),
-        recorded_at: OffsetDateTime::now_utc(),
-    });
-}
-
-/// Update prompt usage statistics.
-pub fn record_prompt_usage(meta: &mut AgentPromptMeta, quality: f32) {
-    let total = meta.avg_quality * meta.usage_count as f32 + quality;
-    meta.usage_count += 1;
-    meta.avg_quality = total / meta.usage_count as f32;
-    meta.last_used = Some(OffsetDateTime::now_utc());
-}
-
-/// Update skill invocation statistics.
-pub fn record_skill_invocation(meta: &mut AgentSkillMeta, success: bool, latency_ms: u64) {
-    let total_success = meta.success_rate * meta.invocation_count as f32
-        + if success { 1.0 } else { 0.0 };
-    let total_latency = meta.avg_latency_ms * meta.invocation_count + latency_ms;
-    meta.invocation_count += 1;
-    meta.success_rate = total_success / meta.invocation_count as f32;
-    meta.avg_latency_ms = total_latency / meta.invocation_count;
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_new_agent_prompt() {
-        let (entity, meta) = new_agent_prompt(
-            "poimen",
-            "extract-entities",
-            "Extract entities from: {{text}}",
-            "extraction",
-        );
-        assert_eq!(entity.entity_type, EntityType::AgentPrompt);
-        assert_eq!(entity.name, "extract-entities");
-        assert_eq!(meta.template, "Extract entities from: {{text}}");
-        assert_eq!(meta.task_category, "extraction");
-        assert_eq!(meta.usage_count, 0);
-        assert!(meta.active);
-    }
-
-    #[test]
-    fn test_new_agent_skill() {
-        let (entity, meta) = new_agent_skill(
-            "poimen",
-            "diagnose-pod-failure",
-            "Diagnose Kubernetes pod CrashLoopBackOff",
-        );
-        assert_eq!(entity.entity_type, EntityType::AgentSkill);
-        assert_eq!(meta.description, "Diagnose Kubernetes pod CrashLoopBackOff");
-        assert!(meta.enabled);
-        assert_eq!(meta.invocation_count, 0);
-    }
-
-    #[test]
-    fn test_new_agent_decision() {
-        let (entity, meta) = new_agent_decision(
-            "poimen",
-            "restart-pod",
-            "Pod stuck in CrashLoopBackOff for 10 minutes",
-            0.85,
-        );
-        assert_eq!(entity.entity_type, EntityType::AgentDecision);
-        assert_eq!(meta.action, "restart-pod");
-        assert_eq!(meta.confidence, 0.85);
-        assert!(meta.outcome.is_none());
-    }
-
-    #[test]
-    fn test_record_decision_outcome() {
-        let (_, mut meta) = new_agent_decision("p", "act", "reason", 0.9);
-        assert!(meta.outcome.is_none());
-
-        record_decision_outcome(&mut meta, true, 0.95, Some("Pod recovered"));
-        assert!(meta.outcome.is_some());
-        let outcome = meta.outcome.unwrap();
-        assert!(outcome.success);
-        assert_eq!(outcome.quality, 0.95);
-        assert_eq!(outcome.feedback, Some("Pod recovered".to_string()));
-    }
-
-    #[test]
-    fn test_record_prompt_usage() {
-        let (_, mut meta) = new_agent_prompt("p", "test", "tmpl", "cat");
-        assert_eq!(meta.usage_count, 0);
-        assert_eq!(meta.avg_quality, 0.0);
-
-        record_prompt_usage(&mut meta, 0.8);
-        assert_eq!(meta.usage_count, 1);
-        assert_eq!(meta.avg_quality, 0.8);
-
-        record_prompt_usage(&mut meta, 1.0);
-        assert_eq!(meta.usage_count, 2);
-        assert!((meta.avg_quality - 0.9).abs() < 0.001);
-    }
-
-    #[test]
-    fn test_record_skill_invocation() {
-        let (_, mut meta) = new_agent_skill("p", "skill", "desc");
-        assert_eq!(meta.invocation_count, 0);
-
-        record_skill_invocation(&mut meta, true, 100);
-        assert_eq!(meta.invocation_count, 1);
-        assert_eq!(meta.success_rate, 1.0);
-        assert_eq!(meta.avg_latency_ms, 100);
-
-        record_skill_invocation(&mut meta, false, 200);
-        assert_eq!(meta.invocation_count, 2);
-        assert_eq!(meta.success_rate, 0.5);
-        assert_eq!(meta.avg_latency_ms, 150);
-    }
-
-    #[test]
-    fn test_entity_type_round_trip_agent_types() {
-        for ty in &[
-            EntityType::AgentPrompt,
-            EntityType::AgentSkill,
-            EntityType::AgentDecision,
-        ] {
-            let s = ty.as_str();
-            assert_eq!(EntityType::from_str(s), *ty);
-        }
-    }
-
-    #[test]
-    fn test_agent_prompt_serialization() {
-        let (_, meta) = new_agent_prompt("p", "test", "tmpl {{x}}", "cat");
-        let json = serde_json::to_string(&meta).unwrap();
-        let deserialized: AgentPromptMeta = serde_json::from_str(&json).unwrap();
-        assert_eq!(deserialized.template, "tmpl {{x}}");
-        assert_eq!(deserialized.task_category, "cat");
-    }
-}

crates/mem-core/src/entity.rs

@@ -8,7 +8,7 @@ use time::OffsetDateTime;
 use std::fmt;
 
 /// Entity type classification (extensible enum).
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Hash)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize, Hash)]
 #[serde(rename_all = "snake_case")]
 pub enum EntityType {
     Person,
@@ -17,13 +17,6 @@ pub enum EntityType {
     Location,
     Event,
     Organization,
-    /// Agent prompt template tracked as a first-class entity.
-    /// Enables the agent to learn which prompts produce good results.
-    AgentPrompt,
-    /// Agent skill — a reusable capability the agent has learned.
-    AgentSkill,
-    /// Agent decision — a recorded choice with reasoning and outcome.
-    AgentDecision,
     Unknown,
 }
 
@@ -36,9 +29,6 @@ impl EntityType {
             Self::Location => "location",
             Self::Event => "event",
             Self::Organization => "organization",
-            Self::AgentPrompt => "agent_prompt",
-            Self::AgentSkill => "agent_skill",
-            Self::AgentDecision => "agent_decision",
             Self::Unknown => "unknown",
         }
     }
@@ -51,24 +41,11 @@ impl EntityType {
             "location" => Self::Location,
             "event" => Self::Event,
             "organization" => Self::Organization,
-            "agent_prompt" => Self::AgentPrompt,
-            "agent_skill" => Self::AgentSkill,
-            "agent_decision" => Self::AgentDecision,
             _ => Self::Unknown,
         }
     }
 }
 
-impl<'de> serde::Deserialize<'de> for EntityType {
-    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
-    where
-        D: serde::Deserializer<'de>,
-    {
-        let s = String::deserialize(deserializer)?;
-        Ok(Self::from_str(&s))
-    }
-}
-
 impl fmt::Display for EntityType {
     fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
         write!(f, "{}", self.as_str())
@@ -198,9 +175,6 @@ mod tests {
             EntityType::Person,
             EntityType::Tool,
             EntityType::Concept,
-            EntityType::AgentPrompt,
-            EntityType::AgentSkill,
-            EntityType::AgentDecision,
         ] {
             let s = ty.as_str();
             assert_eq!(EntityType::from_str(s), *ty);

crates/mem-core/src/lib.rs

@@ -12,7 +12,6 @@ pub mod scoring;
 pub mod entity;
 pub mod edge;
 pub mod community;
-pub mod agent_entity;
 
 pub use gate_parser::{GateResponse, ParseError, parse_gate_response};
 
@@ -31,4 +30,3 @@ pub use scoring::{DocumentScorer, ScoringPipeline, GlobalTfIdfScorer, ProjectTfI
 pub use entity::{Entity, EntityType};
 pub use edge::{Edge, ContradictionStatus};
 pub use community::Community;
-pub use agent_entity::{AgentPromptMeta, AgentSkillMeta, AgentDecisionMeta, DecisionOutcome};

crates/mem-ingest/src/authentik_jwt.rs

@@ -52,24 +52,13 @@ impl AuthentikJwtIssuer {
 
     /// From environment: AUTHENTIK_ISSUER, AUTHENTIK_CLIENT_ID, AUTHENTIK_CLIENT_SECRET
     pub fn from_env() -> Result<Self> {
-        // Support both naming conventions: AUTHENTIK_* and memory-agent-oidc secret keys
         let issuer = std::env::var("AUTHENTIK_ISSUER")
-            .or_else(|_| std::env::var("ISSUER"))
+            .map_err(|_| anyhow!("AUTHENTIK_ISSUER not set"))?;
-            .map_err(|_| anyhow!("AUTHENTIK_ISSUER or ISSUER not set"))?;
         let client_id = std::env::var("AUTHENTIK_CLIENT_ID")
-            .or_else(|_| std::env::var("CLIENT_ID"))
+            .map_err(|_| anyhow!("AUTHENTIK_CLIENT_ID not set"))?;
-            .map_err(|_| anyhow!("AUTHENTIK_CLIENT_ID or CLIENT_ID not set"))?;
         let client_secret = std::env::var("AUTHENTIK_CLIENT_SECRET")
-            .or_else(|_| std::env::var("CLIENT_SECRET"))
+            .map_err(|_| anyhow!("AUTHENTIK_CLIENT_SECRET not set"))?;
-            .map_err(|_| anyhow!("AUTHENTIK_CLIENT_SECRET or CLIENT_SECRET not set"))?;
 
-        tracing::info!(
-            target: "observability",
-            event = "authentik_jwt_init",
-            issuer = %issuer,
-            client_id = %client_id,
-            "Authentik JWT issuer initialized"
-        );
         Ok(Self::new(&issuer, &client_id, &client_secret))
     }
 
@@ -103,25 +92,12 @@ impl AuthentikJwtIssuer {
         let client = reqwest::Client::new();
         
         // Authentik OAuth2 token endpoint
-        // Use TOKEN_URL env var if set, otherwise derive from issuer
+        let token_url = format!("{}/token/", self.issuer_url.trim_end_matches('/'));
-        let token_url = std::env::var("TOKEN_URL")
-            .or_else(|_| std::env::var("AUTHENTIK_TOKEN_URL"))
-            .unwrap_or_else(|_| {
-                // Derive: strip app-specific path, use global token endpoint
-                // e.g., https://authentik.riotpiao.com/application/o/memory-agent/
-                //     -> https://authentik.riotpiao.com/application/o/token/
-                if let Some(base) = self.issuer_url.rfind("/o/") {
-                    format!("{}/o/token/", &self.issuer_url[..base])
-                } else {
-                    format!("{}/token/", self.issuer_url.trim_end_matches('/'))
-                }
-            });
         
         let params = [
             ("grant_type", "client_credentials"),
             ("client_id", &self.client_id),
             ("client_secret", &self.client_secret),
-            ("scope", "openid roles"),
         ];
 
         let response = client

crates/mem-ingest/src/entity_extractor.rs

@@ -22,15 +22,11 @@ use tokio::sync::Mutex;
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ExtractedEntity {
     pub name: String,
-    #[serde(alias = "type")]
     pub entity_type: EntityType,
     pub summary: String,
-    #[serde(default = "default_confidence")]
     pub confidence: f32,
 }
 
-fn default_confidence() -> f32 { 0.8 }
-
 impl ExtractedEntity {
     /// Convert to domain model (Phase 1 type)
     pub fn to_domain(&self, project_id: &str) -> Entity {
@@ -66,35 +62,6 @@ impl LlmEntityExtractor {
 
     /// Parse extraction response JSON
     /// Format: { "entities": [{ "name": "...", "type": "...", "summary": "..." }, ...] }
-    /// Clean LLM response: strip thinking tags, markdown fences, extract JSON
-    fn clean_llm_response(text: &str) -> String {
-        let mut result = text.to_string();
-        // Remove <think>...</think> blocks
-        while let Some(start) = result.find("<think>") {
-            if let Some(end) = result.find("</think>") {
-                result = format!("{}{}", &result[..start], &result[end + 8..]);
-            } else {
-                break;
-            }
-        }
-        // Remove markdown code fences
-        result = result.replace("```json", "").replace("```", "");
-        // Find JSON object
-        let trimmed = result.trim();
-        if let Some(start) = trimmed.find('{') {
-            if let Some(end) = trimmed.rfind('}') {
-                return trimmed[start..=end].to_string();
-            }
-        }
-        // Maybe it's a JSON array — wrap in object
-        if let Some(start) = trimmed.find('[') {
-            if let Some(end) = trimmed.rfind(']') {
-                return format!("{{\"entities\": {}}}", &trimmed[start..=end]);
-            }
-        }
-        trimmed.to_string()
-    }
-
     fn parse_extraction(response: &str) -> Result<Vec<ExtractedEntity>> {
         #[derive(Deserialize)]
         struct Response {
@@ -156,7 +123,7 @@ impl LlmEntityExtractor {
                 {"role": "user", "content": prompt}
             ],
             "temperature": 0.3,
-            "max_tokens": 12000
+            "max_tokens": 500
         });
         
         let response = client
@@ -164,7 +131,7 @@ impl LlmEntityExtractor {
             .header("Authorization", auth_header)
             .header("Content-Type", "application/json")
             .json(&payload)
-            .timeout(std::time::Duration::from_secs(90))
+            .timeout(std::time::Duration::from_secs(30))
             .send()
             .await?;
         
@@ -179,28 +146,12 @@ impl LlmEntityExtractor {
         }
         
         let data: serde_json::Value = response.json().await?;
-        // Extract content — some models put JSON in "content", others in "reasoning"
+        let content = data["choices"][0]["message"]["content"]
-        let msg = &data["choices"][0]["message"];
+            .as_str()
-        let raw_content = msg["content"].as_str().unwrap_or("").to_string();
+            .unwrap_or("{}")
-        let raw_reasoning = msg["reasoning"].as_str().unwrap_or("").to_string();
+            .to_string();
         
-        // Use content if non-empty, otherwise try reasoning field
+        tracing::debug!("LLM response (via Authentik JWT): {}", content);
-        let raw = if !raw_content.trim().is_empty() { &raw_content } else { &raw_reasoning };
-        let content = Self::clean_llm_response(raw);
-        
-        let tokens = &data["usage"];
-        tracing::info!(
-            target: "observability",
-            event = "llm_entity_call",
-            model = %model,
-            endpoint = %endpoint,
-            raw_len = raw.len(),
-            cleaned_len = content.len(),
-            prompt_tokens = %tokens["prompt_tokens"],
-            completion_tokens = %tokens["completion_tokens"],
-            has_reasoning = !raw_reasoning.is_empty(),
-            "LLM entity extraction call complete"
-        );
         Ok(content)
     }
     
@@ -282,27 +233,14 @@ Respond in JSON:
             );
 
             let reflection = if std::env::var("LLM_ENDPOINT").is_ok() {
-                self.call_llm_endpoint(&reflection_prompt).await.unwrap_or_else(|e| {
+                self.call_llm_endpoint(&reflection_prompt).await.unwrap_or_else(|_| self.simulate_llm(&reflection_prompt).unwrap_or_default())
-                    tracing::warn!("Reflection LLM call failed: {}, skipping verification", e);
-                    String::new()
-                })
             } else {
                 self.simulate_llm(&reflection_prompt)?
             };
+            let verified = Self::parse_reflection(&reflection)?;
 
-            // If reflection succeeded, filter entities; otherwise keep all
+            // Filter: keep only entities marked present
-            if !reflection.is_empty() {
-                match Self::parse_reflection(&reflection) {
-                    Ok(verified) => {
             entities.retain(|e| verified.iter().any(|(name, present)| name == &e.name && *present));
-                    }
-                    Err(e) => {
-                        tracing::warn!("Reflection parse failed: {}, keeping all entities", e);
-                    }
-                }
-            } else {
-                tracing::info!("Reflection skipped, keeping {} unverified entities", entities.len());
-            }
 
             // Adjust confidence for reflected entities (slight penalty for needing verification)
             for entity in &mut entities {

crates/mem-ingest/src/fact_extractor.rs

@@ -1,12 +1,12 @@
 //! Fact extraction: Identify relationships between entities
 //!
-//! Three implementations:
+//! Two implementations:
 //! 1. SimpleFactExtractor: Pattern-based (verbs + wiki links)
-//! 2. LlmFactExtractor: LLM-based extraction with entity context
+//! 2. LlmFactExtractor: LLM-based (placeholder for production)
-//! 3. Fallback chain: LLM → Simple pattern matching
 //!
-//! Aligned with Zep paper §2.2.2: Facts as edges between entity pairs,
+//! CRAP: 12 (Simple pattern matching + LLM placeholder)
-//! with temporal extraction and dedup against existing edges.
+//! SOLID: Trait-based (Open/Closed)
+//! DRY: Reuses EntityExtractor pattern
 
 use anyhow::Result;
 use async_trait::async_trait;
@@ -27,18 +27,20 @@ pub struct ExtractedFact {
 pub trait FactExtractor: Send + Sync {
     async fn extract(&self, text: &str) -> Result<Vec<ExtractedFact>>;
     
-    /// Extract facts with entity context (Zep §2.2.2: facts between known entities)
+    /// Extract facts with GRM context (optional, defaults to extract())
     async fn extract_with_context(
         &self,
         text: &str,
         _entity_contexts: &[crate::grm_retriever::EntityContext],
     ) -> Result<Vec<ExtractedFact>> {
+        // Default: ignore context, use plain extraction
         self.extract(text).await
     }
 }
 
 /// Simple fact extractor based on verb patterns
 /// Pattern: [[Entity1]] verb [[Entity2]]
+/// Common verbs: uses, manages, runs, deployed_to, works_with
 pub struct SimpleFactExtractor;
 
 #[async_trait]
@@ -46,15 +48,17 @@ impl FactExtractor for SimpleFactExtractor {
     async fn extract(&self, text: &str) -> Result<Vec<ExtractedFact>> {
         let mut facts = vec![];
 
+        // Extract [[Entity]] patterns
         let entity_pattern = Regex::new(r"\[\[([^\]]+)\]\]")?;
-        let _entities: Vec<String> = entity_pattern
+        let entities: Vec<String> = entity_pattern
             .captures_iter(text)
             .filter_map(|cap| cap.get(1).map(|m| m.as_str().to_string()))
             .collect();
 
-        let verbs = ["uses", "manages", "runs", "deployed_to", "works_with",
+        // Common relationship verbs
-                      "depends_on", "contains", "extends", "implements", "connects_to"];
+        let verbs = ["uses", "manages", "runs", "deployed_to", "works_with"];
 
+        // Simple heuristic: if two entities appear close together with a verb between them
         for verb in &verbs {
             let pattern = format!(
                 r"\[\[([^\]]+)\]\].*?{}.*?\[\[([^\]]+)\]\]",
@@ -67,7 +71,12 @@ impl FactExtractor for SimpleFactExtractor {
                             source_entity_id: src.as_str().to_string(),
                             target_entity_id: tgt.as_str().to_string(),
                             relation_type: verb.to_uppercase(),
-                            fact: format!("{} {} {}", src.as_str(), verb, tgt.as_str()),
+                            fact: format!(
+                                "{} {} {}",
+                                src.as_str(),
+                                verb,
+                                tgt.as_str()
+                            ),
                         });
                     }
                 }
@@ -78,252 +87,19 @@ impl FactExtractor for SimpleFactExtractor {
     }
 }
 
-/// LLM-based fact extractor (Zep §2.2.2 alignment)
+/// LLM-based fact extractor (placeholder for production)
-/// Extracts relationships between entity pairs using LLM
+/// TODO (Phase 2.6): Implement with real LLM API
-pub struct LlmFactExtractor {
+/// TODO (Phase 2.6): Support complex relationships (3-way, temporal, conditional)
-    model_name: String,
+pub struct LlmFactExtractor;
-    jwt_issuer: Option<std::sync::Arc<tokio::sync::Mutex<crate::authentik_jwt::AuthentikJwtIssuer>>>,
-}
-
-impl LlmFactExtractor {
-    pub fn new(model_name: &str) -> Self {
-        let jwt_issuer = crate::authentik_jwt::AuthentikJwtIssuer::from_env().ok();
-        Self {
-            model_name: model_name.to_string(),
-            jwt_issuer: jwt_issuer.map(|iss| std::sync::Arc::new(tokio::sync::Mutex::new(iss))),
-        }
-    }
-
-    /// Clean LLM response: strip thinking tags, markdown fences, extract JSON
-    fn clean_llm_response(text: &str) -> String {
-        let mut result = text.to_string();
-        while let Some(start) = result.find("<think>") {
-            if let Some(end) = result.find("</think>") {
-                result = format!("{}{}", &result[..start], &result[end + 8..]);
-            } else { break; }
-        }
-        result = result.replace("```json", "").replace("```", "");
-        let trimmed = result.trim();
-        if let Some(start) = trimmed.find('{') {
-            if let Some(end) = trimmed.rfind('}') {
-                return trimmed[start..=end].to_string();
-            }
-        }
-        if let Some(start) = trimmed.find('[') {
-            if let Some(end) = trimmed.rfind(']') {
-                return format!("{{\"facts\": {}}}", &trimmed[start..=end]);
-            }
-        }
-        trimmed.to_string()
-    }
-
-    async fn call_llm(&self, prompt: &str) -> Result<String> {
-        let endpoint = std::env::var("LLM_ENDPOINT")
-            .unwrap_or_else(|_| "http://localhost:11434/v1/chat/completions".to_string());
-
-        // Get auth header: Authentik JWT if configured, else API key
-        let auth_header = if let Some(jwt_issuer) = &self.jwt_issuer {
-            let issuer = jwt_issuer.lock().await;
-            match issuer.get_access_token().await {
-                Ok(token) => format!("Bearer {}", token),
-                Err(e) => {
-                    tracing::warn!(target: "observability", event = "fact_jwt_fallback", error = %e, "JWT failed, using API key");
-                    let key = std::env::var("LLM_API_KEY").unwrap_or_else(|_| "default-key".to_string());
-                    format!("Bearer {}", key)
-                }
-            }
-        } else {
-            let key = std::env::var("LLM_API_KEY")
-                .or_else(|_| std::env::var("MEM_API_KEY"))
-                .unwrap_or_else(|_| "default-key".to_string());
-            format!("Bearer {}", key)
-        };
-
-        let start = std::time::Instant::now();
-        let client = reqwest::Client::new();
-        let payload = serde_json::json!({
-            "model": self.model_name,
-            "messages": [
-                {"role": "system", "content": "You are a fact extraction specialist. Extract relationships between entities from text. Output ONLY valid JSON."},
-                {"role": "user", "content": prompt}
-            ],
-            "max_tokens": 12000,
-            "temperature": 0.1
-        });
-
-        let response = client
-            .post(&endpoint)
-            .header("Authorization", &auth_header)
-            .header("Content-Type", "application/json")
-            .json(&payload)
-            .timeout(std::time::Duration::from_secs(120))
-            .send()
-            .await?;
-
-        let status = response.status();
-        if !status.is_success() {
-            let body = response.text().await.unwrap_or_default();
-            tracing::warn!(target: "observability", event = "fact_llm_error", status = %status, body = %body, "Fact LLM call failed");
-            return Err(anyhow::anyhow!("LLM API error: {}", status));
-        }
-
-        let elapsed = start.elapsed();
-        let data: serde_json::Value = response.json().await?;
-        
-        // Handle both content and reasoning fields (ornith uses reasoning)
-        let msg = &data["choices"][0]["message"];
-        let raw_content = msg["content"].as_str().unwrap_or("").to_string();
-        let raw_reasoning = msg["reasoning"].as_str().unwrap_or("").to_string();
-        let raw = if !raw_content.trim().is_empty() { &raw_content } else { &raw_reasoning };
-        let cleaned = Self::clean_llm_response(raw);
-
-        let tokens = &data["usage"];
-        tracing::info!(
-            target: "observability",
-            event = "llm_fact_call",
-            model = %self.model_name,
-            endpoint = %endpoint,
-            raw_len = raw.len(),
-            cleaned_len = cleaned.len(),
-            prompt_tokens = %tokens["prompt_tokens"],
-            completion_tokens = %tokens["completion_tokens"],
-            duration_ms = elapsed.as_millis() as u64,
-            has_reasoning = !raw_reasoning.is_empty(),
-            "LLM fact extraction call complete"
-        );
-        Ok(cleaned)
-    }
-}
 
 #[async_trait]
 impl FactExtractor for LlmFactExtractor {
-    async fn extract(&self, text: &str) -> Result<Vec<ExtractedFact>> {
+    async fn extract(&self, _text: &str) -> Result<Vec<ExtractedFact>> {
-        self.extract_with_context(text, &[]).await
+        // TODO (Phase 2.6): Implement LLM-based extraction
-    }
+        // Pattern: Send text to api.riotpiao.com with prompt
-
+        // Parse response for [source, relation, target] tuples
-    async fn extract_with_context(
-        &self,
-        text: &str,
-        entity_contexts: &[crate::grm_retriever::EntityContext],
-    ) -> Result<Vec<ExtractedFact>> {
-        // Build entity list for prompt
-        let entity_names: Vec<&str> = entity_contexts
-            .iter()
-            .map(|e| e.entity_name.as_str())
-            .collect();
-
-        if entity_names.is_empty() {
-            tracing::debug!("No entities provided, skipping fact extraction");
-            return Ok(vec![]);
-        }
-
-        let prompt = format!(
-            r#"Extract relationships (facts) between these entities from the text.
-
-Entities: {:?}
-
-Text:
-"{}"
-
-For each relationship provide:
-- source: Entity name (must be from the list above)
-- target: Entity name (must be from the list above)  
-- relation: Verb/predicate describing the relationship (e.g., "uses", "manages", "is_part_of", "deployed_on")
-- fact: One-sentence natural language description
-
-CRITICAL: Only extract relationships EXPLICITLY stated or strongly implied. Source and target must both be from the entity list.
-
-Respond in JSON:
-{{"facts": [{{"source": "...", "target": "...", "relation": "...", "fact": "..."}}, ...]}}
-"#,
-            entity_names, text
-        );
-
-        let llm_ok = std::env::var("LLM_ENDPOINT").is_ok();
-        let response = if llm_ok {
-            match self.call_llm(&prompt).await {
-                Ok(r) => r,
-                Err(e) => {
-                    tracing::warn!("Fact extraction LLM failed: {}, returning empty", e);
-                    return Ok(vec![]);
-                }
-            }
-        } else {
-            tracing::debug!("LLM_ENDPOINT not set, skipping LLM fact extraction");
-            return Ok(vec![]);
-        };
-
-        // Parse response
-        #[derive(Deserialize)]
-        struct FactResponse {
-            facts: Vec<RawFact>,
-        }
-        #[derive(Deserialize)]
-        struct RawFact {
-            source: String,
-            target: String,
-            relation: String,
-            fact: String,
-        }
-
-        // Try parsing, if trailing chars error try trimming to valid JSON
-        let parsed = match serde_json::from_str::<FactResponse>(&response) {
-            Ok(r) => Ok(r),
-            Err(e) if e.to_string().contains("trailing") => {
-                // Find the closing of the top-level object and retry
-                let mut depth = 0i32;
-                let mut end = 0;
-                for (i, c) in response.char_indices() {
-                    match c {
-                        '{' | '[' => depth += 1,
-                        '}' | ']' => { depth -= 1; if depth == 0 { end = i + 1; break; } },
-                        _ => {}
-                    }
-                }
-                if end > 0 {
-                    serde_json::from_str::<FactResponse>(&response[..end])
-                } else {
-                    Err(e)
-                }
-            }
-            Err(e) => Err(e),
-        };
-        match parsed {
-            Ok(parsed) => {
-                let facts: Vec<ExtractedFact> = parsed.facts
-                    .into_iter()
-                    .filter(|f| {
-                        // Validate source and target are known entities
-                        let src_ok = entity_names.iter().any(|e| e.eq_ignore_ascii_case(&f.source));
-                        let tgt_ok = entity_names.iter().any(|e| e.eq_ignore_ascii_case(&f.target));
-                        if !src_ok || !tgt_ok {
-                            tracing::debug!(
-                                "Dropping fact with unknown entity: {} -> {}",
-                                f.source, f.target
-                            );
-                        }
-                        src_ok && tgt_ok && f.source != f.target
-                    })
-                    .map(|f| ExtractedFact {
-                        source_entity_id: f.source,
-                        target_entity_id: f.target,
-                        relation_type: f.relation.to_uppercase(),
-                        fact: f.fact,
-                    })
-                    .collect();
-
-                tracing::info!(
-                    "LLM fact extraction: {} facts from {} entities",
-                    facts.len(), entity_names.len()
-                );
-                Ok(facts)
-            }
-            Err(e) => {
-                tracing::warn!("Fact extraction JSON parse failed: {}", e);
         Ok(vec![])
     }
-        }
-    }
 }
 
 #[cfg(test)]
@@ -334,38 +110,9 @@ mod tests {
     async fn test_simple_fact_extraction() {
         let extractor = SimpleFactExtractor;
         let text = "[[Rock]] uses [[Kubernetes]] and [[ArgoCD]]";
+
         let facts = extractor.extract(text).await.unwrap();
-        assert!(!facts.is_empty());
+        assert!(facts.len() > 0);
         assert!(facts.iter().any(|f| f.relation_type == "USES"));
     }
-
-    #[tokio::test]
-    async fn test_simple_no_wiki_links() {
-        let extractor = SimpleFactExtractor;
-        let text = "Kubernetes uses etcd for storage";
-        let facts = extractor.extract(text).await.unwrap();
-        assert!(facts.is_empty()); // No [[wiki links]]
-    }
-
-    #[test]
-    fn test_clean_llm_response() {
-        let input = r#"<think>reasoning here</think>{"facts": [{"source": "A", "target": "B", "relation": "uses", "fact": "A uses B"}]}"#;
-        let cleaned = LlmFactExtractor::clean_llm_response(input);
-        assert!(cleaned.starts_with("{"));
-        assert!(cleaned.contains("facts"));
-    }
-
-    #[test]
-    fn test_strip_thinking_no_tags() {
-        let input = r#"{"facts": []}"#;
-        let cleaned = LlmFactExtractor::clean_llm_response(input);
-        assert_eq!(cleaned, input);
-    }
-
-    #[tokio::test]
-    async fn test_llm_fact_no_entities_returns_empty() {
-        let extractor = LlmFactExtractor::new("test");
-        let facts = extractor.extract_with_context("some text", &[]).await.unwrap();
-        assert!(facts.is_empty());
-    }
 }

crates/mem-store/migrations/009_temporal_edge_migration.sql

@@ -1,67 +0,0 @@
--- Migration 009: Temporal edge schema (Zep paper §2.2.2)
--- Replaces old memory_edge (child_sha/parent_sha node graph)
--- with temporal edge schema supporting relation types, facts, and validity periods.
--- Idempotent: safe to run multiple times.
-
--- Rename old table if it still exists (skip if already migrated)
-DO $$
-BEGIN
-    IF EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = 'memory_edge'
-               AND EXISTS (SELECT 1 FROM information_schema.columns 
-                          WHERE table_name = 'memory_edge' AND column_name = 'child_sha'))
-    THEN
-        ALTER TABLE memory_edge RENAME TO memory_edge_legacy;
-    END IF;
-END $$;
-
--- Create temporal edge table
-CREATE TABLE IF NOT EXISTS memory_edge (
-    id TEXT PRIMARY KEY,
-    project_id TEXT NOT NULL DEFAULT 'default',
-    source_id TEXT NOT NULL,
-    target_id TEXT NOT NULL,
-    relation_type TEXT NOT NULL DEFAULT '',
-    fact TEXT NOT NULL DEFAULT '',
-    weight REAL NOT NULL DEFAULT 1.0,
-    strength REAL DEFAULT 1.0,
-    confidence REAL DEFAULT 0.8,
-    t_valid TIMESTAMPTZ,
-    t_invalid TIMESTAMPTZ,
-    t_created TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-    t_expired TIMESTAMPTZ,
-    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-    episode_id TEXT,
-    deleted_at TIMESTAMPTZ
-);
-
--- Ensure app user owns the table
-DO $$ BEGIN
-    IF EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'app') THEN
-        ALTER TABLE memory_edge OWNER TO app;
-    END IF;
-END $$;
-
-CREATE INDEX IF NOT EXISTS idx_memory_edge_source ON memory_edge(source_id);
-CREATE INDEX IF NOT EXISTS idx_memory_edge_target ON memory_edge(target_id);
-CREATE INDEX IF NOT EXISTS idx_memory_edge_project ON memory_edge(project_id);
-CREATE INDEX IF NOT EXISTS idx_memory_edge_relation ON memory_edge(relation_type);
-
--- Ensure memory_entity has all columns code expects
-ALTER TABLE memory_entity ADD COLUMN IF NOT EXISTS deleted_at TIMESTAMPTZ;
-ALTER TABLE memory_entity ADD COLUMN IF NOT EXISTS source_count INTEGER DEFAULT 1;
-
--- Unique constraint for entity upsert dedup
-DO $$
-BEGIN
-    -- Dedup existing rows before creating unique index
-    DELETE FROM memory_entity a USING memory_entity b
-    WHERE a.project_id = b.project_id AND a.name = b.name
-      AND a.t_created < b.t_created;
-EXCEPTION WHEN OTHERS THEN NULL;
-END $$;
-CREATE UNIQUE INDEX IF NOT EXISTS idx_memory_entity_project_name ON memory_entity(project_id, name);
-
--- ROLLBACK instructions:
--- DROP TABLE IF EXISTS memory_edge;
--- ALTER TABLE IF EXISTS memory_edge_legacy RENAME TO memory_edge;

k8s/app/config.yaml

@@ -20,6 +20,7 @@ data:
   # OpenSearch
   OPENSEARCH_HOST: "opensearch.poimen.svc.cluster.local:9200"
   # Obsidian
+  OBSIDIAN_URL: "http://obsidian-server.poimen.svc.cluster.local:8080"
   # LLM Configuration (for entity extraction)
   LLM_ENDPOINT: "http://api-internal.riotpiao.com:8000/v1/chat/completions"
   LLM_MODEL: "qwen:7b"

k8s/app/deployment.yaml

@@ -1,6 +1,6 @@
 # Poimen Memory API Server
-# Serves HTTP endpoints for memory ingest, query, visualization.
+# Serves 7 HTTP endpoints for memory ingest, query, and management.
-# Connects to memory-db (pgvector) + api.riotpiao.com (LLM via Authentik JWT).
+# Connects to memory-db (pgvector) for persistent storage.
 apiVersion: apps/v1
 kind: Deployment
 metadata:
@@ -60,43 +60,13 @@ spec:
                   key: password
             - name: DATABASE_URL
               value: "postgresql://$(DATABASE_USER):$(DATABASE_PASSWORD)@$(DATABASE_HOST):$(DATABASE_PORT)/$(DATABASE_NAME)?sslmode=disable"
-
+            # LLM Gateway API key
-            # LLM via api.riotpiao.com (Authentik JWT auth)
-            - name: LLM_ENDPOINT
-              value: "https://api.riotpiao.com/v1/chat/completions"
-            - name: LLM_API_BASE
-              value: "https://api.riotpiao.com/v1"
-            - name: LLM_MODEL
-              value: "ornith:35b"
-
-            # Authentik service account (memory-agent-oidc secret)
-            - name: AUTHENTIK_ISSUER
-              valueFrom:
-                secretKeyRef:
-                  name: memory-agent-oidc
-                  key: ISSUER
-            - name: AUTHENTIK_CLIENT_ID
-              valueFrom:
-                secretKeyRef:
-                  name: memory-agent-oidc
-                  key: CLIENT_ID
-            - name: AUTHENTIK_CLIENT_SECRET
-              valueFrom:
-                secretKeyRef:
-                  name: memory-agent-oidc
-                  key: CLIENT_SECRET
-            - name: TOKEN_URL
-              valueFrom:
-                secretKeyRef:
-                  name: memory-agent-oidc
-                  key: TOKEN_URL
-
-            # Server config
             - name: MEM_API_KEY
               valueFrom:
                 secretKeyRef:
                   name: poimen-memory-secrets
                   key: llm-api-key
+            # Server config (from ConfigMap)
             - name: MEM_PORT
               value: "8080"
             - name: MEM_HOME
@@ -104,7 +74,10 @@ spec:
           envFrom:
             - configMapRef:
                 name: poimen-memory-config
-          command: ["/app/mem"]
+            - secretRef:
+                name: poimen-memory-auth
+            - secretRef:
+                name: poimen-memory-secrets
           args:
             - serve
             - --port
@@ -137,6 +110,7 @@ spec:
         - name: tmp
           emptyDir:
             sizeLimit: 64Mi
+      # Tolerate control-plane nodes
       tolerations:
         - key: node-role.kubernetes.io/control-plane
           operator: Exists

k8s/app/kustomization.yaml

@@ -6,7 +6,7 @@ resources:
   - deployment.yaml
   - service.yaml
   - config.yaml
-  # obsidian.yaml retired — reference docs now via memory graph
+  - obsidian.yaml
   # Legacy secret managed separately
   # - secrets.yaml
 generators:

k8s/app/obsidian.yaml

@@ -0,0 +1,159 @@
+---
+# Obsidian server deployment
+# Serves local vault with web UI and API
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: obsidian-server
+  namespace: poimen
+  labels:
+    app.kubernetes.io/name: obsidian-server
+    app.kubernetes.io/part-of: poimen-memory
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app.kubernetes.io/name: obsidian-server
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/name: obsidian-server
+        app.kubernetes.io/part-of: poimen-memory
+    spec:
+      serviceAccountName: obsidian-server
+      securityContext:
+        runAsNonRoot: true
+        runAsUser: 1000
+        runAsGroup: 1000
+        fsGroup: 1000
+        seccompProfile:
+          type: RuntimeDefault
+      initContainers:
+      - name: git-sync-init
+        image: alpine/git:latest
+        securityContext:
+          runAsNonRoot: false
+          runAsUser: 0
+          allowPrivilegeEscalation: false
+          capabilities:
+            drop:
+              - ALL
+            add:
+              - CHOWN
+              - DAC_OVERRIDE
+        command:
+        - sh
+        - -c
+        - |
+          export GIT_SSH_COMMAND="ssh -i /root/.ssh/id_ed25519 -o StrictHostKeyChecking=no"
+          git config --global --add safe.directory /vault
+          if [ -d /vault/.git ]; then
+            cd /vault && git pull origin main || true
+          else
+            # Clone into temp, move contents into vault
+            rm -rf /tmp/repo
+            git clone ssh://[email protected]:2222/rock/poimen-obesdient-memory.git /tmp/repo
+            cp -a /tmp/repo/. /vault/
+            rm -rf /tmp/repo
+          fi
+          chown -R 1000:1000 /vault
+        volumeMounts:
+        - name: vault
+          mountPath: /vault
+        - name: ssh-key
+          mountPath: /root/.ssh
+          readOnly: true
+      containers:
+      - name: obsidian-server
+        image: ppatlabs/obsidian:latest
+        imagePullPolicy: IfNotPresent
+        securityContext:
+          allowPrivilegeEscalation: false
+          capabilities:
+            drop:
+              - ALL
+        ports:
+        - name: http
+          containerPort: 27124
+          protocol: TCP
+        env:
+        - name: VAULT_NAME
+          value: poimen-vault
+        - name: VAULT_PATH
+          value: /vault
+        - name: REST_API_ENABLED
+          value: "true"
+        - name: REST_API_PORT
+          value: "8080"
+        volumeMounts:
+        - name: vault
+          mountPath: /vault
+        - name: config
+          mountPath: /config
+        resources:
+          requests:
+            cpu: 100m
+            memory: 256Mi
+          limits:
+            cpu: 500m
+            memory: 512Mi
+        livenessProbe:
+          httpGet:
+            path: /
+            port: http
+            scheme: HTTPS
+          initialDelaySeconds: 30
+          periodSeconds: 10
+          timeoutSeconds: 5
+        readinessProbe:
+          httpGet:
+            path: /
+            port: http
+            scheme: HTTPS
+          initialDelaySeconds: 15
+          periodSeconds: 5
+          timeoutSeconds: 5
+      volumes:
+      - name: vault
+        persistentVolumeClaim:
+          claimName: obsidian-vault
+      - name: config
+        emptyDir: {}
+      - name: ssh-key
+        secret:
+          secretName: obsidian-git-ssh
+          defaultMode: 0400
+
+# PVC managed by homelab repo (k8s/infra/databases/obsidian-vault-pvc.yaml)
+
+---
+# Service for Obsidian server
+apiVersion: v1
+kind: Service
+metadata:
+  name: obsidian-server
+  namespace: poimen
+  labels:
+    app.kubernetes.io/name: obsidian-server
+spec:
+  type: ClusterIP
+  ports:
+  - name: http
+    port: 80
+    targetPort: 27124
+    protocol: TCP
+  selector:
+    app.kubernetes.io/name: obsidian-server
+
+---
+# ServiceAccount for Obsidian
+apiVersion: v1
+kind: ServiceAccount
+metadata:
+  name: obsidian-server
+  namespace: poimen
+  labels:
+    app.kubernetes.io/name: obsidian-server
+
+# Ingress managed by homelab repo (obsidian.riotpiao.com)
+# See: homelab/k8s/bootstrap/ingress/ingress.yaml