Add workspace and memory system (OpenClaw-inspired)

Implements persistent memory for agents with hybrid search:

- Database-backed workspace with PostgreSQL (not filesystem)
- Memory documents: MEMORY.md, daily logs, identity files
- Chunked content with FTS (tsvector) + vector (pgvector) indexes
- Reciprocal Rank Fusion (RRF) for hybrid search combining BM25 and semantic
- Memory tools: memory_search, memory_write, memory_read
- Proactive heartbeat system for periodic execution (30 min default)
- OpenAI embeddings provider (text-embedding-3-small)

Key patterns from OpenClaw:
- "Memory is files, not RAM" - explicit persistence required
- Two-tier memory: daily logs (raw) + curated MEMORY.md
- Session isolation via user_id/agent_id scoping

Co-Authored-By: Claude Opus 4.5 <[email protected]>

migrations/V2__workspace_memory.sql

@@ -0,0 +1,159 @@
+-- NEAR Agent Database Schema
+-- V2: Workspace and memory system (OpenClaw-inspired)
+--
+-- This migration adds:
+-- 1. Persistent memory documents (MEMORY.md, daily logs, identity files)
+-- 2. Chunked content for hybrid search (FTS + vector)
+-- 3. Heartbeat state for proactive execution
+
+-- Enable pgvector extension for semantic search
+-- NOTE: This requires pgvector to be installed on the PostgreSQL server
+-- Install via: CREATE EXTENSION vector; (requires superuser or rds_superuser)
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- ==================== Memory Documents ====================
+-- Stores full documents like MEMORY.md, daily logs, identity files
+-- Think of this as the filesystem equivalent, but in PostgreSQL
+
+CREATE TABLE memory_documents (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    -- Ownership: who this document belongs to
+    user_id TEXT NOT NULL,              -- User identifier (from channel)
+    agent_id UUID,                      -- NULL = shared across all agents for this user
+
+    -- Document type and content
+    doc_type TEXT NOT NULL,             -- 'memory', 'daily_log', 'identity', 'soul', 'agents', 'user', 'heartbeat'
+    title TEXT,                         -- Optional title (e.g., date for daily logs)
+    content TEXT NOT NULL,              -- Full document content
+
+    -- Timestamps
+    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+
+    -- Flexible metadata (tags, source, etc.)
+    metadata JSONB NOT NULL DEFAULT '{}',
+
+    -- Ensure one document per type per user (for singleton docs like MEMORY.md)
+    -- Daily logs use title as the date discriminator
+    CONSTRAINT unique_doc_per_user_type UNIQUE (user_id, agent_id, doc_type, title)
+);
+
+-- Indexes for common queries
+CREATE INDEX idx_memory_documents_user ON memory_documents(user_id);
+CREATE INDEX idx_memory_documents_user_type ON memory_documents(user_id, doc_type);
+CREATE INDEX idx_memory_documents_updated ON memory_documents(updated_at DESC);
+
+-- ==================== Memory Chunks ====================
+-- Documents are chunked for search. Each chunk has:
+-- 1. Full-text search vector (tsvector) for keyword matching
+-- 2. Embedding vector for semantic similarity
+--
+-- Hybrid search combines both using Reciprocal Rank Fusion (RRF)
+
+CREATE TABLE memory_chunks (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    document_id UUID NOT NULL REFERENCES memory_documents(id) ON DELETE CASCADE,
+
+    -- Chunk position and content
+    chunk_index INT NOT NULL,           -- Position in document (0-based)
+    content TEXT NOT NULL,              -- Chunk text (~800 tokens with 15% overlap)
+
+    -- Full-text search: auto-generated tsvector
+    content_tsv TSVECTOR GENERATED ALWAYS AS (to_tsvector('english', content)) STORED,
+
+    -- Semantic search: embedding vector (OpenAI text-embedding-ada-002 = 1536 dims)
+    -- NULL until embeddings are generated
+    embedding VECTOR(1536),
+
+    -- Timestamps
+    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+
+    -- Each chunk index unique per document
+    CONSTRAINT unique_chunk_per_doc UNIQUE (document_id, chunk_index)
+);
+
+-- GIN index for full-text search
+CREATE INDEX idx_memory_chunks_tsv ON memory_chunks USING GIN(content_tsv);
+
+-- HNSW index for vector similarity (cosine distance)
+-- HNSW is faster than IVFFlat for reads, slightly slower for writes
+CREATE INDEX idx_memory_chunks_embedding ON memory_chunks
+    USING hnsw(embedding vector_cosine_ops)
+    WITH (m = 16, ef_construction = 64);
+
+-- Index for document lookups
+CREATE INDEX idx_memory_chunks_document ON memory_chunks(document_id);
+
+-- ==================== Heartbeat State ====================
+-- Tracks periodic heartbeat execution per user/agent
+
+CREATE TABLE heartbeat_state (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id TEXT NOT NULL,
+    agent_id UUID,                      -- NULL = default agent for user
+
+    -- Timing
+    last_run TIMESTAMPTZ,               -- When heartbeat last executed
+    next_run TIMESTAMPTZ,               -- Scheduled next execution
+    interval_seconds INT NOT NULL DEFAULT 1800,  -- 30 minutes default
+
+    -- State
+    enabled BOOLEAN NOT NULL DEFAULT true,
+    consecutive_failures INT NOT NULL DEFAULT 0,
+
+    -- Last check timestamps (for batched monitoring)
+    -- e.g., {"email": "2024-01-15T10:00:00Z", "calendar": "2024-01-15T10:00:00Z"}
+    last_checks JSONB NOT NULL DEFAULT '{}',
+
+    -- Ensure one heartbeat config per user/agent
+    CONSTRAINT unique_heartbeat_per_user UNIQUE (user_id, agent_id)
+);
+
+CREATE INDEX idx_heartbeat_user ON heartbeat_state(user_id);
+CREATE INDEX idx_heartbeat_next_run ON heartbeat_state(next_run) WHERE enabled = true;
+
+-- ==================== Helper Functions ====================
+
+-- Function to update updated_at timestamp
+CREATE OR REPLACE FUNCTION update_updated_at_column()
+RETURNS TRIGGER AS $$
+BEGIN
+    NEW.updated_at = NOW();
+    RETURN NEW;
+END;
+$$ language 'plpgsql';
+
+-- Trigger to auto-update updated_at on memory_documents
+CREATE TRIGGER update_memory_documents_updated_at
+    BEFORE UPDATE ON memory_documents
+    FOR EACH ROW
+    EXECUTE FUNCTION update_updated_at_column();
+
+-- ==================== Views ====================
+
+-- View for documents with chunk counts (useful for debugging)
+CREATE VIEW memory_documents_summary AS
+SELECT
+    d.id,
+    d.user_id,
+    d.doc_type,
+    d.title,
+    d.created_at,
+    d.updated_at,
+    COUNT(c.id) as chunk_count,
+    COUNT(c.embedding) as embedded_chunk_count
+FROM memory_documents d
+LEFT JOIN memory_chunks c ON c.document_id = d.id
+GROUP BY d.id;
+
+-- View for pending embedding work
+CREATE VIEW chunks_pending_embedding AS
+SELECT
+    c.id as chunk_id,
+    c.document_id,
+    d.user_id,
+    d.doc_type,
+    LENGTH(c.content) as content_length
+FROM memory_chunks c
+JOIN memory_documents d ON d.id = c.document_id
+WHERE c.embedding IS NULL;