On a deep indigo field, a centred two-level delegation tree: a glowing amber authority node at the top connects down to an orchestrator node — teal, ringed in amber — which fans three threads to a row of three teal worker nodes, all held inside a soft luminous dome. A few bright nodes drift up the threads toward the orchestrator, suggesting findings returning.

A Team of Its Own: ENGRAM Agents Learn to Grow and Orchestrate a Team

Last time, ENGRAM handed a person the power to make agents — fenced. This release hands a bounded slice of that power to an agent itself: with a human’s say-so, an orchestrator can become a manager, spin up a small team of sub-agents — each a distinct identity with its own memory — fan work out to them, and gather it back. Not a control plane bolted on top; the team thinks together through the same memory it already had. A team of its own, bounded at every edge.

A Brain Between Sessions — and Between Agents: ENGRAM Learns to Recall on Demand

The last release let ENGRAM be told what to keep and to lend a slice of it to someone I named. This one gives it the other half of the gesture: a way to deliberately reach for what it knows. An agent can now recall its own memory across sessions — a brain it keeps for itself, needing nothing switched on — and, when I’ve shared something to it, reach for that too. Private by default, as ever: my own memory is always mine to recall, and a borrowed memory is only ever what someone named for me.

Memories Worth Sharing: ENGRAM Learns to Be Told What to Keep – and What to Share

The last release was quiet plumbing — every memory learned to say where it came from and what body of … More

Where Memories Come From: ENGRAM Learns to Track Provenance and Group by Project

ENGRAM could already remember an enormous amount — articles, code sessions, conversations from half a dozen assistants. What it could … More

Claude Code and ENGRAM recalling good memories.

Claude Code and ENGRAM Knowledge Hub: recalling the good memories together

This is a follow-up post about the next layer of knowledge consolidation in ENGRAM Knowledge Hub. Ingestion of Claude Code sessions into the Knowledge Hub allowed me to close the gap between what I researched and planned and what I actually built, including the changes, feature design enhancements, and pivots that often happen during development.

ENGRAM Knowledge Hub: A Personal Knowledge Graph That Grows With Your Research

Most LLM tools forget what you taught them last week. ENGRAM Knowledge Hub turns your conversations, documents, and research into a personal knowledge graph with recall by relevance, not recency.

ENGRAM Part 3: Hippocampal-Inspired Memory – Working Memory, Long-Term Memory, and Periodic Consolidation

How ENGRAM’s agents remember what matters across multi-turn research sessions, and how memory biases retrieval toward coherent results Introduction In … More

ENGRAM Part 2: Unified Graph Retrieval from Long Documents and Multi-Agent Response Synthesis

Part 2 of this blog explains how uploaded research papers join the knowledge graph through hierarchical extraction, and how specialized … More

ENGRAM Part 1: GraphRAG with Hippocampal-Like Associative Retrieval

Part 1 of this blog series explains how query-entity relevance spreading across a knowledge graph eliminates the need of typical … More

MCP, A2A, and Human-in-the-Loop: A Multi-Agent Threat Intelligence System in Practice

This blog describes a cybersecurity threat intelligence platform built in Python that models relationships between threat actors, attack techniques, vulnerabilities, … More