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+
+This is some sketches about how to add an agentic manager with memory.
+
+The existing 'react' manager is going to stay.  The new one will be called
+'react_mem'.  The 'react_mem' module is to going to be a drop-in replacement
+for 'react' so that the end user can decide which to deploy.
+
+## Interaction start
+
+1. RETRIEVE CONTEXT
+   │
+   ├── Long-term facts
+   │   └── Embed user query → retrieve relevant subgraph
+   │
+   ├── Episodic memory
+   │   └── Embed user query → find similar past episodes
+   │
+   └── Conversation memory
+       └── Pull summary/key points from prior invocations in this conversation
+   
+2. INITIALIZE WORKING MEMORY
+   └── Empty (or seed with "Goal: <user query>")
+   
+   
+## Core loop
+
+┌─────────────────────────────────────────────────────────────────┐
+│                         ASSEMBLE CONTEXT                        │
+│                                                                 │
+│  • System prompt / agent persona                                │
+│  • Long-term facts (retrieved at invocation start)              │
+│  • Relevant episodes (retrieved at invocation start)            │
+│  • Conversation memory (what's happened this conversation)      │
+│  • Working memory (steps so far this invocation)                │
+│  • Current user query                                           │
+└─────────────────────────────────────────────────────────────────┘
+                              │
+                              ▼
+┌─────────────────────────────────────────────────────────────────┐
+│                           REASON                                │
+│                                                                 │
+│  LLM generates:                                                 │
+│  • Thought (reasoning trace)                                    │
+│  • Decision: which tool + parameters, OR final answer           │
+└─────────────────────────────────────────────────────────────────┘
+                              │
+                              ▼
+                    ┌─────────────────────┐
+                    │   Final answer?     │
+                    └─────────────────────┘
+                         │           │
+                        YES          NO
+                         │           │
+                         ▼           ▼
+              ┌──────────────┐  ┌─────────────────────────────────┐
+              │   COMPLETE   │  │              ACT                │
+              │  (exit loop) │  │                                 │
+              └──────────────┘  │  Execute tool, get observation  │
+                                └─────────────────────────────────┘
+                                                 │
+                                                 ▼
+                                ┌─────────────────────────────────┐
+                                │      UPDATE WORKING MEMORY      │
+                                │                                 │
+                                │  Append: thought, action, result│
+                                │                                 │
+                                │  If working memory too large:   │
+                                │  → Compress/summarize older     │
+                                │    steps, keep recent raw       │
+                                └─────────────────────────────────┘
+                                                 │
+                                                 ▼
+                                          (loop back to
+                                          ASSEMBLE CONTEXT)
+                                          
+                                          
+## Invocation end
+
+1. EXTRACT & STORE
+   │
+   ├── Conversation memory
+   │   └── Summarize this invocation: "User asked X, agent did Y, result was Z"
+   │       Append to conversation memory
+   │
+   ├── Facts (optional)
+   │   └── Did the agent learn anything worth persisting?
+   │       If yes → StoreFact (could be automatic extraction or agent-driven)
+   │
+   └── Episode (if task-like)
+       └── RecordEpisode: goal, key steps, outcome, lessons
+           Tag with embeddings for future retrieval
+
+2. DISCARD WORKING MEMORY
+   └── No longer needed (it's been summarized into conversation memory)
+   
+   
+## Conversation end
+
+1. PROMOTE TO LONG-TERM
+   │
+   └── Scan conversation memory for facts worth persisting
+       → Store to knowledge graph
+   
+2. RECORD CONVERSATION-LEVEL EPISODE (optional)
+   │
+   └── If the conversation had an overarching goal/theme,
+       capture as a higher-level episode
+
+## Refinements to consider
+
+Refinements to consider:
+Mid-invocation retrieval
+The initial retrieval might not be enough. Agent could have tools to pull in more:
+
+RetrieveFacts(query) — dig deeper if initial context insufficient
+RecallEpisodes(query) — "have I seen this specific problem before?"
+
+Working memory compression trigger
+
+Option A: Every N steps, auto-compress
+Option B: When token count exceeds threshold
+Option C: Agent explicitly decides ("I should summarize before continuing")
+
+Fact/episode extraction
+
+Option A: Agent explicitly calls StoreFact / RecordEpisode as part of its reasoning
+Option B: Post-invocation LLM pass: "Given this invocation, extract any facts/episodes worth storing"