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