name: memory-orchestration description: Analyze context management, memory systems, and state continuity in agent frameworks. Use when (1) understanding how prompts are assembled, (2) evaluating eviction policies for context overflow, (3) mapping memory tiers (short-term/long-term), (4) analyzing token budget management, or (5) comparing context strategies across frameworks.

Memory Orchestration

Analyzes context management and memory systems.

Process

Trace context assembly — How prompts are built from components
Identify eviction policies — How context overflow is handled
Map memory tiers — Short-term (RAM) to long-term (DB)
Analyze token management — Counting, budgeting, truncation

Context Assembly Analysis

Standard Assembly Order

┌─────────────────────────────────────────┐
│ 1. System Prompt                        │
│    - Role definition                    │
│    - Behavioral guidelines              │
│    - Output format instructions         │
├─────────────────────────────────────────┤
│ 2. Retrieved Context / Memory           │
│    - Relevant past interactions         │
│    - Retrieved documents (RAG)          │
│    - User preferences                   │
├─────────────────────────────────────────┤
│ 3. Tool Definitions                     │
│    - Available tools and schemas        │
│    - Usage examples                     │
├─────────────────────────────────────────┤
│ 4. Conversation History                 │
│    - Previous turns (user/assistant)    │
│    - Prior tool calls and results       │
├─────────────────────────────────────────┤
│ 5. Current Input                        │
│    - User's current message             │
│    - Any attachments/context            │
├─────────────────────────────────────────┤
│ 6. Agent Scratchpad (Optional)          │
│    - Current thinking/planning          │
│    - Intermediate results               │
└─────────────────────────────────────────┘

Assembly Patterns

Template-Based

PROMPT_TEMPLATE = """
{system_prompt}

## Available Tools
{tool_descriptions}

## Conversation
{history}

## Current Request
{user_input}
"""

prompt = PROMPT_TEMPLATE.format(
    system_prompt=self.system_prompt,
    tool_descriptions=self._format_tools(),
    history=self._format_history(),
    user_input=message
)

Message List (Chat API)

messages = [
    {"role": "system", "content": system_prompt},
    *self._get_history_messages(),
    {"role": "user", "content": user_input}
]

Programmatic Assembly

def build_prompt(self, input):
    builder = PromptBuilder()
    builder.add_system(self.system_prompt)
    builder.add_context(self.memory.retrieve(input))
    builder.add_tools(self.tools)
    builder.add_history(self.history, max_tokens=2000)
    builder.add_user(input)
    return builder.build()

Eviction Policies

FIFO (First In, First Out)

def trim_history(self, max_messages: int):
    while len(self.history) > max_messages:
        self.history.pop(0)  # Remove oldest

Pros: Simple, predictable Cons: May lose important early context

Sliding Window

def get_context_window(self, max_tokens: int):
    window = []
    token_count = 0
    for msg in reversed(self.history):
        msg_tokens = count_tokens(msg)
        if token_count + msg_tokens > max_tokens:
            break
        window.insert(0, msg)
        token_count += msg_tokens
    return window

Pros: Token-aware, keeps recent Cons: Still loses old context

Summarization

def summarize_and_trim(self, max_tokens: int):
    if self.total_tokens < max_tokens:
        return
    
    # Summarize oldest messages
    old_messages = self.history[:len(self.history)//2]
    summary = self.llm.summarize(old_messages)
    
    # Replace with summary
    self.history = [
        {"role": "system", "content": f"Previous conversation summary: {summary}"},
        *self.history[len(self.history)//2:]
    ]

Pros: Preserves context semantically Cons: Expensive (LLM call), lossy

Vector Store Swapping

def manage_context(self, current_input: str, max_tokens: int):
    # Move old messages to vector store
    if self.total_tokens > max_tokens:
        to_archive = self.history[:-10]
        self.vector_store.add(to_archive)
        self.history = self.history[-10:]
    
    # Retrieve relevant context
    relevant = self.vector_store.search(current_input, k=5)
    return self._build_prompt(relevant, self.history)

Pros: Scalable, relevance-based Cons: Complex, retrieval quality matters

Importance Scoring

def score_and_trim(self, max_tokens: int):
    scored = []
    for msg in self.history:
        score = self._compute_importance(msg)
        scored.append((score, msg))
    
    # Keep highest scoring until budget
    scored.sort(reverse=True)
    kept = []
    tokens = 0
    for score, msg in scored:
        if tokens + count_tokens(msg) > max_tokens:
            break
        kept.append(msg)
        tokens += count_tokens(msg)
    
    # Restore chronological order
    self.history = sorted(kept, key=lambda m: m['timestamp'])

Pros: Keeps important context Cons: Expensive to compute

Memory Tier Mapping

┌─────────────────────────────────────────────────────┐
│                  MEMORY TIERS                        │
├─────────────────────────────────────────────────────┤
│ Tier 1: Working Memory (In-Prompt)                  │
│ ├── Current conversation turns                      │
│ ├── Active tool results                             │
│ └── Immediate scratchpad                            │
│ Latency: 0ms | Capacity: Context window             │
├─────────────────────────────────────────────────────┤
│ Tier 2: Session Memory (RAM)                        │
│ ├── Full conversation history                       │
│ ├── Session state                                   │
│ └── Cached retrievals                               │
│ Latency: <1ms | Capacity: GB                        │
├─────────────────────────────────────────────────────┤
│ Tier 3: Persistent Memory (Database)                │
│ ├── Vector store (semantic search)                  │
│ ├── SQL/Document store (structured)                 │
│ └── User profiles and preferences                   │
│ Latency: 10-100ms | Capacity: TB+                   │
└─────────────────────────────────────────────────────┘

Tier Promotion/Demotion

class MemoryManager:
    def on_turn_end(self, turn):
        # Tier 1 → Tier 2: Move from prompt to session
        self.session_memory.add(turn)
        
        # Tier 2 → Tier 3: Persist important turns
        if self.should_persist(turn):
            self.persistent_memory.add(turn)
    
    def on_session_end(self):
        # Tier 2 → Tier 3: Archive session
        summary = self.summarize_session()
        self.persistent_memory.add(summary)

Token Management

Counting Strategies

Method	Accuracy	Speed
`tiktoken`	Exact	Fast
`len(text) / 4`	Rough estimate	Instant
API response	Post-hoc	After call
Tokenizer model	Exact	Medium

Budget Allocation

class TokenBudget:
    def __init__(self, total: int = 8000):
        self.total = total
        self.allocations = {
            'system': 1000,
            'tools': 1500,
            'history': 4000,
            'input': 1000,
            'output_reserve': 500
        }
    
    def remaining_for_history(self, used: dict) -> int:
        fixed = used.get('system', 0) + used.get('tools', 0)
        return self.total - fixed - self.allocations['output_reserve']

Output Template

## Memory Orchestration Analysis: [Framework Name]

### Context Assembly
- **Order**: [System → Memory → Tools → History → Input]
- **Method**: [Template/Message List/Programmatic]
- **Location**: `path/to/prompt_builder.py`

### Eviction Policy
- **Strategy**: [FIFO/Window/Summarization/Vector/Importance]
- **Trigger**: [Token count/Message count/Explicit]
- **Location**: `path/to/memory.py:L45`

### Memory Tiers

| Tier | Storage | Capacity | Retrieval |
|------|---------|----------|-----------|
| Working | In-prompt | ~4K tokens | Immediate |
| Session | Dict/List | Unlimited | Direct |
| Persistent | [Chroma/Pinecone/SQL] | Unlimited | Semantic |

### Token Management
- **Counting**: [tiktoken/estimate/API]
- **Budget Allocation**: [Description]
- **Overflow Handling**: [Truncate/Summarize/Error]

Integration

Prerequisite: codebase-mapping to identify memory files
Feeds into: comparative-matrix for context strategies
Related: control-loop-extraction for scratchpad usage

memory-orchestration

$ Installieren