LangChain published a framework today for thinking about continual learning in AI agents — and it’s one of the clearest mental models for this problem that’s appeared in the wild. This guide takes that framework and turns it into a practical implementation playbook, with code examples for each layer and decision criteria for choosing between them.

The three layers, briefly: agents can learn through context (runtime-injected instructions), storage (external memory), or weights (model fine-tuning). Each has different costs, speeds, and durability characteristics.

Layer 1: In-Context Learning

In-context learning is the fastest and cheapest layer. You modify what the agent knows by modifying what you inject into its context at runtime — system prompts, retrieved documents, user-specific configuration.

When to use it

  • You need immediate behavior change without deployment
  • The change is user-specific or task-specific
  • You’re still exploring what behavior you actually want

Implementation: Dynamic System Prompts

from langchain_anthropic import ChatAnthropic
from langchain_core.messages import SystemMessage, HumanMessage

def build_agent_with_context(user_preferences: dict, task_context: str):
    """Build an agent with dynamically injected context."""
    
    # Assemble system prompt from dynamic context
    system_prompt = f"""You are a helpful AI assistant.
    
User preferences:
- Communication style: {user_preferences.get('style', 'professional')}
- Expertise level: {user_preferences.get('expertise', 'intermediate')}
- Preferred tools: {', '.join(user_preferences.get('tools', []))}

Current task context:
{task_context}

Adapt your responses to match these preferences and context.
"""
    
    model = ChatAnthropic(model="claude-sonnet-4-6")
    
    return model, SystemMessage(content=system_prompt)

# Usage
preferences = {
    "style": "concise",
    "expertise": "senior engineer",
    "tools": ["Python", "LangGraph", "Chroma"]
}

task_context = "Working on a RAG pipeline for document retrieval. Current issue: slow embedding generation."

model, system_msg = build_agent_with_context(preferences, task_context)

Implementation: Context File Loading (OpenClaw Pattern)

import os
from pathlib import Path

def load_agent_context(workspace_dir: str) -> str:
    """Load agent context from workspace files — OpenClaw SOUL.md/MEMORY.md pattern."""
    context_parts = []
    
    for filename in ["SOUL.md", "MEMORY.md", "USER.md"]:
        filepath = Path(workspace_dir) / filename
        if filepath.exists():
            content = filepath.read_text()
            context_parts.append(f"### {filename}\n{content}")
    
    # Load today's memory
    from datetime import date
    today = date.today().isoformat()
    memory_file = Path(workspace_dir) / "memory" / f"{today}.md"
    if memory_file.exists():
        context_parts.append(f"### Today's Context\n{memory_file.read_text()}")
    
    return "\n\n".join(context_parts)

Durability: Context-layer changes last only as long as you inject them. If your system prompt changes, you need to update every place that prompt is assembled.

Layer 2: In-Storage Learning

In-storage learning persists information in external systems — vector stores, databases, file systems — that the agent reads from at runtime. Unlike context, storage-layer memory survives model updates, harness changes, and long time horizons.

When to use it

  • Knowledge accumulates over time and needs to outlast sessions
  • Multiple agents need access to a shared knowledge base
  • Information is too voluminous to include in every context window

Implementation: Vector Store Memory with LangChain + Chroma

from langchain_chroma import Chroma
from langchain_anthropic import AnthropicEmbeddings
from langchain.memory import VectorStoreRetrieverMemory
from langchain_core.documents import Document
from datetime import datetime

# Initialize the vector store
embeddings = AnthropicEmbeddings()
vectorstore = Chroma(
    collection_name="agent_memory",
    embedding_function=embeddings,
    persist_directory="./agent_memory_db"
)

# Memory retriever — fetch top 5 relevant memories for each query
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
memory = VectorStoreRetrieverMemory(retriever=retriever)

def save_to_memory(observation: str, metadata: dict = None):
    """Save an observation to persistent memory."""
    doc = Document(
        page_content=observation,
        metadata={
            "timestamp": datetime.utcnow().isoformat(),
            "source": "agent_observation",
            **(metadata or {})
        }
    )
    vectorstore.add_documents([doc])
    print(f"Saved to memory: {observation[:80]}...")

def retrieve_relevant_memory(query: str) -> list[str]:
    """Retrieve memories relevant to the current context."""
    docs = retriever.get_relevant_documents(query)
    return [doc.page_content for doc in docs]

# Usage in an agent loop
def agent_with_memory(user_input: str):
    # Retrieve relevant past context
    relevant_memories = retrieve_relevant_memory(user_input)
    memory_context = "\n".join(relevant_memories) if relevant_memories else "No relevant prior context."
    
    # Build augmented prompt
    prompt = f"""Prior context from memory:
{memory_context}

Current user request:
{user_input}"""
    
    # Run agent...
    response = "..."  # agent invocation here
    
    # Save this interaction to memory
    save_to_memory(
        f"User asked: {user_input}\nAgent responded: {response}",
        metadata={"type": "interaction"}
    )
    
    return response

Implementation: Structured Long-Term Memory

import json
from pathlib import Path
from datetime import date

class StructuredAgentMemory:
    """Structured memory for tracking learned preferences and facts."""
    
    def __init__(self, memory_file: str = "agent_longterm.json"):
        self.memory_file = Path(memory_file)
        self.memory = self._load()
    
    def _load(self) -> dict:
        if self.memory_file.exists():
            return json.loads(self.memory_file.read_text())
        return {"preferences": {}, "facts": [], "lessons": []}
    
    def _save(self):
        self.memory_file.write_text(json.dumps(self.memory, indent=2))
    
    def learn_preference(self, key: str, value: str):
        """Record a learned user preference."""
        self.memory["preferences"][key] = {
            "value": value,
            "learned_at": date.today().isoformat()
        }
        self._save()
    
    def learn_lesson(self, lesson: str):
        """Record a lesson from a failure or correction."""
        self.memory["lessons"].append({
            "lesson": lesson,
            "learned_at": date.today().isoformat()
        })
        self._save()
    
    def get_context_summary(self) -> str:
        """Summarize memory for injection into context."""
        lines = []
        
        if self.memory["preferences"]:
            lines.append("Learned preferences:")
            for k, v in self.memory["preferences"].items():
                lines.append(f"  - {k}: {v['value']}")
        
        if self.memory["lessons"]:
            lines.append("\nLessons learned:")
            for item in self.memory["lessons"][-5:]:  # Last 5 lessons
                lines.append(f"  - {item['lesson']}")
        
        return "\n".join(lines)

Durability: Storage-layer memory persists indefinitely. The risk is stale or contradictory information accumulating. Build in periodic memory review and consolidation.

Layer 3: In-Weights Learning (Fine-Tuning)

Fine-tuning updates the model’s weights to encode behavior that needs to be robust across all contexts — not just when you inject the right instructions. This is the most expensive and slowest layer.

When to use it

  • Behavior must be consistent regardless of context injection
  • You have sufficient labeled examples (typically thousands)
  • The capability gap can’t be closed by better prompting or retrieval

When NOT to use it

  • The behavior you want can be achieved with a good system prompt (use Layer 1)
  • The knowledge needs to be updateable frequently (use Layer 2)
  • You have fewer than a few hundred examples (not enough signal)

Implementation: Fine-Tuning Data Preparation

import json
from dataclasses import dataclass, asdict

@dataclass
class FinetuningExample:
    """A single training example for agent fine-tuning."""
    system: str
    human: str
    assistant: str

def prepare_finetuning_dataset(
    interaction_logs: list[dict],
    output_file: str = "finetuning_data.jsonl"
):
    """
    Convert interaction logs into fine-tuning format.
    Filter for high-quality interactions only.
    """
    examples = []
    
    for log in interaction_logs:
        # Only include interactions marked as successful
        if not log.get("success", False):
            continue
        
        # Only include interactions with explicit positive feedback
        if log.get("user_rating", 0) < 4:
            continue
        
        example = FinetuningExample(
            system=log["system_prompt"],
            human=log["user_input"],
            assistant=log["agent_response"]
        )
        examples.append(example)
    
    # Write to JSONL
    with open(output_file, "w") as f:
        for ex in examples:
            f.write(json.dumps(asdict(ex)) + "\n")
    
    print(f"Prepared {len(examples)} fine-tuning examples → {output_file}")
    return examples

The catastrophic forgetting problem: When you fine-tune on new data, the model may degrade on tasks it previously handled well. Always evaluate on a held-out set that covers your full capability surface, not just the new behavior you’re training.

Decision Tree: Which Layer Do You Need?

Start here: What behavior change do you need?
│
├─ Can a better system prompt solve it?
│   └─ YES → Layer 1 (in-context). Ship immediately.
│
├─ Does it require knowledge that persists across sessions?
│   └─ YES → Layer 2 (in-storage). Build retrieval pipeline.
│
├─ Must the behavior be consistent even without careful prompting?
│   └─ YES → Layer 3 (in-weights). Fine-tune. Accept cost.
│
└─ Are you unsure?
    └─ Start with Layer 1. Move to Layer 2 if you hit session
       boundary problems. Only move to Layer 3 if Layers 1+2
       demonstrably can't solve the problem.

The most common mistake: skipping directly to Layer 3 (fine-tuning) for problems that Layer 1 or 2 would solve in an afternoon.

Combining All Three Layers

Production systems often use all three. Here’s a pattern:

class ThreeLayerAgent:
    def __init__(self, base_model: str, memory_db_path: str, longterm_memory_path: str):
        self.model = ChatAnthropic(model=base_model)
        self.vector_memory = Chroma(persist_directory=memory_db_path, ...)
        self.structured_memory = StructuredAgentMemory(longterm_memory_path)
    
    def run(self, user_input: str, session_context: str = "") -> str:
        # Layer 2: retrieve relevant stored memory
        relevant_past = self.vector_memory.similarity_search(user_input, k=3)
        
        # Layer 1: assemble dynamic context
        system = f"""You are a helpful agent.
        
{self.structured_memory.get_context_summary()}

Relevant past context:
{chr(10).join([d.page_content for d in relevant_past])}

Session context: {session_context}
"""
        # Layer 3 is baked into the model weights — no code needed at runtime
        response = self.model.invoke([
            SystemMessage(content=system),
            HumanMessage(content=user_input)
        ])
        
        # Update Layer 2 storage
        self.vector_memory.add_documents([Document(
            page_content=f"Q: {user_input}\nA: {response.content}"
        )])
        
        return response.content

Sources

  1. LangChain Blog: Continual learning for AI agents
  2. GitHub: langchain-ai/deepagents
  3. Chroma Context-1 documentation

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