The memory layer your AI agents have been missing

import autogen
from crewlayer import CrewLayerClient

cl = CrewLayerClient(api_key="crwl_...")
AGENT_ID = "550e8400-e29b-41d4-a716-446655440000"

class MemoryAssistant(autogen.AssistantAgent):
    def generate_reply(self, messages, sender, **kwargs):
        last_msg = messages[-1]["content"]

        # Pull relevant memories before responding
        memories = cl.memory.recall(AGENT_ID, query=last_msg, top_k=5)
        if memories:
            mem_ctx = "\n".join(f"- {m['content']}" for m in memories)
            messages = [
                {"role": "system", "content": f"Memory:\n{mem_ctx}"}
            ] + messages

        reply = super().generate_reply(messages, sender, **kwargs)

        # Log the action
        cl.actions.log(AGENT_ID, tool_name="generate_reply",
                       input_params={"query": last_msg},
                       output_result={"reply": reply}, status="success")
        return reply

from llama_index.core import VectorStoreIndex
from crewlayer import CrewLayerClient

cl = CrewLayerClient(api_key="crwl_...")
AGENT_ID = "550e8400-e29b-41d4-a716-446655440000"

index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()

def query_with_memory(question: str) -> str:
    # Enrich query with agent's long-term memory
    past = cl.memory.recall(AGENT_ID, query=question, top_k=3)
    enriched = question
    if past:
        enriched += "\n\n[Agent memory]\n"
        enriched += "\n".join(m["content"] for m in past)

    response = query_engine.query(enriched)

    # Persist response as a new memory
    cl.memory.save(AGENT_ID, content=str(response), importance=0.7)
    return str(response)

import { streamText } from 'ai'
import { openai } from '@ai-sdk/openai'
import { CrewLayerClient } from '@crewlayer/sdk'

const cl = new CrewLayerClient({ apiKey: process.env.CREWLAYER_API_KEY! })
const AGENT_ID = '550e8400-e29b-41d4-a716-446655440000'

export async function POST(req: Request) {
  const { messages } = await req.json()
  const lastMsg = messages.at(-1)?.content ?? ''

  // Pull relevant memories for context
  const memories = await cl.memory.recall(AGENT_ID, {
    query: lastMsg, topK: 5
  })
  const memCtx = memories.map(m => `- ${m.content}`).join('\n')

  const result = streamText({
    model: openai('gpt-4o'),
    system: `You are a helpful assistant.\n\nMemory:\n${memCtx}`,
    messages,
    onFinish: async ({ text }) => {
      // Persist assistant reply as a new memory
      await cl.memory.save(AGENT_ID, { content: text, importance: 0.6 })
    }
  })

  return result.toDataStreamResponse()
}