Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 6 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Enhance your Google ADK agent with persistent memory using Zep. Configure Zep to store agent conversation history, past routing decisions, and user preferences. The agent should consult its Zep memory when making new routing decisions, learning from previous interactions to optimize performance and cost over time. Provide the Python code for initializing Zep and integrating it into an agent's context retrieval process. ```python
from zep_python import ZepClient
from zep_python.memory import Memory # Initialize Zep client
zep_client = ZepClient("http://localhost:8000") # Replace with your Zep server URL # Example of integrating Zep memory into an agent function
def agent_with_memory(prompt: str, user_id: str) -> str: session_id = f"user-{user_id}" # Retrieve memory for the session memory = zep_client.get_memory(session_id) # Use memory for context in routing decisions # For instance, if user prefers 'fast' over 'cheap' based on past interactions # After decision, add current interaction to memory # zep_client.add_messages(session_id, [{'role': 'human', 'content': prompt}, {'role': 'ai', 'content': 'response'}]) return "Decision based on memory and current prompt"
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Google ADK Multi-Model Inference Routing with DeepSeek R1 for Cerebras/Trainium Optimization
Inspired by AWS's strategy of deploying specialized AI chips like Cerebras' Wafer-Scale Engine alongside cost-effective Trainium processors, this challenge involves building an intelligent multi-agent system using Google ADK. The system will act as a dynamic inference router, intelligently dispatching requests to different AI models (e.g., DeepSeek R1) or simulated hardware endpoints based on real-time performance metrics, cost constraints, and security policies. Agents within the Google ADK framework will analyze incoming inference queries, assess their complexity, and determine the optimal model and hardware configuration for execution. Key aspects include integrating persistent memory management with Zep for agent state, enforcing security policies with Lakera to protect against malicious inputs, and leveraging Ray Tune for optimizing model routing decisions through experimentation. The goal is to maximize inference throughput and minimize cost while ensuring security and reliability for critical AI workloads.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.