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Implements agents using Deep Agents.
Passed 22 of 23 test cases with the skill, and 6 of 23 without it.
and 26 more in the full report
17 fixed5 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write a minimal Python script to instantiate and invoke a basic agent using the deepagents framework with default settings to send a 'Hello!' message.
| Checked | Without skill | With skill |
|---|---|---|
| Imports create_deep_agent from deepagents. | Missed | Passed |
| Invokes agent with a user message dictionary. | Missed | Passed |
The instructions your model reads when the skill loads.
Deep Agents provides a batteries-included agent harness built on LangGraph:
create_deep_agent: Factory function that creates a configured agenttask toolThe agent returned is a compiled LangGraph StateGraph, compatible with streaming, checkpointing, and LangGraph Studio.
# Core
from deepagents import create_deep_agent
# Subagents
from deepagents import CompiledSubAgent
# Backends
from deepagents.backends import (
StateBackend, # Ephemeral (default)
FilesystemBackend, # Real disk
StoreBackend, # Persistent cross-thread
CompositeBackend, # Route paths to backends
)
# LangGraph (for checkpointing, store, streaming)
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.store.memory import InMemoryStore
# LangChain (for custom models, tools)
from langchain.chat_models import init_chat_model
from langchain_core.tools import tool
test cases passed, vs 6 / 23 without
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