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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns.
Passed 22 of 24 test cases with the skill, and 20 of 24 without it.
2 fixed20 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
For a long-running customer support agent in LangChain where full message history exceeds token limits, I need to preserve historical context across dozens of interactions by dynamically condensing old context without dropping history completely. Which memory class should be instantiated, and what required component must be supplied to its constructor?
| Checked | Without skill | With skill |
|---|---|---|
| uses ConversationSummaryMemory | Missed | Passed |
| passes llm parameter to memory | Passed | Passed |
The instructions your model reads when the skill loads.
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
Autonomous systems that use LLMs to decide which actions to take.
Agent Types:
Sequences of calls to LLMs or other utilities.
Chain Types:
Systems for maintaining context across interactions.
Memory Types:
test cases passed, vs 20 / 24 without
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