TapeAgents unifies agent development and optimization by treating every interaction as a replayable, structured log.
Record agent sessions as tapes, replay them for debugging, reuse them for training.
TapeAgents introduces a structured logging framework that records agent sessions as reusable "tapes," enabling both development debugging and systematic optimization of LLM agents.
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https://arxiv.org/abs/2412.08445
🤔 Original Problem:
Existing frameworks either focus on agent development or optimization, but not both. Developers lack tools to effectively debug, audit, and improve LLM agents while maintaining session persistence and reproducibility.
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🛠️ Solution in this Paper:
→ TapeAgents centers around a granular, structured log called "tape" that serves as both session memory and resumable state
→ The framework uses nodes as basic units that process LLM outputs to generate new tape steps
→ Agents compose nodes and can have subagents, forming hierarchical teams
→ Environment reacts to agent actions by adding observation steps to the tape
→ The tape-centric design enables session persistence, debugging, and optimization through rich metadata
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💡 Key Insights:
→ Combining low-level control with high-level agent building paradigms enables both development and optimization
→ Structured logs with rich metadata facilitate training data generation
→ Resumable state machine architecture allows step-by-step debugging
→ Tape reuse across agents enables systematic evaluation and improvement
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📊 Results:
→ Successfully fine-tuned a cost-effective form-filling assistant achieving 76.6% GREADTH score
→ Reduced costs by 300x while maintaining performance using 8B parameter model
→ Demonstrated effectiveness across multiple use cases including web search, data science, and math problem-solving
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