Report Abuse

Basic Information

TapeAgents is a Python framework for building, debugging, serving and improving LLM-based agents using a structured, replayable session log called a tape. The tape records agent thoughts, actions, control-flow steps and environment observations so agents reason by processing and appending to that log. The project supports constructing agents as low-level state machines, as high-level multi-agent teams, or as mono-agents guided by multiple prompts. It includes an introductory Jupyter notebook and example scripts demonstrating real tasks and benchmarks. TapeAgents is intended for developers and researchers who need reproducible agent execution, fine-grained control over prompts and state, and a workflow that spans development to deployment. The repo provides installation instructions, examples, tools for running agents with streaming LLM responses, and links to documentation and a technical report describing the design and research behind the approach.

Links

Categorization

App Details

Features
TapeAgents centers on a tape data structure that makes agent sessions replayable and editable. The framework exposes APIs to render tapes into prompts and to generate next steps using LLM streams. It supports agent configurations including state-machine nodes, multi-agent team patterns, and prompt-guided mono-agents. Developer tooling includes TapeAgent studio and TapeBrowser for debugging and inspection, response streaming for serving agents, and structured metadata linking tapes, steps, LLM calls and agent configurations to enable analysis and optimization. Example modules show integration with web search, code interpreter style tools, AutoGen-style team programming, benchmark tasks like GAIA and WorkArena, and finetuning workflows such as GSM-8k tuning. Packaging and examples are provided for quick start via pip and for building from source.
Use Cases
The tape-centric design improves reproducibility and observability because every decision and observation is recorded and can be replayed or modified. Developers can debug agents interactively, change prompts or team structure and resume sessions when the new agent can continue from an existing tape. The structured metadata supports evaluating successful tapes and using them to optimize agent configuration or finetune smaller models. Built-in examples demonstrate practical uses such as planning plus web search, browser automation benchmarks, multi-agent data science workflows, and tape improvement agents that revisit prior runs. The framework also supports serving agents with streaming responses, and the documentation plus a technical report aid deeper understanding and adoption for research and production development.

Please fill the required fields*