---
license: other
license_name: research-only-mixed-upstream
license_link: https://github.com/zzh237/AgentBake
pretty_name: AgentBake Trace Library (100 heterogeneous agents, 7 frameworks)
size_categories:
- 1K<n<10K
tags:
- agents
- personalization
- benchmark
- multi-agent
- traces
---

# AgentBake Trace Library

Execution-trace corpus for the **AgentBake benchmark** (*A Personalization Layer
and Benchmark for Heterogeneous Agents*). 100 heterogeneous agents spanning
seven frameworks (AutoGen, CrewAI, LangChain, LangGraph, LlamaIndex,
PydanticAI, Strands), each contributing ~10 recorded multi-turn scenarios.

## Contents

```
good_by_framework/
  autogen/<agent>/scenario_XXX/
    *__trace_sequence.json   # per-step activations: input text, output, tools, timing
    *__topology.json         # agent graph: nodes, roles, declared capabilities
    *__prebuilt_eval.json    # LLM-judge scores (coherence, goal success, ...)
    selection_metadata.json
  crewai/ ... langchain/ ... langgraph/ ... llamaindex/ ... pydanticai/ ... strands/
```

~4,900 files, ~6.2 GB. 16/14/15/30/5/10/10 agents per framework.

## Provenance

Traces were generated by executing rebuilt open-source agents (149-agent
adapter library, pinned upstream repos + commit SHAs in the companion code
repo) on synthetic task scenarios, with **Qwen3-32B** (`qwen.qwen3-32b-v1:0`,
via Amazon Bedrock) as the agent backend LLM and as the prebuilt evaluator
judge. The trace content (inputs, outputs, topologies, judge scores) is
generated data; upstream agent *code* is not included here — see the code
repository for the adapter library and per-repo licenses.

## Usage

```python
from huggingface_hub import snapshot_download
snapshot_download("zzh237/agentbake-traces", repo_type="dataset",
                  local_dir="data/multiagent_traces")
```

Then run the AgentBake benchmark from the code repo:
https://github.com/zzh237/AgentBake

## Intended use

Research on agent personalization, orchestration-policy learning, and
benchmark evaluation. Scenarios are synthetic; no real user data.