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metadata 
pretty_name: Open Agent Leaderboard Results
license: cdla-permissive-2.0
tags:
  - benchmark
  - leaderboard
  - agents
  - evaluation
  - ai-agents
  - agent-evaluation
language:
  - en
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Open Agent Leaderboard Results

Detailed evaluation results for general-purpose AI agents across diverse real-world benchmarks — without domain-specific tuning.

Benchmarks

Benchmark Task ID Description
AppWorld appworld App-based task completion in simulated smartphone environments
BrowseComp+ browsecomp_plus Web browsing and complex information retrieval
SWE-bench swebench Software engineering issue resolution on real GitHub repos
TauBench-Airline taubench_airline Customer service agent evaluation (airline domain)
TauBench-Retail taubench_retail Customer service agent evaluation (retail domain)
TauBench-Telecom taubench_telecom Customer service agent evaluation (telecom domain)

The overall score is a weighted average: each TauBench sub-task gets 1/12 weight (1/4 total for TauBench), all others get 1/4 each.

Agents Evaluated

Agent Framework
Claude Code claude-code
OpenAI Solo openai-agents-python
Smolagent smolagents
React litellm
React + Shortlisting litellm + exgentic

Models

Results are reported for each agent × model combination: Claude Opus 4.5, Gemini 3 Pro, GPT-5.2, DeepSeek V3.2, Kimi K2.5.

Submitting new results

This dataset is the source of truth for the Open Agent Leaderboard. To add results for a new model, agent, or benchmark:

  1. Run evaluations using the Exgentic framework
  2. Open a PR on this dataset adding your rows to the parquet file in data/

Each row represents one (agent, model, benchmark) combination. Required fields:

Field Description
agent Agent identifier (e.g., claude_code)
agent_name Display name (e.g., Claude Code CLI)
model Model identifier (e.g., openai_Azure_DeepSeek-V3.2)
model_name Display name (e.g., openai/azure/DeepSeek-V3.2)
benchmark Benchmark identifier (e.g., swebench)
benchmark_name Display name (e.g., SWE-bench)
benchmark_score Primary score (0-1)
planned_sessions Number of tasks attempted
total_sessions Number of sessions completed
successful_sessions Number of sessions that passed

See the existing data for the full schema and examples.

Schema

Column Type Description
agent / agent_name string Agent identifier and display name
model / model_name string Model identifier and display name
benchmark / benchmark_name string Benchmark identifier and display name
benchmark_score float Primary success rate (0-1)
average_score float Average score across sessions
average_agent_cost float Average cost per task (USD)
average_steps float Average number of agent steps per task
average_action_count float Average number of actions per task
average_invalid_action_count float Average invalid actions per task
percent_successful float Fraction of tasks that succeeded
percent_finished float Fraction of tasks that completed (success or fail)
percent_error float Fraction of tasks that errored
total_agent_cost float Total cost across all tasks (USD)
planned_sessions / total_sessions / successful_sessions int Session counts