Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →In agentic-arena’s mock tool-use comparison, a hand-written standard-library loop and LangGraph tied at an estimated 753.5 prompt tokens per item. The other frameworks ranged from 794.0 (Pydantic AI, 1.05× the baseline) to 856.9 (OpenAI Agents SDK, 1.14×), with smolagents far above the group at 2,935.5 (3.90×). These are project-reported figures from scripted runs, measured with a character-based estimator. They describe how much each adapter pushes into the prompt under identical conditions. They are not a live provider bill, and they say nothing about which framework gives better answers.
The headline numbers
The table below uses the project’s tool_use task with 15 items. Each value is a mean of estimated prompt tokens per item, and the multiple is calculated against the vanilla baseline. The figures come from the agentic-arena measured findings page, which states that all reported numbers were regenerated by continuous integration on a clean Linux install.
As an Amazon Associate I earn from qualifying purchases.
| Adapter | Mean prompt tokens per item | Multiple of vanilla |
|---|---|---|
| vanilla (standard-library baseline) | 753.5 | 1.00× |
| LangGraph | 753.5 | 1.00× |
| Pydantic AI | 794.0 | 1.05× |
| Microsoft Agent Framework | 802.0 | 1.06× |
| Google ADK | 836.1 | 1.11× |
| OpenAI Agents SDK | 856.9 | 1.14× |
| smolagents | 2,935.5 | 3.90× |
Six of the seven adapters sit within 1.15× of the baseline. smolagents is the clear outlier in this configuration. LangGraph matched the baseline byte for byte in the cited comparison, so the difference between it and the vanilla loop is zero on this measure.
What was held constant
The project says it kept the model, gateway, tools, task specification, evaluation set, and iteration budget identical across adapters. In mock mode it replayed byte-identical scripted turns, so the only variable was the framework’s own request construction and control flow. The methodology page describes these controls and the cost-estimation definitions.
#1 Best Overall
- STRATEGIC EXPANSION GAMEPLAY: Introduces Division M, a brand-new Agent type that transforms how you play Agent Avenue by adding deeper tactical decisions and unpredictable outcomes.
- NEW DANGER ZONE MECHANIC: Special agents create a high-stakes “danger zone” around your home space, increasing tension and forcing players to rethink positioning and strategy.
- ENHANCES BASE GAME EXPERIENCE: Designed to seamlessly integrate with the original Agent Avenue board game, adding fresh challenges and extended replay value.
- INCREASED PLAYER ENGAGEMENT: Elevates excitement with dynamic interactions, making every round more competitive, suspenseful, and engaging for all players.
- PERFECT FOR GAME NIGHT & FANS: Ideal for families, strategy gamers, and fans of Agent Avenue looking to expand gameplay with new twists and advanced mechanics.
That design answers a narrow question: how much does each adapter place on the wire, and how does it behave when the script misbehaves? It does not answer whether a model using one framework reaches a better result on live tasks. The author of the write-up makes the same distinction explicitly, quoted at the end of this article.
Why six adapters cluster and smolagents stands apart
The six close adapters differ in the tool schema, not the conversation
On the first turn, the project reports identical 472-character messages for the first six adapters. The spread comes from the serialized tools block, which the project measured in characters:
| Adapter | Serialized tool block (characters, first turn) |
|---|---|
| vanilla and LangGraph | 637 |
| Pydantic AI | 715 |
| Google ADK | 735 |
| Microsoft Agent Framework | 740 |
| OpenAI Agents SDK | 837 |
The extra characters come from schema decoration and formatting. The framework overhead page names fields such as title, additionalProperties, and strict: true as sources of added schema characters, rather than a more compact serialization.
Free tools Windows power users keep installed
One-click scans. No signup required.
Rank #2
- HIGH QUALITY - The future is here and it's ready to play! Coder Mindz is the only board game and STEM toy, that teaches Coding and Artificial Intelligence concepts using a fun gameplay.
- EASY PLAY - Use it at home, in school, coding clubs, Montessori, STEM clubs, boys girls scout, summer clubs, tutoring, after school, day care, maker space, hackathons and for Girls who code!
- YOUNG INVENTOR - Created by Samaira, a 9 year old girl and covered by over 100 Media and News, including TIME, NBC TODAY Show, Business Insider, Yahoo Finance, NBC Bay Area, Sony, Mercury News and many more. Her first game is now used in over 600 schools worldwide.
- FIRST EVER AI GAME and FREE CURRICULUM - The only game that introduces kids to many AI concepts. Teaches Image Recognition, Training, Inference, Data, Adaptive Learning, Autonomous and more. Also teaches Coding concepts like Loops, Functions, Conditionals and Algorithm writing and more. FREE CURRICULUM available to download on website (limited time only)
- THINK AI - Artificial Intelligence is a big and emerging branch. The “Intelligence” in machines is programmed by “Training”. Once trained the machines “Infer” and start behaving “Autonomously”. Training involves Back-propagation which is Retraining or Fine Tuning. Using bots and code card this game sneakily introduces all those concepts which form foundation of today’s AI world. Learning Coding and AI concept helps you connect with real coding and AI.
The project also corrected an earlier version of its comparison. Some adapters had looked cheaper because they omitted tool parameters or descriptions. Once the schemas were equalized, the apparent advantage disappeared, and no adapter was leaner than the baseline after the correction.
smolagents sends a templated prompt that restates the tools
The smolagents ToolCallingAgent adds a templated system prompt. The arena asked for a 384-character system prompt, but the framework sent 4,207 characters. According to the project, that prompt includes prose restating tools that are also sent in schema form, which is the main reason its per-item figure is so high.
The project frames the prompt as scaffolding for models that cannot call tools natively, so it should not be read as waste in every deployment. A team using a model with native tool calling would be paying for that scaffolding without needing it. A team using a model without that capability may be getting something it needs. The measurement shows the cost; it does not decide whether the cost is worth it.
Rank #3
- Udderly hilarious board game for family and friends game nights. Fun for big groups of 4-20+ players
- Easy to learn, quick to play and endlessly repayable board game. This version comes with 20 extra questions
- Think the same to win the game. Flip over a question and guess what your family and friends are thinking
- If your answer is in the majority, you win cows. If you’re the odd one out, you’re stuck with the pink cow of doom
- One of the best board games for families, adults, teens and kids aged 10+. Perfect icebreaker game. Easy and fun for everyone! Perfect as a Thanksgiving or Christmas game
smolagents also has a separate CodeAgent entry, which the project reports at 6.95× the baseline prompt tokens. It should be compared as its own configuration rather than folded into the ToolCallingAgent figure.
What the estimator can and cannot tell you
The prompt-token figures use len(text) // 4, a character-count estimate, not a real byte-pair-encoding tokenizer. The overhead page notes that JSON punctuation inflates the estimate, so the absolute numbers are not the counts a provider would bill.
- Use it for: ranking adapters against each other under the same scripted tools and turns.
- Do not use it for: forecasting a monthly bill. Actual cost depends on the provider’s tokenizer, your usage pattern, and current pricing, none of which this mock measurement establishes.
Short requests versus long loops
Fixed per-request overhead matters most on short tasks. The project also ran a scripted conversation extended to 30 tool-calling turns and recorded estimated prompt tokens at selected requests:
Rank #4
- REIMAGINING OF FAVORITE STRATEGY GAME: The Cosmolancer game is a galactic reimagining of legendary designer Reiner Knizia’s Kingdoms game! Play as a Cosmolancer (space photographer) and compete to be the best
- FAST-PACED TILE LAYING GAME: In this highly competitive tile placement game, try to collect the most Interstellar Credits by strategically placing Photo Op tiles and Imaging Devices on the board. But watch out for Hazards and Black Holes
- BY AWARD-WINNING DESIGNER REINER KNIZIA: Set in a fantastical galaxy of Imposing Squids and Galactic Hounds, the Cosmolancer board game is a spacefaring take on Reiner Knizia’s acclaimed Kingdoms game
- SABOTAGE WITH HAZARDS: Stay alert! Opponents can play Hazard tiles, such as the Abominable Worm and Cosmopoachers, to sabotage photos and subtract credits from another player’s score
- WOODEN TOKENS AND CUSTOM ARTWORK: Includes premium components, wooden tokens, and custom artwork by Hugo Cuellar and Charlie Layton
| Request | vanilla (estimated prompt tokens) | smolagents (estimated prompt tokens) | smolagents ÷ vanilla |
|---|---|---|---|
| 1 | 121 | 1,069 | 8.83× |
| 11 | 1,531 | 2,551 | 1.67× |
| 31 | 4,350 | 5,515 | 1.27× |
The growth table uses a smaller arena prompt than the headline table, so compare ratios within this table rather than absolute values across the two.
In this test, every request carried the full conversation history. The project reports that none of the seven adapters dropped, windowed, or summarized history on the default path. Each additional turn added 136.7 to 148.2 estimated tokens across the frameworks in the scripted setup. The project’s reading is that the fixed per-request overhead shrinks as a share of the cumulative prompt as turns accumulate. That is a property of this scripted run, not a universal cost curve for every deployed agent.
Multi-agent delegation
The findings page also measures a three-role pipeline (researcher, writer, editor). In that setup, the vanilla and LangGraph multi-agent versions made 2.00× the single-agent LLM calls and used 2.50× the prompt tokens. The graph machinery itself produced no measured difference between those two variants.
Best Value
- ESP32-S3 1.28inch Double Eye AIoT Development Board.240 × 240 Resolution,Dual IPS Displays,SPI Communication
- Controlled Via I2C Interface For Flexible Operation And Smooth UI Interaction, With Interrupt Support
- Onboard Audio Codec Chip.Supports High-Quality Audio Processing, Providing Clear And High-Quality Audio Input And Output
- Supports AI Speech Interaction.Allows Access To Online Large Model Platforms Such As DeepSeek, Doubao, Etc.
- ESP32-S3-DualEye-LCD-1.28 currently provides two development tools and frameworks, Arduino IDE and ESP-IDF, providing flexible development options, you can choose the right development tool according to your project needs and personal habits.Please contact with us to get the development resources if needed.
The project also reports other delegation mechanisms with different costs, including handoffs decided by the model and sub-agents invoked as tools. Each is a separate measured setup. The 2.00× and 2.50× multipliers should not be generalized to every multi-agent design.
Scripted fault behavior
The decision guide’s scripted resilience arena reports fault-recovery scores out of eight:
- 8/8 for vanilla, Pydantic AI, Microsoft Agent Framework, and smolagents
- 7/8 for LangGraph and OpenAI Agents SDK
- 6/8 for Google ADK
A separate provider-fault probe sent scripted HTTP 429 (rate-limit) responses. The project reports that every framework survived one scripted 429, while the vanilla loop did not. smolagents alone survived three consecutive 429s, with a measured delay of roughly two to four minutes. These are results from project scripts. They are not a live reliability ranking, and provider behavior in production will differ. The decision guide covers the scope of these scripts.
How to use these figures when choosing an adapter
Keep the following questions separate, because the benchmark measures some of them and not others:
- Request size under identical tools: compare the headline table, and check whether the extra schema or prompt text is needed for your model.
- Tool-call robustness: review the malformed and unknown-tool behavior, noting that it comes from scripted tests.
- Provider faults: review the 429 probe, but test retry behavior against your own provider’s limits.
- Call count and growth: count model calls for the delegation pattern you plan to use, and estimate prompt growth for your expected turn count.
- History management: the tested adapters kept full history by default, so plan your own truncation or summarization if your sessions are long.
- Real cost and quality: measure with your provider’s tokenizer, your pricing, and your own evaluation set. The benchmark does not test live answer quality or pricing.
What the author says
Rashid Mahmood published the results on DEV Community on September 30, 2026. He wrote: “That makes these wire and behaviour measurements. They say nothing about which framework writes better answers.”
Quick Recap
Primary sources
- agentic-arena measured findings: tables, caveats, and reproducibility commands.
- Framework overhead: schema and system-prompt causes, the estimator, and the 30-turn growth test.
- Methodology: controls, replay design, and cost-estimation definitions.
- Decision guide: scope of the scripted resilience and provider-fault tests.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
