ThinkChain

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About ThinkChain​

ThinkChain provides access to a variety of popular LLMs, such as DeepSeek-R1, DeepScaleR, Qwen2.5 and SmolLM2. A simple Solidity interface makes it easy for smart contracts to construct prompts and decode replies entirely on-chain. Completion requests are charged in Ether.

Our project addresses three critical challenges in blockchain-based AI integration:

1. Scalability

Traditional on-chain AI computation faces severe scalability limitations due to the computational overhead. We solved this through EigenLayer co-processors, where network operators execute computations off-chain, significantly reducing the blockchain resource burden while maintaining decentralization.

2. Integration

Directly porting existing AI implementations to Solidity is impractical due to the EVM's computational constraints. However, by utilizing Cartesi's RISC-V virtual machine with Linux compatibility, we can execute deterministic AI inference for LLM models using traditional software off-chain and expose its results in EVM smart contracts.

3. True Decentralized Verification

Current blockchain AI projects often compromise on decentralization through various trust assumptions:

• Some rely on Trusted Execution Environments (TEEs), requiring trust in third parties
• Others use zkTLS, which still involves trusting external entities
• Many solutions don't address trust verification at all

Our solution provides robust verification without centralized trust points, maintaining the core promise of blockchain technology while enabling advanced AI capabilities.

This combination of features makes our project particularly valuable for smart contracts that would benefit from on-chain access to LLMs where verification and trust are critical. Examples of use cases include AI agents, AI-assisted decision-making, data analysis, and content generation.

It is worth mentioning that we have run into some challenges during the development of ThinkChain during the Cartesi x EigenLayer Experiment Week. Integrating diverse technologies (React frontend, Python backend, and Solidity smart contracts) presented significant challenges. While our team had strong expertise in Solidity, Python, and LLM models, our limited frontend experience caused delays. The complexity of learning multiple technologies can be particularly challenging for newcomers during hackathons.

We also encountered issues with large machine snapshots (13GB) with coprocessor, where publishing the machine was too slow and occasionally failed with timeouts and other errors. We worked around this by making multiple publish attempts until successful. Furthermore, we experienced high disk usage (up to 100GB) because the coprocessor maintained multiple copies of machine instances (.tar, .gnutar, image/, .car, docker images). Some team members were unable to run the full machine due to excessive disk space consumption.

What's next​

We have several future improvements in our backlog:

• Offload matrix multiplication operations, which would greatly improve performance and reduce costs for users;
• Make the completion request cost calculation more realistic, taking into consideration base layer transaction fees, infrastructure costs, among other factors;
• Implementing an explorer web UI for visualizing past completion requests and inferences
• Support payments in ERC-20 tokens
• Implement a proof-of-concept application that encodes prompts and decodes responses entirely on-chain