OpenGradient's LangChain integration brings TEE-secured LLM inference in addition to verifiable ML inference to AI agents without context window pollution, enabling sophisticated ML operations while empowering vertical AI agent capabilities in solving domain-specific problems.
Building AI agents that can access ML models and data-rich workflows enables them to leverage specialized knowledge and intelligence. For example, an LLM-powered AI agent can utilize risk models to evaluate portfolios, or employ sybil resistance models to detect malicious actors operating on blockchain networks.
The new OpenGradient LangChain integration elegantly unlocks these exciting new use-cases, enabling AI agents to leverage powerful ML models deployed on a decentralized, verifiable network. Check out the pull request here.
Unlocking Specialized Models for AI Agents
The OpenGradient LangChain integration represents a breakthrough in AI development by connecting OpenGradient’s decentralized AI infrastructure with LangChain’s powerful and flexible agentic reasoning framework.
Developers can now build powerful agents that have access to specialized ML models that can incorporate specialized models into their reasoning, while ensuring that each individual model inference is secured by cryptography and hardware enclaves.
OpenGradient’s approach allows agent developers to overcome the current problems with embedding specialized model capabilities into agentic reasoning such as:
- Degraded agent performance due to context window pollution
- Limited ML functionality to preserve context space
- Forced multi-tool chaining for parameter specifications
- Complex external infrastructure to offload ML operations
OpenGradient's LangChain toolkit eliminates these tradeoffs by allowing developers to encapsulate all data processing logic within the tool definition itself, while providing comprehensive services around hosting, inference, and data provenance.
Why Context Windows Matter
Context windows are the lifeblood of large language model agents. Every token counts, and anything that consumes unnecessary space directly impacts your agent's ability to maintain conversation history, follow complex instructions, or reason effectively.
Imagine your agent needs to access a forecasting model that requires 1,000 live data points as input. With current tooling your agent must:
- Call a data-gathering tool that returns 1,000 live data points
- Pass all 1,000 data points to your model inference tool
- Every subsequent message would carry those two 1,000 number messages in context
With OpenGradient's custom tools approach:
- Your tool definition handles data gathering automatically
- The model processes data outside the agent's context
- Only the relevant results return to the agent
This keeps your agent's context window clean and focused on high-value conversation rather than raw data processing.
The OpenGradient Advantage
By utilizing running your model inferences through OpenGradient, LangChain toolcalls gain all the benefits of OpenGradient's infrastructure.
Verifiable Inference
OpenGradient's network offers a flexible array of offerings when it comes to securing AI inference. This includes cryptographic schemes like Zero-Knowledge Machine Learning (ZKML) and Trusted Execution Environments (TEEs). These give users the ability to perform trustless, verifiable model execution – critical for applications where transparency and security are paramount.
Comprehensive Offerings
In addition to secure inference, OpenGradient also handles model hosting and execution trace provenance while providing access to SOTA custom tooling. OpenGradient’s flexible infrastructure offerings for hardware enclaves also allows developers to build verifiable data pipelines to make computational workflows end-to-end verifiable.
Decentralization
AI inferences on the OpenGradient network are run through decentralized nodes and recorded on OpenGradient’s blockchain, and are verified by all nodes on our network. This gives developers an easy way of verifying the individual model inferences made on the network.
Seamless Developer Integration
Any model on the OpenGradient network can instantly be used as part of a custom tool. If you have your own model you’d like to turn into an agent tool, simply upload to our model hub and it’s ready to go!
Real-World Applications
Our toolkit provides complete freedom to implement custom data processing pipelines, integrate live data feeds, and build specialized tools tailored to your specific use cases. Some common applications that we’ve seen created from our Langchain integration include:
- Financial analysis agents using spot forecasting and volatility models
- DeFi optimization through yield farming prediction models
- Healthcare assistants leveraging medical imaging models while maintaining patient conversation
- Content moderation systems using multiple specialized classification models without performance degradation
- Research agents that dynamically pull and analyze data through custom ML pipelines
Getting Started
For first time OpenGradient users, you can read about the OpenGradient SDK here.
Implementation is straightforward. Set up an OpenGradient API key through our SDK
pip install opengradient
opengradient config init
Then simply install the package
pip install -U LangChain-opengradient
And import our toolkit into your agent application. Then you’re ready to start using OpenGradient’s toolkit to build custom tools for any LangChain application!
from LangChain_opengradient import OpenGradientToolkit
import opengradient as og
toolkit = OpenGradientToolkit(
private_key="your-api-key"
)
You can train a model, upload it to the OpenGradient hub, and immediately build tools to use in your agents.
For more information and example custom tools, visit our integration page on Langchain!
What's Next?
The OpenGradient LangChain integration represents a significant step forward in building more capable, efficient AI agents. By combining the flexibility of LangChain's agent framework with OpenGradient's decentralized infrastructure, developers can build agents that are now empowered by specialized models.
Ready to elevate your agents? Check out the comprehensive tutorial for detailed examples and implementation guides.
Learn more in our Documentation.
Learn more and see examples in Langchain’s Documentation
Follow us on X and join us on Discord. Developers - Sign up to gain early access to our solutions.