NearAI LangChain Integration

nearai_langchain provides seamless integration between NearAI and LangChain, allowing developers to use NearAI's capabilities within their LangChain applications.

🎯 Key Purposes

1. Libraries Support

   • Langchain
   • Langgraph
   • Coinbase AgentKit

2. Optional NearAI Inference Integration

   • Access model inference through NearAI's optimized infrastructure
   • Maintain compatibility with standard LangChain for other use cases
   • Seamlessly switch between NearAI and LangChain inference

3. NearAI Registry Integration

   • Register and manage agents in the NearAI registry
   • Optionally, enable agent-to-agent interaction and make your agents callable by other agents in the NearAI registry
   • Auto-generate or validate agent metadata
   • Example metadata.json:

     {
      "name": "langchain_fireworks_example_usage",
      "version": "0.0.1",
      "description": "NEAR AI - Langchain Fireworks example usage",
      "category": "agent",
      "tags": [],
      "details": {
         "agent": {
            "defaults": {
              "model": "qwen2p5-72b-instruct",
              // Optionally, specify "model_provider". fireworks is used by default in nearai.
              "inference_framework": "nearai"  // Use "langchain" or "nearai" for inference. Optional. Default is "nearai".
            },
            "framework": "agentkit"  // Used by nearai hub to assign correct dependencies
         }
      },
      "show_entry": true
     }
     

4. Agent Intercommunication

   • Upload agents to be used by other agents
   • Call other agents from the registry in your own agents
   • Framework-agnostic: works with both NearAI and LangChain inference

5. Benchmarking and Evaluation

   • Run popular or user owned benchmarks on agents
   • Optionally, upload evaluation results to NearAI evaluation table
   • Support for both NearAI and LangChain inference frameworks

🚀 Quick Start

Your agent code and metadata.json must be in the same directory. You must run your agent script from that directory:

# Directory structure:
my_agent/
  ├── metadata.json
  ├── agent.py
  └── [other files...]

# Navigate to agent directory
cd my_agent

# Run your agent locally
python3.11 agent.py

# Or upload agent to nearai registry. Run from `nearai` repo:
nearai registry upload <path_to_your_agent>/my_agent
# Go to app.near.ai and lookup your agent. Run it.

Example agent code:

from langchain_core.messages import SystemMessage

from nearai_langchain.orchestrator import NearAILangchainOrchestrator, RunMode

orchestrator = NearAILangchainOrchestrator(globals())
# To continue conversation on existing thread in local mode:
# orchestrator = NearAILangchainOrchestrator(globals(), thread_id="thread_xxxxxx")

# Langchain chat model that can be passed to other LangChain & LangGraph libraries.
model = orchestrator.chat_model

# NEAR AI environment.
# In remote mode thread is assigned, user messages are given, and an agent is run at least once per user message.
# In local mode an agent is responsible to get and upload user messages.
env = orchestrator.env

if orchestrator.run_mode == RunMode.LOCAL:
    print("English -> Italian Translator")
    user_input = input("\nEnglish: ")
    env.add_user_message(user_input)

messages = [SystemMessage("Translate the following from English into Italian")] + env.list_messages()

result = model.invoke(messages)
print(result)
env.add_reply(result.content)
if orchestrator.run_mode == RunMode.LOCAL:
    print("-------------------")
    print(result.content)
    print("-------------------")

# Run once per user message.
env.mark_done()

See examples/ folder for more examples.

📦 Installation

pip install nearai-langchain

🛠️ Development Setup

1. Clone the repository:

   git clone https://github.com/nearai/nearai_langchain.git
   cd nearai_langchain
   

2. Install dependencies:

   ./install.sh
   

3. Development tools:

• Run format check: ./scripts/format_check.sh
• Run linting: ./scripts/lint_check.sh
• Run type check: ./scripts/type_check.sh