Phoenix - Your AI AssistantBuild AI Agent with LangChain & LangGraph for Automation
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What you get with this Offer
This AI Agent can automate workflows, handle conversations, analyze documents, extract insights, and connect with APIs or external tools.
It will be built using modular graph-based architecture, allowing complex decision-making and reasoning across multiple steps — perfect for building autonomous or semi-autonomous agents.
Depending on your requirements, the agent can:
Integrate with APIs (Google, Slack, Jira, CRM, databases, etc.)
Process and summarize large datasets or documents (PDFs, text, CSV, etc.)
Perform RAG-based retrieval for domain-specific Q&A
Automate repetitive tasks or workflows
Support multi-agent collaboration (LangGraph nodes)
You’ll receive:
✅ A fully functional AI Agent implemented in Python using LangChain & LangGraph
✅ Clean, production-ready code
✅ Step-by-step documentation
✅ Optional cloud deployment (AWS, GCP, or local server)
Whether you need an intelligent assistant, data-processing bot, or automated reasoning system — I’ll build an AI Agent that actually acts intelligently, not just chats.
What the Freelancer needs to start the work
To start building your custom AI Agent, please provide the following details:
1. Use Case or Goal – What do you want the AI Agent to do? (e.g., automate data tasks, build a chatbot, analyze documents, connect to APIs, etc.)
2. Data or Knowledge Source – Any files, documents, or database/API access the agent should use for retrieval or automation.
3. Preferred LLM or Platform – Specify if you want to use OpenAI, Anthropic, Ollama, or a custom model.
4. Integrations (if any) – APIs, services, or tools the AI Agent should connect with (Slack, Jira, CRM, etc.).
5. Workflow or Interaction Style – Should the agent run automatically, respond to prompts, or act as part of a multi-agent system?
Once I have this information, I’ll create a clear plan and begin building your LangChain + LangGraph AI Agent.