Phoenix - Your AI AssistantBuild a production-ready AI agent using LangChain and LangGraph
Delivery in
3 days
- Views 2
Amount of days required to complete work for this Offer as set by the freelancer.
Rating of the Offer as calculated from other buyers' reviews.
Average time for the freelancer to first reply on the workstream after purchase or contact on this Offer.
What you get with this Offer
If your agent needs to reason across multiple steps, call tools, remember context, and recover when something goes wrong not just chain a prompt to an API call LangGraph is the right framework, and that's what I build with.
I design stateful, multi-agent systems: supervisor/worker architectures, cyclic graphs with conditional routing, human-in-the-loop checkpoints where a person needs to approve or correct before the agent proceeds, and persistent memory across sessions. This is for teams building an actual product or internal system on top of an LLM, not a one-off automation.
I work directly in Python, integrate with your existing backend, and ship agents that handle failure gracefully instead of breaking the moment an API call times out or a model returns something unexpected.
Features
Multi-agent orchestration (supervisor, worker, and router patterns)
Stateful graphs with conditional branching and cycles
Tool/function calling with structured outputs
RAG integration (vector store retrieval, re-ranking, citation handling)
Persistent memory / conversation state across sessions
Human-in-the-loop approval steps where needed
Guardrails, error handling, and automatic retries
Backend integration (FastAPI/REST) so it plugs into your existing app
Documentation covering the graph logic and how to extend it
I design stateful, multi-agent systems: supervisor/worker architectures, cyclic graphs with conditional routing, human-in-the-loop checkpoints where a person needs to approve or correct before the agent proceeds, and persistent memory across sessions. This is for teams building an actual product or internal system on top of an LLM, not a one-off automation.
I work directly in Python, integrate with your existing backend, and ship agents that handle failure gracefully instead of breaking the moment an API call times out or a model returns something unexpected.
Features
Multi-agent orchestration (supervisor, worker, and router patterns)
Stateful graphs with conditional branching and cycles
Tool/function calling with structured outputs
RAG integration (vector store retrieval, re-ranking, citation handling)
Persistent memory / conversation state across sessions
Human-in-the-loop approval steps where needed
Guardrails, error handling, and automatic retries
Backend integration (FastAPI/REST) so it plugs into your existing app
Documentation covering the graph logic and how to extend it
What the Freelancer needs to start the work
What the agent needs to accomplish, and where a human should stay in the loop vs. fully autonomous
Your current stack (backend language/framework, database, vector store if any)
Data sources: documents, APIs, internal tools it needs access to
LLM provider preference (OpenAI, Anthropic, open-source/self-hosted)
How end users interact with it: API, chat UI, Slack, internal tool
Expected scale (requests/day, concurrent sessions) if relevant