Langchain Developer for AI Chat Agents mvp
Budget: $30 – $250 USD
We’re working on a project about AI psychologists, and overall, most of it is done, but I’m not satisfied with the architecture of how the AI "psychologist" interacts with the user.
Here’s the breakdown:
We’ve built a collection of AI personas, each with distinct expertise and behavioral profiles—carefully defined to handle specific domains effectively.
On the user side, we maintain a context layer: a repository of verified facts about each individual, updated as we go, ensuring we’re working from accurate, grounded data without any guesswork.
Then there’s the session manager—it’s the backbone of the system. It orchestrates the conversation, tracks state across interactions, and retains key details like ongoing tasks, resolved issues, and defined objectives.
Here’s how it operates:
When a user engages, we pull their profile and confirmed history, then pair it with the selected AI persona. Every message gets processed through a large language model, guided by a context that includes:
The user’s inferred personality and knowledge base
A fact-checked dataset of their background and current concerns
A relevant history of prior exchanges
Strict directives to prioritize accuracy
And a detailed spec for the persona’s behavior and tone
Post-session, we extract and store:
Newly uncovered user details, tagged with confidence levels
Updates on problem-solving progress
A concise session summary for continuity
When the user returns, the system reloads this context, enabling seamless resumption—maintaining conversational flow and preserving memory of past interactions with precision.
We are going to use langchain.
Because
1. For AI personas, we can use langchain agents with custom defined
2. with context layer, we can store verified user data in vector db and implement langchain' RAG to fetch relevant user details dynamically.
3. For session manager
- We can use conversation memory to persist ongoing interactions and utilize multi-context memory to maintain structured memory
- After that we will store interaction summaries with langchain's summarization chains for efficient recall.
1. do you feel confident you can implement this idea with LangChain?
2. if so how much work time do you think it would take? To get to MVP just with the service not counting UI etc
Here’s the breakdown:
We’ve built a collection of AI personas, each with distinct expertise and behavioral profiles—carefully defined to handle specific domains effectively.
On the user side, we maintain a context layer: a repository of verified facts about each individual, updated as we go, ensuring we’re working from accurate, grounded data without any guesswork.
Then there’s the session manager—it’s the backbone of the system. It orchestrates the conversation, tracks state across interactions, and retains key details like ongoing tasks, resolved issues, and defined objectives.
Here’s how it operates:
When a user engages, we pull their profile and confirmed history, then pair it with the selected AI persona. Every message gets processed through a large language model, guided by a context that includes:
The user’s inferred personality and knowledge base
A fact-checked dataset of their background and current concerns
A relevant history of prior exchanges
Strict directives to prioritize accuracy
And a detailed spec for the persona’s behavior and tone
Post-session, we extract and store:
Newly uncovered user details, tagged with confidence levels
Updates on problem-solving progress
A concise session summary for continuity
When the user returns, the system reloads this context, enabling seamless resumption—maintaining conversational flow and preserving memory of past interactions with precision.
We are going to use langchain.
Because
1. For AI personas, we can use langchain agents with custom defined
2. with context layer, we can store verified user data in vector db and implement langchain' RAG to fetch relevant user details dynamically.
3. For session manager
- We can use conversation memory to persist ongoing interactions and utilize multi-context memory to maintain structured memory
- After that we will store interaction summaries with langchain's summarization chains for efficient recall.
1. do you feel confident you can implement this idea with LangChain?
2. if so how much work time do you think it would take? To get to MVP just with the service not counting UI etc