Session management in agentic workflows refers to how an AI agent (or system of agents) maintains context, state, and continuity across multiple interactions or steps in a task. In simpler terms, it’s how an agent “remembers” what’s going on — what’s been done, what’s being worked on, and what still needs to be done — across time.

⚙️ Typical Components

1. Session ID – unique identifier for each workflow or user conversation.
2. State store – keeps relevant data (messages, files, task progress, variables).
3. Lifecycle – defines when a session starts, updates, expires, or restarts.
4. Persistence – optional saving of sessions (to Redis, SQLite, etc.) for long-term tasks.

In multi-agent systems, each agent might share the same session context or have its own local session that contributes to a shared memory graph.

Here’s a toy version showing how session management might look in an agentic workflow:

import uuid

# In-memory session store
sessions = {}

def start_session():
    session_id = str(uuid.uuid4())
    sessions[session_id] = {"messages": [], "variables": {}}
    return session_id

def add_message(session_id, role, content):
    sessions[session_id]["messages"].append({"role": role, "content": content})

def get_context(session_id):
    return sessions[session_id]["messages"]

# Example usage:
sid = start_session()
add_message(sid, "user", "Write a function to fetch stock prices")
add_message(sid, "agent", "Here’s a Python function using yfinance...")
add_message(sid, "user", "Now add error handling")

# The agent can now recall context:
for msg in get_context(sid):
    print(f"{msg['role']}: {msg['content']}")

In production systems (like OpenAI’s agents SDK, LangChain, or LlamaIndex), session management is built into the agent framework. Now it’s built up, the next layer is tool use and persistent memory, which together make session management long-term and actionable.

One can connect sessions to a simple SQLite database to store message history.

In an agentic system, the agent doesn’t just chat — it acts through tools (e.g., APIs, Python functions, databases). That’s the backbone of how LLM-driven agents (like OpenAI’s new Agents SDK or LangChain’s chains) remember and reason across actions. What’s more, it can also turn that session history into embeddings — vectorized representations of content — stored in a retrieval database (like Pinecone, FAISS, or Elasticsearch). This allows agents to recall similar past experiences or facts semantically, not just literally.