Mem0 — a memory layer for agents

The core problem: LLMs are stateless

Every call to a language model starts fresh. The model has no persistent state of its own — whatever context you send is all it knows about the world beyond its training data. This means “memory” is always your responsibility as the developer: you decide what goes into the prompt, and you pay (in tokens and latency) for every word of it.

The naive approach is context stuffing — prepending the full conversation history to every new turn. It works for short sessions but breaks down quickly:

• Cost scales linearly. Ten prior sessions of 2,000 tokens each means 20,000 tokens in the context before the user even types their first word.
• Relevance collapses. A month-old discussion about a cancelled order is almost never relevant to today’s shipping question, yet it still occupies prime context real estate.
• Context limits bite. Even with very large windows, there is a ceiling, and retrieval quality often degrades long before you reach it.

What you actually want is selective long-term memory: keep the facts that matter, discard the noise, and inject only what is relevant for the current turn.

What Mem0 is

Mem0 is a memory layer — a service that sits between your agent and the underlying LLM, handling the extraction, storage, and retrieval of salient facts across sessions. The core idea:

1. After (or during) a conversation turn, Mem0 passes the exchange to an LLM that extracts discrete memory items — compact facts like “User prefers vegetarian food” or “User’s timezone is Asia/Kolkata”.
2. Those items are stored in a vector store (Mem0 manages this for you, or you can point it at your own).
3. On the next turn, before you call your agent LLM, you query Mem0 with the current message. It performs a semantic search over stored memories and returns the top-k most relevant ones.
4. You inject those memories into your system prompt. The agent sees only what is relevant — not the full history.

The extraction step is the key innovation. Rather than storing raw conversation turns, Mem0 distills them into a structured fact graph that can be searched efficiently and stays compact even after hundreds of sessions.

Memory types

Mem0 organizes memories along three axes, which map directly to the taxonomy from the broader agent memory literature:

In practice, user memory is the most commonly used — it is what lets your agent feel like it knows a returning customer.

The architecture at a glance

Why this beats naive context-stuffing

The extracted facts are typically one short sentence each. Even a user with 200 sessions might yield 40 distinct memory items. Injecting the top 5 relevant ones costs a few hundred tokens at most.

The Mem0 Python API — core shape

The canonical workflow has two operations: add memories from a completed conversation, and search for relevant memories at the start of the next turn.

from mem0 import Memory

# Initialise — Mem0 manages the vector store and extraction LLM internally.
# Pass a config dict to customise the LLM provider or vector store backend.
# See current Mem0 docs for the full config schema.
m = Memory()

# --- After a conversation turn ---
# `messages` follows the standard OpenAI-style chat format.
messages = [
    {"role": "user",      "content": "I'm vegetarian and I live in Berlin."},
    {"role": "assistant", "content": "Got it — I'll keep that in mind!"},
]

# add() sends the exchange to the extraction LLM, which distills facts,
# then stores them in the vector store under the given user_id.
result = m.add(messages, user_id="alice")
# result contains the memory items that were written (ids, text, etc.)

# --- At the start of the NEXT session ---
user_message = "Can you suggest a restaurant near me for tonight?"

# search() embeds the query and returns the top-k most semantically
# relevant stored memories for this user.
relevant = m.search(query=user_message, user_id="alice")

# relevant is a list of memory objects; inject their text into the prompt.
memory_block = "\n".join(item["memory"] for item in relevant["results"])

system_prompt = f"""You are a helpful assistant.

What you know about this user:
{memory_block}
"""

# Now call your agent LLM with system_prompt + the current user_message.

A few things worth noting about this shape:

• user_id is the key that scopes memories. Use a stable identifier — a database primary key works well.
• The extraction step happens inside add(). You do not need to manually decide which facts to store; the extraction LLM does that.
• search() returns ranked results — you can take [:3] or [:5] to stay within a token budget.
• For agent-scoped or session-scoped memories, Mem0 also accepts agent_id and run_id parameters on both calls. Check the current docs for the exact keyword argument names, as these evolve across releases.

Updating and deleting memories

Mem0 tracks memory items by ID. When the extraction LLM detects a contradiction — say, a user who previously said “I live in Berlin” now says “I just moved to Amsterdam” — Mem0 can update the existing item rather than create a duplicate. This conflict resolution is handled internally, but you can also call m.update() or m.delete() explicitly if you need programmatic control over what is stored.

Putting it together in an agent loop

A realistic agent session looks like this in pseudocode:

def agent_turn(user_id: str, user_message: str) -> str:
    # 1. Retrieve relevant memories for this user
    relevant = m.search(query=user_message, user_id=user_id)
    memory_text = "\n".join(r["memory"] for r in relevant["results"][:5])

    # 2. Build the prompt with injected memories
    system = f"You are a helpful assistant.\n\nAbout this user:\n{memory_text}"

    # 3. Call your agent LLM (any provider)
    response = call_llm(system=system, user=user_message)

    # 4. Store the new exchange as memories for future sessions
    m.add(
        [
            {"role": "user",      "content": user_message},
            {"role": "assistant", "content": response},
        ],
        user_id=user_id,
    )

    return response

The loop is the same regardless of which underlying LLM or framework you use. Mem0 is framework-agnostic — it works equally well with a raw OpenAI call, a LangChain chain, or a LangGraph agent.