zep-ag2

Zep memory integration for AG2

Installation

pip install zep-ag2

Identifiers in Zep v4

Zep v4 addresses every user, thread, and graph by a server-generated UUID. A create call takes no client-chosen name. The public API of this package takes user_uuid, thread_uuid, and graph_uuid.

For a new user, create the user and the thread one time with create_user and create_thread, read the UUIDs from the responses, and store them in your own database. The package does not resolve a name at run time.

from zep_ag2 import create_thread, create_user

user = await create_user(zep, first_name="Jane", email="jane@example.com")
thread = await create_thread(zep, user_uuid=user.uuid_)

# Store these three values in your application database.
user.uuid_, user.graph_uuid, thread.uuid_

Migrating from V3

Follow the V3 to V4 migration guide for the general procedure. The API changes that are specific to this package are:

• ensure_user(client, *, user_id, ...) and ensure_thread(client, *, thread_id, user_id) are removed. create_user(client, *, first_name=None, last_name=None, email=None, on_created=None) and create_thread(client, *, user_uuid) replace them. They take no client-chosen name, and they return the created User and Thread. The on_created hook now receives the UUID of the user.
• ZepMemoryManager(client, user_id, session_id=None, ...) is now ZepMemoryManager(client, user_uuid, thread_uuid=None, *, graph_uuid=None, sync_client=None, ...). Give the stored user UUID, thread UUID, and user graph UUID here. The user_id and session_id properties are now user_uuid and thread_uuid.
• The lazy provisioning path of ZepMemoryManager is removed: ensure_user_and_thread(), and the constructor arguments first_name, last_name, email, and on_created.
• ZepGraphMemoryManager(client, graph_id) is now ZepGraphMemoryManager(client, graph_uuid, *, sync_client=None). The graph_id property is now graph_uuid.
• create_search_memory_tool(client, user_id, session_id=None, ...) is now create_search_memory_tool(client, graph_uuid, ...). Give the stored user graph UUID.
• create_add_memory_tool(client, user_id, session_id=None) is now create_add_memory_tool(client, graph_uuid, thread_uuid=None). Give the stored user graph UUID and thread UUID.
• create_search_graph_tool(client, user_id=None, graph_id=None, ...) and create_add_graph_data_tool(client, user_id=None, graph_id=None) now take one graph_uuid. Give the stored user graph UUID or the stored UUID of a named graph.
• The search_filters argument of create_search_memory_tool and create_search_graph_tool is now filters.
• register_all_tools(agent, executor, client, user_id, session_id=None, graph_id=None) is now register_all_tools(agent, executor, client, graph_uuid, thread_uuid=None, memory_graph_uuid=None). The graph tools use graph_uuid. The memory tools use memory_graph_uuid, which defaults to graph_uuid. When graph_uuid is a named graph, give the stored user graph UUID as memory_graph_uuid.
• The user_id and thread_id fields of ContextInput are now user_uuid and thread_uuid.

For an existing V3 user, thread, or graph, do not call create_user, create_thread, or graph.create. Get its UUID one time with the backfill in the migration guide, and store it.

Quick Start

System Message Injection

import asyncio
import os
from autogen import AssistantAgent, UserProxyAgent, LLMConfig
from zep_cloud.client import AsyncZep, Zep
from zep_ag2 import ZepMemoryManager, create_thread, create_user, register_all_tools


async def main():
    # The async helpers use AsyncZep. The synchronous AG2 tools use Zep.
    zep = AsyncZep(api_key=os.environ["ZEP_API_KEY"])
    sync_zep = Zep(api_key=os.environ["ZEP_API_KEY"])

    # Create these one time and store the UUIDs in your own database.
    user = await create_user(zep, first_name="Jane", email="jane@example.com")
    thread = await create_thread(zep, user_uuid=user.uuid_)
    user_uuid, graph_uuid, thread_uuid = user.uuid_, user.graph_uuid, thread.uuid_

    llm_config = LLMConfig({"model": "gpt-5-mini", "api_key": os.environ["OPENAI_API_KEY"]})

    assistant = AssistantAgent(
        name="assistant",
        llm_config=llm_config,
        system_message="You are a helpful assistant with long-term memory.",
    )
    user_proxy = UserProxyAgent(
        name="user",
        human_input_mode="NEVER",
        code_execution_config=False,
        is_termination_msg=lambda msg: "TERMINATE" in (msg.get("content") or ""),
    )

    # Enrich agent with memory context. The UUIDs come from your database.
    memory_mgr = ZepMemoryManager(zep, user_uuid, thread_uuid, graph_uuid=graph_uuid)
    await memory_mgr.enrich_system_message(assistant, query="conversation topic")

    # Register memory tools. AG2 calls them synchronously, so they take the sync client.
    register_all_tools(assistant, user_proxy, sync_zep, graph_uuid, thread_uuid)

    result = user_proxy.initiate_chat(assistant, message="What do you remember about me?")


asyncio.run(main())

Tool-Based Memory Access

from zep_cloud.client import Zep
from zep_ag2 import create_search_graph_tool, create_add_graph_data_tool

# The tools take the synchronous Zep client.
sync_zep = Zep(api_key=os.environ["ZEP_API_KEY"])

# Create tools bound to the graph of one user
search_tool = create_search_graph_tool(sync_zep, graph_uuid)
add_tool = create_add_graph_data_tool(sync_zep, graph_uuid)

# Register with AG2's decorator pattern
assistant.register_for_llm(description="Search knowledge graph")(search_tool)
user_proxy.register_for_execution()(search_tool)

assistant.register_for_llm(description="Add to knowledge graph")(add_tool)
user_proxy.register_for_execution()(add_tool)

Automatic Memory Loop (attach_to_agent)

AG2 has no native memory interface -- ConversableAgent.register_hook is the framework's closest thing to a per-turn seam. attach_to_agent wires a fully automatic inject+persist loop onto that seam, so you don't have to call enrich_system_message/add_messages yourself on every turn:

from zep_ag2 import ZepMemoryManager

# The hooks run in synchronous AG2 code, so they call the synchronous Zep client.
manager = ZepMemoryManager(zep, user_uuid, thread_uuid, graph_uuid=graph_uuid, sync_client=sync_zep)
manager.attach_to_agent(assistant)

# Every message the agent receives is persisted and used to refresh its system
# message; every reply the agent sends is persisted automatically too.
user_proxy.initiate_chat(assistant, message="My name is Alice.")

See "The automatic memory loop, precisely" below for the exact hook wiring and what is (and isn't) covered.

Features

• Tool-based memory access — Register Zep search/add as AG2 tools via @register_for_llm
• System message injection — Automatically enrich agent context with relevant memories
• Automatic memory loop — attach_to_agent wires inject-on-receive and persist-on-send onto AG2's register_hook seam
• Knowledge graph — Access Zep's knowledge graph from AG2 agents
• Conversation memory — Store and retrieve thread-based conversation history
• Out-of-band provisioning — create_user/create_thread for explicit, fail-loud setup before the first turn
• Sync tool execution — Tools call the synchronous Zep client, which matches AG2's synchronous tool execution

The automatic memory loop, precisely

ZepMemoryManager.attach_to_agent(agent) registers two hooks on ConversableAgent:

• process_last_received_message fires for every message the agent receives. It persists the message and retrieves context with the synchronous sync_client of the manager, then replaces the agent's system message with its original text plus the freshly-rendered context_template. The hook always returns the message content unmodified -- this is a side channel, not a message transform.
• process_message_before_send fires for every message the agent sends. AG2's contract here is clean enough to persist through: the hook receives the outgoing message (a str, or a dict with a content key) and returns it unchanged after persisting it as an assistant message. This completes the loop -- previously (manual invocation only), persisting the assistant's reply required an explicit add_messages() call.

Both hooks wrap their entire body in try/except, so a Zep outage never breaks the agent's conversation loop -- on failure, the incoming hook simply skips the system-message update and the outgoing hook skips persistence, in both cases still returning the message unchanged.

attach_to_agent is optional and additive: enrich_system_message/add_messages remain available for manual, explicit control (e.g. if you want to persist only some turns, or inject context at a different point than "on receive").

Multi-agent caveat: one manager per Zep thread

Attach attach_to_agent to exactly one agent per Zep thread -- normally the user-facing agent. Each attached manager registers both hooks, so if two agents in a two-agent conversation each call attach_to_agent with managers pointing at the same thread_uuid, every turn is persisted twice with conflicting roles: agent A's outgoing hook (process_message_before_send) persists its reply as role="assistant", and agent B's incoming hook (process_last_received_message) persists that same content again as role="user" (and vice versa for the reverse direction). This silently doubles and mislabels the thread's history -- it is not caught or deduplicated by the package.

Correct wiring -- attach only to the user-facing agent, leaving the other agent unmanaged:

manager = ZepMemoryManager(zep, user_uuid, thread_uuid, graph_uuid=graph_uuid, sync_client=sync_zep)

# Attach to ONE agent only (e.g. the user-facing assistant) ...
manager.attach_to_agent(assistant)

# ... and leave the other agent in the conversation unattached.
# Do NOT also call some_other_manager.attach_to_agent(other_agent) with a
# manager pointing at the same thread_uuid.

If both agents genuinely need their own automatic loop, create a second thread and give each manager a distinct thread_uuid instead of sharing one thread:

assistant_thread = await create_thread(zep, user_uuid=user_uuid)
critic_thread = await create_thread(zep, user_uuid=user_uuid)

assistant_manager = ZepMemoryManager(zep, user_uuid, assistant_thread.uuid_, sync_client=sync_zep)
assistant_manager.attach_to_agent(assistant)

critic_manager = ZepMemoryManager(zep, user_uuid, critic_thread.uuid_, sync_client=sync_zep)
critic_manager.attach_to_agent(critic)

Provisioning

Create the Zep user and thread out-of-band, before the first turn. ZepMemoryManager takes the UUIDs of resources that already exist, and it never creates a resource on a memory path. Both helpers raise on every failure, so a misconfiguration is caught early. Pass first_name, last_name, and email so Zep can anchor the identity node of the user in the graph, and on_created to run one-time setup (ontology, custom instructions) after the user is created:

from zep_ag2 import create_thread, create_user


async def setup_new_user(
    zep, user_uuid: str
) -> None: ...  # example one-time setup, such as an ontology


user = await create_user(
    zep,
    first_name="Jane",
    last_name="Smith",
    email="jane@example.com",
    on_created=setup_new_user,
)
thread = await create_thread(zep, user_uuid=user.uuid_)

manager = ZepMemoryManager(zep, user.uuid_, thread.uuid_, graph_uuid=user.graph_uuid)

The manager needs the UUID of the graph of the user for graph search. Pass graph_uuid from user.graph_uuid. When you do not pass it, the manager reads it one time with user.get(user_uuid) and caches it.

Per-user manager instances. Like the sibling Zep integrations, a ZepMemoryManager is scoped to one (user_uuid, thread_uuid) pair for the lifetime of the instance -- create one manager per user/thread rather than sharing a single instance across users.

Custom context retrieval with context_builder

By default, context is retrieved via thread.get_context(...) (or, inside process_user_message, via thread.add_messages(..., return_context=True)). Pass context_builder to replace this with custom logic -- e.g. a filtered graph search, or a different graph entirely:

from zep_ag2.memory import ContextInput


async def my_builder(ctx: ContextInput) -> str | None:
    user = await ctx.zep.user.get(ctx.user_uuid)
    pager = await ctx.zep.graph.search_edges(
        user.graph_uuid,
        query=ctx.user_message,
    )
    edges = pager.items or []
    if not edges:
        return None
    return "\n".join(edge.fact for edge in edges if edge.fact)


manager = ZepMemoryManager(
    zep,
    user_uuid,
    thread_uuid,
    context_builder=my_builder,
)

ContextInput.agent is the AG2 agent in scope when the builder is invoked via attach_to_agent's automatic loop, and None otherwise (e.g. a manual process_user_message/enrich_system_message call with no agent involved). If the builder raises, a warning is logged and context injection is skipped for that call -- the builder never raises into process_user_message/get_memory_context/enrich_system_message.

Concurrency. Inside process_user_message, when context_builder is set, persistence (thread.add_messages without return_context) and the builder run concurrently via asyncio.gather(..., return_exceptions=True), with per-side isolation: a builder failure never blocks the message from being persisted, and a persistence failure never prevents the builder's context from being returned.

Customizing the injected context template

The retrieved context (whether from the default retrieval or a context_builder) is wrapped in context_template before being injected into the agent's system message. Override it with your own wording, as long as it contains a literal {context} placeholder:

manager = ZepMemoryManager(
    zep,
    user_uuid,
    thread_uuid,
    context_template="Relevant background:\n{context}",
)

The template is rendered via template.replace("{context}", context_text) -- never str.format -- so context text containing {, }, or % is always safe to inject.

API Reference

ZepMemoryManager

Manages Zep memory for AG2 agents via system message injection and an optional automatic loop.

• ZepMemoryManager(client, user_uuid, thread_uuid=None, *, graph_uuid=None, sync_client=None, context_builder=None, context_template=DEFAULT_CONTEXT_TEMPLATE) — Initialize with an AsyncZep client. The synchronous methods call sync_client, a zep_cloud.client.Zep client, and raise TypeError when it is not set.
• attach_to_agent(agent) — Register the automatic inject+persist loop (see above). Needs sync_client.
• get_memory_context_sync(query=None, limit=5) and enrich_system_message_sync(agent, query=None, limit=5) — Synchronous forms. They need sync_client.
• await process_user_message(user_message, *, agent=None) — Persist a user turn and retrieve context in one call
• await resolve_graph_uuid() — Return the UUID of the graph of the user, and cache it
• await enrich_system_message(agent, query=None, limit=5) — Inject memory context
• await get_memory_context(query=None, limit=5) — Get formatted context string
• await add_messages(messages) — Store messages in Zep thread
• await get_session_facts() — Get extracted session facts

ZepGraphMemoryManager

Manages Zep knowledge graph for AG2 agents.

• ZepGraphMemoryManager(client, graph_uuid, *, sync_client=None) — Initialize with an AsyncZep client and the UUID of a graph. search_sync and add_data_sync call sync_client, a zep_cloud.client.Zep client, and raise TypeError when it is not set.
• await search(query, limit=5, scope="edges") — Search the graph
• await add_data(data, data_type="text") — Add data to the graph
• await enrich_system_message(agent, query=None, limit=5) — Inject graph context

Provisioning

• await create_user(client, *, first_name=None, last_name=None, email=None, on_created=None) — Create a Zep user and return it; read user.uuid_ and user.graph_uuid
• await create_thread(client, *, user_uuid) — Create a Zep thread and return it; read thread.uuid_

Tool Factories

All tool factories return synchronous callables (AG2 executes tools synchronously). Each factory and register_all_tools take a synchronous zep_cloud.client.Zep client and use it for every call. An AsyncZep client raises TypeError when you create the tools. The async helpers (create_user, create_thread, and the manager methods) keep AsyncZep.

create_search_memory_tool and create_search_graph_tool follow a pin-or-expose pattern: every search parameter (scope, reranker, limit, mmr_lambda, center_node_uuid) is exposed to the model by default. The scope selects the v4 search method: edges, nodes, episodes, observations, thread_summaries, or auto. The auto scope calls graph.get_context, which accepts neither a reranker nor a limit. The package sends limit as the page_size of the v4 search call.

Each scope accepts only some rerankers. edges accepts all five, nodes accepts all but episode_mentions, episodes accepts rrf and mmr, and observations and thread_summaries accept rrf, mmr, and cross_encoder. The tool schema offers only valid choices: episode_mentions appears only when filters sets episode_uuids, and node_distance needs center_node_uuid. If the model sends a pair that Zep rejects, the tool does not call Zep. It returns an error message that names the pair and lists the valid rerankers for the scope, so the model can call the tool again. The auto scope takes no reranker: if the model sends a reranker with auto, the tool returns an error and does not call Zep. For mmr with no mmr_lambda, the tool sends 0.5. A pinned pair that is not valid, which includes a pinned reranker with the auto scope, raises ValueError when you create the tool.

Use pinned_params to fix a parameter to a constant (hidden from the model), or hidden_params to remove it from the schema without pinning (Zep's own default applies):

# Model chooses scope/reranker/limit/mmr_lambda/center_node_uuid freely (all exposed by default)
tool = create_search_graph_tool(sync_zep, graph_uuid)

# Pin scope to "nodes" and limit to 5 -- hidden from the model, always sent as given
tool = create_search_graph_tool(sync_zep, graph_uuid, pinned_params={"scope": "nodes", "limit": 5})

# Hide reranker entirely -- omitted from the schema AND the SDK call (Zep's default applies)
tool = create_search_graph_tool(sync_zep, graph_uuid, hidden_params={"reranker"})

• create_search_memory_tool(client, graph_uuid, *, pinned_params=None, hidden_params=None, filters=None, bfs_origin_node_uuids=None, scope=None, limit=None) — Search conversation memory
• create_add_memory_tool(client, graph_uuid, thread_uuid=None) — Add conversation memory
• create_search_graph_tool(client, graph_uuid, *, pinned_params=None, hidden_params=None, filters=None, bfs_origin_node_uuids=None, scope=None, limit=None) — Search knowledge graph
• create_add_graph_data_tool(client, graph_uuid) — Add graph data
• register_all_tools(agent, executor, client, graph_uuid, thread_uuid=None, memory_graph_uuid=None) — Register all tools at once

Examples

• Basic Memory — System message injection + memory tools
• Graph Memory — Knowledge graph with ZepGraphMemoryManager
• Search Tools — Read-only search tool registration
• Full Tools — All tools in a GroupChat with multiple agents
• Manual Test — End-to-end integration test with real APIs

Configuration

Environment Variables

# Required
export ZEP_API_KEY="your-zep-cloud-api-key"

# Required for examples that use LLM
export OPENAI_API_KEY="your-openai-api-key"

Development

make install        # Install dev dependencies
make pre-commit     # Format, lint, type-check, test
make ci             # CI validation

Requirements

• Python 3.11+
• ag2>=0.9.0
• zep-cloud>=4.0.0,<5

License

Apache-2.0 — see LICENSE for details.

Support

• Zep Documentation
• AG2 Documentation
• GitHub Issues

Contributing

Contributions are welcome! Please see our Contributing Guide for details.