redis-agent-memory

Developer-friendly & type-safe Python SDK specifically catered to leverage redis-agent-memory API.

Summary

Redis Agent Memory API: API for storing, retrieving, and searching Redis Agent Memory session memory and long-term memory.

Authentication depends on your deployment. Use the credential issued for your Agent Memory service or store. For self-managed deployments, see the self-managed Agent Memory guide for authentication options.

Documentation

• Developer guide
• Python SDK quickstart
• REST API reference (HTTP endpoints and schemas, not Python method pages)
• REST API quickstart

Recent changes

Namespace resources (since 0.4.0). Create and manage namespaces with list_namespaces, create_namespace, get_namespace, update_namespace, and delete_namespace. Bind a session or long-term memory to a namespace resource with namespace_ref.

Namespace routing (0.5.0). Define a store-wide policy with put_namespace_routing to organize long-term memories in shared namespaces. accept rules select a destination; reject rules can block a memory from entering that destination, even when a caller names it explicitly. The policy only targets shared namespaces. To route a memory that is already in a shared namespace, call move_long_term_memories(memory_ids=["mem-1"]) without namespace_ref. Memories in personal namespaces or no namespace are not eligible for automatic routing. A new memory with neither namespace field remains unnamespaced.

Document ingestion (MemoryETL, 0.5.0). Generate long-term memories from documents, such as policies, before an agent has any sessions. This complements extraction from session events. Configure a document Extractor, submit a revision with create_document_ingestion, then check its asynchronous status and list the memories it produced. Pass document content as a base64-encoded UTF-8 string.

Summary Views (0.5.0). Materialize summaries of long-term memories. The server assigns owner_id. Visibility defaults to private; set SHARED to publish a view of shared memories to store-authorized callers. Filter sources with namespace_ref, and group by owner, session, namespace ID, or memory type. A result read uses that same scope. The service checks the contributing records and withholds invalid output while repair is pending. Choose text or structured output, then refresh on demand or use continuous refresh. Summary Views must be enabled in the deployment.

Ordering. Filter-only long-term search accepts sort_by (createdAt or updatedAt) and sort_order. Do not combine sort with text. list_namespaces accepts the same kind of sort and defaults to id ascending.

Session rows. list_sessions still returns session IDs. list_session_summaries returns owner, timestamps, and namespace, with caller-selected order.

Ingestion hooks. When a store selects an input hook, memory and document writes require idempotency_key. A stopped hook returns HTTP 422, and a retryable hook returns HTTP 503. Read one decision with get_ingestion_hook_status, or list them with list_ingestion_hook_statuses.

Source evidence. A memory source can be a document or a session. Page the evidence for one source with list_memory_source_evidence.

Legacy namespace labels. The flat namespace string is deprecated on memories. It does not create or resolve a namespace resource. Do not send it together with namespace_ref. Summary View filters, grouping, and result scope accept namespace_ref only.

HTTP 424. When a memory or model-provider dependency cannot complete the request, the SDK raises MemoryFailedDependencyErrorResponseContent. FailedDependencyErrorResponseContent still represents other 424 failed-dependency errors.

SDK Installation

The SDK can be installed with uv, pip, or poetry package managers.

uv

uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.

uv add redis-agent-memory

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install redis-agent-memory

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add redis-agent-memory

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from redis-agent-memory python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "redis-agent-memory",
# ]
# ///

from redis_agent_memory import AgentMemory

sdk = AgentMemory(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Create a namespace

Create a shared root namespace. Replace ns-product in the following examples with the returned namespace ID.

# Synchronous Example
from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.create_namespace(request={
        "name": "product",
        "scope": models.NamespaceScope.SHARED,
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.create_namespace_async(request={
            "name": "product",
            "scope": models.NamespaceScope.SHARED,
        })

        # Handle response
        print(res)

asyncio.run(main())

Add a session event

Append a chat turn. The server extracts long-term memory from the session asynchronously. Optionally bind the session with namespaceRef so extracted memories land in that namespace.

# Synchronous Example
from redis_agent_memory import AgentMemory, models
from redis_agent_memory.utils import parse_datetime


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.add_session_event(actor_id="user-42", role=models.MessageRole.USER, content=[
        {
            "text": "What were the action items from last week's meeting?",
        },
    ], created_at=parse_datetime("2024-03-15T10:00:00Z"), session_id="session-user42-20240315", namespace_ref={
        "namespace_id": "ns-product",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models
from redis_agent_memory.utils import parse_datetime

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.add_session_event_async(actor_id="user-42", role=models.MessageRole.USER, content=[
            {
                "text": "What were the action items from last week's meeting?",
            },
        ], created_at=parse_datetime("2024-03-15T10:00:00Z"), session_id="session-user42-20240315", namespace_ref={
            "namespace_id": "ns-product",
        })

        # Handle response
        print(res)

asyncio.run(main())

Get session memory

Read the conversation for a session -- this should contain the messages you added:

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.get_session_memory(session_id="session-user42-20240315")

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.get_session_memory_async(session_id="session-user42-20240315")

        # Handle response
        print(res)

asyncio.run(main())

Search long-term memory

Search long-term memory the server extracted or you created manually. In this example, we limit the search to the namespace identified by namespaceRef.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.search_long_term_memory(request={
        "text": "preferred theme",
        "filter_": {
            "namespace_ref": {
                "eq": "ns-product",
            },
        },
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.search_long_term_memory_async(request={
            "text": "preferred theme",
            "filter_": {
                "namespace_ref": {
                    "eq": "ns-product",
                },
            },
        })

        # Handle response
        print(res)

asyncio.run(main())

Create long-term memories

Optionally write long-term memories yourself instead of waiting for extraction.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.bulk_create_long_term_memories(memories=[
        {
            "id": "mem-1",
            "text": "The user prefers dark mode",
            "owner_id": "user-1",
            "namespace_ref": {
                "namespace_id": "ns-product",
            },
        },
    ])

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.bulk_create_long_term_memories_async(memories=[
            {
                "id": "mem-1",
                "text": "The user prefers dark mode",
                "owner_id": "user-1",
                "namespace_ref": {
                    "namespace_id": "ns-product",
                },
            },
        ])

        # Handle response
        print(res)

asyncio.run(main())

Move long-term memories

Optionally move existing memories into a namespace. Omit namespaceRef to auto-route memories already in a shared namespace when store routing is enabled.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.move_long_term_memories(memory_ids=[
        "mem-1",
    ], namespace_ref={
        "namespace_id": "ns-product",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.move_long_term_memories_async(memory_ids=[
            "mem-1",
        ], namespace_ref={
            "namespace_id": "ns-product",
        })

        # Handle response
        print(res)

asyncio.run(main())

Set namespace routing policy

Replace the store routing policy. Accept rules pick a shared namespace from memory text. Reject rules can veto that destination, including explicit placements.

# Synchronous Example
from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.put_namespace_routing(policy={
        "enabled": True,
        "rules": [
            {
                "id": "invoices",
                "destination": {
                    "namespace_id": "ns-product",
                },
                "enabled": True,
                "action": models.RoutingRuleAction.ACCEPT,
                "strategies": [
                    {
                        "kind": models.RouteStrategyKind.MATCHER_SPEC,
                        "matcher": {
                            "kind": models.MatcherKind.REGEX,
                            "regex": {
                                "pattern": "invoice",
                            },
                        },
                    },
                ],
            },
        ],
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.put_namespace_routing_async(policy={
            "enabled": True,
            "rules": [
                {
                    "id": "invoices",
                    "destination": {
                        "namespace_id": "ns-product",
                    },
                    "enabled": True,
                    "action": models.RoutingRuleAction.ACCEPT,
                    "strategies": [
                        {
                            "kind": models.RouteStrategyKind.MATCHER_SPEC,
                            "matcher": {
                                "kind": models.MatcherKind.REGEX,
                                "regex": {
                                    "pattern": "invoice",
                                },
                            },
                        },
                    ],
                },
            ],
        })

        # Handle response
        print(res)

asyncio.run(main())

Get namespace routing policy

Read the store routing policy. An empty disabled policy is returned when none has been saved.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.get_namespace_routing()

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.get_namespace_routing_async()

        # Handle response
        print(res)

asyncio.run(main())

Submit a document for extraction

Use an Extractor configured for documents in the control plane. The response accepts an asynchronous job; poll its status before reading outputs. Reuse the idempotency key only for the same submission.

# Synchronous Example
from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.create_document_ingestion(document_id="customer-preferences", revision="v1", content="Q3VzdG9tZXIgcHJlZmVycyBlbWFpbCB1cGRhdGVzLg==", media_type=models.DocumentMediaType.TEXT_PLAIN, owner_id="user-123", extractor_id="document-extractor", idempotency_key="customer-preferences-v1")

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.create_document_ingestion_async(document_id="customer-preferences", revision="v1", content="Q3VzdG9tZXIgcHJlZmVycyBlbWFpbCB1cGRhdGVzLg==", media_type=models.DocumentMediaType.TEXT_PLAIN, owner_id="user-123", extractor_id="document-extractor", idempotency_key="customer-preferences-v1")

        # Handle response
        print(res)

asyncio.run(main())

Check document ingestion

Replace the ingestion ID with the ID returned by submission. Wait for a terminal status before listing the extracted memories.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.get_document_ingestion(ingestion_id="0123456789abcdef0123456789abcdef")

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.get_document_ingestion_async(ingestion_id="0123456789abcdef0123456789abcdef")

        # Handle response
        print(res)

asyncio.run(main())

Read document ingestion outputs

List the memories created by an ingestion. Use limit and offset to read more pages.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.list_document_ingestion_outputs(ingestion_id="0123456789abcdef0123456789abcdef")

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.list_document_ingestion_outputs_async(ingestion_id="0123456789abcdef0123456789abcdef")

        # Handle response
        print(res)

asyncio.run(main())

Create a Summary View

Create an owner-grouped view of long-term memories. Summary Views must be enabled in the deployment.

# Synchronous Example
from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.create_summary_view(name="Customer preferences", source=models.SummaryViewSource.LONG_TERM, group_by=[
        models.SummaryViewGroupField.OWNER_ID,
    ], output={
        "format_": models.SummaryViewOutputFormat.TEXT,
        "instructions": "Summarize the customer's preferences.",
    }, refresh_policy={
        "mode": models.SummaryViewRefreshMode.MANUAL,
    }, filter_={
        "namespace_ref": {
            "eq": "ns-product",
        },
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.create_summary_view_async(name="Customer preferences", source=models.SummaryViewSource.LONG_TERM, group_by=[
            models.SummaryViewGroupField.OWNER_ID,
        ], output={
            "format_": models.SummaryViewOutputFormat.TEXT,
            "instructions": "Summarize the customer's preferences.",
        }, refresh_policy={
            "mode": models.SummaryViewRefreshMode.MANUAL,
        }, filter_={
            "namespace_ref": {
                "eq": "ns-product",
            },
        })

        # Handle response
        print(res)

asyncio.run(main())

Refresh a Summary View

Queue a refresh for one owner. Replace the view ID with the ID returned when creating the view.

# Synchronous Example
from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.refresh_summary_view(view_id="0123456789abcdef0123456789abcdef", target=models.SummaryViewRefreshTarget.PARTITION, scope={
        "owner_id": "user-123",
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory, models

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.refresh_summary_view_async(view_id="0123456789abcdef0123456789abcdef", target=models.SummaryViewRefreshTarget.PARTITION, scope={
            "owner_id": "user-123",
        })

        # Handle response
        print(res)

asyncio.run(main())

Read a Summary View result

Read the latest result for the same owner scope. Refresh runs asynchronously, so inspect the returned status.

# Synchronous Example
from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.get_summary_view_result(view_id="0123456789abcdef0123456789abcdef", owner_id="user-123")

    # Handle response
    print(res)

The same SDK client can also be used to make asynchronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from redis_agent_memory import AgentMemory

async def main():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:

        res = await agent_memory.get_summary_view_result_async(view_id="0123456789abcdef0123456789abcdef", owner_id="user-123")

        # Handle response
        print(res)

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name	Type	Scheme	Environment Variable
api_key	http	HTTP Bearer	AGENT_MEMORY_API_KEY

To authenticate with the API the api_key parameter must be set when initializing the SDK client instance. For example:

from redis_agent_memory import AgentMemory


with AgentMemory(
    "https://api.example.com",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.health()

    # Handle response
    print(res)

Available Resources and Operations

Available methods

AgentMemory SDK

• health - Return information about the operational status of the service.
• create_document_ingestion - Submit one revisioned document for extraction.
• get_document_ingestion
• list_document_ingestion_outputs
• store_health - Returns read-only health for applicable store-scoped RAM features.
• list_ingestion_hook_statuses - List hook decisions in stable newest-first order. Pages can be empty after owner filtering; continue while nextCursor is present. Internal run reservations are omitted.
• get_ingestion_hook_status - Read a hook decision and application status. Requires authenticated store access and, when enforced, matching owner scope. Content and secrets are never returned.
• bulk_delete_long_term_memories - Deletes long-term memories in bulk by their IDs.
• bulk_create_long_term_memories - Creates long-term memories in bulk. Records without namespace or namespaceRef stay un-namespaced. Automatic routing does not place them in a shared namespace. An explicit namespaceRef must pass destination admission and is never rerouted. Admission failures are reported per record in errors.
• move_long_term_memories - Moves long-term memories to an explicit namespaceRef, or automatically routes a memory that is already in a shared namespace when namespaceRef is omitted. Personal and un-namespaced memories are not automatic-routing candidates and keep their current placement with a per-item error. If routing is disabled or no shared destination qualifies, the memory also keeps its current placement and receives a per-item error. An explicit destination must pass admission and is never rerouted. Returns 409 when the explicit destination is archived; automatic routing and admission failures are reported per item in errors.
• search_long_term_memory - Runs a semantic search on long-term memory with filtering options.
• get_long_term_memory - Returns a long-term memory by its ID.
• update_long_term_memory - Partially updates a long-term memory by its ID. The merged record must pass admission at its destination. Updates never select a different destination automatically.
• update_long_term_memory_fields - Atomically updates a custom-typed long-term memory's text and/or attributes, scoped to the caller's bound memory type. Used by the custom-extraction worker. New text must pass destination admission before the update is applied.
• list_memory_source_links - Pages source links for one long-term memory. Tokens bind this query and expire.
• list_memory_source_evidence - Pages extraction evidence for one source of a memory, in append order. Tokens bind the store, current owner, memory and source, and expire.
• get_namespace_routing - Returns the store's namespace routing policy. An empty disabled policy is returned when none has been saved.
• put_namespace_routing - Replaces the store's namespace routing policy. Namespaces remain hierarchy records; routing is an ordered store-level document of plug-in strategies.
• list_namespaces - Lists namespace roots or direct children.
• create_namespace - Creates a namespace resource as a root, direct child, or explicit hierarchy path. Returns 409 when an active or archived namespace already occupies the requested location.
• delete_namespace - Deletes an empty leaf namespace. Returns 409 when the namespace has children or memory placements, or when it changed concurrently.
• get_namespace - Gets one namespace by its canonical ID.
• update_namespace - Renames or archives a namespace. Returns 409 when the namespace changed concurrently, an active or archived sibling already uses the requested name, or the namespace is archived.
• list_sessions - Returns a paginated list of session IDs for a store.
• add_session_event - Appends a single event to a session. Creates the session if it does not exist. If sessionId is omitted, the server generates one.
• delete_session_memory - Deletes the session memory for a session.
• get_session_memory - Returns the session memory for a session.
• delete_session_event - Deletes a single event from a session by event ID.
• get_session_event - Returns a single event from a session by event ID.
• list_session_summaries - Returns paginated session rows with metadata and caller-selected order. The ID-only ListSessions operation remains unchanged.
• list_source_memories - Pages memories linked to one source. A source link does not grant access to a memory in another owner or namespace.
• list_summary_views - Lists Summary View definitions and high-level state. Partition values are not included.
• create_summary_view - Creates a Summary View definition and asynchronously enqueues its initial reconciliation.
• delete_summary_view - Marks a Summary View deleting and asynchronously removes its schedule and materializations in bounded batches. Repeated requests are idempotent.
• get_summary_view - Returns one Summary View definition without partition values.
• update_summary_view - Updates mutable Summary View fields. Source is immutable. Material changes increment definitionVersion; name- or description-only changes do not enqueue refresh work.
• refresh_summary_view - Asynchronously enqueues one exact partition refresh or a complete view reconciliation. It never performs generation synchronously.
• get_summary_view_result - Returns one authorized exact Summary View partition after validating its contributing source records. Invalid output is withheld while repair is pending.

Global Parameters

A parameter is configured globally. This parameter may be set on the SDK client instance itself during initialization. When configured as an option during SDK initialization, This global value will be used as the default on the operations that use it. When such operations are called, there is a place in each to override the global value, if needed.

For example, you can set storeId to "<id>" at SDK initialization and then you do not have to pass the same value on calls to operations like create_document_ingestion. But if you want to do so you may, which will locally override the global setting. See the example code below for a demonstration.

Available Globals

The following global parameter is available. Global parameters can also be set via environment variable.

Name	Type	Description	Environment
store_id	str	The store_id parameter.	AGENT_MEMORY_STORE_ID

Example

from redis_agent_memory import AgentMemory, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.create_document_ingestion(document_id="customer-preferences", revision="v1", content="Q3VzdG9tZXIgcHJlZmVycyBlbWFpbCB1cGRhdGVzLg==", media_type=models.DocumentMediaType.TEXT_PLAIN, owner_id="user-123", extractor_id="document-extractor", idempotency_key="customer-preferences-v1")

    # Handle response
    print(res)

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from redis_agent_memory import AgentMemory
from redis_agent_memory.utils import BackoffStrategy, RetryConfig


with AgentMemory(
    "https://api.example.com",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.health(,
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from redis_agent_memory import AgentMemory
from redis_agent_memory.utils import BackoffStrategy, RetryConfig


with AgentMemory(
    "https://api.example.com",
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:

    res = agent_memory.health()

    # Handle response
    print(res)

Error Handling

AgentMemoryError is the base class for all HTTP error responses. It has the following properties:

Property	Type	Description
err.message	str	Error message
err.status_code	int	HTTP response status code eg 404
err.headers	httpx.Headers	HTTP response headers
err.body	str	HTTP body. Can be empty string if no body is returned.
err.raw_response	httpx.Response	Raw HTTP response
err.data		Optional. Some errors may contain structured data. See Error Classes.

Example

from redis_agent_memory import AgentMemory, errors, models


with AgentMemory(
    "https://api.example.com",
    store_id="<id>",
    api_key="<AGENT_MEMORY_API_KEY>",
) as agent_memory:
    res = None
    try:

        res = agent_memory.create_document_ingestion(document_id="customer-preferences", revision="v1", content="Q3VzdG9tZXIgcHJlZmVycyBlbWFpbCB1cGRhdGVzLg==", media_type=models.DocumentMediaType.TEXT_PLAIN, owner_id="user-123", extractor_id="document-extractor", idempotency_key="customer-preferences-v1")

        # Handle response
        print(res)


    except errors.AgentMemoryError as e:
        # The base class for HTTP error responses
        print(e.message)
        print(e.status_code)
        print(e.body)
        print(e.headers)
        print(e.raw_response)

        # Depending on the method different errors may be thrown
        if isinstance(e, errors.BadRequestErrorResponseContent):
            print(e.data.title)  # str
            print(e.data.status)  # Optional[int]
            print(e.data.detail)  # Optional[str]
            print(e.data.instance)  # Optional[str]
            print(e.data.type)  # models.BadRequestErrorType

Error Classes

Primary errors:

• AgentMemoryError: The base class for HTTP error responses.
  • BadRequestErrorResponseContent: Request validation or input decoding failed. Status code 400. *
  • AuthenticationErrorResponseContent: Authentication credentials are missing, malformed, or invalid. Status code 401. *
  • ForbiddenErrorResponseContent: The caller is authenticated but not allowed to access the requested resource. Status code 403. *
  • UnexpectedErrorResponseContent: The service failed with an unexpected internal error. Status code 500. *
  • NotFoundErrorResponseContent: The requested resource does not exist. Status code 404. *
  • TimeoutErrorResponseContent: The request timed out before the service could complete it. Status code 408. *

Less common errors (15)

Network errors:

• httpx.RequestError: Base class for request errors.
  • httpx.ConnectError: HTTP client was unable to make a request to a server.
  • httpx.TimeoutException: HTTP request timed out.

Inherit from AgentMemoryError:

• ResourceSuspendedErrorResponseContent: The requested resource exists but is suspended by an administrator. Status code 423. Applicable to 30 of 38 methods.*
• FailedDependencyErrorResponseContent: A dependent resource required to process the request is unavailable or unhealthy. Status code 424. Applicable to 30 of 38 methods.*
• TooManyRequestsErrorResponseContent: The service rejected the request because rate limits were exceeded. Status code 429. Applicable to 27 of 38 methods.*
• PayloadTooLargeErrorResponseContent: The request payload exceeds the maximum supported size. Status code 413. Applicable to 24 of 38 methods.*
• ConflictErrorResponseContent: The request conflicts with the current state of the resource. Status code 409. Applicable to 8 of 38 methods.*
• MemoryFailedDependencyErrorResponseContent: A resource or model-provider dependency required by Agent Memory could not complete the request. Status code 424. Applicable to 7 of 38 methods.*
• SummaryViewConflictErrorResponseContent: A Summary View operation conflicts with the current definition or partition state. Status code 409. Applicable to 5 of 38 methods.*
• IngestionHookStoppedErrorResponseContent: The ingestion hook stopped this operation without applying unapproved content. Status code 422. Applicable to 3 of 38 methods.*
• SummaryViewQuotaErrorResponseContent: A Summary View operation exceeded an enabled store quota. Exact ceilings are deployment configuration established from benchmark evidence. Status code 429. Applicable to 3 of 38 methods.*
• IngestionHookRetryErrorResponseContent: A saved ingestion hook decision is waiting for its next bounded attempt. Status code 503. Applicable to 3 of 38 methods.*
• ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via the cause attribute.

* Check the method documentation to see if the error is applicable.

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from redis_agent_memory import AgentMemory
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = AgentMemory(client=http_client)

or you could wrap the client with your own custom logic:

from redis_agent_memory import AgentMemory
from redis_agent_memory.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = AgentMemory(async_client=CustomClient(httpx.AsyncClient()))

httpx2 (Pydantic's httpx fork)

httpx2 is Pydantic's maintained fork of httpx. To run this SDK on httpx2, call alias_httpx() at your program's entry point, before importing the SDK, so every import httpx — including the ones inside the SDK — resolves to httpx2:

import httpx2

httpx2.alias_httpx()

from redis_agent_memory import AgentMemory

s = AgentMemory()

An SDK can also be generated against httpx2 directly, so it depends on the fork instead of httpx, by setting python.httpClientLibrary: httpx2 in gen.yaml.

Resource Management

The AgentMemory class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from redis_agent_memory import AgentMemory
def main():

    with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:
        # Rest of application here...


# Or when using async:
async def amain():

    async with AgentMemory(
        "https://api.example.com",
        store_id="<id>",
        api_key="<AGENT_MEMORY_API_KEY>",
    ) as agent_memory:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from redis_agent_memory import AgentMemory
import logging

logging.basicConfig(level=logging.DEBUG)
s = AgentMemory(server_url="https://example.com", debug_logger=logging.getLogger("redis_agent_memory"))

You can also enable a default debug logger by setting an environment variable AGENT_MEMORY_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.