vectorize-io__hindsight/hindsight-docs/docs-integrations/haystack.md

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34	Haystack Persistent Memory with Hindsight | Integration	Add persistent long-term memory to Haystack agents with Hindsight. Provides retain/recall/reflect Tools plus a HindsightMemoryWrapper with optional auto-recall and auto-retain.

Haystack

Persistent long-term memory for Haystack agents via Hindsight. The hindsight-haystack package gives you two complementary patterns:

• create_hindsight_tools(...) — Returns a list of Haystack Tools (retain_memory, recall_memory, reflect_on_memory) the model can call directly inside a turn.
• HindsightMemoryWrapper — A Haystack Toolset that bundles the same tools and adds optional auto-recall (inject relevant memories into the system prompt before each turn) and auto-retain (store user + assistant messages after each turn).

View Changelog →

Installation

pip install hindsight-haystack

Quick Start

from hindsight_client import Hindsight
from hindsight_haystack import create_hindsight_tools
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

client = Hindsight(base_url="http://localhost:8888")

tools = create_hindsight_tools(
    client=client,
    bank_id="user-123",
    mission="Track user preferences",
)

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=tools,
    system_prompt=(
        "You are a helpful assistant with long-term memory. "
        "Use retain_memory to store important facts. "
        "Use recall_memory to search memory before answering."
    ),
)

result = agent.run(messages=[ChatMessage.from_user("Remember that I prefer dark mode")])
print(result["messages"][-1].text)

Automatic Memory with HindsightMemoryWrapper

For automatic recall and retain without relying on the agent to call tools:

from hindsight_haystack import HindsightMemoryWrapper

toolset = HindsightMemoryWrapper(
    client=client,
    bank_id="user-123",
    mission="Track user preferences",
    auto_recall=True,   # Inject memories into the system prompt before each turn
    auto_retain=True,   # Store user + assistant messages after each turn
)

agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=toolset,
    system_prompt="You are a helpful assistant with long-term memory.",
)

# Use toolset.run() for automatic memory behavior
result = toolset.run(agent, messages=[ChatMessage.from_user("I prefer dark mode")])

Selective Tools

# Only retain + recall (no reflect)
tools = create_hindsight_tools(
    client=client,
    bank_id="user-123",
    include_reflect=False,
)

Configuration

Call configure() once to set connection defaults so you can omit client=/hindsight_api_url= on every call:

from hindsight_haystack import configure

configure(
    hindsight_api_url="http://localhost:8888",
    api_key="your-api-key",
    budget="mid",
    tags=["source:haystack"],
    context="my-app",
    mission="Track user preferences",
)

tools = create_hindsight_tools(bank_id="user-123")

The API URL defaults to Hindsight Cloud (https://api.hindsight.vectorize.io), and the API key falls back to the HINDSIGHT_API_KEY environment variable.

Requirements

• Python 3.10+
• haystack-ai >= 2.12.0
• hindsight-client >= 0.4.0

Prerequisites

A running Hindsight instance:

Hindsight Cloud (recommended): Sign up — no self-hosting required.

Self-hosted:

pip install hindsight-all
export HINDSIGHT_API_LLM_API_KEY=your-api-key
hindsight-api  # starts on http://localhost:8888