hindsight-openai-agents

OpenAI Agents SDK integration for Hindsight — persistent long-term memory for AI agents.

Provides FunctionTool instances that give OpenAI Agents SDK agents the ability to store, search, and synthesize memories across conversations.

Prerequisites

• A running Hindsight instance (self-hosted via Docker or Hindsight Cloud)
• Python 3.10+

Installation

pip install hindsight-openai-agents openai-agents

hindsight-openai-agents pulls in openai-agents and hindsight-client.

Quick Start

import asyncio
from agents import Agent, Runner
from hindsight_client import Hindsight
from hindsight_openai_agents import create_hindsight_tools

async def main():
    client = Hindsight(base_url="http://localhost:8888")
    await client.acreate_bank(bank_id="user-123")

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

    agent = Agent(
        name="assistant",
        instructions="You are a helpful assistant with long-term memory. Use hindsight_retain to store important facts. Use hindsight_recall to search memory before answering.",
        tools=tools,
    )

    # Store a memory
    result = await Runner.run(agent, "Remember that I prefer dark mode")
    print(result.final_output)

    # Hindsight processes retained content asynchronously (fact extraction,
    # entity resolution, embeddings). A brief pause ensures memories are
    # searchable before the next recall. In production, this delay is only
    # needed when retain and recall happen back-to-back in the same script.
    await asyncio.sleep(3)

    # Recall it later
    result = await Runner.run(agent, "What are my UI preferences?")
    print(result.final_output)

    # Clean up
    await client.aclose()

asyncio.run(main())

The agent gets three tools:

• hindsight_retain — Store information to long-term memory
• hindsight_recall — Search long-term memory for relevant facts
• hindsight_reflect — Synthesize a reasoned answer from memories

Auto-Inject Memories with memory_instructions()

Instead of relying on the agent to call hindsight_recall explicitly, you can auto-inject relevant memories into the system prompt on every turn:

from hindsight_openai_agents import create_hindsight_tools, memory_instructions

agent = Agent(
    name="assistant",
    instructions=memory_instructions(
        client=client,
        bank_id="user-123",
        base_instructions="You are a helpful assistant with long-term memory.",
    ),
    tools=create_hindsight_tools(
        client=client,
        bank_id="user-123",
        include_recall=False,  # recall handled by memory_instructions
    ),
)

memory_instructions() returns an async callable compatible with Agent(instructions=...). On each turn it recalls relevant memories and appends them to your base instructions. If recall fails or returns nothing, it gracefully falls back to base_instructions alone.

Selecting Tools

Include only the tools you need:

tools = create_hindsight_tools(
    client=client,
    bank_id="user-123",
    include_retain=True,
    include_recall=True,
    include_reflect=False,  # Omit reflect
)

Global Configuration

Instead of passing a client to every call, configure once:

from hindsight_openai_agents import configure, create_hindsight_tools

configure(
    hindsight_api_url="http://localhost:8888",
    api_key="your-api-key",       # Or set HINDSIGHT_API_KEY env var
    budget="mid",                  # Recall budget: low/mid/high
    max_tokens=4096,               # Max tokens for recall results
    tags=["env:prod"],             # Tags for stored memories
    recall_tags=["scope:global"],  # Tags to filter recall
    recall_tags_match="any",       # Tag match mode
)

# Now create tools without passing client
tools = create_hindsight_tools(bank_id="user-123")

Memory Scoping with Tags

Use tags to partition memories by topic, session, or user:

# Store memories tagged by source
tools = create_hindsight_tools(
    client=client,
    bank_id="user-123",
    tags=["source:chat", "session:abc"],
    recall_tags=["source:chat"],
    recall_tags_match="any",
)

Configuration Reference

Production Patterns

Error Handling

Tools surface errors to the agent as tool error results. The OpenAI Agents SDK catches exceptions from tools automatically and returns them as error strings, allowing the agent to handle failures gracefully:

from hindsight_openai_agents.errors import HindsightError

# The agent will see error messages and can decide how to proceed
result = await Runner.run(agent, "What do you remember about me?")
print(result.final_output)

Bank Lifecycle

Create banks before first use and clean up when done:

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

    # Create bank (idempotent)
    await client.acreate_bank(bank_id="user-123")

    tools = create_hindsight_tools(client=client, bank_id="user-123")
    # ... use tools ...

    # Optional: delete bank when no longer needed
    await client.adelete_bank(bank_id="user-123")

Multi-Agent Workflows

Give each agent its own memory bank, or share a bank across agents:

# Per-agent memory
researcher_tools = create_hindsight_tools(client=client, bank_id="researcher-memory")
writer_tools = create_hindsight_tools(client=client, bank_id="writer-memory")

# Shared memory across agents
shared_tools = create_hindsight_tools(
    client=client,
    bank_id="team-shared",
    tags=["team:content"],
)

Requirements

• Python >= 3.10
• openai-agents >= 0.7.0
• hindsight-client >= 0.4.0

Documentation

• Integration docs
• Hindsight API docs