cross-domain-agent-knowledge

name	cross-domain-agent-knowledge
title	Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2507.06229
keywords	["Agent Memory","Knowledge Transfer","Cross-Domain Learning","Agent Frameworks","Experience Sharing"]
description	Create a universal memory infrastructure enabling agents across different frameworks to share experience trajectories without retraining. Improve agent performance by retrieving workflows from related domains and applying diagnostic fixes.

Agent KB: Universal Knowledge Infrastructure for Heterogeneous Agents

Agent frameworks like smolagents and OpenHands operate in isolation, causing each agent to rediscover solutions and repeat mistakes independently. Agent KB establishes a shared knowledge repository that allows agents across heterogeneous frameworks to benefit from experience accumulated in other domains. By implementing hybrid retrieval (for planning seeds and feedback fixes) with a safeguard mechanism that prevents harmful knowledge transfer, the system achieves substantial improvements without requiring framework modifications or model retraining.

The core problem is knowledge silos: agents accumulate valuable trajectories but cannot share them across framework boundaries, creating duplicated effort and preventing collective intelligence from emerging.

Core Concept

Agent KB operates on three key principles:

1. Universal memory interface: Trajectories are compiled into a standardized knowledge base accessible via lightweight APIs, agnostic to underlying agent framework
2. Hybrid retrieval strategy: Planning stage retrieves seed workflows from other domains; feedback stage applies diagnostic corrections based on failures
3. Disagreement gating: Before applying retrieved knowledge, verify it aligns with the current task and agent's reasoning to prevent negative transfer

This architecture enables seamless knowledge sharing without requiring agents to be aware of framework differences.

Architecture Overview

• Knowledge aggregation layer: Collects trajectories from multiple frameworks and standardizes format
• Planning retriever: Seed-retrieves high-level workflows relevant to current problem
• Feedback fixer: Extracts diagnostic patterns from failed trajectories to suggest corrections
• Disagreement gate: Validator that ensures retrieved knowledge enhances rather than disrupts reasoning
• Lightweight APIs: Framework-agnostic interfaces for knowledge access
• Multi-framework support: Compatible with smolagents, OpenHands, LangChain, and custom agents

Implementation

Set up the knowledge base infrastructure to aggregate trajectories from multiple sources:

from agent_kb.knowledge_base import KnowledgeBase
from agent_kb.standardizers import TrajectoryStandardizer

# Initialize universal knowledge repository
kb = KnowledgeBase(storage=)

standardizer = TrajectoryStandardizer()


trajectories_smolagents = load_smolagents_trajectories()
trajectories_openhands = load_openhands_trajectories()

 trajectory  trajectories_smolagents + trajectories_openhands:
    
    standardized = standardizer.standardize(
        trajectory=trajectory,
        framework=  
    )

    
    kb.add_trajectory(
        trajectory=standardized,
        metadata={
            : trajectory.get(),
            : trajectory.get(),
            : trajectory.get(),
            : trajectory.get()
        }
    )

()