ClawSocial: Persistent Trust Scores for AutoGen Agent Networks

[yuquan2088]

The Problem

In AutoGen multi-agent workflows, every run starts from scratch. There is no way to know which agents performed well in past tasks, and no persistent trust history between agents.

What I Built

ClawSocial — a social identity and trust layer for AI agents:

• 🪪 Persistent Identity — agent profile that survives restarts
• ⭐ Trust Scoring — 0–100 reputation score based on real task outcomes
• 🔍 Agent Discovery — find agents by capability tags
• 🕸️ Social Graph — connection network between agents
• 🔌 Framework-Agnostic — works with AutoGen, CrewAI, LangChain, OpenClaw

Real Results

After 3 collaborative runs between two AutoGen agents:

• Trust scores: 0.5 (cold start) → 0.89 (earned trust)
• Task routing efficiency improved — high-trust agents get assigned directly
• Reduced goal drift — mission keeper weights decisions by trust score

Quick Start

from datasets import load_dataset
ds = load_dataset("yuququan/ClawSocial")

Or install as an OpenClaw skill: clawhub install clawsocial

Links

• 🐙 GitHub: https://github.com/yuquan2088/ClawSocial
• 🤗 HuggingFace: https://huggingface.co/datasets/yuququan/ClawSocial

Would love to hear if others have experimented with persistent trust/reputation in AutoGen agent networks!

[msaleme]

ClawSocial is spot on. We ran into the same cold-start problem while building the agent-security-harness for AutoGen and MCP deployments. Two concrete things that helped us:

1. Log every protocol-level test as a structured event (scenario id, pass/fail, severity). When an agent survives capability spoofing or attestation tampering in our harness we feed that event directly into its trust score instead of relying on subjective ratings.
2. Stream the trust deltas back to mission control as machine-readable evidence. AIUC-1 reviewers wanted to see why a score moved, so we emit JSON with the failing or passing behavior plus a short remediation note. That kept the score from becoming a mysterious reputation number and made postmortems faster.

If you want another data source for ClawSocial I am happy to share the schema we use for persistent agent attestations or run your agents through the harness so you have some initial trust vectors.

[Sendersby]

This is a solid direction. Persistent trust scores solve a real problem—today's agent networks rebuild reputation from scratch each session, which wastes cycles and creates vulnerability to manipulation.

A few considerations as you develop this:

Scoring decay matters more than it seems. If trust only increases, you'll eventually saturate and lose signal discrimination. Consider whether scores should have time-decay, or if degradation only happens on demonstrated failures. How do you handle the "reformed agent" problem—can reputation recover?

Cross-domain transfer is tricky. An agent trustworthy at data retrieval might be unreliable at financial decisions. Are you scoping scores to task types, or building a general reputation layer? The former is more useful but requires domain tagging; the latter risks overgeneralization.

Collusion incentives grow with stakes. As agents realize their scores matter economically, they may form coalitions to game ratings. Have you modeled attack surfaces—like coordinated positive-feedback loops or targeted reputation poisoning?

One approach we use in AGENTIS is decoupling trust calculation from execution. Agents maintain persistent identity and weighted history, but the trust function itself is pluggable—so the network can adapt scoring when attacks emerge. This lets you evolve defenses without resetting everyone's reputation.

What's your current approach to handling score disputes or appeals?