RepKit — A Reputation SDK for AI Agents

Status: Pre-release — Star this repo to follow launch updates.

RepKit turns every agent interaction into an evaluation event. When Agent A delegates to Agent B, Agent A observes the outcome. That observation becomes data. Accumulated data becomes reputation.

Because a benchmark is a snapshot — reputation is a trajectory.

Full product overview at reputagent.com/repkit

The Problem

Over 40% of agentic AI projects will be canceled by 2027 (Gartner). Teams can't answer a simple question: "Can I trust this agent?"

Benchmarks measure capability at one moment. They don't tell you if an agent is consistent, how it handles edge cases, or whether it's improving over time.

RepKit makes continuous evaluation operational infrastructure — not a gate before deployment, but a system that runs during production.

How It Works

Interaction → Evaluation → Accumulation → Reputation

1. Interaction — Agent A delegates a task to Agent B
2. Evaluation — Agent A observes the outcome and logs it via RepKit
3. Accumulation — Evaluations aggregate across interactions and time
4. Reputation — Trust signals power routing, access, and governance decisions

API Preview

from repkit import RepKit

rk = RepKit(api_key="rk_...")

# Log an evaluation from an agent-to-agent interaction
rk.log_interaction_evaluation(
    interaction_id="txn-789",
    agent="agent-123",
    dimensions={
        "accuracy": 0.95,
        "safety": 0.88,
        "helpfulness": 0.93
    }
)

# Query reputation — accumulated from all evaluations
rep = rk.get_reputation("agent-123")
print(rep.score)        # 7.8
print(rep.trend)        # "improving"
print(rep.eval_count)   # 142

import { RepKit } from "@reputagent/repkit";

const rk = new RepKit({ apiKey: "rk_..." });

await rk.logEvaluation({
  interactionId: "txn-789",
  agent: "agent-123",
  dimensions: { accuracy: 0.95, safety: 0.88, helpfulness: 0.93 },
});

const rep = await rk.getReputation("agent-123");

What Reputation Powers

Design Principles

• Evidence over assertions — RepKit aggregates structured evaluation inputs over time, not single-run judgments
• Reputation over scores — Signals accumulate across interactions and versions, producing durable reputation
• Signals, not decisions — RepKit computes reputation signals; enforcement remains under your control

What RepKit Does Not Do

RepKit records evaluations, computes reputation, and exposes results via API. It does not:

• Mandate a specific judge model or evaluator
• Require a routing framework or agent runtime
• Enforce decisions — you remain in control

Built on Documented Patterns

RepKit implements concepts from the ReputAgent evaluation patterns library:

• LLM-as-Judge — Automated evaluation using language models
• Human-in-the-Loop — Human oversight for high-stakes decisions
• Reflection Pattern — Agents that evaluate their own outputs
• Red Teaming — Adversarial testing for robustness

Avoids documented failure modes:

• Sycophancy Amplification — Agents that agree rather than evaluate honestly
• Hallucination Propagation — Errors that cascade through agent chains
• Mutual Validation Trap — Agents that validate each other's mistakes

Related

• reputagent-data — Open dataset of 404 entries: failure modes, evaluation patterns, use cases, glossary, ecosystem tools, and research index
• Agent Playground — Pre-production testing where agents build track record through real multi-agent scenarios
• ReputAgent — The full platform for agent reputation and evaluation

Get Early Access

RepKit is in pre-release. Request early access at reputagent.com/repkit.

License

Apache-2.0 — see LICENSE.

Patent pending. RepKit represents one embodiment of the claimed inventions. Descriptions here are illustrative and do not limit the scope of current or future claims.