Agent-ModernColBERT
by lightonai
150M late-interaction retriever optimized for agentic reasoning traces
lightonai/Agent-ModernColBERTmixpeek://text_extractor@v1/lighton_agent_moderncolbert_v1Overview
Agent-ModernColBERT is a 150M parameter late-interaction retrieval model from LightOn, specifically trained on agentic retrieval data where queries contain reasoning traces alongside the search intent. Built on ModernBERT architecture via PyLate, it achieves 72.53% accuracy on BrowseComp-Plus — exceeding configurations using GPT-5 + Qwen3-8B despite being 26x smaller than AgentIR-4B. This makes it uniquely suited for AI agent tool-use pipelines where the query is a chain-of-thought reasoning trace, not a clean user query.
Architecture
ModernBERT-based late-interaction model trained with PyLate on AgentIR data. Uses per-token 128-dim embeddings with MaxSim scoring, like ColBERT. The key innovation is training on reasoning trace + query pairs, so the model learns to extract search intent from noisy agentic context — function calls, intermediate thoughts, and partial conclusions.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "text_embeddings",
version: "v1",
parameters: { model_id: "lightonai/Agent-ModernColBERT" },
},
});Capabilities
- Retrieval from AI agent reasoning traces (not just clean queries)
- Late-interaction scoring for fine-grained token matching
- Tiny model footprint (150M) with outsized agentic performance
- Compatible with standard ColBERT indexing and serving
- Strong zero-shot transfer to general retrieval tasks
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| BrowseComp-Plus | Accuracy | 72.53% | Exceeds GPT-5 + Qwen3-8B setup |
| AgentIR | Retrieval Acc | Competitive with 4B models | At 150M params (26x smaller) |
Performance
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Specification
Build a pipeline with Agent-ModernColBERT
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Open Studio