ColGrep-Agent-2B is a lightweight code-localization agent designed to identify the files and line ranges relevant to a GitHub issue, bug report, feature request, or natural-language question about a repository.
Given a question such as "Where are retries configured?" or "Why does the export crash on empty files?", the agent iteratively searches the repository using ColGrep, reads candidate files through a read-only terminal, and returns the precise code locations relevant to the task.
The model powers colgrep --agent and is designed to offload repository exploration from larger coding agents. Rather than consuming thousands of tokens searching through files before making a fix, a larger model can delegate code localization to ColGrep-Agent-2B and receive a focused list of relevant locations.
Its small footprint enables practical local inference, with GPU acceleration when available, without requiring Python, a separate inference server, or an API key.
The colgrep --install-agent command downloads the default colgrep-agent-2B-Q8_0.gguf model (approximately 2.5 GB), verifies that it loads, and prepares the prompt cache to accelerate the first inference.
Installation is required only once. No Python environment, external inference server, or API key is necessary.
colgrep --agent "where does the agent decide which GPU backend llama.cpp uses"
The following example shows a real session on ColGrep's own source code, running on an M3 Pro with Metal acceleration. Individual tool results have been omitted for readability.
The output follows the path/to/file:START-END format, making it directly usable by developers, scripts, and other coding agents.
Agent progress is printed to stderr only when running in a terminal, while stdout contains only the resulting code locations. This separation makes the command easy to compose with other tools.
Additional usage examples
Scope the search to a directory and display the matching code lines:
colgrep --agent "crash when the config file is empty" ./backend -c
Return structured JSON for downstream applications:
colgrep --agent --json "where are retries configured"
ColGrep can be integrated into existing coding-agent workflows, allowing larger models to delegate repository exploration to a smaller specialized model.
For example, once configured, Claude Code can delegate open-ended questions such as "Where is this behavior implemented?" to colgrep --agent, receiving exact file paths and line ranges instead of performing every exploratory search itself.
This division of labor allows larger coding agents to reserve more of their context and computation for understanding issues, planning changes, and generating patches.
ColGrep is a semantic code search tool for terminals and coding agents, built on the NextPlaid search engine. It combines natural-language search with regex filtering to find code by both meaning and exact matches.
ColGrep-Agent-2B extends this search functionality with an iterative search-and-read loop. Rather than relying on a single search query, the agent follows leads, inspects candidate files, refines its searches, and identifies relevant code locations over multiple turns.
ColGREP-Agent-2B is built on MiniCPM5-2B and trained in two stages: a short supervised fine-tuning (SFT) cold start followed by reinforcement learning (RL) using GRPO.
We construct multilingual code-localization environments from nebius/SWE-rebench, internlm/SWE-Fixer-Train-110K, and additional scraped repositories covering multiple programming languages. Each training instance pairs a repository with an issue description and a dedicated bug-fix commit. We use the pre-patch repository state as the search environment and derive gold file- and line-level localization targets from the corresponding patch. These data sources are used across both the SFT and RL stages.
We first perform a short cold-start SFT phase using traces generated and filtered with Qwen3.5-27B as a teacher model. This stage teaches tool-call formatting, command ordering, argument and flag usage, and effective repository exploration strategies, with an emphasis on semantic search through ColGrep rather than traditional grep-based search. We then train for 500 steps using GRPO in a dedicated code-localization environment, rewarding accurate file- and line-level identification while penalizing unnecessary exploration steps.
The model operates in a repository exploration environment with access to three tools:
Tool
Purpose
colgrep
Search code semantically, with optional regex and path filters.
terminal
Read files and inspect the repository through read-only commands.
finish
Submit relevant locations as path/to/file:START-END.
The agent has a 10-turn budget to search and inspect the repository before submitting its final prediction as a set of relevant file paths and line ranges.
We evaluate ColGREP-Agent-2B on SWE-bench Lite and Multi-SWE-bench Flash, adapting both benchmarks into code-localization tasks following the methodology described in our paper. Rather than generating patches or resolving issues end-to-end, the model is tasked with identifying the files and lines of code relevant to each issue. This allows us to evaluate localization accuracy independently of code generation.
| Model | Harness | SWE-bench Lite | Multi-SWE-bench Flash
All models use the ColGREP harness. File and line F1 measure overlap with reference files and lines, respectively. Scores are percentages averaged over five seeds (300 instances per benchmark per seed), using strict predictions without rescue, a 10-turn limit with mandatory final submission, and temperature 0.6 without reasoning for comparison models.
@misc{colgrepagent_2026,
title = {ColGrep-Agent},
author = {Lasbordes, Maxence and Sourty, Raphael and Sinha, Aarush and Chatelain, Amélie},
year = {2026},
howpublished = {\url{https://huggingface.co/blog/lightonai/colgrep-agent/}}
}
Files
13 files · 5.05 GBWeights 5.03 GB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("lightonai/colgrep-agent-2B", revision="f798a61129c6decb5166864e0a3c98373e62c764")
model = AutoModelForCausalLM.from_pretrained("lightonai/colgrep-agent-2B", revision="f798a61129c6decb5166864e0a3c98373e62c764", device_map="auto")
