Model reference · open weights

colgrep-agent

LLMs lightonai Text gen 2 builds Open weights 21 dl/mo

colgrep-agent is an open-weight language model from lightonai. colgrep-agent-2B (BF16) weighs 5.0 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • ColGrep-Agent-2B is a 2.5B parameter text-generation model by lightonai designed to locate specific files and line ranges within a code repository.
  • It supports a context length of 131072 tokens and operates in English under the apache-2.0 licence.

Summary of the lightonai/colgrep-agent-2B model card, 2026-10-12

What it is

Released bylightonai
Released2026-10-03
Parameters2.5B
VRAM5.0 GB for the weights

What it runs on

Memory and cards for colgrep-agent-2B (BF16)

5.0 GBweights, file size
43 MBcache per 1K tokens
647 MBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB164all 128K11.6 GB
RTX 4060 Ti 16 GB276all 128K15.4 GB
RTX 3090 24 GB5012all 128K23.4 GB
RTX 4090 24 GB5012all 128K23.4 GB
RTX 5090 32 GB7117all 128K31.0 GB
L40S 48 GB10827all 128K44.0 GB
A100 80 GB20551all 128K78.2 GB
H100 80 GB19448all 128K78.1 GB
RTX PRO 6000 Blackwell 96 GB23959all 128K93.8 GB
DGX Spark (GB10) 128 GB unified27769all 128K107 GB
H200 141 GB36491all 128K138 GB
B200 180 GB472118all 128K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
16.0 GB7.1 GB
57.4 GB12.7 GB
88.5 GB17.0 GB
1611.3 GB28.2 GB
3217.0 GB50.8 GB
6428.2 GB95.9 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
colgrep-agent-2B (above) ↗ 2.5BBF16 5.0 GBRTX 3060 12 GB
colgrep-agent-2B-GGUF ↗
7 builds: Q4_0 1.5 GB … F16 5.0 GB
GGUF 1.6 GBRTX 3060 12 GB

From the model card

What lightonai says about colgrep-agent

Read the model card

📄 Paper | 📝 Blog Post | 💻 GitHub | 🤗 Model | 📦 GGUF

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.

🚀 Quick start

1. Install ColGrep and the model

Homebrew (macOS / Linux)

brew install lightonai/tap/colgrep && colgrep --install-agent

Shell installer (macOS / Linux)

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.sh | sh && "${CARGO_HOME:-$HOME/.cargo}/bin/colgrep" --install-agent

Windows (PowerShell)

irm https://github.com/lightonai/next-plaid/releases/latest/download/colgrep-installer.ps1 | iex
colgrep --install-agent

Cargo (from source)

cargo install colgrep && colgrep --install-agent

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.

2. Index a repository

cd /path/to/project
colgrep init

After the initial indexing, subsequent searches automatically detect modified files and update the index before answering.

3. Ask a question

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.

$ colgrep --agent "where does the agent decide which GPU backend llama.cpp uses"
◆ colgrep agent  · colgrep-agent-2B-Q8_0.gguf (GPU)
❯ where does the agent decide which GPU backend llama.cpp uses

  ● colgrep   llama.cpp GPU backend
  ● terminal  cat -n agent/src/llm/llama.rs
  ● terminal  cat -n agent/src/llm/server.rs
  ● terminal  cat -n agent/src/engine.rs
  ● terminal  cat -n agent/src/llm/llama.rs | sed -n '80,150p'
  ● terminal  cat -n agent/src/llm/server.rs | sed -n '65,130p'
  ● colgrep   find_gpu
  ● terminal  cat -n agent/src/llm/server.rs | sed -n '1,100p'
  ● finish    agent/src/llm/server.rs:103-105, agent/src/llm/server.rs:65-69, …

✓ 5 locations · 9 turns · 2 searches · 6 terminal commands · 21.7s

agent/src/llm/server.rs:103-105
agent/src/llm/server.rs:65-69
agent/src/llm/llama.rs:101-101
agent/src/llm/llama.rs:115-116
agent/src/engine.rs:57-67

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"

🤖 Integration with coding agents

ColGrep can be integrated into existing coding-agent workflows, allowing larger models to delegate repository exploration to a smaller specialized model.

Install the corresponding hooks and skills:

colgrep --install-claude-code   # Claude Code
colgrep --install-codex         # Codex
colgrep --install-opencode      # OpenCode

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

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.

🧠 Training and harness

ColGREP-Agent-2B is built on MiniCPM5-2B and trained in two stages: a short supervised fine-tuning (SFT) cold start foll

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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