ColGrep-Agent-2B Guide: Find Code Locations Before Writing a Patch

ColGrep-Agent-2B helps with the step before a coding patch: finding the files and line ranges that matter. That is useful when a repository is unfamiliar, a bug report names a behavior rather than a function, or a coding assistant needs a smaller evidence set to inspect.

The appropriate acceptance test is whether the returned locations explain the requested behavior. It is not whether the model writes a correct patch. This guide covers documented local setup and a review workflow for handing located code to your normal development tools.

What ColGrep-Agent-2B is built to return

LightOn's card describes a MiniCPM5-2B-derived model specialized for code localization. It searches and inspects repository content, then returns file paths with line ranges. The CLI integrates it through colgrep --agent, using local GGUF inference. The default documented Q8_0 artifact is about 2.5 GB. Official model card.

The reported localization evaluations concern file and line selection. They do not establish a patch-resolution rate for an autonomous coding agent. A correct location can still be followed by a wrong edit, and a useful patch may need more context than the first returned span.

Keep this scope in your interface. Label the output as “candidate locations” until a developer has inspected the spans. If you pass them to another assistant, include the question and repository revision so the evidence can be interpreted correctly.

Follow the documented local setup

The model card's Homebrew path installs ColGrep from LightOn's tap and then prepares the local agent:

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

These are documented commands for a reader's environment; we have not installed or executed them as part of this guide. Check the current CLI instructions for your operating system and confirm where the model artifact and search index will be stored. ColGrep project documentation.

Record the CLI version, model file and repository commit before an evaluation. Use a small repository you are entitled to inspect. Check the tool's repository scope and exclusions before pointing it at a larger workspace with generated files, dependencies or unrelated projects.

The default artifact size helps plan a download, but it is not peak memory for a full localization run. Observe indexing, model loading and search separately if you are deciding whether the tool suits a laptop development workflow.

Ask for a behavior, then verify the locations

A useful question names the observable behavior and the area of uncertainty. For example: “Where is an expired session token rejected before an account settings request reaches the handler?” This gives the search a concrete path to follow without assuming a function name.

Run the question through colgrep --agent using the syntax documented by your installed version. Save the returned paths and ranges with the query. Open each result in an editor and verify that the lines exist at the recorded revision.

Look beyond an isolated matching word. For the session example, the relevant evidence may include middleware, a token validation function and the route that invokes it. A file mentioning “session” in a comment is not enough. Trace the caller and the outcome of rejection.

Keep exact search available alongside semantic localization. Error strings, configuration keys and function names can provide direct anchors. If the query includes a literal identifier, confirm it appears in the returned code instead of assuming a semantic match is sufficient.

Build a localization test set from your own repository

Ask a maintainer to choose questions with known answers. Include one narrow function lookup, one behavior spread across several files and one obsolete feature that no longer exists. The last case checks whether the tool can avoid presenting unrelated code as a confident answer.

For each question, define acceptable files and the evidence that must be present. Avoid requiring one exact line span when nearby context is equally useful. Also flag excessive spans that force a reviewer to read hundreds of irrelevant lines.

Score usefulness alongside coverage. A result that includes every source file may contain the answer, but it has not reduced the developer's search burden. Record review time only if you actually measure it; do not convert a good-looking example into a productivity percentage.

Hand evidence to a coding assistant carefully

Create a compact handoff containing the behavior question, commit identifier, candidate paths and a short explanation of why each span matters. Let the coding assistant inspect current source before editing. Line numbers can drift after another developer's change.

Separate source discovery from permission to modify files. A read-only search result should not trigger an automatic patch simply because the next tool has write access. When a change is authorized, keep normal tests and code review in the workflow.

If you switch model files or update ColGrep, repeat the known-question set. Also rebuild or refresh an index when the CLI requires it after repository changes. A stale search result can look like a model mistake while pointing accurately to code that existed earlier.

The related MiniCPM5 guide is useful background for the base-model family, but ColGrep-Agent's task-specific behavior and CLI integration need their own evaluation.

Worked repository question: finding a retry policy

Consider a hypothetical service that calls an external API and retries some failures. The bug report says requests with an invalid credential appear to retry several times before returning an error. The developer needs to locate the retry decision and understand where authentication errors enter that decision.

Write the localization question around the behavior: “Where does this service decide which API failures should be retried, and how are authentication failures classified?” Avoid supplying a guessed filename. Keep the repository commit and the bug report's exact error message with the query.

The useful evidence set may contain the client wrapper, an error classification helper and a caller that sets retry limits. Inspect whether the returned spans cover those roles. A single function named retry might belong to an unrelated background job and therefore fail the question.

Trace a concrete path through the located code. Identify the error object created for an authentication failure, the conditional that categorizes it and the loop or library that repeats the call. Write the chain in your review note. This turns a candidate span into evidence for a proposed change.

Now add a second query asking where the retry limit comes from. A configuration default, environment variable and per-request override can all matter. The answer is useful only if it shows which value reaches the failing request, not merely where a variable with the same name is declared.

Before handing the evidence to a patching assistant, state the uncertainty that remains. For example, the classification code may be located while the actual vendor error shape is unknown. That remaining question needs a fixture or log review, not another confident guess about the repository.

Evaluate precision, coverage and inspection burden

Define an expected evidence set for each known question. Use maintained implementation files and relevant tests where possible. Keep generated clients and dependency copies separate because they can resemble the code while offering the wrong place to make a change.

Coverage asks whether the returned set contains the required evidence. Precision asks how much of the returned set is relevant. A result with every file has broad coverage but poor precision; a narrow span in one helper can be precise while missing the caller that changes its behavior.

Line-level review also needs context. A conditional may be the important line, but a reviewer needs its inputs and the consequence of taking each branch. Define acceptable ranges that include those dependencies rather than requiring a bare line number as the only correct answer.

Measure inspection burden only through actual review. You can record how many candidate files were opened and how many irrelevant spans were discarded. If you measure review time, describe the reviewer and task conditions. Do not extrapolate a small familiar repository into a general percentage of engineering time saved.

Keep failed questions in the set. The most informative failures often involve vague behavior, symbols that changed names or several implementations of the same concept. Those cases tell you whether better question wording, exact search or a fresh index is the appropriate next step.

Compare with exact search on the same questions

Use a literal search when the bug report includes a distinctive string. Search the message, configuration key or symbol and record the maintained source locations. Then compare the agent's candidate set with that direct anchor. This helps determine whether semantic localization adds useful context beyond an obvious match.

For behavior-only questions, ask a maintainer to identify the expected code without disclosing the path in the query. That avoids a test in which the answer is already embedded in the prompt. Include a few queries that use the user's vocabulary rather than the implementation's terminology.

Do not treat exact search and agent localization as competitors that require one universal winner. Literal anchors can be efficient for known identifiers; a localization loop can help trace behavior across files. Use the route that reduces uncertainty for the particular question.

Record the repository revision and index state for both methods. Comparing an agent's stale index with exact search on a newer working tree can produce a misleading conclusion about model quality. If there are uncommitted changes, record whether each search path can see them.

When a question refers to an old feature, inspect version history separately. A current-code search may correctly fail because the implementation was removed. Do not ask the agent to manufacture a present location for a historical behavior. Frame a new question around the relevant commit or branch instead.

Keep repository scope and generated code visible

A monorepo can contain several services, test fixtures and multiple versions of a client. Start with the application area relevant to the question, then expand deliberately. If scope is too narrow, record the missing dependency; if it is too broad, watch for similarly named components in unrelated packages.

Read the CLI's exclusion and indexing instructions for the installed version. Check how generated files, vendored dependencies and ignored paths are handled. Do not assume that every file excluded from an index is irrelevant to runtime behavior; occasionally a generated schema explains an important contract even though the maintained source is elsewhere.

Create a fixture repository for evaluating source changes. Rename a function, move a module and remove an obsolete file, then refresh the tool as documented and repeat the known questions. These tests show whether the search results follow current source rather than silently returning old line ranges.

Keep candidate locations relative to the repository root. An absolute path from one developer's machine can be unusable in another checkout. Pair the relative path with the commit identifier and re-open the current file before editing.

For private code, review the tool and any connected integrations according to your development policy. The documented local GGUF path is one component. Do not infer that every plugin or subsequent coding assistant uses the same data path. Keep those integrations explicit in the handoff.

Turn a location result into a reviewable patch brief

Write a patch brief containing the observed behavior, the located evidence chain and the intended change. Include a concrete acceptance case, such as “an invalid credential returns after one classified failure,” only when that behavior is the approved requirement. The localization model does not decide the product's policy.

List the tests that should change or be added, and explain what uncertainty each resolves. A retry classification fix may need an authentication failure fixture, a retryable server-error fixture and a limit case. Those test ideas arise from the behavior trace rather than from the model's confidence.

Ask the editing workflow to inspect the files at the current revision and report any mismatch with the brief. If lines have moved or another developer already changed the classification, update the brief before applying a patch. Evidence should guide the work without becoming a stale instruction.

Keep the localization result in the change record when it helps reviewers understand how the affected path was found. It is an aid to review, not a correctness certificate. The final patch still needs its own tests and human assessment of the intended behavior.

Conclusion

Adopt ColGrep-Agent-2B when its candidate locations reduce the code you need to inspect. Keep the question, revision and reviewed spans together, then use your established coding and testing process for the patch. Localization is a valuable deliverable even when editing remains a separate step.

FAQ

Does ColGrep-Agent-2B fix bugs by itself?

Its documented specialization is code localization. Treat returned files and lines as evidence for a separate editing workflow.

Does the local agent require a hosted model API?

The documented CLI route runs a local GGUF model. Check your CLI configuration and any additional integrations separately.

How should I judge a successful result?

Verify that the spans exist, explain the requested behavior and omit unnecessary context. Then inspect callers and dependencies before proposing a patch.