> ## Documentation Index > Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details. # Optimization Algorithms > What each optimization algorithm changes, what it needs, how many suite runs it costs, and when to use it. On this page we show which algorithm to pick for improving your agent, from the guided loop to the DSPy optimizers, when the metric is a scenario suite. The metric is what sets the cost: every metric call is one simulated conversation, so an optimizer that plans hundreds of calls costs hundreds of conversations. The table counts those calls on the six-scenario example of [Optimize with DSPy](/docs/improve-your-agent/optimize-with-dspy). ## Algorithms side by side | Algorithm | Changes | Needs | Metric calls on 6 scenarios | Tracked in LangWatch | Use when | | - | - | - | - | - | - | | Guided loop (Langy or your coding agent) | Prompt, tool descriptions, tool code, harness settings, one hypothesis per run | The suite and the traces | 6 to 12 per hypothesis | Every run in Agent Testing | The cause is in code, a tool or the harness, or you want to review each change | | BootstrapFewShot | Adds few-shot examples taken from passing runs | A metric | About 20 | Yes | The instructions are right and the model needs examples of the format | | BootstrapFewShotWithRandomSearch | Same, and searches over sets of examples | A metric | 50 to 100 | Yes | Few-shot examples help and you can afford the search | | COPRO | Rewrites the instructions, breadth-first then depth-first | A metric | About 50 with `breadth=3, depth=2` | Yes | Instructions only, on a small budget | | MIPROv2 | Proposes instructions and few-shot sets, then searches over the combinations | A metric | 52 zero-shot with `num_candidates=4, num_trials=6`: 10 to bootstrap, 42 over seven full evaluations. 42 minutes and \$0.93 on the example | Yes | The default choice for instructions, with or without examples | | GEPA | Rewrites the instructions from text feedback, keeps a Pareto front of candidates | A metric that returns a score and feedback text | Set `max_metric_calls`: 48 took 51 runs, 31 minutes and \$0.64 on the example | Yes | The judge's reasoning is the feedback, which is the scenario case | | SIMBA | Rewrites instructions and examples from self-reflection over mini-batches | A metric | About 60 with `max_steps=4` | No | Larger example sets, where mini-batches pay off | The instruction optimizers above (COPRO, MIPROv2, GEPA, SIMBA) rewrite the instructions of every predictor in the program, and on a `dspy.ReAct` program the instructions contain the tool descriptions, so those optimizers change the tools as well. `BootstrapFewShot` and `BootstrapFewShotWithRandomSearch` leave the instructions alone and only add demonstrations. See [Optimize with DSPy](/docs/improve-your-agent/optimize-with-dspy). ## Which algorithm to pick * **Start with the guided loop.** Most of the turns in a long conversation come from a tool contract or a harness setting, and an algorithm cannot change either, while one hypothesis in the guided loop costs one suite run. * **GEPA when the metric returns text feedback.** The scenario judge writes why a candidate failed, and GEPA's reflection reads that text and proposes the next candidate from it, without you reviewing each step. * **MIPROv2 when the metric is a number.** Use MIPROv2 with an evaluator that returns a score and no reasoning, or with a dataset of expected outputs; LangWatch tracks every trial. * **COPRO on a small budget**, when you want the instructions rewritten in a few calls and have no examples to add. * **BootstrapFewShot when the format is the problem**, meaning the instructions are right and the model gets the shape of the answer wrong. ## Tracking a run `langwatch.dspy.init(experiment=..., optimizer=optimizer)` records every trial of a tracked optimizer in **Experiments**, with the score, the instructions of each predictor and the examples it chose. The tracked optimizers are BootstrapFewShot, BootstrapFewShotWithRandomSearch, COPRO, MIPROv2 and GEPA. For any other optimizer, the [custom optimizer](/docs/dspy-visualization/custom-optimizer) page shows how to log a step yourself. **Also check:** [Improve your agent](/docs/improve-your-agent/overview) (the loop the algorithms run inside), [DSPy visualization](/docs/dspy-visualization/quickstart), [Prompt optimization with Langy](/docs/langy/prompt-optimization). This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.