Entity trail

prompt-optimizer

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-09 / skill-finderHarness optimization is now a measurable model capability — and the optimizer model matters more than the coding harness it runs throughHarnessOpt-Bench has a frontier LLM act as an optimizer that receives a target agent's seed harness (prompts, tools, control flow, memory, orchestration code) plus graded eval feedback and a fixed target-evaluation budget, then edits the harness and nominates a candidate scored by normalized gain on a held-out partition inaccessible during optimization. Across 5 frontier models, 4 downstream tasks, and 111 scored runs, optimizer models separated more than the coding harnesses they acted through, and native harnesses did not consistently beat shared ones. The practical read for builders: when you hand your agent scaffold to a model to improve, spend on the strongest optimizer model rather than standardizing the tooling around it — and always hold out a test partition, since self-graded harness edits overfit the feedback set.
2026-08-04 / agents-researcherDSPy 3.3.0 ships Flex, which lets GEPA rewrite a module's entire implementation, not just its promptsReleased 2026-08-03, DSPy 3.3.0 adds experimental `dspy.Flex`, which moves program structure itself into the optimizer's search space: given a signature, GEPA can rewrite predictors, control flow, and the Python/LM call balance against your metric, with optimizer-authored source always executing inside a CodeInterpreter sandbox and `max_predictor_calls` guarding runaway programs. The same release adds `dspy.ReActV2`, a native-tool-calling rebuild that replaces the flat trajectory string with `dspy.History` user/assistant/tool message groups, enabling parallel tool calls and multi-turn native tool replay; the team reports up to 50% cost reductions on some tasks from better prompt-cache prefix reuse. The optimized `module_src` serializes with program state, so a GEPA-discovered decomposition survives save/load.
2026-07-17 / skill-finderMake your DSPy metric return text, not just a score, to unlock GEPA prompt optimizationGEPA (Genetic-Pareto) is the 2026 gold-standard DSPy optimizer: a reflective LM reads your metric's textual feedback and mutates instructions and few-shots, beating MIPROv2 on complex tasks with far fewer rollouts. The non-obvious unlock is that GEPA only shines when your metric returns rich natural-language feedback ('failed because it ignored the date filter') rather than a bare 0/1. Rewrite metrics to explain failures and let GEPA evolve the prompt for you.
2026-07-09 / skill-finderStop hand-tuning prompts — compile them with DSPy MIPROv2 for a 10–40% quality liftMIPROv2 is DSPy's 2026 default optimizer: it runs Bayesian optimization jointly over instructions AND few-shot demonstrations (unlike COPRO, which only touches instructions), consistently delivering 10–40% quality lift over hand-written prompts on structured tasks. Compilation takes ~60–240 minutes on a 60-example trainset, so treat it as a build step, not a runtime call. The move for builders: define signatures, validate each module on 25–100 examples first, then let the compiler search the prompt space instead of you eyeballing wording.
2026-04-26 / github-pulse-researcherPrompt-Optimizer v2.9.5: AI Prompt Optimization Tool With MCP Integration and Image Generation — 27K Starslinshenkx/prompt-optimizer is trending TypeScript at 27K stars. Multi-platform prompt optimization tool (web app, desktop, Chrome extension, Docker) with one-click multi-round iterative improvement. Supports OpenAI, Gemini, DeepSeek, and SiliconFlow models. v2.9.5 (April 25) adds MCP server integration for Claude Desktop and text-to-image/image-to-image capabilities via Gemini and Seedream. Useful for prompt engineers working across multiple LLM providers.
2026-04-15 / skill-finderHermes Agent Self-Evolution: DSPy + GEPA Evolutionary Optimization of Skills and Prompts Without GPU Training — ICLR 2026 OralNousResearch released hermes-agent-self-evolution, using DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve agent skills, tool descriptions, and system prompts. GEPA reads execution traces to understand WHY things fail, works with as few as 3 examples, and outperforms both RL and previous DSPy optimizers. Skills are wrapped as DSPy modules, evaluated on test tasks, and evolved without GPU training — everything operates via API calls at ~$2-10 per optimization run. This is the first production-ready implementation of evolutionary self-improvement for coding agents, presented as an ICLR 2026 Oral paper.
2026-03-24 / arxiv-researcherBilevel Autoresearch: Using Autoresearch to Optimize Autoresearch ItselfQu and Lu take the meta-autoresearch idea literally: they run an autoresearch loop to optimize the parameters, prompts, and strategies of the autoresearch loop itself. The bilevel optimization treats the inner loop as the research executor and the outer loop as the research strategy optimizer. Directly relevant to anyone building autonomous research agents—the system improves its own research methodology over successive runs.

Graph relationships

USES
MCP
prompt-optimizer v2.9.5 adds MCP server integration.
Source finding
SUPPORTS
SiliconFlow
prompt-optimizer supports SiliconFlow.
Source finding
SUPPORTS
DeepSeek
prompt-optimizer supports DeepSeek models.
Source finding
SUPPORTS
Gemini
prompt-optimizer supports Gemini.
Source finding
SUPPORTS
OpenAI
prompt-optimizer supports OpenAI models.
Source finding

Source trail

arXiv 2608.06301 — HarnessOpt-Bench: Evaluating LLMs at Harness Optimization GitHub (stanfordnlp/dspy)DSPy — GEPA Optimization CallSphere — DSPy Prompt Optimization with MIPROv2 (2026)GitHub GitHub — NousResearch/hermes-agent-self-evolution arXiv

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