Braintrust is the best Promptfoo alternative for teams that have moved past local testing and need to measure AI agents in production. Braintrust gives you detailed traces across every agent step, evaluations that run against live traffic and plug into CI/CD, and a shared interface where PMs and engineers collaborate on prompt iteration.
For solo developers who just want to run evals locally, the best open-source alternatives are: DeepEval for Python-native pytest metrics and RAGAS for RAG-specific retrieval and generation scoring.
Promptfoo (10.8k GitHub stars) is a CLI-first prompt testing and red-teaming tool. It runs locally, uses YAML configs stored in your repo, and scans for 50+ vulnerability types. For a solo developer testing prompts on a laptop, it is a solid option.
The friction starts when a second engineer asks, "Where do I find the results from last week's eval run?" While Promptfoo writes results to local files or CI artifacts, there is no persistent dashboard, no shared experiment history, and no way for a PM to stay in the loop on the latest eval run. While a great fit for local evals, production evaluation -- the part where you score live traffic and catch regressions after deployment -- is not covered.
Here is where the gaps show up concretely:
None of this makes Promptfoo bad. It makes it a testing tool for the pre-deployment phase. If your needs have grown past that into production scoring, team collaboration, or release governance, you need something else.
Promptfoo is a great open-source platform for a single developer looking to run evals locally. For solo developers looking for alternatives to compare, the open-source platforms below can address those needs.
What it is: A Python-native LLM evaluation framework built on pytest. Where Promptfoo uses YAML assertions, DeepEval uses Python test cases with typed metric objects.
GitHub: 13.9k stars | License: Apache 2.0 | Language: Python
DeepEval ships 50+ built-in metrics including G-Eval, hallucination detection, answer relevancy, contextual recall, and faithfulness. Each metric returns a score between 0 and 1 with a natural-language explanation, which makes debugging faster than reading pass/fail assertions.
Where DeepEval goes beyond Promptfoo:
Where it falls short: Python-only. The framework funnels users toward Confident AI, its hosted commercial layer, for dashboards and team features. The open-source version has no persistent UI.
Best for: Python-focused teams who want out-of-the-box metrics inside their existing test suite.
What it is: A metrics library for evaluating retrieval-augmented generation pipelines. Not a full eval framework -- a focused scoring toolkit.
GitHub: 12.8k stars | License: Apache 2.0 | Language: Python
RAGAS provides metrics Promptfoo does not have built in: context precision, context recall, faithfulness, answer relevancy, and noise sensitivity. These score the two stages of a RAG pipeline separately. Context precision and recall measure whether the retriever pulled the right documents. Faithfulness measures whether the generator's answer is grounded in those documents rather than hallucinated.
The original RAGAS paper introduced reference-free evaluation, meaning you can score outputs without manually writing ground-truth labels for every test case. For teams with hundreds of documents in their knowledge base, this removes the biggest bottleneck in setting up evals.
At Braintrust, we support RAGAS metrics in our eval pipelines. Running RAGAS locally is a great way to measure some local RAG evals, but if you want to run these in production, you can give it a go on Braintrust.
Where RAGAS goes beyond Promptfoo:
Where it falls short: No UI, no experiment tracking, no CI/CD integration, no production monitoring. You call it from a Python script and get scores back. It only applies to RAG pipelines. Chatbots, agents, and classification systems without a retrieval step will not benefit.
Best for: Teams running RAG pipelines who need targeted retrieval and generation quality scores. Pair it with Braintrust for the evaluation infrastructure.
Open-source tooling is great for the core use case of one developer running offline evals on their machine. As companies scale, their needs change. While testing a prompt locally before shipping may work at the start, once your agent is in production, you need to actively track logs, monitor quality, view individual traces, and allow the team to iterate on the prompts.
Open-source tools fundamentally do not support this level of collaboration without hosting the entire infrastructure yourself. Once you hit any sort of scale, it becomes important to look at tooling that tracks production traces and gets the whole team involved.
Braintrust is the graduation path from the open-source tools covered above. It connects evaluation to the rest of your development lifecycle. Where Promptfoo runs tests locally on your test data, Braintrust runs evals at all stages of the development cycle. Developers can run evals locally, engineering leadership can verify that changes do not degrade quality in CI/CD, and the whole company has visibility into the performance of your agent in production.
The production-to-eval pipeline. Braintrust captures production traces through native integrations with OpenTelemetry, the Vercel AI SDK, OpenAI Agents SDK, LangChain, Google ADK, and more (25+ framework and SDK integrations total). Those traces flow into datasets that you can filter, annotate, and use as evaluation inputs. When a user reports a bad response, you pull that trace into a test case with one click. Over time, your eval suite is built from real failures, not synthetic guesses.
Loop AI. Loop is Braintrust's AI assistant for evaluation workflows. Use natural language to analyze production logs, generate datasets from log patterns, create scoring functions based on identified issues, and generate filters to surface problems. A product manager can type "create a dataset from logs with errors" and Loop builds it. This eliminates the cold-start problem where teams know they need evals but nobody wants to spend a week writing test cases.
CI/CD quality gates. Braintrust's GitHub Action runs evaluations on every pull request and blocks merges if scores drop below thresholds you define. This works like type checking or lint rules but for LLM output quality. A prompt change that regresses faithfulness by 5% gets flagged before it reaches production, not after.
Brainstore. Braintrust built a custom high-performance datastore optimized for evaluation data. It uses object storage and a streaming Rust engine to load spans in real time. Queries against large experiment histories run up to 80x faster than traditional databases. When you are comparing 50 experiments across thousands of test cases, the speed difference between waiting 30 seconds and getting results in under a second changes how you work.
Playground. The Braintrust Playground lets engineers and product managers test prompts against real production data side by side. Compare model outputs, tweak system prompts, and immediately run evaluations on changes. The playground is not a toy sandbox. It operates on the same datasets and scorers you use in CI, so what works in the playground works in the pipeline.
| Capability | Promptfoo | Braintrust |
|---|---|---|
| Eval execution | CLI, local or CI | CLI, CI, web UI, API |
| Results storage | Local JSON / CI artifacts | Persistent cloud with 80x-fast query engine |
| Production monitoring | No | Yes, via trace ingestion and online scoring |
| CI/CD quality gates | GitHub Action (pass/fail) | GitHub Action with threshold-based merge blocking |
| Prompt management | YAML config files in repo | Versioned prompts with environment-based deployment |
| Team collaboration | Git-based (config files) | Web UI with shared experiments, annotations, datasets |
| AI-assisted evaluation | No | Loop AI generates datasets, scorers, and filters from logs |
| Red teaming / security | 50+ vulnerability plugins | Via integration (red teaming is not Braintrust's focus) |
| Framework integrations | Model-agnostic via providers | 25+ native integrations (OpenTelemetry, Vercel AI SDK, LangChain, etc.) |
| Pricing | Free OSS / Enterprise (contact sales) | Free (1M spans, unlimited users) / Pro ($249/mo flat) / Enterprise |
| Plan | Cost | What you get |
|---|---|---|
| Free | $0 | 1M trace spans/month, unlimited users, core eval features, 14-day retention |
| Pro | $249/month (flat) | Unlimited spans, 50K scores, 30-day retention, priority support |
| Enterprise | Custom | SSO, dedicated support, self-hosted deployment options |
| Tool | Type | Starting price | Best for | Key differentiator |
|---|---|---|---|---|
| Promptfoo | OSS CLI | Free (Enterprise: contact sales) | Solo devs, red teaming, CLI-first testing | 50+ red-team vulnerability plugins |
| DeepEval | OSS Framework | Free (Confident AI: contact sales) | Python teams, pytest-based eval, metric depth | 50+ research-backed metrics with explanations |
| RAGAS | OSS Library | Free | RAG pipeline scoring | Reference-free retrieval + generation metrics |
| Braintrust | Hosted Platform | Free (Pro: $249/mo) | Teams needing full eval lifecycle + production monitoring | Production to eval to deploy loop with Loop AI |
Start by identifying which stage of the eval lifecycle you are stuck at.
If you are a solo developer testing prompts before committing code, Promptfoo's CLI and YAML configs are fast and free. You do not need a platform yet. Run npx promptfoo@latest init and go.
If you are a Python team that wants structured metrics inside your test suite, DeepEval drops into pytest and gives you 50+ scored metrics with explanations. Pair it with RAGAS if you are building a RAG pipeline and want retrieval-specific scoring on top.
If you need production visibility into what your LLM app is actually doing, Braintrust gives you tracing, cost tracking, and prompt versioning. You can use the managed cloud or explore self-hosted deployment on the Enterprise plan if data residency matters.
If your team has outgrown local eval and needs evaluation connected to production, deployment, and collaboration, Braintrust is the answer. It is the only tool on this list that closes the loop between what happens in production and what you test in CI. Loop AI removes the cold-start problem. Quality gates prevent regressions from shipping. And the free tier is generous enough (1M spans, unlimited users) that you can evaluate it on real workloads before deciding.
Promptfoo got you started. It made evaluation feel like writing tests, which was the right mental model for a solo developer shipping their first LLM feature. The problem is that LLM quality does not end at merge. It starts there.
Production traffic surfaces failure modes that synthetic test cases miss. Regressions happen when a model provider updates weights or a teammate edits a system prompt. Quality drift is invisible unless you are scoring live responses and comparing them to baselines.
Braintrust connects those stages. Production traces become evaluation datasets. Evaluation scores gate deployments. Deployment outcomes feed the next iteration. Notion, Stripe, and Vercel run this loop in production today. The free tier gives you 1M trace spans and unlimited users, enough to build the workflow before committing budget. If your team is shipping LLM features to real users and you want to know whether they are actually working, that is where Braintrust starts.
Promptfoo is built for local, CLI-driven evaluation and red teaming. It works well for individual developers testing prompts before committing code. Teams typically look for alternatives when they need persistent dashboards to compare experiments across team members, production monitoring to score live traffic, or collaboration features that let non-engineers participate in the evaluation workflow. Braintrust addresses all three by connecting production data to evaluation and providing a shared web UI alongside CLI and API access.
For teams that need evaluation connected to production monitoring and deployment governance, Braintrust is the strongest alternative. It closes the loop between what happens in production and what you test in CI, with features like Loop AI for automated dataset and scorer generation, quality gates that block regressions from merging, and a persistent experiment history queryable through Brainstore. The free tier includes 1M trace spans and unlimited users, so teams can evaluate it on real workloads without financial commitment.
The two tools solve different problems. Promptfoo is a testing framework focused on pre-deployment eval and security scanning, with strong red-teaming capabilities across 50+ vulnerability types. Braintrust is a platform that connects evaluation to production monitoring, deployment gates, and team collaboration. When comparing them directly: Braintrust stores results persistently rather than in local files, provides a web UI for non-technical team members, offers AI-assisted evaluation through Loop, and monitors production quality after deployment. Promptfoo has stronger built-in red-teaming and runs with zero cloud dependencies. If security scanning is your primary need, Promptfoo wins. If end-to-end evaluation governance is the goal, Braintrust is the better fit.
The answer depends on your stack and stage. For Python teams that want metrics inside pytest, DeepEval offers the deepest library of scored, explained evaluation metrics. For RAG-specific pipelines, RAGAS provides targeted retrieval and generation quality scores. For the full evaluation lifecycle connecting production data, testing, deployment gates, and team collaboration, Braintrust is the most complete platform available. Its combination of Loop AI, CI/CD quality gates, and production monitoring makes it the right choice for teams shipping LLM features at scale.
Yes. Some teams use Promptfoo for red-teaming and security scanning during development while using Braintrust for production monitoring, evaluation management, and deployment governance. Promptfoo handles adversarial testing before code is merged. Braintrust handles quality scoring after deployment and provides the persistent experiment history and collaboration layer that Promptfoo does not offer. The two tools do not conflict because they operate at different stages of the lifecycle.
Start by identifying what you are trying to measure. If you are testing whether prompts produce correct outputs, begin with Promptfoo or DeepEval to run assertion-based or metric-based tests against a small set of examples. If you are running a RAG pipeline, add RAGAS metrics to score retrieval and generation quality separately. Once you have production traffic, set up Braintrust to trace live requests and surface quality issues you did not anticipate in testing. The Braintrust free tier includes 1M spans and requires no credit card, making it a low-friction starting point for production-stage evaluation.
Testing checks whether a specific prompt produces an expected output given a specific input. It is binary: pass or fail. Evaluation scores the quality of outputs across multiple dimensions (accuracy, faithfulness, relevance, safety) using numeric metrics. Testing tells you "this prompt broke." Evaluation tells you "this prompt scores 0.72 on faithfulness, down from 0.85 last week." Promptfoo sits closer to the testing end of this spectrum with its assertion-based approach. Braintrust and DeepEval sit closer to the evaluation end, with scored metrics, explanations, and historical comparison across experiments.
Promptfoo is still one of the strongest open-source options for LLM red teaming, with 50+ plugins covering prompt injection, PII leakage, jailbreaks, and more. DeepEval added red-teaming support for 40+ vulnerability categories and runs natively in Python. For teams that need red teaming as part of a broader evaluation platform rather than a standalone scanner, Braintrust can integrate with dedicated security tools while handling the quality evaluation and production monitoring side of the workflow. If red teaming is your only requirement, Promptfoo remains the best fit.