Braintrust Alternatives 2026: Top 7 for Agent Observability

Braintrust is an eval-first AI platform. It shines at regression testing: write a scorer, run a suite across prompts and models, catch the diff before the PR merges. For teams whose bottleneck is "did this change break behavior X," it is a strong tool.

The trouble starts when eval regression is not the bottleneck. Braintrust was designed around a prompt and a completion with a scorer attached. The industry has moved past that. The unit of work today is an agent: it runs for minutes, calls a dozen tools, spawns a sub-agent, fails four tool calls deep, and re-sends its entire conversation on every turn. Braintrust shows you the trace, but the UX is built for scoring, not for reading what the agent did and finding where it went wrong. That is a different product.

Laminar is that product. It was built for AI agents from the ground up, and every choice in it (how traces are stored, how outcomes are tracked, how you debug, how you query) follows from understanding the shape of an agent trace. This article ranks the top Braintrust alternatives for 2026, ordered by how well they solve agent observability and debugging rather than eval-first regression.

1. Laminar. Apache 2.0, OpenTelemetry-native, built for AI agents. 20x trace compression, the lowest pricing on the market, Signals, a coding-agent-driven debugger, SQL over all platform data, and a code-first eval SDK. The direct Braintrust alternative if your primary pain is shipping and debugging agents, not CI regression.
2. Langfuse. MIT-licensed, prompt-first, strong observation model. Closest feature-to-feature swap for Braintrust on a permissive OSS license, though its eval SDK is thinner than Braintrust's.
3. Arize Phoenix. Elastic License 2.0, OpenTelemetry-native via OpenInference. Solid eval harness, notebook-friendly.
4. LangSmith. Closed source, LangChain-first. Strong eval harness plus LangGraph Studio for LangGraph users.
5. Weights & Biases Weave. Closed source. Fits if your ML team already lives in W&B and wants evals next to experiments.
6. Helicone. Apache 2.0 proxy. No real eval harness, but cheap observability when eval is not the need.
7. Traceloop / OpenLLMetry. Vendor-neutral OpenTelemetry instrumentation. Useful as a license-portable ingest layer that works with most of the backends above.

One-line rule: pick Laminar if your workload is agents, Langfuse if you want OSS evals with prompt management, Phoenix if you want OpenInference compatibility, LangSmith if you are locked to LangGraph.

Braintrust is not broken. It is specialized for the eval loop. The reason Laminar wins for agents is that it is specialized for the opposite thing, and the feature set tells one story: shipping AI agents means Laminar. Here is that story in the order it matters.

Agents re-send the full conversation on every turn. A 30-turn run that has k unique messages ends up carrying on the order of k(k+1)/2 messages across its spans, the same context copied over and over. Generic observability tools store every copy. Laminar understands the structure of an agent trace: it hashes each message, stores every unique message once per trace, and reconstructs the full trace byte-for-byte at query time. The result is an average 20x reduction in trace storage, and up to 50x on the longest agent runs. (Read the full write-up.)

That compression is not just a storage detail, it is the foundation of the pricing. Because Laminar stores a fraction of the bytes for the same agent run, it can charge on data volume and still come in below everyone else. Pricing is on data volume and retention with no seat fees and no per-score unit counting: Free is 1GB with 7-day retention, Hobby is $30/month for 3GB then $2/GB at 30-day retention, and Pro is $150/month for 10GB then $1.50/GB at 6-month retention with unlimited seats. Compare that with Braintrust Pro at $249/month for 5GB and 50k scores, with self-host gated behind an Enterprise contract. Laminar has the best pricing on the market for agents, and the reason is that it understands agents.

Agent traces are large and, for a human, hard to read at scale. One trace you can open and skim. Ten thousand you cannot. Signals are how Laminar answers the questions you actually have in production: did the agent get stuck, did the user give up, did this run fail for a different reason than last week's.

A Signal is an instruction written in plain language, paired with a JSON output schema. It reads every trace and produces a structured event when it sees what you described. "Agent looped on the same tool without making progress." Laminar extracts it, backfills it across your history, and fires on every new trace going forward. The result is a stream of events you can query, cluster, and alert on. Braintrust has scorers that run on a dataset; Signals are a different primitive built because we understand what an agent trace contains and how teams need to slice it.

Building an agent is a loop: run it, read what it did, change something, run it again. Laminar's debugger is that loop, built so your coding agent runs it. Start your agent with LMNR_DEBUG=true and the run is traced into a session. Then Claude Code, Cursor, or Codex reads the resulting trace, edits your code, and reruns, with each rerun served from cache up to the point it is testing so the turn is fast and cheap.

The whole loop is driven through the Laminar CLI, so the coding agent reruns from a specific state without you babysitting the terminal. A long agent run can take minutes end to end, and the call you are fixing is often three-quarters of the way through; caching the prefix means the coding agent can take that turn dozens of times in the span it would take to run live once. Braintrust's playground iterates on a single prompt against a dataset. The Laminar debugger iterates on a real captured agent trace, and hands the loop to the agent writing your code.

Agent traces are complex and raise questions a chart was never going to answer: "how many runs called tool X more than five times and then errored," "which model version regressed on this Signal last week." Laminar gives you raw SQL over all platform data, traces, spans, signal events, evaluations, and metadata, and it is reachable wherever you or your coding agent are working: the in-app SQL editor, the lmnr-cli sql query command, the MCP server, and the SQL API. No dataset export, no notebook loop. Braintrust's analysis is notebook or API-driven; Laminar makes the raw trace queryable in product.

This is the one place Braintrust is genuinely strong, and Laminar competes on its own terms. Laminar's evals follow a code-first, barebones-SDK philosophy: a dataset of datapoints, an executor function (your agent or a piece of it), and evaluator functions that score the output. You write them as plain Python or TypeScript and run python my_eval.py, tsx my-eval.ts, or lmnr eval. Every run produces structured EVALUATION, EXECUTOR, and EVALUATOR spans you can query in SQL alongside your production traces.

The point is versatility: because the SDK is thin and code-first, you can evaluate any part of an agent, a single tool, a sub-agent, an end-to-end run, without contorting it into a prompt-and-scorer shape. That design comes from understanding agents.

Laminar ships a production-ready Helm chart: clone, apply, run. No enterprise sales call, no proprietary operator, no "contact us for self-host." Every feature is on the OSS image, including Signals, the SQL editor, the debugger, and evals. This is the sharp line with Braintrust, whose self-host is Enterprise-only and whose Brainstore storage layer is proprietary. Laminar self-host is free, Apache 2.0, every feature included, and because the data is OpenTelemetry-native it stays in a format you can move.

Most eval-first tools, Braintrust included, were designed around a prompt/completion pair with a scorer attached. Agent observability is a different problem:

• Long traces. Thousands of spans across LLM calls, tool calls, retries, and sub-agent invocations, with the conversation re-sent on every turn.
• Non-deterministic control flow. The agent decides the next step. Every run has a different shape.
• Nested causality. A failure at span 1,800 can be caused by a bad retrieval at span 42. You follow the chain, not the list.
• Session continuity. Agents pause and resume. A task spans multiple process runs. The trace has to stitch.

Everything below claims to handle this. The ranking reflects how well they actually do.

License: Apache 2.0. Deployment: Cloud, or self-hosted via the official Helm chart in minutes. Repo: github.com/lmnr-ai/lmnr.

Laminar was built from day one for AI agents. Where Braintrust organizes around eval suites and scorers, Laminar organizes around the agent run: the conversation, the spans that produced it, the outcomes you track across thousands of them, and the loop you debug it in. The six points above are the whole pitch. The transcript view renders a trace as the conversation it was (what the agent said, what the user said back, what each tool call did), so a 2,000-span run is a ten-second read instead of a ten-minute one, and 20x compression, Signals, the debugger, SQL, and a code-first eval SDK build on top of that.

Native SDKs for Python and TypeScript auto-instrument LangChain, LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK, Browser Use, and more. Because Laminar is OTel-native, OpenInference and OpenLLMetry spans flow in without re-instrumenting, so migrating off Braintrust is mostly pointing the exporter elsewhere.

• Your entire workflow is CI-driven eval regression with scorer sweeps across prompts and models. Braintrust is still strongest in that narrow lane.
• You do not have nested tool use or agents. A single-call logging tool is enough.

License: MIT. Deployment: Cloud, self-host. Repo: github.com/langfuse/langfuse.

If you like Braintrust's model but need a permissive OSS license, Langfuse is the closest swap. Prompt versioning, typed observations (generations, spans, events), an eval harness with LLM-as-judge and custom scorers, and a self-host that includes every feature on the free image.

Strengths:

• MIT license. Fully open source, free self-host with all features.
• Strong prompt management: versioning, tagging, release channels.
• Eval harness with scorers, human feedback, and CI integration.

Weaknesses:

• Observation-first data model. Long agent runs render as a list of observations rather than a transcript.
• Unit-based Cloud pricing (traces + observations + scores) adds up on agent workloads.
• No raw SQL over traces in product, no natural-language outcome tracking, no trace compression.

Pricing: Free tier includes 50k observations with 30-day retention. Core $29/month. Pro $199/month. Self-host is free with all features. See the full Langfuse comparison.

License: Elastic License 2.0. Deployment: Self-host (pip install or Helm), Arize AX managed option.

Phoenix is the open-source side of Arize. It ships OpenInference, the most widely adopted OTel semantic conventions for LLM spans, and a strong eval harness.

Strengths:

• OpenTelemetry-native via OpenInference. Instrument once, send anywhere.
• Phoenix Evals: mature library of LLM-as-judge templates.
• Notebook-friendly; runs in Colab or locally.

Weaknesses:

• Span-tree-first trace UX. No transcript view.
• Elastic License 2.0 is not OSI-approved open source. ELv2 prohibits offering Phoenix as a hosted service to third parties. For teams whose legal team uses the OSI definition, this is a blocker.
• Graduation path to Arize AX is a separate contract with span-based pricing.

Pricing: Phoenix OSS is free. Arize AX: Free tier 25k spans + 1GB, Pro $50/month for 50k spans + 10GB. Full comparison: Arize Phoenix alternatives 2026.

License: Closed source. Deployment: Cloud, hybrid, self-hosted (Enterprise only).

LangSmith is LangChain's managed platform. Strong eval harness, and LangGraph Studio is the best agent IDE available if your stack is LangGraph.

Strengths:

• LangGraph Studio (real agent IDE, not just a viewer).
• Mature eval harness and dataset experiments.
• OpenTelemetry support added in March 2026.

Weaknesses:

• Closed source. Self-hosting is Enterprise-only.
• Seat-based pricing ($39/seat/month on Plus) gets expensive with larger teams.
• Tightest fit is still LangChain. Teams on other frameworks get less value.

Pricing: Developer free with 5k base traces/month. Plus $39/seat/month plus $0.50 per 1k base traces.

License: Closed source. Deployment: Cloud, on-prem for Enterprise.

Weave plugs tracing and evals into the existing W&B console. If your team already evaluates models there, agents get the same tooling.

Strengths:

• Native W&B integration.
• Strong eval framework with scorers and comparisons.
• Good for teams evaluating models and agents on the same platform.

Weaknesses:

• Trace UX borrowed from ML experiment tracking. Not agent-first.
• Weak on realtime trace viewing during long runs.
• Closed source.

Pricing: Free tier with limited storage. Paid plans scale with volume and seats.

License: Apache 2.0. Deployment: Cloud, self-host.

Helicone is a proxy that sits in front of the LLM provider and logs every request. Simplest integration of any tool in this list: change a base URL. Lightweight eval hooks, but not a replacement for Braintrust's scorer harness.

Strengths:

• Zero-code proxy integration.
• Caching, rate-limit handling, and retries built into the proxy.
• Cheap to get started.

Weaknesses:

• Request/response focused, not span-based. Multi-step agents are stitched together after the fact.
• Eval tooling is light compared to Braintrust, Phoenix, or Langfuse.
• Proxy model adds a hop to every LLM call.

Pricing: Free tier. Paid plans scale with request volume.

License: Apache 2.0 (OpenLLMetry SDK). Deployment: Cloud backend, vendor-neutral SDK.

Traceloop's value is the OpenLLMetry SDK: vendor-neutral OpenTelemetry instrumentation for LLMs. Traceloop's own backend is one place the traces can go. Most backends in this list (Laminar, Langfuse, Phoenix, LangSmith) can also ingest OpenLLMetry spans, which makes OpenLLMetry the safest instrumentation choice for teams that want portability.

Strengths:

• OTel-native. Works with any compatible backend.
• Active open-source community.

Weaknesses:

• The backend UX is less agent-specific than Laminar or LangSmith.
• Primary value is the SDK, not the product.

Braintrust's $249/month entry price for Pro is the highest paid-entry price in this list. Laminar's $150/month Pro buys more data (10GB vs 5GB) at longer retention (6 months vs 30 days), and because Laminar compresses agent traces ~20x, that 10GB holds far more real agent traffic than the raw number suggests.

The line that matters for Braintrust alternatives: if OSS self-host is a requirement, Braintrust is out, and Laminar, Langfuse, Phoenix, and Helicone are your options. Of those, Laminar is Apache 2.0 (OSI) and ships every feature on the free self-host image.

Answer these in order. Stop at the first yes.

1. Are you shipping and debugging AI agents and want a transcript view, 20x compression, Signals, SQL, and a coding-agent debugger? → Laminar.
2. Do you want OSS evals with strong prompt management? → Langfuse.
3. Are you already on Arize or need OpenInference compatibility? → Phoenix.
4. Are you committed to LangChain or LangGraph and want an agent IDE? → LangSmith.
5. Does your ML team live in W&B? → Weave.
6. Do you just need cheap request/response logs? → Helicone.
7. Do you want vendor-neutral instrumentation and will decide the backend later? → OpenLLMetry plus any of the above.

Straightforward because both products speak OpenTelemetry.

1. Switch the exporter. Braintrust's TypeScript and Python SDKs are OTel-based. Point the OTLP exporter at Laminar's endpoint and traces land. If you prefer Laminar's native SDK, Python and TypeScript both follow the same auto-instrumentation pattern. Start with the Laminar quickstart.
2. Recreate production outcomes as Signals. Keep offline Braintrust evals running if they are wired into CI. For production outcome tracking, recreate the important scorers as Signals so they backfill across history and fire on new traces going forward.
3. Port the evals you want in code. Laminar's eval SDK is code-first; port the scorers that matter as evaluator functions and run them with python my_eval.py or lmnr eval.
4. Run side-by-side during the transition. OTel supports multiple exporters. Send to both backends until you trust the new pipeline, then turn off the old one.

We built Laminar because no eval-first tool solved our own problem: debugging a 30-minute browser agent that failed at minute 18, with no idea which of 2,000 spans to look at first, and paying to store the same conversation re-sent on every one of those spans.

The transcript view was the first thing we built, then 20x compression so storing agent traffic stopped being the expensive part, then Signals because the failure mode you care about today is not the one your scorers captured a month ago, then the debugger so the coding agent writing your agent could run the fix loop itself. Every one of those came from understanding agents, which is the thing eval-first tools were not built around.

If you are looking at Braintrust alternatives because your workload is less about CI regression and more about shipping agents and figuring out what is going wrong in production, that is the reason to try Laminar first.

Start with the free tier: 1GB of traces, 7-day retention. Instrument one agent. If you do not see the difference in the first hour, come back and tell us why.

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The best Braintrust alternative in 2026 is Laminar. It is Apache 2.0 licensed, OpenTelemetry-native, and built specifically for AI agents, with 20x trace compression, the lowest pricing on the market, Signals for natural-language outcome tracking, a coding-agent-driven debugger, raw SQL over all platform data, and a code-first eval SDK. Langfuse is the best alternative if you want an OSS eval harness with prompt management; Phoenix is the best alternative if you want OpenInference compatibility; LangSmith is the best alternative for LangGraph-committed teams.

Laminar is the best open-source Braintrust alternative. It is Apache 2.0 (OSI-approved), self-hosts in one command via an official Helm chart, and ships every feature on the free image, including Signals, the SQL editor, the debugger, and evals. Braintrust itself is closed source with Enterprise-only self-host. Langfuse (MIT) and Helicone (Apache 2.0) are the other permissive-license options.

Laminar is the cheapest Braintrust alternative for agent workloads. It bills on data volume with no seat fees and no per-score unit counting, and it compresses agent traces ~20x before storing them, so the same dollar covers far more real traffic. Free is 1GB at 7-day retention, Hobby is $30/month for 3GB, and Pro is $150/month for 10GB at 6-month retention with unlimited seats, versus Braintrust Pro at $249/month for 5GB plus 50k scores.

No. Braintrust is a closed-source commercial SaaS, and its Brainstore storage layer is proprietary. Self-hosting requires an Enterprise "hybrid deployment" contract. The AI proxy they publish on GitHub is open source, but the platform itself is not. If you need OSS self-host, Laminar (Apache 2.0), Langfuse (MIT), and Helicone (Apache 2.0) are the options.

Yes. Laminar ships a code-first eval SDK: you define a dataset, an executor function, and evaluator functions in plain Python or TypeScript and run them with python my_eval.py, tsx my-eval.ts, or lmnr eval. Every run produces structured EVALUATION, EXECUTOR, and EVALUATOR spans you can query in SQL next to production traces. Braintrust is still strongest at CI-driven scorer sweeps across prompts and models; for most teams, Laminar's code-first SDK plus Signals for production outcomes covers the eval need.

Laminar exposes raw SQL over all platform data: traces, spans, signal events, evaluations, and metadata. You can query it from the in-app SQL editor, the lmnr-cli sql query command, an MCP server (so your coding agent can query it), or the SQL API. Braintrust analysis is notebook or API-driven; there is no in-product SQL over traces.

Braintrust is eval-first: scorers, datasets, CI regression, prompt comparisons. Laminar is agent-first: transcript view, 20x trace compression, Signals, a coding-agent debugger, SQL over all platform data, and a code-first eval SDK. Both ingest OpenTelemetry. Licenses differ: Braintrust is closed-source SaaS with proprietary Brainstore storage, Laminar is Apache 2.0 with free Helm chart self-host. Pricing differs: Braintrust Pro is $249/month for 5GB + 50k scores; Laminar Pro is $150/month for 10GB at 6-month retention.

Braintrust Starter is free with 1GB and 10k scores at 14-day retention; Pro is $249/month for 5GB and 50k scores at 30-day retention. Laminar Free is 1GB at 7-day retention; Hobby is $30/month for 3GB at 30-day retention; Pro is $150/month for 10GB at 6-month retention, unlimited seats. Laminar bills on data volume only and compresses agent traces ~20x; Braintrust bills on data plus score count. For agent workloads with many per-span outcomes, Laminar is more predictable and lower cost.

Agent observability is the practice of capturing and debugging the full execution of an AI agent, including every LLM call, tool call, retrieval, and sub-agent invocation. It differs from classical LLM observability because agent runs are long, non-deterministic, deeply nested, and re-send their full conversation on every turn. Agent-specific tooling renders the run as a transcript, compresses the repeated context, supports natural-language outcome tracking, and lets a coding agent re-run the agent from any point. See our explainer on agent observability for the longer version.

Yes, and several teams do. OpenTelemetry supports multiple exporters. You can instrument once, send traces to Laminar for production debugging, and keep Braintrust wired into CI for regression testing. Over time, Laminar Signals often replace the production-facing subset of Braintrust scorers because they backfill across history and fire on new traces automatically, and Laminar's code-first eval SDK can absorb the rest.

Last updated: June 2026. Verify features and pricing against each vendor's current documentation before committing.