Best 5 Literal AI Alternatives in 2026 (Migration Guide)

Literal AI’s hosted platform has been discontinued. If you built LLM tracing, evaluation, prompt management, or A/B testing on Literal AI, the managed product you relied on is gone, and this post is the migration guide. Chainlit, the open-source framework Literal AI’s team also built, moved to community maintenance on May 1, 2025. The Literal AI self-hosting Docker option ended on October 31, 2025. In 2026 the hosted cloud service is no longer available. What remains is the open-source Data Layer, which keeps your data but not the managed platform. This guide ranks five alternatives and walks through moving your traces, datasets, and prompt versions to a new home.

Literal AI did several things well. The Chainlit integration was tight, multimodal logging across vision, audio, and video was clean, and the Prompt Playground had a good UX for iterating prompts against live models. None of that is a reason to stay, because the hosted product is no longer there to stay on. The question now is which destination minimizes migration friction while preserving the parts of the workflow you actually used.

TL;DR: pick by migration priority

What happened to Literal AI

Literal AI was built by a Paris-based team, founded in 2023, the same team behind Chainlit, the open-source Python framework for conversational AI used by 80,000-plus developers. The platform was a multimodal LLM observability and evaluation product: LLM tracing, session and in-context debugging, datasets, offline and online evals, A/B testing, prompt versioning with a Prompt API and a Prompt Playground, and multimodal logging across vision, audio, and video. Pricing ran Basic (free, 10,000 log units a month, 30-day retention, 3 seats), Pro, and Enterprise.

The wind-down happened in stages across 2025 and into 2026:

1. May 1, 2025 — Chainlit, the open-source framework, moved to community maintenance. The original team formally stepped back under a Maintainer Agreement. The framework stayed open source and usable, but active development by the founding team ended.
2. October 31, 2025 — Literal AI’s self-hosting and enterprise Docker-image option ended. Teams running Literal AI inside their own VPC lost the supported deployment path.
3. 2026 — Literal AI’s hosted cloud service is no longer available. The managed dashboards, online evals, A/B testing UI, and Prompt Playground are gone.

The migration path Literal AI itself points users to is the open-source Data Layer for self-hosting only. That preserves your stored data, traces, datasets, and prompt versions, but it is a storage layer, not the product. You keep the database; you lose the platform built on top of it. That is the gap this guide is about. If you are reading this, you have data in Literal AI and a deadline, so the rest of the post is about getting you off cleanly.

What you lose when the Literal AI hosted platform goes away

The open-source Data Layer is real and useful, but it pays to be precise about what it does and does not give back. The Data Layer is a schema and a set of database adapters. It stores threads, steps, generations, datasets, and prompt versions. It does not render dashboards, run evaluators, or host a playground.

Concretely, when the Literal AI hosted platform goes away you lose:

• Hosted dashboards. The trace explorer, session timelines, and the latency and cost views were rendered by the managed product. The Data Layer holds the raw rows; the UI that made them legible is not part of it.
• Online evals. Evaluators that scored production traffic continuously ran on the hosted platform. With the Data Layer alone, there is no scoring engine running against live traces.
• The Prompt Playground. The interactive surface for iterating a prompt against a live model and comparing versions side by side was a hosted feature. The Prompt API could read and write versions; the Playground that made them editable by hand is gone.
• The A/B testing UI. Splitting traffic across prompt versions and reading the scored comparison was a managed surface, not a Data Layer capability.
• Managed retention. The 30-day retention on Basic, and longer windows on paid tiers, were operated by the hosted service. Self-hosting the Data Layer makes retention your database’s problem and your storage bill.

The honest summary: the Data Layer keeps your data, so you do not lose your history, but you lose the product. A destination tool has to put the dashboards, the eval engine, the prompt surface, and the retention story back. That is the bar the five alternatives below are measured against.

What to look for in a Literal AI replacement

A Literal AI migration is not a generic “best observability tool” search; it is shaped by which surfaces you actually used. Score replacements on the seven axes that map to the Literal AI feature set you are migrating off.

1. Future AGI: Best for closing the loop

Verdict: Future AGI is the closest functional match for the full Literal AI feature set, and it adds the one thing Literal AI never shipped. Literal AI traced, scored, and let you A/B prompt versions, then stopped at the score. Future AGI takes that score and keeps going: it clusters the failures, runs an optimizer, and rewrites the prompt. You get tracing, datasets, offline and online evals, and prompt management, the surfaces you are migrating off, plus a self-improving loop on top. For a team that has to rebuild its stack anyway, rebuilding on something that does more than Literal AI did is the pragmatic move.

What it covers from the Literal AI feature set:

• Tracing and session debugging. traceAI is an OpenTelemetry-based observability SDK that captures spans, traces, and per-session timelines, with auto-instrumentation for OpenAI, LangChain, Groq, and more. Because it is OTel-standard, the trace format is portable, so you are not migrating onto another proprietary format you would have to leave later.
• Datasets and offline evals. ai-evaluation ships 50-plus pre-built evaluators across RAG faithfulness, context relevance, answer correctness, agent trajectory, tool-call accuracy, hallucination, groundedness, and toxicity, with error localization that pinpoints which input field caused a failure. Datasets are managed through futureagi-sdk, which also handles human-in-the-loop annotation queues.
• Online evals. Evaluators run against live traces, scoring production output without ground truth, the online-eval surface Literal AI ran on its hosted platform.
• Prompt management. The prompt workbench handles versioning and side-by-side A/B comparison, replacing Literal AI’s prompt versioning and Prompt Playground for hands-on iteration.
• Multimodal logging. Future AGI’s Turing judge models handle text and image inputs, so multimodal traces are captured and scorable, not dropped on migration.
• The optimizer loop. agent-opt consumes eval scores and rewrites prompts through ProTeGi (gradient-based), GEPA (genetic), and MetaPrompt algorithms. Literal AI’s A/B testing was static; Future AGI’s loop is self-improving.
• Runtime guardrails and a gateway. Agent Command Center is an OpenAI-compatible LLM gateway with 18-plus built-in guardrail scanners and 100-plus providers, and Protect runs inline. Neither was part of Literal AI.

Migration from Literal AI: Three pieces. Export traces, datasets, and prompt versions from the Data Layer or the hosted export endpoints. Replace the trace pipeline: Literal AI used its own SDK and decorators, so the cutover is swapping that for a one-time traceAI SDK initialization, after which auto-instrumentation captures spans with no per-call-site change for OpenAI and LangChain calls. Re-create datasets and evals: Literal AI’s offline and online evals are re-defined against the ai-evaluation catalog, and custom evals become EvalTemplate definitions. Timeline: five to ten engineering days for under 200 prompt templates, including a shadow-traffic period.

Where it falls short:

• agent-opt is opt-in. Start with traceAI plus ai-evaluation in week one, and turn the optimizer on once eval baselines stabilize. The loop compounds value over weeks, not on day one.
• The platform surface is broader than Literal AI’s single-product simplicity. A one-person side project that only used Literal AI’s tracing may not need the full eval and optimizer stack on day one.

Pricing: Free tier with 100K traces a month. Scale tier from $99 a month with the full eval suite, agent-opt, and RBAC. Enterprise custom, with SOC 2 Type II, HIPAA, GDPR, and CCPA certified.

Score: 7 of 7 axes.

2. Langfuse: Best for OSS self-host continuity

Verdict: Langfuse is the pick when the migration priority is keeping a self-host posture without a vendor’s hosted cloud in the path. The Literal AI self-hosting Docker option ended on October 31, 2025; Langfuse Core is MIT-licensed and you run it yourself, so the deployment model you wanted is back. The prompt-management surface is the deepest in open source, and the tracing surface is deep. The trade-off is that Langfuse is an observation and prompt layer, not a closed loop.

What it covers from the Literal AI feature set:

• Self-host continuity. Langfuse Core is MIT. Self-host on Postgres, ClickHouse, Redis, and S3, and trace volume is bounded by your cluster, not a vendor tier or a discontinued Docker image.
• Deep tracing. OpenTelemetry-native traces, per-session timelines, and agent traces with tool-call spans, with prompt-version tagging on every trace.
• A deep prompt registry. Slugged prompts, version labels, label-based deploys with fast rollback, and prompt-linked evaluators that run on promotion. This is the surface closest to Literal AI’s prompt versioning.
• Datasets and offline evals. Version-controlled datasets linked to prompt versions, with LLM-as-judge and custom scorers, plus CI experiments before prompt promotion.

Migration from Literal AI: Three pieces. Export traces and datasets from the Data Layer, swap Literal AI’s SDK and decorators for the langfuse SDK or raw OpenTelemetry emitters, and recreate evals as Langfuse LLM-as-judge or custom scorers. Prompt versions pulled from Literal AI’s Prompt API are re-created as Langfuse prompts. Timeline: five to eight engineering days.

Where it falls short:

• No optimizer. Langfuse stores prompts and traces; it does not rewrite them from eval scores.
• No native online-eval engine matching a continuous production-scoring service; you wire scoring jobs yourself.
• No interactive prompt playground at the depth of Literal AI’s; iteration is more registry-and-CI than hands-on UI.
• Self-host burden compounds above 5 to 10M traces a month. ClickHouse and Postgres tuning land on the platform team.

Pricing: Hobby free with 50K units a month. Core $29 a month plus usage. Pro $199 a month. Enterprise typically $2,499 a month. Self-host of Core is MIT.

Score: 5 of 7 axes (missing: online evals, prompt playground).

3. Braintrust: Best for hosted-managed continuity

Verdict: Braintrust is the pick when the migration priority is keeping a hosted-managed posture with no infrastructure to run, the same low-ops experience Literal AI’s cloud gave you. Braintrust is built around the eval loop: a hosted experiment grid where you score prompt versions against datasets, with the open-source Autoevals package supplying a broad set of pre-built scorers. For a team that liked Literal AI’s hosted A/B and eval workflow and does not want to operate a database, Braintrust is the lowest-friction managed swap.

What it covers from the Literal AI feature set:

• Hosted, no infrastructure. Braintrust is a managed product, so you do not stand up the storage layer the Data Layer would require.
• A broad eval catalog. The Autoevals package ships factuality, relevance, summarization, and more scorers out of the box, well past a hand-rolled set.
• The experiment grid and A/B testing. Side-by-side scored comparison of prompt and model versions against a dataset is the core surface, the closest match for Literal AI’s A/B testing UI.
• Playground and prompt iteration. A hosted playground for iterating prompts against eval scores, a reasonable analog for the Prompt Playground.

Migration from Literal AI: Three pieces. Export datasets and prompt versions, replace Literal AI’s SDK with the Braintrust SDK for logging and experiment capture, and re-author evals as Autoevals or custom scorers. Datasets re-upload as Braintrust datasets. Timeline: five to seven engineering days.

Where it falls short:

• No optimizer loop. Braintrust scores experiments; it does not rewrite prompts from the scores.
• No runtime guardrails or gateway; runtime enforcement is out of scope.
• Tracing depth is real but eval-centric; teams wanting the deepest agent-trace surface often pair it with a dedicated tracer.
• Pricing scales with eval and span volume; heavy continuous-eval workloads should model the bill first.

Score: 5 of 7 axes (missing: optimizer-driven loop, runtime guardrails and gateway).

4. Comet Opik: Best for a vendor-backed OSS stack with multimodal logging

Verdict: Comet Opik is the pick when the migration priority is multimodal logging parity plus institutional backing. Literal AI logged vision, audio, and video, and Opik captures multimodal traces. It is Comet’s open-source LLM observability project, so you get a self-hostable tracing and evaluation stack backed by an established ML-tooling company rather than a discontinued startup product. For a team whose Literal AI usage leaned on multimodal traces and whose security review wants a vendor that is not going anywhere, Opik fits.

What it covers from the Literal AI feature set:

• Multimodal trace logging. Opik captures image and multimodal traces, the surface Literal AI handled across vision, audio, and video.
• Open-source and self-hostable. Run Opik locally or in your VPC; trace volume is not gated by a logs tier or a discontinued Docker image.
• Tracing plus evaluation in one OSS tool. Span capture, LLM-as-judge evaluators, and a metric set, with a hosted option for teams that do not want to self-host.
• Datasets and experiments. Dataset-driven evaluation and experiment comparison are first-class, covering the dataset surface you used in Literal AI.

Migration from Literal AI: Three pieces. Export traces and datasets, swap Literal AI’s SDK for the Opik SDK and its trace decorator, and re-author evals as Opik metrics or LLM-as-judge evaluators. Datasets re-upload. Timeline: five to eight engineering days.

Where it falls short:

• No optimizer loop. Opik observes and evaluates; it does not rewrite prompts from scores.
• No interactive prompt playground at the depth of Literal AI’s; prompt management is lighter than the registry-first tools here.
• No native online-eval engine for continuous production scoring as a first-class managed surface.
• Self-host operations, while lighter than some, still need a platform owner.

Score: 5 of 7 axes (missing: optimizer loop, prompt playground).

5. LangSmith: Best for Prompt Playground parity

Verdict: LangSmith is the pick when the surface you will miss most is the Prompt Playground. It ships a hosted prompt hub with a playground and a prompt-comparison view, the closest like-for-like with Literal AI’s prompt versioning plus Prompt Playground combination, alongside capable tracing and an eval surface. The honest caveat is coupling: LangSmith is tightly tied to LangChain and increasingly positioned as the LangChain Inc product surface, so it fits best if your stack is already LangChain-shaped.

What it covers from the Literal AI feature set:

• A hosted prompt hub and playground. Versioned prompts, an interactive playground for iterating against live models, and side-by-side comparison, the nearest match for the Prompt Playground UX you are losing.
• Tracing and session debugging. Per-run traces with step-level detail and tool-call spans, hosted, with no infrastructure to operate.
• Datasets and offline evals. Dataset-driven evaluation with LLM-as-judge and custom evaluators, and experiment comparison across runs.
• Online evals. Evaluators can score production traffic on the hosted platform, covering Literal AI’s online-eval surface.

Migration from Literal AI: Three pieces. Export datasets and prompt versions, replace Literal AI’s SDK and decorators with LangSmith tracing instrumentation, and re-create evals as LangSmith evaluators. Prompts re-created in the LangSmith prompt hub. Timeline: five to eight engineering days, lighter if your code is already LangChain-based.

Where it falls short:

• Tight LangChain coupling. If your application does not use LangChain, LangSmith is a heavier fit than a framework-neutral tool.
• No optimizer loop; LangSmith scores and compares but does not rewrite prompts from outcomes.
• No runtime guardrails or gateway for inline enforcement.
• Multimodal logging is thinner than Literal AI’s vision-audio-video coverage; confirm parity for your workload before cutover.

Score: 5 of 7 axes (missing: optimizer loop, runtime guardrails; multimodal partial).

Capability matrix

✓ native and first-class · ◐ partial or workaround · ✗ not available

How to migrate off Literal AI

Literal AI was an SDK-and-decorator product, not a base_url-style proxy, so the cutover is not a one-line config change. It is three concrete pieces of work: export, re-instrument, and re-create. Plan them as a sequence, with a shadow period before you flip production.

Step 1: Export from the open-source Data Layer

If you migrated to the Data Layer for self-hosting, your traces, datasets, and prompt versions already live in your own database, so you keep your history. Dump the relevant tables, threads, steps, generations, datasets, and prompt versions, as JSON or CSV.

If you were still on the hosted platform while it was available, export traces and datasets through the export endpoints and pull prompt versions through the Prompt API before access ends. Persist one file per dataset and one file per prompt template with versions as an array. Treat this export as the snapshot you migrate from.

Step 2: Re-instrument the trace pipeline

Literal AI captured traces through its own SDK and decorators wrapped around the LLM call. Migrating means replacing that instrumentation, but the cutover is replacing the trace pipeline, not editing every call by hand.

With Future AGI, traceAI auto-instruments through OpenTelemetry. A one-time SDK initialization wraps the OpenAI and LangChain calls at import time, so spans are captured with no per-call-site change for the common providers:

# pip install futureagi
from fi_instrumentation import FITracer, TracerProvider, BatchSpanProcessor, HTTPSpanExporter

exporter = HTTPSpanExporter(endpoint="https://api.futureagi.com/traces")
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(exporter))
# OpenAI and LangChain calls are now traced automatically

The same shape applies to the other destinations: Langfuse and LangSmith both capture traces after a one-time SDK initialization, and Opik uses a trace decorator. The decorators or wrapped clients Literal AI required are removed. For framework-specific call sites, expect a manual pass. For hundreds of call sites, script the change with a codemod and run a shadow period where both pipelines record traces in parallel before cutover.

Step 3: Port prompt versions into the destination registry

Literal AI’s Prompt API let you read prompt versions programmatically. Pull every version while you have access, then re-author them into the destination’s prompt registry. Variable substitution and simple templating are mechanical; templates with Literal-AI-specific syntax need a manual pass.

On Future AGI, prompt versions land in the prompt workbench with versioning and A/B comparison. On Langfuse they become slugged, version-labeled prompts. On LangSmith they go into the prompt hub. Preserve version metadata so your rollback history survives the move. For under 100 templates, plan three to four days; above 200, plan a full sprint.

Step 4: Re-create datasets and evals

Literal AI ran both offline and online evals, and those do not port automatically. Re-upload your exported datasets first, then re-define the evaluators. Offline evals map onto a pre-built catalog where one exists: Future AGI’s ai-evaluation ships 50-plus built-in evaluators covering RAG, agent trajectory, function calling, hallucination, groundedness, and toxicity, so common metrics are a configuration step rather than custom code. Custom Literal AI evals become EvalTemplate definitions on Future AGI, or custom scorers on Langfuse, Braintrust, Opik, and LangSmith. Online evals, the ones that scored live production traffic, need a destination that runs scoring against live traces; confirm that surface exists before you commit. Budget two to four days for a typical custom-eval surface.

Step 5: Confirm multimodal logging

Literal AI logged vision, audio, and video. If your application is multimodal, do not assume parity. Capture one multimodal trace end to end on the destination during the shadow period and verify the image, audio, or video payload is stored and viewable. Future AGI and Comet Opik both handle multimodal traces; some tools log image attachments but not audio or video. Catching a gap here during the shadow period is far cheaper than catching it after cutover.

Decision framework: Choose X if

Choose Future AGI if you want the full Literal AI feature set, tracing, datasets, offline and online evals, prompt management, in one platform, plus an optimizer loop that rewrites prompts from eval scores. Pick this when production agent workloads are a real line item and you would rather rebuild on something that does more than Literal AI did than rebuild on a like-for-like.

Choose Langfuse if the migration priority is OSS self-host continuity, an MIT-licensed trace and prompt store you run yourself, with no vendor cloud in the path and no discontinued Docker image to worry about. Pick this when the platform team can absorb the ClickHouse self-host burden.

Choose Braintrust if the migration priority is hosted-managed continuity, the same no-infrastructure posture Literal AI’s cloud gave you, with a rigorous scored experiment grid and a broad autoeval set.

Choose Comet Opik if the migration priority is multimodal logging parity plus a vendor-backed open-source stack, and you want institutional backing a security review will accept.

Choose LangSmith if the surface you will miss most is the Prompt Playground and your stack is already LangChain-shaped. Pick this for the closest hosted prompt-hub-and-playground analog.

When to just self-host the Data Layer

Calibrated honesty matters here. For a narrow set of teams, the open-source Data Layer alone is a defensible answer, at least for a while.

If your Literal AI usage was almost entirely trace storage, you queried traces through your own tools and never leaned on the hosted dashboards, online evals, or the Prompt Playground, then self-hosting the Data Layer keeps your data with the least disruption. You lose the rendered UI, but if you were not using it, that is not a loss.

The honest framing: the Data Layer is a reasonable bridge and a poor destination. The moment you need dashboards, an eval engine, a prompt surface anyone edits by hand, or managed retention, you are rebuilding the product yourself, and at that point picking one of the five alternatives above is less work than maintaining a homegrown platform on a storage schema.

What we did not include

Three products show up in other observability listicles that we left out of this migration guide. Arize Phoenix is a strong local-first, OpenInference-native tracing tool, but its prompt-management and A/B surfaces are lighter than Literal AI’s, so it is not a like-for-like for teams that used the Prompt Playground heavily; worth a look if tracing was your only Literal AI surface. Helicone is a capable lightweight observability proxy, but its eval and prompt surfaces are thinner than the Literal AI feature set. Vellum is an enterprise prompt-workflow product whose workflow-builder shape is different enough from Literal AI’s observability-first product that the migration is not a direct mapping.

Related reading

• Future AGI vs Langfuse in 2026
• Best LLM Evaluation Tools in 2026
• Best Braintrust Alternatives in 2026
• Best Comet Opik Alternatives in 2026
• Best Arize Phoenix Alternatives in 2026

Sources

• Literal AI product overview and tier structure, literalai.com (Basic free / Pro / Enterprise; tracing, datasets, evals, prompt management, multimodal logging)
• Chainlit open-source repository and maintenance announcement, github.com/Chainlit/chainlit (community maintenance from May 1, 2025)
• Literal AI self-hosting and Data Layer documentation (self-hosting Docker option ended October 31, 2025; open-source Data Layer for self-hosting)
• Langfuse open-source repository, github.com/langfuse/langfuse (MIT)
• Langfuse pricing page, langfuse.com/pricing
• Braintrust product page and Autoevals package, braintrust.dev
• Comet Opik open-source repository, github.com/comet-ml/opik
• LangSmith product page and prompt hub, smith.langchain.com
• Future AGI traceAI, github.com/future-agi/traceAI (Apache 2.0)
• Future AGI ai-evaluation, github.com/future-agi/ai-evaluation (Apache 2.0)
• Future AGI agent-opt, github.com/future-agi/agent-opt (Apache 2.0)
• Future AGI Agent Command Center, docs.futureagi.com/docs/command-center