Literal AI

AI native Linear connected to the codebase.

Routing & Optimization

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Founded

2023

Location

Newcastle, United Kingdom

Employees

Funding

$5M

Literal AI — Observability and Evaluation for LLM/RAG in Production

**Summary:** Literal AI is a production platform for RAG and LLM applications that focuses on observability, evaluation, and analytics. It helps teams test, monitor, debug, and improve LLM- and agent-driven apps with multimodal logging, prompt workflows, datasets, experiments, and online evaluation. The company has announced service discontinuation effective October 31, 2025—see the docs for the migration guide and export steps.

Website: [Literal AI](https://www.literalai.com)

Docs: [Overview](https://docs.literalai.com/get-started/overview) • [Quick Start](https://docs.literalai.com/get-started/quick-start) • [Home + Discontinuation Notice](https://docs.literalai.com/)

What Literal AI Does

Literal AI provides the “operational layer” around your LLM or agent stack, not orchestration. You integrate a lightweight Python or TypeScript SDK (or framework callbacks) to capture traces, prompts, scores, and user feedback from real traffic, then use the UI and APIs to evaluate and improve quality.

Focus areas: **observability, evaluation, analytics** for RAG pipelines, agents, and chat assistants

Works with your stack: **OpenAI**, **LangChain/LangGraph**, **LlamaIndex**, plus others via SDK

Deployment options: hosted and enterprise self-hosting (Docker-based)

Learn more: [Homepage](https://www.literalai.com) • [Integrations](https://docs.literalai.com/integrations/openai) • [Self-hosting](https://docs.literalai.com/self-hosting/get-started)

Key Capabilities

**Multimodal logging and traces:** Centralize runs, chain steps, tool calls, retrieval steps, latency, cost, and errors. [Logs guide](https://docs.literalai.com/guides/logs)

**Prompt lifecycle management:** Version prompts, compare providers/settings, and A/B test changes. [Prompts guide](https://docs.literalai.com/guides/prompts) • [Prompt playground deep dive](https://www.literalai.com/blog/deep-dive-on-the-prompt-playground-and-capabilities)

**Datasets and experiments:** Build datasets (often from production data) and run experiments to **prevent regressions** before rollout. [Datasets](https://docs.literalai.com/guides/dataset) • [Experiments](https://docs.literalai.com/guides/experiment)

**Online evaluation rules:** Define rules to score agent/workflow quality on live traffic and catch issues early. [Online evaluation update](https://www.literalai.com/blog/product-update-online-evaluation) • [Evaluation guide](https://docs.literalai.com/guides/evaluation)

**Framework-native integrations:** Plug into [OpenAI](https://docs.literalai.com/integrations/openai), [LangChain/LangGraph](https://docs.literalai.com/integrations/langchain), and [LlamaIndex](https://docs.literalai.com/integrations/llama-index); use SDKs for others.

**SDKs:** Python and TypeScript for quick instrumentation. [Quick Start](https://docs.literalai.com/get-started/quick-start)

How It Fits in Your Stack

Literal AI is not an orchestrator and does not ship its own agent.

It layers onto frameworks like [LangChain/LangGraph](https://docs.literalai.com/integrations/langchain) and [LlamaIndex](https://docs.literalai.com/integrations/llama-index) to provide telemetry, evaluation, and prompt tooling for your existing workflows.

It instruments providers like [OpenAI](https://docs.literalai.com/integrations/openai) and supports others via the SDK.

Context: Users note the clear **scope vs orchestration** boundary, which makes Literal complementary to popular agent frameworks. See discussion in [PromptEngineering subreddit](https://www.reddit.com/r/PromptEngineering/comments/1f8u360/update_your_prompt_or_llm_in_production_and_pray/) and reinforcement from the [company user](https://www.reddit.com/user/chainlit/comments/).

Common Use Cases

**RAG pipelines:** Trace retrieval steps, measure grounding quality, and evaluate answers with datasets and online rules. Reference: [RAG observability blog](https://www.literalai.com/blog/observing-the-rag-process-step-by-step)

**Agentic apps:** Log chain/runs and tool calls; apply online evaluation to monitor success/failure modes.

**Prompt lifecycle:** Version, A/B test, and promote prompts based on measured performance deltas.

**Pre-production testing:** Run experiments on curated datasets to avoid regressions on rollout.

**Production analytics:** Track cost, latency, errors, and user feedback to inform prompt/model updates.

Who It’s For

Product and engineering teams shipping LLM or RAG apps that require robust **evaluation, monitoring, and analytics** in production.

Teams using **LangChain/LangGraph** or **LlamaIndex** that want observability and scoring without changing orchestration.

Organizations that prefer or require **self-hosting** for data control and compliance.

Pros and Cons (Market Feedback)

Pros:

**Clear scope** as an observability/evaluation layer, not orchestration .

**Practical online evaluation** and **deep prompt playground** speed iteration and guardrails .

Recognized for **comprehensive RAG observability** .

Cons:

**Limited third‑party reviews**; listings exist but lack depth .

**Discontinuation risk:** service ends Oct 31, 2025—migration planning required .

Deployment, Pricing, and Free Tier

Deployment: Hosted; enterprise **self-hosting** via Docker .

Free tier: A monthly free allowance is referenced in the [release notes](https://docs.literalai.com/more/release-notes). Confirm availability given the discontinuation timeline.

Getting Started

1. Review the [Overview](https://docs.literalai.com/get-started/overview) and [Quick Start](https://docs.literalai.com/get-started/quick-start) for Python/TypeScript setup.

2. Add integrations for [OpenAI](https://docs.literalai.com/integrations/openai), [LangChain/LangGraph](https://docs.literalai.com/integrations/langchain), or [LlamaIndex](https://docs.literalai.com/integrations/llama-index).

3. Set up logging, prompts, datasets, experiments, and evaluation using the guides: [Logs](https://docs.literalai.com/guides/logs) • [Prompts](https://docs.literalai.com/guides/prompts) • [Dataset](https://docs.literalai.com/guides/dataset) • [Experiment](https://docs.literalai.com/guides/experiment) • [Evaluation](https://docs.literalai.com/guides/evaluation).

Important Notice: Discontinuation and Migration

Literal AI has announced the service will be discontinued on October 31, 2025. The docs provide a migration guide and data export steps. If you rely on the platform, plan and execute your migration and export data ahead of the shutdown: [Docs home + migration notice](https://docs.literalai.com/).

Team

Founders: Dan (CEO) and Willy (CTO). See the core team on the [Team page](https://www.literalai.com/team).

Notable Resources

Product updates and deep dives: [Online Evaluation](https://www.literalai.com/blog/product-update-online-evaluation) • [Prompt Playground](https://www.literalai.com/blog/deep-dive-on-the-prompt-playground-and-capabilities) • [RAG Observability](https://www.literalai.com/blog/observing-the-rag-process-step-by-step)

Docs hub: [Docs Overview](https://docs.literalai.com/get-started/overview) • [Quick Start](https://docs.literalai.com/get-started/quick-start) • [Release Notes](https://docs.literalai.com/more/release-notes)

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