Lucidic AI

What do they actually do

Lucidic AI provides SDKs (Python and TypeScript) and a web dashboard that automatically instruments LLM calls and captures multi‑step agent runs as sessions, steps, and events. Teams install a short quickstart to begin recording traces from OpenAI, Anthropic, or Gemini and see them in the UI (Quickstart, Introduction).

The product focuses on debugging, testing, and simulation for agent workflows: session tracing and replay, step‑level “time‑travel” re‑runs, mass simulations to run hundreds of test cases in parallel, custom rubrics and A/B experiments for evaluation, and a prompt database with versioning. It also provides cost/token tracking, auto‑instrumentation (via OpenTelemetry), and integrations with popular LLM providers and frameworks like LangChain and PydanticAI (Sessions & Steps, Time Travel, Mass Simulations, Rubrics/Experiments, Prompt DB, Integrations/How it works).

The company targets engineering teams building agentic workflows and shows early testimonials (e.g., engineers at Expedia and Palantir). Lucidic is a YC Winter 2025 company, with public docs and a live dashboard (homepage, YC profile).

Who are their target customer(s)

• Agent developers / backend engineers building multi‑step agent flows: They can’t reliably see or replay the exact sequence of LLM calls, inputs, and outputs when something breaks, so debugging is slow and often requires re‑running whole flows. Lucidic captures sessions and steps for inspection and replay (Quickstart, Sessions & Steps).
• Prompt engineers / ML engineers iterating on behavior: They change prompts without a reproducible way to test impact or manage versions. Lucidic provides a prompt database with versioning plus rubrics and experiments to compare configurations (Prompt DB, Rubrics/Experiments).
• SREs and observability engineers responsible for reliability and costs: They struggle to trace agent failures across services and track token/cost usage. Lucidic offers automatic instrumentation, cost/token tracking, and integrations with common LLM providers and frameworks (Integrations/How it works).
• QA / test engineers hunting edge cases: Manual testing misses rare failures and it’s impractical to re‑run many end‑to‑end agent executions. Lucidic’s mass simulations replay logs and run hundreds of tests in parallel to reproduce and quantify failures (Mass Simulations).
• Product owners / compliance or policy owners: They need auditable checks that agents follow rules and meet success criteria before rollout. Lucidic’s rubrics and experiments make pass/fail tests and tracked comparisons explicit for policy enforcement (Rubrics/Experiments, YC description).

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led pilots with YC network and early adopters (e.g., logos in testimonials) using the two‑line SDK quickstart to instrument a live stack, reproduce a recent failure via session replay/time‑travel, and deliver a short ROI summary on reduced debugging time (Quickstart, homepage, YC profile).
• First 50: Shift to developer‑led growth with templates, tutorials, and integrations so small teams can self‑serve; run targeted outreach to prompt/SRE teams and offer a 4–6 week validation using mass simulations and rubrics to quantify stability gains (Integrations intro, Mass Simulations, Rubrics).
• First 100: Productize sales: standard pilot→SLA plays, security/compliance packaging, and onboarding playbooks; add partner integrations and co‑sell with LLM providers and agent frameworks so Lucidic SDKs/templates are bundled in partner flows (docs/trust & integrations, homepage).

What is the rough total addressable market

Top-down context:

Direct LLM/agent observability is estimated around ~$510M in 2024, with projections to ~$8.1B by 2034, indicating a small but fast‑growing niche (Market.us). Adjacent categories like prompt‑engineering (~$222M in 2023 → ~$2.06B by 2030) and broader observability (~$2.9B in 2025 → ~$6.1B by 2030) suggest expansion paths but involve significant overlap (Grand View Research, Mordor Intelligence).

Bottom-up calculation:

Assume 10,000–20,000 organizations building production AI agents in the near term, with 25–40% adopting dedicated LLM/agent observability at $30k–$75k ACV; that implies a direct TAM on the order of ~$75M–$600M today, with room to grow as AI adoption broadens (AI Index adoption context).

Assumptions:

• 10k–20k organizations actively building multi‑step agent workflows over the next few years; adoption informed by high enterprise AI usage rates.
• Typical ACV for observability/testing in the $30k–$75k range, reflecting early‑stage, team‑sized deployments.
• Significant overlap with MLOps/observability budgets; figures reflect dedicated agent/LLM observability spend only.

Who are some of their notable competitors

• LangSmith (LangChain): End‑to‑end traces of multi‑step agents with dashboards, cost/latency insights, and built‑in evals; overlaps on tracing, replay, and experiments, tightly integrated with the LangChain stack (LangSmith, observability).
• Langfuse: Open‑source LLM observability with traces, prompt versioning, datasets/experiments, and eval scores; similar coverage to Lucidic but positioned for self‑hosting over a closed SaaS (observability, evaluation).
• Weights & Biases (W&B Weave): General ML experiment tracking and model ops with added LLM tracing, evaluations, and monitoring; overlaps on experiment logging and trace inspection but is not agent‑first (W&B Weave, LLM monitoring).
• PromptLayer: Prompt registry, versioning, A/B testing, backtests, and evaluations; competes on prompt lifecycle and testing but lacks full session replay/time‑travel and large‑scale agent simulation focus (site, docs).
• OpenAI Evals: Evaluation framework and hosted service for building rubrics and automated graders; overlaps on evals but not on automatic session tracing, replay, or agent re‑run UI (guide, repo).