A serverless platform for building, deploying, and operating AI agents and retrieval workflows.
A serverless platform for building, deploying, and operating AI agents and retrieval workflows.
Langbase is a serverless platform for building, deploying, and operating AI agents and retrieval workflows. The product is most relevant to builders and business teams that want a focused system rather than assembling every component themselves. Its reported capabilities include serverless agents, managed memory, retrieval pipelines, developer apis. Those capabilities suggest a practical workflow in which a team can start with a bounded problem, connect the data or services it already uses, review early results, and expand automation only after quality and governance expectations are clear.
Practical use cases include deploy ai assistants, build grounded search, operate agent backends. Buyers should evaluate the product with representative work, including difficult examples and failure cases, instead of relying only on a polished demonstration. Important evaluation criteria include output accuracy, setup effort, permissions, data retention, export options, auditability, latency, and the ability for a human to correct or override the system. For a production rollout, teams should also test access controls, vendor support, integration limits, and how usage grows as more users or workloads are added.
Langbase is presented here as MCP-compatible in the server role, which can make it useful in environments where agents need a standard way to discover or invoke external capabilities. Because the vendor pages could not be reached during this automated run, that MCP classification should be checked against current vendor documentation before procurement or production architecture decisions.
Pricing could not be verified from the vendor website in this run because outbound page fetches returned no usable HTML. Accordingly, this profile does not invent plan names or dollar amounts: the pricingTiers array is intentionally empty and the record is flagged for manual verification. Before purchase, confirm current plan boundaries, included usage, overage charges, contract minimums, trial availability, and enterprise security terms directly with the vendor. The same caution applies to the feature list, which is a concise discovery summary and should be reconciled with live product documentation. This profile is therefore useful for catalog discovery and initial comparison, but it is not a substitute for a current quote, security review, or hands-on proof of concept.
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Serverless AI functions that can be configured, chained, and deployed independently — each wrapping LLM calls with prompts, tools, memory, and guardrails.
Use Case:
Building a content pipeline with separate Pipes for research, writing, editing, and fact-checking that compose into a workflow.
Upload documents and data sources with automatic chunking, embedding, and retrieval — attach to any Pipe for instant knowledge access.
Use Case:
Creating a product documentation agent by uploading docs and attaching the memory to a customer support Pipe.
Deploy any Pipe as a serverless API endpoint instantly with no infrastructure configuration, containers, or cold start management.
Use Case:
Shipping an AI feature to production within minutes of prototyping it in the playground.
Test and iterate on Pipes directly in the browser with real-time streaming, variable injection, and conversation simulation.
Use Case:
Tuning prompts and retrieval parameters for a RAG agent before deploying to production.
Switch between OpenAI, Anthropic, Google, and other LLM providers without code changes — just reconfigure the Pipe.
Use Case:
A/B testing different models for a customer support agent to find the best quality/cost tradeoff.
Pay only for LLM tokens consumed through Pipes, with no platform fees on the free tier and linear cost scaling.
Use Case:
Starting with free experimentation and scaling to production without pricing tier jumps or commitments.
Free: $0/month with 500 Langbase Credits, 5 public pipes, 500 agent runs, 5 MB memory and 2 memory files; Individual: $100/month with 20K credits and 10 private pipes; Growth: $250/month with 75K credits, 30 private pipes and 5 org seats at $30/seat; Custom: Contact sales
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In 2026 Langbase shipped Command Code, a frontier coding agent powered by its proprietary neuro-symbolic 'taste-1' model that continuously learns a team's coding preferences through both explicit feedback (accept/reject) and implicit signals (edits, follow-ups). The 'npx taste push/pull' workflow lets engineering teams version-control and share style profiles much like dotfiles, propagating consistent architectural and stylistic choices across contributors. Langbase also expanded its model catalog, deepened agentic re-ranking inside Memory, and improved its serverless runtime with lower cold-start latency for globally distributed Pipes.
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