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AI developer platform🔴Developer
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Langbase

A serverless platform for building, deploying, and operating AI agents and retrieval workflows.

Starting atFree
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In Plain English

A serverless platform for building, deploying, and operating AI agents and retrieval workflows.

OverviewFeaturesPricingUse CasesIntegrationsLimitationsFAQAlternatives

Overview

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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Key Features

Composable Pipes+

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.

Managed Memory (RAG)+

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.

One-Click Deployment+

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.

Integrated 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.

Multi-Model Support+

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.

Usage-Based Pricing+

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.

Pricing Plans

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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See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

🎯

Deploy AI assistants

⚡

Build grounded search

🔧

Operate agent backends

Integration Ecosystem

10 integrations

Langbase works with these platforms and services:

🧠 LLM Providers
OpenAIAnthropicGoogleMistralCoheretogether-aigroqfireworks
💬 Communication
Email
🔗 Other
api
View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Langbase doesn't handle well:

  • ⚠Langbase trades flexibility for managed convenience: deep customization of retrieval pipelines, embedding strategies, or low-level model serving is limited compared to self-hosted stacks. On-prem or air-gapped deployment is not a first-class option, which restricts use in highly regulated environments. Complex multi-agent orchestration with long-running state, human-in-the-loop checkpoints, or cyclical graphs is less mature than purpose-built frameworks like LangGraph or CrewAI. Documentation and community resources, while growing, remain smaller than the LangChain/LlamaIndex ecosystem. Pricing is usage-based, so high-volume memory queries or large-context LLM calls can become expensive without active monitoring.

Pros & Cons

✓ Pros

  • ✓Serverless APIs reduce initial runtime and deployment work
  • ✓Managed memory and retrieval cover common assistant requirements
  • ✓MCP server support can expose platform capabilities to compatible clients

✗ Cons

  • ✗Platform abstraction can make low-level debugging and portability harder
  • ✗Cost depends on request volume, storage, and underlying model usage
  • ✗Current quotas and commercial terms require manual verification

Frequently Asked Questions

What exactly are 'Pipes' in Langbase and how do they differ from a regular API call?+

Pipes are serverless AI agent endpoints that bundle a prompt, model configuration, tools, memory connections, and guardrails into a single deployable unit. Unlike a raw LLM API call, a Pipe is versioned, observable, and model-agnostic — you can swap from GPT-4 to Claude to Llama without changing your application code, and every invocation is logged with cost, latency, and quality metrics.

How does Langbase Memory compare to running my own vector database?+

Langbase Memory is a fully managed RAG layer that handles document ingestion, chunking, embedding generation, vector storage, semantic retrieval, and agentic re-ranking out of the box. Compared to self-hosting Pinecone, Weaviate, or pgvector, you skip the work of choosing embedding models, tuning chunk sizes, and building retrieval logic — but you trade some flexibility and pay per query rather than per stored vector.

What is Command Code and the taste-1 model?+

Command Code is Langbase's frontier coding agent powered by taste-1, a proprietary neuro-symbolic AI model developed by Langbase that continuously learns a developer's or team's coding preferences through explicit and implicit feedback. Teams can share taste profiles using 'npx taste push/pull,' so consistent style and architectural choices propagate across contributors automatically.

Can I use Langbase with open-source or self-hosted models?+

Yes. Langbase supports hundreds of LLMs including open-source models served via providers like Together AI, Groq, Fireworks, and Anyscale, alongside hosted models from OpenAI, Anthropic, Google, Mistral, and Cohere. You configure the model per Pipe, and Langbase handles routing, retries, and observability uniformly.

Is Langbase suitable for production workloads or just prototyping?+

Langbase is built specifically for production. The serverless runtime is globally distributed for low-latency inference, every Pipe ships with built-in logging and analytics, deployments are instant and versioned, and the platform exposes evaluation tooling for regression-testing agent quality. Many teams use it as their primary AI infrastructure rather than a prototyping sandbox.
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What's New in 2026

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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Quick Info

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AI developer platform

Website

langbase.com
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