LangChain vs Langfuse

New LangChain Blog Posts: Snyk Case Study, Stripe Link Integration, Deep Agents Updates

New blog posts added: 'How Snyk turned an internal support agent into a customer feature' (Oct 8, 2026), 'Agents that can pay: building Restock with Stripe's Link and Managed Deep Agents' (Oct 8, 2026), 'Revamping Skills in Deep Agents' (Oct 7, 2026), 'Managed Deep Agents v0.9: schedules, per-run configuration, and Slack reactions' (Oct 7, 2026), and 'How to Build a Model Router in the Harness' (Oct 1, 2026).

New in LangSmith: Engine v2, Managed Deep Agents, Fine-Tuning, and More

LangChain announces LangSmith Engine v2 with red teaming and automated testing, Managed Deep Agents v0.8 with new auth, memory, and channels, Trajectories for readable agent sessions, and LangSmith Fine-Tuning, among other updates.

Versos AI Launches NVIDIA NeMo Agents for Video Training Data Curation

Versos AI, a provider of video training data infrastructure for library owners and AI labs, announced a new capability built with NVIDIA NeMo. This allows AI teams to describe their dataset needs in natural language, which the platform then uses to search, evaluate, and assemble rights-cleared video footage into structured training datasets. The workflow accelerates model development by reducing manual search and review time. It also enhances discoverability of premium video libraries for AI partnerships. The technology leverages NVIDIA CUDA for accelerated inference, NVIDIA Nemotron Ultra for agent reasoning, and LangChain for agent orchestration. Versos AI will demonstrate this at IBC2026 in Amsterdam.

• Versos AI launched a new capability built with NVIDIA NeMo.
• The system interprets natural language requests to find and assemble video training data.
• Built on NVIDIA CUDA Toolkit, NVIDIA Nemotron Ultra, and LangChain.

See which projects drive your usage

A new chart shows your organization's units over time, grouped by project or type, with a CSV export. On Langfuse Cloud it is on the Billing page; self-hosted deployments get a new Usage page.

Langfuse CLI 1.0 and new evaluator features

Langfuse CLI 1.0 released, along with new evaluator template gallery and reusable evaluators for production evaluations.

Deploying Langfuse Open-Source LLM Observability

This technical guide explains how to deploy Langfuse, an open-source observability platform for LLM applications, using Docker Compose. The deployment uses PostgreSQL for metadata, ClickHouse for trace and metrics analytics, Redis for cache/queueing, and S3-compatible object storage for media/exports, with Traefik and Let's Encrypt providing TLS. The article includes required prerequisites (Linux server 4 vCPU / 16GB RAM, Docker + Docker Compose, domain A record), step-by-step environment and docker-compose configuration, first-run setup (create organization/project and API keys), and a test-trace example using the Langfuse SDK and an OpenAI-compatible client. Publication date: 2026-08-12.

• Langfuse is an open-source observability platform for LLM applications that traces prompts/responses, tracks token usage and cost, and provides debugging analytics.
• The guide deploys Langfuse via Docker Compose using Traefik (TLS), PostgreSQL (metadata), ClickHouse (trace/metrics analytics), Redis (cache/queue), and S3-compatible object storage.
• Container images and versions shown include traefik:v3.7.0, postgres:17, clickhouse/clickhouse-server:26.5.1-alpine, and redis:7-alpine; Langfuse images used are langfuse/langfuse:3 and langfuse/langfuse-worker:3.