Agenta — Open-source LLMOps platform for prompt management, evaluation, and observability. Best for Teams that need an open-source LLMOps platform with prompt versioning and evaluation, Product managers and domain experts who want to experiment with prompts without coding, Developers building LLM applications with multi-step agents needing full trace evaluation. Free to start; paid plans from $49/mo.
Teams that need an open-source LLMOps platform with prompt versioning and evaluationProduct managers and domain experts who want to experiment with prompts without codingDevelopers building LLM applications with multi-step agents needing full trace evaluationOrganizations that prefer self-hosted infrastructure for data privacy and control
Not ideal for
Small projects that only need a lightweight prompt editor without evaluation or observabilityTeams that prefer a fully managed SaaS solution with zero setup and maintenance overheadUsers looking for a simple chat interface or API-only tool without a collaborative UIEnterprise teams requiring advanced security features like SSO or role-based access control (not mentioned)
Agenta is a strong open-source choice for teams needing structured LLMOps with prompt versioning, evaluation, and observability. It’s ideal for collaborative teams but requires self-hosting for full control.
Agenta shines for teams that want to move from ad-hoc prompt management to a structured LLMOps workflow. Its key advantage is the integration of prompt engineering, evaluation, and observability in one open-source platform, reducing tool sprawl. The unified playground allows comparing prompts and models side-by-side, and the version history ensures traceability. Evaluation is robust with LLM-as-a-judge, custom code evaluators, and human annotation, plus the ability to evaluate full traces for agents. Observability features include tracing every request and annotating traces for debugging. The UI enables non-technical team members like PMs and domain experts to experiment without code, while the API provides parity for developers. However, as an open-source tool, you'll need to handle deployment and maintenance. Compared to commercial alternatives like LangSmith or Weights & Biases, Agenta offers more transparency and no vendor lock-in but may require more setup effort. Consider Agenta if you value open-source, need collaborative prompt workflows, and want to keep data on-premises. Pass if you prefer a fully managed SaaS with minimal ops overhead.
Skip Agenta if Skip Agenta if you need a fully managed LLMOps platform with zero infrastructure overhead and prefer turnkey SaaS pricing over open-source self-hosting.
Latest from Agenta
Updated 4 days ago
Across the latest 10 updates: 8 feature updates and 2 news mentions.
How likely is Agenta to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Agenta
Agenta is an open-source LLMOps platform that helps teams build reliable LLM applications by integrating prompt management, evaluation, and observability into a single workflow. Designed for product managers, developers, and domain experts, it centralizes prompts, evaluations, and traces to replace scattered tools like Slack, Google Sheets, and emails. Key features include a unified playground for side-by-side prompt and model comparison, automated evaluation with LLM-as-a-judge or custom evaluators, full trace evaluation for multi-step agents, and human annotation for feedback. Agenta also offers version history for prompts, model-agnostic support (works with any LLM provider), and the ability to turn production traces into test cases. It integrates with LangChain, LlamaIndex, and OpenAI, and provides both a UI for non-coders and a full API for programmatic control. Compared to closed-source alternatives, Agenta’s open-source model gives teams full control over their LLM development infrastructure.
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Key Features
Prompt management with version history
Unified playground for comparing prompts and models side-by-side
Automated evaluation using LLM-as-a-judge or custom code evaluators
Full trace evaluation for multi-step agents
Human annotation and feedback integration
Observability with request tracing and debugging
Turn production traces into test cases with one click
Model-agnostic support (any LLM provider)
UI for non-technical team members to edit prompts safely
Full API and UI parity for programmatic control
Monitor performance and detect regressions with live evaluations
Real-world workflow fit
Concrete scenarios for the personas Agenta actually fits — and what changes day-one when you adopt it.
Product Manager
You need to iterate on a customer support chatbot prompt without involving developers.
Outcome: Use Agenta's playground to compare 3 prompt variants with different models, run an automatic evaluator on 50 test cases, and pick the best performing version—all without writing code.
ML Engineer
You want to set up a CI/CD pipeline for prompts that automatically gates deployments based on evaluation scores.
Outcome: Configure webhooks in Agenta to trigger a GitHub Action, run evaluations on test sets, and only deploy prompts that exceed a 90% accuracy threshold, reducing regressions in production.
Startup CTO
You need to self-host an LLMOps platform on your own cloud to keep sensitive data private.
Outcome: Deploy Agenta via Docker or Kubernetes on AWS, integrate OpenTelemetry tracing, and use the self-hosted instance for all prompt management and evaluation, staying fully compliant with data policies.
Use Cases
Version prompts and collaborate with product managers using the playground
Run automatic and human evaluations on LLM outputs before production
Monitor production traces with OpenTelemetry to debug agent behavior
Capture user feedback and convert production failures into test sets
Build a CI/CD pipeline for prompts with automated evaluation gates
Self-host Agenta to keep LLM data within your infrastructure
Models Under the Hood
OpenAI GPT-4oOpenAI o1GPT-5.5 (if available via API)Cohere Command R+Llama 3.3 70BAny local or custom model via API
Limitations
The Hobby plan is limited to 2 users and 5k traces per month with 30-day retention. Pro plan caps at 10 seats. Trace overage costs can add up ($5 per 10k). Self-hosted enterprise requires contacting sales. The platform is web-only with API/CLI; no native mobile or desktop app.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agenta tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/month
Ideal for
Solo developer or two-person team exploring LLMOps with low volume (up to 5K traces/mo).
What this tier adds
Free entry point with unlimited prompts, 2 seats, 20 evaluations/mo, 5K traces/mo, 30-day retention, and community support.
Pro
$49/month
Ideal for
Small team (3-10 members) needing more evaluations and traces with 90-day retention for active projects.
What this tier adds
Adds unlimited evaluations, 10K traces/mo ($5/10K overage), in-app support, 90-day retention, and $20/seat for additional users up to 10.
Business
$399/month
Ideal for
Mid-size to large team (unlimited seats) with high trace volume (1M/mo) requiring RBAC, SOC2, and private Slack support.
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Pro plan: $20/seat/month for additional users beyond 3 included, max 10 seats
•Pro plan: $5 per 10k traces over the 10k/month included
•Business plan: $5 per 10k traces over the 1M/month included
•Enterprise self-hosting requires contacting sales for custom pricing
Where the pricing makes sense
The company stage and team size where Agenta's pricing actually pencils out — and where peers do it cheaper.
Agenta's Hobby tier ($0/mo, 2 users, 5K traces) is generous for solo developers or very small teams evaluating LLMOps. The Pro tier ($49/mo for 3 users) is competitive compared to LangSmith's $99/mo Developer tier, but trace overage ($5/10K) can surprise heavy users. Business ($399/mo) for unlimited seats and 1M traces suits mid-size teams, though LangSmith Team ($199/mo) may be cheaper for fewer traces. Enterprise is custom. Self-hosting is free but requires ops effort.
Setup time & first value
How long it actually takes to get something useful out of Agenta — broken out by persona, not the marketing-page minute.
For cloud-hosted Agenta: sign up, create a project, and start using the playground within 5 minutes. For self-hosting: deploy via Docker Compose or Kubernetes (requires 15-30 minutes of DevOps setup). Integrating OpenTelemetry tracing into your Python app takes about 10 minutes of code changes.
Switching to or from Agenta
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From LangSmith: export evaluations and traces via API, then import test sets into Agenta manually using SDK.
→From spreadsheet-based prompt management: manually copy prompts into Agenta's playground and set up version history.
→From custom in-house solution: use Agenta's API/SDK to programmatically insert prompts and traces.
Migrating out
↗To LangSmith: export Agenta traces using OpenTelemetry collector, and manually re-create evaluation sets.
↗To Weights & Biases Prompts: use Agenta's API to extract prompts and evaluations, then import via W&B API.
↗To a Git-based system: export prompts as JSON files from Agenta and version them in your repository.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-02-11: Published blog series on CI/CD for LLM prompts, prompt drift detection, and prompt versioning best practices.
•2026-02-11: Published guide for product teams on managing prompts without code.
