OpenAgents
OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing.
Pick OpenAgents when you want a readable, forkable three-agent stack and you're prepared to own the maintenance. The Data Agent's Python/SQL execution and the 200+ plugin wiring give you something concrete to inspect, and docker-compose keeps local evaluation cheap. Pass if you need a maintained release cadence, SLAs or a support contract — the project's own news stops in October 2023.
Verified 2h ago · liveness 87/100 · cite: rightaichoice.com/tools/openagents
- Researchers who want open code to evaluate how language agents fail on real-world tasks
- Developers seeking an Apache-2.0 full-stack baseline to fork into a custom agent app
- Analysts who want an agent that writes and runs Python and SQL for data search, cleanup and charts
- Teams that must self-host to keep agent data off a vendor cloud
- Non-technical users expecting a polished consumer product with responsive support
- Teams that need a maintained release cadence — the project's news stops in October 2023
- Production workloads depending on SLAs, guaranteed uptime or a support contract
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Skip OpenAgents if you need a supported product with a maintenance cadence — its public news stops October 2023, there is no SLA, and the Web Agent's Chrome extension breaks as sites change.
OpenAgents costs $0 in both forms: Apache-2.0 source you self-host, and a free hosted demo. The real spend is engineering time — Docker setup plus ongoing plugin and Chrome-extension upkeep. Compare against ChatGPT Plus (roughly $20/mo scale) if you want a managed assistant, or AutoGen (free framework) if you only need scaffolding, not a running three-agent stack.
In short
OpenAgents — OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing. Best for Researchers who want open code to evaluate how language agents fail on real-world tasks, Developers seeking an Apache-2.0 full-stack baseline to fork into a custom agent app, Analysts who want an agent that writes and runs Python and SQL for data search, cleanup and charts. Free to use.
What's new in OpenAgents
Checked 7 days agoAcross the latest 4 updates: 1 feature update, 2 launches and 1 news mention.
We've reached 3,000 users!
The project marked 3,000 users and asked for patience as the demo servers handled unexpectedly high traffic.
Try out our Lemur, the SOTA and open-sourced foundation models for language agents
Release of Lemur, open-sourced foundation models for language agent tasks, reported as matching ChatGPT on 15 agent tasks.
We've released OpenAgents platform code for all three agents, server backend and frontend
Platform code for the Data, Plugins and Web agents plus the server backend and frontend became available for localhost setup.
We've released OpenAgents demos, including Data, Plugins, and Web agents
Initial public demos for the three agents shipped alongside tutorials and use cases.
What people actually say about OpenAgents — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
56 mentions across 3 sources (Hacker News, YouTube, Bluesky) · researched Jul 17, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Fully open-source (Apache 2.0) with modifiable code for transparency.
- +Model-agnostic: works with Claude, GPT, Gemini, and more.
- +Supports multi-agent orchestration in a single workspace.
- +Local deployment via Docker ensures data privacy.
- +A2A-compatible framework for inter-agent communication.
- −Breaking API changes in v0.2.0 caused migration headaches.
- −Security vulnerability (CVE-2025-6282) requires urgent patching.
- −Steep learning curve for non-developers; Docker required.
- −Limited updates since late 2023 raise maintenance concerns.
- −Web UI can be rough and lacks polish of commercial tools.
Viability Score
How well maintained and how widely used is OpenAgents? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Data Agent writes and executes Python and SQL to search, handle, manipulate and visualize data
- Plugins Agent connects to 200+ handpicked third-party daily-use plugins
- Web Agent drives a Chrome extension to navigate and explore websites automatically
- Auto Plugin Selection searches for and suggests plugins matching a described task
- Concurrent plugin use chains Klook, a currency converter and WeatherViz in one request
- Chat-based web UI optimized for swift responses and graceful handling of common failures
- Free hosted online demo covering the Data, Plugins and Web agents
- Full-stack localhost deployment of frontend, backend and real_agents
- Dockerfile and docker-compose.yml for running the whole stack locally
- Apache-2.0 codebase for forking and redistribution
- Open-sourced Lemur foundation models for language agent tasks
- Google Maps route planning handled conversationally by the Web Agent
- Twitter posting and Google Form completion from plain-language requests
- READMEs in English, Chinese, Japanese and Korean
About OpenAgents
OpenAgents is an Apache-2.0 platform from the xlang-ai team, published as a COLM 2024 paper, for using and hosting language agents in everyday tasks. It ships three working agents behind one chat web UI: a Data Agent that writes and executes Python and SQL to search, handle, manipulate and visualize data; a Plugins Agent wired to 200+ handpicked daily-use tools; and a Web Agent that drives a Chrome extension to navigate and explore sites on its own. All three are hosted on a free public demo. The README frames it as a two-sided project. General users get a chat front end optimized for swift responses and graceful handling of common failures. Developers and researchers get the full stack — frontend, backend and real_agents folders — plus a Dockerfile and docker-compose.yml to stand the whole thing up on localhost as a base for building and evaluating new agents. Docs ship in English, Chinese, Japanese and Korean, alongside a code of conduct and contribution guide. Two plugin behaviors are worth noting before you fork. Auto Plugin Selection searches for and suggests plugins matching a described task, and concurrent plugin use can chain Klook, a currency converter and WeatherViz into a single trip plan. The Web Agent handles Google Maps navigation, Twitter posting and Google Form sign-ups from plain language. The team also open-sourced Lemur, foundation models for language agent tasks, reported as matching ChatGPT on 15 agent tasks. Positioning sits between managed assistants like ChatGPT Plus and code-first agent frameworks such as AutoGen and LangGraph. You trade product polish for Apache-2.0 code you can read, fork and self-host. The repo carries 4.9k stars and 527 forks across 61 commits, and its news feed stops at the October 2023 3,000-user milestone — budget accordingly.
Behind the Verdict
The most common reason to open this repo is to read it, not to deploy it. If you're a researcher who wants a full-stack reference for how a data agent, a tool-calling agent and a browser-driving agent are wired together, OpenAgents gives you three distinct architectures in one Apache-2.0 codebase with a COLM 2024 paper behind them. That's a rare combination and it's the real product here. We'd reach for it when the task involves letting an agent run Python or SQL against a dataset and report back with charts. The Data Agent's search, handle, manipulate and visualize loop is the most self-contained of the three, and it's the one that holds up best outside a live demo. Docker-compose keeps local evaluation cheap enough to run on a laptop. Where it bites: the Plugins Agent is only as reliable as the third-party services it points at, and those change without warning. Concurrent plugin chaining (Klook plus a currency converter plus WeatherViz for a trip plan) is impressive when it works and brittle when one endpoint shifts. Plan on debugging that yourself; there's no vendor to escalate to. The Web Agent's Chrome-extension approach is instructive but thin-skinned. Google Maps navigation, Twitter posting and Google Form completion are documented, and any major site redesign can break the flows. For production browser automation, a maintained commercial service or a framework with active releases will save you time. The stale signal matters. The news feed stops at the October 2023 3,000-user milestone and 61 commits, so treat OpenAgents as a study and starting point rather than something you inherit updates from. If you need a cadence of releases and fixes, look at LangGraph or AutoGen instead; if you want code you can read end to end and reshape, this remains a solid
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Real-world workflow fit
Concrete scenarios for the personas OpenAgents actually fits — and what changes day-one when you adopt it.
Points the Data Agent at a CSV and a SQL database, asks in plain English for a cleaned dataset and a chart, and reviews the Python it wrote before rerunning it locally.
Outcome: Reports generated without hand-writing pandas or SQL, with the executed code visible for audit.
Clones the repo, runs docker-compose.yml on localhost, swaps in a custom agent under real_agents and wires it to the existing frontend chat UI.
Outcome: A working internal agent app on day one instead of a blank repository.
Uses the three shipped agents plus the published paper and Lemur models to run real-world tasks and record where each agent fails.
Outcome: Reproducible failure data with open code to cite, rather than a black-box vendor benchmark.
Use Cases
- Query a SQL database using plain English to generate reports without writing code.
- Automate booking a restaurant reservation by chaining a search plugin with a calendar tool.
- Scrape product prices from multiple e-commerce sites and compile them into a spreadsheet.
- Analyze CSV data with Python commands expressed in natural language.
- Use the Web Agent to autonomously fill out online forms or navigate websites.
- Fork the full stack into a custom agent application and swap in your own agent methods.
- Benchmark how a language agent fails on a multi-step real-world task using the published code.
Models Under the Hood
as of 2026-10-18
Limitations
- OpenAgents hosts three language agents — Data, Plugins and Web — but local deployment requires comfort with Docker and a backend/frontend setup.
- The Web Agent depends on a Chrome extension, so it can break when the sites it drives change, and third-party plugin maintenance is on you.
- The hosted demo is described in the project's own news as straining under unexpectedly high traffic, with no SLAs or guaranteed uptime.
- The public news feed stops at October 2023, at the 3,000-user milestone, so no recent release cadence is visible.
as of 2026-09-30
Verification history
We have re-verified OpenAgents 80 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 80 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published OpenAgents tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hosted Demo
$0
Self-hosted (Apache-2.0)
$0
Where the pricing makes sense
The company stage and team size where OpenAgents's pricing actually pencils out — and where peers do it cheaper.
OpenAgents costs $0 in both forms: Apache-2.0 source you self-host, and a free hosted demo. The real spend is engineering time — Docker setup plus ongoing plugin and Chrome-extension upkeep. Compare against ChatGPT Plus (roughly $20/mo scale) if you want a managed assistant, or AutoGen (free framework) if you only need scaffolding, not a running three-agent stack.
Setup time & first value
How long it actually takes to get something useful out of OpenAgents — broken out by persona, not the marketing-page minute.
Hosted demo: minutes, no install. Local self-hosting: an afternoon for a developer comfortable with Docker and Python — clone, run docker-compose.yml, confirm frontend, backend and real_agents all start. Forking into a custom agent: days, depending on how much of the agent layer you replace. Researchers reading the paper and Lemur release first should budget a few hours before touching code.
Switching to or from OpenAgents
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ChatGPT Plus: replicate your data-analysis prompts in the Data Agent, which executes Python and SQL you can inspect.
- →From AutoGen: port your agent roles into real_agents and reuse the shipped chat frontend instead of building one.
- →From a manual browser-scraping script: hand the navigation task to the Web Agent's Chrome extension via conversation.
- ↗To ChatGPT Plus: if you need managed uptime and support, move data-analysis and browsing tasks to a hosted assistant.
- ↗To AutoGen or LangGraph: keep the framework, drop the OpenAgents frontend and hosting layer if you only want orchestration.
- ↗To a custom in-house agent: fork the Apache-2.0 code and replace real_agents, keeping backend and frontend.
Integrations
Resources & Guides
- Resourcegithub.com
OpenAgents/README.md at main · xlang-ai/OpenAgents
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild - xlang-ai/OpenAgents
- Resourcegithub.com
OpenAgents/CONTRIBUTING.md at main · xlang-ai/OpenAgents
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild - xlang-ai/OpenAgents
- Resourcegithub.com
OpenAgents/docker-compose.yml at main · xlang-ai/OpenAgents
[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild - xlang-ai/OpenAgents
Tutorials & Learning
YouTube returned 6 videos for “OpenAgents”, and we withheld 6: 6 could not be judged, because “OpenAgents” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about OpenAgents.
Official links
Tools that pair well with OpenAgents
Common stack mates teams adopt alongside OpenAgents, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Openagents vs Stan Ai
If you manage HOAs or property, STAN.AI delivers purpose-built automation with omni-channel agents, meeting minutes, and bid tracking—saving hours weekly. OpenAgents is an open-source research tool for building custom agents, best for technical users who want full control. Choose STAN.AI for ready-to-use property management; choose OpenAgents for experimentation.
Klippa vs Openagents
If you need a free, open-source platform to experiment with language agents for data analysis, plugins, and web browsing, OpenAgents is the clear choice. For enterprise-grade document processing with OCR, fraud detection, and compliance certifications (ISO 27001, GDPR), Klippa delivers end-to-end automation. Your decision hinges on whether you prioritize customizability and cost savings or robust, production-ready document workflows.
Oneke vs Openagents
Choose OneKE if your goal is to extract structured knowledge (entities, relations, events) from Chinese/English text with a customizable, open-source model. Choose OpenAgents if you need a deployable agent platform that can browse the web, query databases, and leverage hundreds of plugins via a chat interface. They solve fundamentally different problems — one is a specialized extraction engine, the other a general-purpose agent framework.
Openagents vs Sust Global
If your organization needs institutional-grade physical climate risk analytics across millions of assets, Sust Global (now backed by ISS Stoxx) is the specialized choice. If you're a researcher or developer wanting to experiment with language agent frameworks for data, plugins, or web tasks, OpenAgents offers a free, open-source playground that you can run locally. The tools address completely different domains, so your decision hinges on whether you need climate risk intelligence or flexible language agent prototyping.
Deep Waste vs Openagents
OpenAgents and Deep Waste serve entirely different needs. If you're a researcher or developer building custom language agents, OpenAgents is the right open-source foundation. If you need an AI-powered waste sorting solution with engagement features for a campus or community, Deep Waste is purpose-built. Choose based on your domain — there's little overlap.
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