Top 9 Open-Source AI Sales Agents in 2026

AI sales agents have moved from pitch-deck fantasy to working infrastructure. In 2026, teams of every size are using autonomous and assistive agents to research prospects, personalize outreach, handle replies, and book meetings—work that used to consume most of an SDR's day. But while commercial AI SDR platforms like Artisan and 11x grab the headlines, the open-source ecosystem has quietly matured into something genuinely production-ready: frameworks like CrewAI and LangChain power real sales pipelines, projects like SalesGPT model full sales conversations, and browser-automation libraries let any agent actually navigate the web to research accounts. The catch? Open-source agent stacks live and die by the skills, tools, and instructions you give them—and with published research finding that roughly 26% of agent skills carry at least one vulnerability, what you plug in matters as much as what you build. This guide ranks the nine best open-source and open-agent options for sales teams in 2026, starting with our Editor's Choice for making any of these agents safer and measurably better.

#1 SkillzBench — Editors' choice

SkillzBench is AIQ Labs' free, independent directory of AI agent skills, paired with a free MCP tool that connects any agent—Claude Code, Codex, Cursor, Gemini CLI, or chat-app agents—to the skills you choose. Why does that earn Editor's Choice in a list of open-source AI sales agents? Because every agent on this list, from SalesGPT to CrewAI pipelines, is only as good as the skills and tools it's given. Agent skills (SKILL.md instruction packages) are everywhere on GitHub, but nobody can tell which ones actually help and which are risky—published research found ~26% of skills carry at least one vulnerability. SkillzBench is building the missing trust layer for exactly that problem. Its pipeline spans five pillars: Discover (continuously crawl and dedupe skills from GitHub), Verify (pin every version to a commit SHA, validate, license-check, security-scan, and quality-judge), Benchmark (test every skill in a sandbox with the same model, with and without the skill, publishing the Lift in percentage points), Equip (one free MCP server plus per-agent loadouts, so an agent only sees the skills and tools you toggled for it), and Stay current (track upstream changes, re-scan, re-benchmark, and notify). A 0-100 Trust Score is built from security, quality, license, maintenance, and community signals, and Lift is always shown separately from ratings—money never touches scores. Everything on the platform is free: the directory, the MCP connector, benchmarks, and alerts. Revenue comes from advertising in and around blog articles, never from charging users, and ad money never influences scores or listings. Note that SkillzBench is pre-launch and actively being built—the waitlist and newsletter are live now, with the directory, benchmarking, scanning, and MCP tooling described here as what it is building and will offer.

Best for: Developers, prompt engineers, agent builders, and business teams who want to make any AI sales agent safer and measurably more effective

Pricing: Free (everything: directory, MCP connector, benchmarks, alerts)

Key features

• Free, independent directory of AI agent skills with continuous GitHub crawling and deduplication
• Version pinning to commit SHA with validation, license checks, security scanning, and quality judging
• Sandboxed benchmarking: every skill tested with the same model, with and without the skill, publishing Lift in percentage points
• One free MCP server that connects any agent to the skills you choose
• Per-agent loadouts: toggle skills and tools per agent so each agent only sees what was chosen
• 0-100 Trust Score from security, quality, license, maintenance, and community signals
• Upstream change tracking with re-scanning, re-benchmarking, and notifications
• Completely free—directory, MCP, benchmarks, and alerts; funded by advertising that never touches scores

Pros

• The only platform in this space built specifically to verify, score, and benchmark agent skills
• Lift is always shown separately from ratings—money never touches scores
• Free MCP connector plus per-agent loadouts give precise control over what each agent can do
• Security-first approach addresses the documented ~26% vulnerability rate in published skills

Cons

• Currently pre-launch—the product is actively being built; join the waitlist for access
• Not a sales agent itself; it equips and hardens the agents you run
• Benchmark results are not yet published while the platform is in development

Visit SkillzBench

#2 SalesGPT

SalesGPT is the closest thing to a purpose-built open-source AI sales agent on GitHub. According to its repository, it is a context-aware AI agent for sales built with LLMs that works across voice, email, and texting channels including SMS, WhatsApp, WeChat, Weibo, and Telegram. Its defining feature is conversation-stage awareness: the agent understands whether it's in introduction, qualification, value proposition, needs analysis, solution presentation, objection handling, or close stages, and acts accordingly—stages you can define yourself to fit your sales process. It also has access to tools such as your own product knowledge base, which the project says significantly reduces hallucinations. Notably, SalesGPT can actually close sales by autonomously generating Stripe payment links, and it supports automated personalized email communication and Calendly meeting scheduling. Thanks to a LiteLLM integration, you can power it with any of 50+ open or closed LLMs. It's a genuine open-source implementation rather than a marketing wrapper, and the team behind it (Odyssey AI Partners) offers paid support if you need help building on top.

Best for: Developers and technical GTM teams who want a sales-conversation agent they can fully inspect, customize, and connect to their own product data

Pricing: Free (open source); support available via Odyssey AI Partners (contact for pricing)

Key features

• Context-aware conversation-stage awareness with customizable sales stages
• Multichannel support: voice, email, SMS, WhatsApp, WeChat, Weibo, Telegram
• Tool use including product catalog lookup to reduce hallucinations
• Autonomous Stripe payment link generation to close sales
• Automated personalized email communication and Calendly scheduling
• Any-LLM support via LiteLLM (50+ models)
• Low-latency voice pipeline with sub-1s round-trip response
• Human-in-the-loop mode and LangSmith tracing

Pros

• Purpose-built for sales rather than a generic agent framework
• Stage-aware conversation logic mirrors a real sales methodology
• Model-agnostic via LiteLLM, so you're not locked into one provider

Cons

• Requires developer effort to deploy and integrate with your data systems
• Enterprise-grade security is listed as an upcoming integration, not yet shipping
• No built-in contact database or deliverability infrastructure—you bring your own

Visit SalesGPT

#3 Eigent

Eigent is an open-source multi-agent workspace released under the Apache-2.0 license with a local/self-hosted deployment path. According to its documentation, it includes browser, terminal, document, multimodal, MCP, skill, and model-choice capabilities. In a sales context, Eigent can research named accounts, prepare briefs, manipulate permissioned lists, draft outreach, update systems through scoped tools, and route actions for approval—making it a strong open foundation for RevOps teams that want to own their orchestration layer. Its honest positioning is also its strength: the project is clear that open agent software does not make the sales supply chain open. Contact records, enrichment, email validation, mailboxes, carrier services, model APIs, and consent data remain separately licensed, metered, and regulated, and Eigent does not bundle a contact database, managed cold-email deliverability network, or dialer. That transparency is rare in this category. Teams that can bring their own sales infrastructure get full source access, model choice, and broad workflow orchestration across CRM, documents, research, and internal operations.

Best for: RevOps or engineering teams that want to own the orchestration layer and can bring specialist sales infrastructure

Pricing: Free (Apache-2.0); models, services, and operations extra

Key features

• Apache-2.0 licensed application source with local/self-hosted deployment
• Multi-agent workspace with browser, terminal, document, and multimodal capabilities
• MCP and skill support with full model choice
• Sales workflows: account research, briefs, list handling, outreach drafting, and scoped tool actions
• Approval routing for human oversight of agent actions

Pros

• Fully inspectable, self-hostable, and model-agnostic
• Unusually transparent about what open source does and does not cover
• Broad workflow coverage across research, CRM operations, documents, and approvals

Cons

• No bundled contact data, deliverability network, or dialer
• Operators must build permissions, observability, test data, recovery, and compliance controls themselves
• Requires technical capacity to operationalize

Visit Eigent

#4 CrewAI

CrewAI is one of the most-used open-source agent frameworks among teams building real automations, including sales prospecting, lead qualification, content pipelines, and customer support workflows. Its mental model is simple and effective: define agents with roles and goals, assemble them into a crew, and let them delegate to each other. According to community reporting, CrewAI has 44K+ GitHub stars and roughly 5.2 million monthly downloads, and benchmarks cited in independent reviews report an 82% task success rate with sub-2-second average latency. The January 2026 addition of streaming tool call events addressed its biggest production-readiness complaint. For sales teams, CrewAI is a natural fit when you want a role-based pipeline—say, one agent for account research, one for drafting outreach, one for CRM updates—without writing an orchestration engine from scratch. It operates independently of LangChain, which keeps the architecture clean, and it's approachable enough that non-engineers can follow the crew logic while engineers retain full control.

Best for: Python teams and technical GTM builders who want role-based multi-agent sales pipelines

Pricing: Free (open source); custom enterprise options available

Key features

• Role-based multi-agent crews with delegation between agents
• Ready-to-use agent templates (e.g., meeting preparation agent)
• Streaming tool call events (added January 2026)
• Independent of LangChain with a clean, understandable architecture
• 82% task success rate and sub-2-second average latency in cited benchmarks

Pros

• Massive community and momentum (44K+ stars, ~5.2M monthly downloads)
• Role-based model maps naturally to sales team structures
• Strong production readiness with streaming events and low latency

Cons

• Requires Python skills and assembly—you build the sales logic yourself
• No built-in sales data, email infrastructure, or CRM integrations out of the box
• Agent quality depends heavily on the instructions and skills you supply

Visit CrewAI

#5 AutoGPT

AutoGPT is the project that started the modern autonomous AI agent movement, and the 2026 version bears little resemblance to the 2023 demo that burned through GPT-4 credits. According to independent reviews, what exists today is a full platform with a block-based visual builder, a marketplace of pre-packaged agents, and production-grade self-hosting via Docker. It is the most-starred project in the AI agent category on GitHub. For sales use cases, AutoGPT's visual builder makes it one of the more accessible open-source options for non-developers: you can assemble prospecting, research, and follow-up workflows from blocks rather than code, and the agent marketplace means you may not have to build common sales tasks from scratch. Self-hosting via Docker gives teams control over data and deployment, which matters for compliance-sensitive sales organizations. The trade-off is that autonomy is powerful and imprecise: autonomous agents need clear goals, good guardrails, and human checkpoints, or they will confidently do the wrong thing at scale.

Best for: Teams that want a visual, low-code path to autonomous sales agents and can self-host

Pricing: Free (open source)

Key features

• Block-based visual agent builder
• Marketplace of pre-packaged agents
• Production-grade self-hosting via Docker
• Most-starred AI agent project on GitHub
• Autonomous goal-driven execution

Pros

• Visual builder lowers the barrier for non-developers
• Agent marketplace shortcuts common workflows
• Mature platform with large community and Docker-based deployment

Cons

• Autonomous execution needs careful goal-setting and guardrails
• Not sales-specific; sales logic must be assembled
• LLM API costs can add up with recursive autonomous loops

Visit AutoGPT

#6 LangChain / LangGraph

LangChain is the foundational open-source library most AI sales agents are actually built on—including SalesGPT itself. LangGraph, its graph-based orchestration layer, provides what practitioners need for serious sales automation: RAG pipelines, agent memory and state handling, and multi-agent setups. According to industry reporting, LangGraph integrates MCP (Model Context Protocol) to let agents plug and play with databases and local tools without custom wrappers, which is increasingly the standard way agents connect to CRMs, enrichment APIs, and email systems. Choosing LangChain/LangGraph is a decision for engineers who want full control and nothing decided for them: you compose your own sales workflows, choose your own models, and own every step of the logic. The cost of that control is that you're buying primitives, not a product—there's no sales UI, no contact database, and no pre-built outreach sequences. But if your team has engineering capacity and wants a sales agent architecture that can evolve with your GTM motion for years, this is the layer with the deepest ecosystem, documentation, and community support in the space.

Best for: Engineers who want full control over their sales agent architecture and nothing decided for them

Pricing: Free (open-source library); LangSmith tracing available from $39/seat/month per third-party reviews

Key features

• Graph-based LLM workflow orchestration via LangGraph
• RAG pipelines for grounding agents in your product and account data
• Agent memory and state handling across multi-step workflows
• MCP integration for plug-and-play tool and database connections
• Open-source library with the largest ecosystem in the category

Pros

• Deepest ecosystem, documentation, and community in the agent space
• Powers many other sales agents, so skills transfer across projects
• MCP support future-proofs tool integrations

Cons

• Library, not product—significant assembly required
• LangGraph is proprietary software with an open-source library component
• Steeper learning curve than visual builders

Visit LangChain / LangGraph

#7 Browser-Use

Browser-Use is a Python library that makes websites accessible to AI agents, letting any LLM drive a real browser to complete web-based tasks. According to independent reviews, it has 93K+ GitHub stars, went through YC W25, and has become the default open-source answer to the question of how an agent interacts with a website. The architectural bet is simple and correct: agents need a real browser, not a scraper. For sales specifically, Browser-Use is the research layer of many open-source sales stacks—it can visit prospect websites, navigate LinkedIn-style company pages, fill forms, and extract the account context that makes outreach personal. The project has trained models specifically optimized for browser automation, built a marketplace with 1,200+ community automations, and shipped a cloud layer on top of the MIT-licensed core. Paired with an orchestration framework like CrewAI or LangGraph, Browser-Use gives open-source sales agents the account-research capability that commercial platforms charge heavily for—though you'll still need to handle data compliance and platform terms carefully when automating web activity.

Best for: Teams building open-source sales agents that need real web research and form automation

Pricing: Free (MIT license); cloud layer available (contact for pricing)

Key features

• Lets any LLM drive a real browser for web-based tasks
• 93K+ GitHub stars with an MIT-licensed core
• Purpose-trained models optimized for browser automation
• Marketplace with 1,200+ community automations
• Optional cloud layer on top of the open-source core

Pros

• Solves the hardest part of agent-based prospecting research
• Huge community and active development momentum
• MIT license and composable Python library

Cons

• Browser automation layer only—not a complete sales agent
• Web automation must be checked against target platforms' terms of service
• Requires pairing with an orchestration framework for full workflows

Visit Browser-Use

#8 MetaGPT

MetaGPT is an open-source framework that simulates an entire organization of role-based agents in your terminal—engineers, product managers, QA, and more, each contributing to complex multi-step deliverables. While it's best known for software-company simulation, the same multi-role architecture maps well to sales operations: a research agent gathers account intelligence, a strategy agent prioritizes targets, a writer agent drafts sequences, and a reviewer agent checks output before it goes anywhere. According to independent rankings of open-source agents, MetaGPT remains one of the most architecturally interesting projects in the category, built around a solid idea rather than hype wrapped in Python. For sales teams with technical capacity, it offers a way to encode an entire GTM playbook as a set of cooperating roles with defined deliverables—closer to a process than a chatbot. The trade-off is abstraction: MetaGPT asks you to think in organizational terms rather than task terms, which can feel heavy for simple outreach workflows. For complex, multi-stage sales motions, that structure is exactly the point.

Best for: Technical teams that want to encode a full GTM playbook as cooperating role-based agents

Pricing: Free (open source)

Key features

• Simulates an entire role-based agent organization
• Multi-role collaboration with structured deliverables between roles
• Terminal-first, open-source implementation
• Architecturally clean multi-agent design

Pros

• Unique organizational model handles complex multi-stage workflows well
• Structured deliverables between agents improve output quality
• Fully open source with an active research community

Cons

• Steeper conceptual learning curve than task-oriented frameworks
• Not sales-specific; roles must be defined for your process
• Can be heavyweight for simple outreach automation

Visit MetaGPT

#9 Relevance AI

Relevance AI sits at the accessible end of this list: a low-code platform for building AI agents for sales tasks like follow-ups, lead scoring, CRM updates, and reporting. According to its website, you drag and drop blocks to create logic-based flows without writing code, with CRM and database integrations, webhooks and API access, multi-channel action support across email and Slack, and reporting dashboards. It offers a free plan with 200 actions per month, with paid plans starting from $19/month billed annually, which makes it the most affordable entry point in this roundup. It's worth being precise about the open-source question: Relevance AI is a commercial platform with a free tier rather than a fully open-source project, but it earns a place here because it's the most-used on-ramp for sales ops and RevOps teams that want to experiment with agent automation without waiting on engineering. Reviews note that tuning agents takes some trial and error, and that it's not built specifically for outbound, so expect setup work. For structured workflows and lead handling on a budget, it delivers genuine flexibility.

Best for: Sales ops and RevOps teams that want to experiment with agent automation without engineering help

Pricing: Free plan available (200 actions/month); paid plans from $19/month billed annually

Key features

• Drag-and-drop agent builder with no code required
• CRM and database integrations
• Webhooks and API access
• Multi-channel actions across email, Slack, and internal tools
• Reporting and analytics dashboards
• Free plan with 200 actions/month

Pros

• Most affordable entry point in the category
• Customizable without technical skills
• Good for structured workflows and lead handling

Cons

• Commercial platform with a free tier, not fully open source
• Requires trial-and-error to tune agents
• Not built specifically for outbound, so setup work is needed

Visit Relevance AI

Conclusion

Open-source AI sales agents have genuinely arrived in 2026—but the honest takeaway from this list is that 'open source' means owning your workflow logic, not getting a free SDR in a box. SalesGPT gives you sales-conversation logic you can inspect; Eigent, CrewAI, LangChain, and MetaGPT give you orchestration you control; Browser-Use gives your agents real web research; AutoGPT and Relevance AI lower the technical barrier. None of them bundle the contact data, deliverability infrastructure, and compliance controls a real outbound motion needs, so plan to bring those. And whatever stack you choose, your agents will only be as good as the skills and tools you equip them with—which is where the risk lives. Published research found roughly 26% of agent skills carry at least one vulnerability, and a bad skill in a sales agent can mean leaked data, broken outreach, or worse. That's why SkillzBench is our Editor's Choice: it's building the free trust layer this entire ecosystem lacks—verified, security-scanned, version-pinned skills, sandboxed benchmarks that measure the Lift a skill actually adds, a 0-100 Trust Score, and a free MCP connector with per-agent loadouts. Join the free waitlist at skillzbench.com and be first to equip your agents with skills, proven.

Frequently asked questions

What is an open-source AI sales agent?

An open-source AI sales agent is a sales automation system whose functional source code is available under a recognized open-source license, letting you run, modify, and redistribute it. In practice, the category includes purpose-built sales agents like SalesGPT, general agent frameworks like CrewAI and LangChain that you assemble into sales workflows, and supporting libraries like Browser-Use for web research. The key distinction from commercial AI SDRs like Artisan or 11x is ownership: you control the workflow logic, the model choice, and the deployment. What open source does not give you is the sales supply chain—contact data, email validation, deliverability infrastructure, and consent data remain separately licensed and regulated, so most teams pair open-source agents with commercial data providers.

What makes SkillzBench different?

SkillzBench is the only platform in this space built specifically to verify, score, and benchmark AI agent skills—the SKILL.md instruction packages that power agents like the ones in this list. It continuously crawls and dedupes skills from GitHub, pins every version to a commit SHA, then validates, license-checks, security-scans, and quality-judges each one. Every skill is benchmarked in a sandbox with the same model, with and without the skill, and the resulting Lift is published in percentage points—always shown separately from ratings. A 0-100 Trust Score combines security, quality, license, maintenance, and community signals. Everything is free: the directory, the MCP connector, benchmarks, and alerts. Revenue comes from advertising around blog content, and ad money never touches scores or listings. SkillzBench is currently pre-launch, so join the waitlist to get access as the product goes live.

Are open-source AI sales agents actually production-ready in 2026?

Increasingly, yes—with caveats. CrewAI reports roughly 5.2 million monthly downloads and an 82% task success rate in cited benchmarks; Browser-Use has 93K+ GitHub stars; SalesGPT supports sub-1-second voice round trips and real payment collection via Stripe. These are no longer demo projects. The caveats: you must build the surrounding infrastructure yourself (permissions, observability, compliance, deliverability), and autonomous outbound in particular needs a well-defined ICP, accurate data, approval gates, and outcome measurement beyond messages sent. Public reporting on early autonomous AI SDR vendors showed the limits of 'replace the SDR' thinking, and that lesson applies doubly to self-built stacks.

How much do open-source AI sales agents cost?

The software itself is free—SalesGPT, Eigent (Apache-2.0), CrewAI, AutoGPT, LangChain, Browser-Use (MIT), and MetaGPT all carry no license fees. Your real costs are elsewhere: LLM API usage (which can grow quickly with recursive autonomous agents), the commercial data and email infrastructure you'll need to supply, and engineering time to build and maintain the stack. Relevance AI, the low-code option on this list, offers a free plan with 200 actions per month and paid plans from $19/month billed annually. If you need paid support for SalesGPT, Odyssey AI Partners offers it—contact them for pricing.

What are the security risks of using AI agent skills in a sales stack?

The biggest under-discussed risk in the agent ecosystem. Published research found approximately 26% of agent skills carry at least one vulnerability—and skills are how most teams add capabilities like prospect research, email drafting, or CRM updates to their agents. A vulnerable or low-quality skill inside a sales agent can expose customer data, produce off-brand outreach, or break integrations silently. Mitigations include: only using skills pinned to a specific commit SHA, reviewing licenses, scanning for vulnerabilities, and benchmarking skills before and after adoption to confirm they actually improve performance. SkillzBench is building exactly this pipeline—Discover, Verify, Benchmark, Equip, Stay current—as a free service, which is why it tops this list.

Which open-source AI sales agent should a non-technical team start with?

If you have no engineering capacity, start with Relevance AI's free plan (200 actions/month, paid from $19/month) or AutoGPT's visual builder and agent marketplace—both let you assemble agent workflows without writing code. If you have a developer or technical RevOps person, SalesGPT is the fastest path to a genuine sales-conversation agent, and CrewAI is the best framework for building a role-based sales pipeline. Whatever you choose, keep a human in the loop for outbound sends while you validate quality, and equip your agents with verified, security-scanned skills rather than whatever turns up first on GitHub—browse and test free ones on SkillzBench as it launches.