AI Frameworks · Klavis-AI

klavis

Klavis is an open-source Model Context Protocol (MCP) integration platform that connects AI agents to 100+ prebuilt tools and services at scale. It offers three deployment options: cloud-hosted, self-hosted with Docker, or via Python/TypeScript SDKs, with OAuth2 support for secure authentication.

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Source: GitHub — github.com/Klavis-AI/klavis

5.8k

GitHub stars

549

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Klavis-AI/klavis
Owner	Klavis-AI
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	5.8k
Forks	549
Open issues	289
Latest release	python-v2.20.0 (2026-01-29)
Last updated	2026-06-01
Source	https://github.com/Klavis-AI/klavis

What klavis is

Klavis provides MCP server implementations and a Strata connector layer that intelligently routes AI agent function calls to external APIs and tools. Built in Python, it exposes REST, SDK, and stdio interfaces; supports Discord, OAuth2, and includes a sandbox environment for LLM training and reinforcement learning workflows.

Quickstart

Get the klavis source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Klavis-AI/klavis.git2cd klavis3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-tool AI agents requiring reliable function calling

Orchestrate AI agents that need consistent, fault-tolerant access to dozens of external APIs (Gmail, Slack, GitHub, etc.) without rewriting integration logic for each model or use case.

LLM training and reinforcement learning environments

Use the MCP Sandbox to provide scalable, isolated tool environments for training RL agents and collecting interaction data across many concurrent agent instances.

Context-window optimization for LLM applications

Deploy Strata to intelligently filter and summarize tool outputs, reducing token consumption while maintaining semantic fidelity—critical for cost management at scale.

Implementation considerations

Evaluate the 100+ prebuilt integrations against your required tool set; custom MCP server development for missing services requires Python expertise and MCP protocol knowledge.
Choose deployment model (cloud vs. self-hosted) based on data residency, compliance, and operational overhead—cloud-hosted simplifies scaling but introduces external dependency.
Strata context-window optimization requires tuning connector behavior and token budgets per use case; monitor LLM token usage and semantic loss during rollout.
Plan OAuth2 credential management and refresh token lifecycle, especially when integrating with multiple SaaS platforms (Gmail, Slack, etc.).
Test error handling and retry logic under high concurrency; validate that Sandbox or Docker deployments meet throughput SLAs for your agent workload.

When to avoid it — and what to weigh

Proprietary, closed-source tool ecosystem required — If your critical integrations are not among the 100+ prebuilt MCP servers or you cannot contribute custom connectors to an open-source model, integration gaps will require custom development.
Minimal external API dependencies — For purely local or self-contained AI inference workflows with no need for external service integrations, the overhead of MCP layers and agent scaffolding is unnecessary.
Strict vendor lock-in or legacy agent framework requirements — If your organization mandates a specific commercial AI agent platform (e.g., Anthropic Claude, OpenAI GPT) without MCP support, Klavis integration may require architectural changes.
Real-time, sub-100ms latency requirements — MCP protocol overhead, network round-trips to integrations, and Strata routing logic may introduce latency unsuitable for ultra-low-latency trading, robotics, or streaming inference pipelines.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution and no warranty.

Apache-2.0 permits commercial use without requiring commercial licensing. However, verify compliance with any closed-source dependencies in cloud-hosted deployments, and confirm that trademark/branding usage aligns with Klavis AI's policies via their terms of service.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

MCP integration platforms handle external API credentials and OAuth tokens; audit credential storage, transmission, and sandbox isolation. Apache-2.0 provides no security guarantees. For self-hosted, ensure Docker image scanning, dependency audits, and network segmentation. Verify OAuth2 implementation protects token refresh cycles and does not leak credentials in logs or error messages.

Alternatives to consider

Anthropic Model Context Protocol (standard) + custom integrations

Direct MCP support in Claude SDK without vendor abstraction; lower latency but requires building and maintaining your own MCP servers and integration logic.

LangChain Tools / Langraph agents with manual tool bindings

More granular control and framework flexibility; requires explicit tool schema definition and error handling but tightly couples to LangChain ecosystem.

Zapier API / Make (Integromat) for agent-triggered workflows

Mature, no-code integration platform; lacks tight AI agent function-calling semantics and adds latency via webhook polling instead of synchronous MCP protocol.

Software development agency

Build on klavis with DEV.co software developers

Explore Klavis on GitHub, try the cloud platform at klavis.ai, or join the Discord community to see live examples and get implementation support from the team.

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klavis FAQ

Can I use Klavis with any LLM model?

Not explicitly stated in provided data. Klavis provides MCP servers and SDKs; compatibility depends on whether your chosen LLM client (OpenAI, Anthropic, other) supports MCP or if Klavis SDK wraps the model. Requires documentation review.

Do I need to host Klavis myself?

No. Three options: (1) cloud-hosted at klavis.ai, (2) self-host via Docker, or (3) use Python/TypeScript SDK in your own app. Cloud-hosted simplifies scaling; self-hosted gives data residency control.

What if a required integration is not in the 100+ prebuilt list?

You can build a custom MCP server in Python. Klavis publishes the MCP protocol and provides examples. Custom servers can be registered as stdio or networked integrations. Effort depends on target API complexity.

Is there a cost?

Unknown from provided data. Open-source license is free, but cloud-hosted (klavis.ai) may have SaaS pricing; self-hosted has your operational costs. Requires checking pricing page.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If klavis is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to scale your AI agent integrations?

Explore Klavis on GitHub, try the cloud platform at klavis.ai, or join the Discord community to see live examples and get implementation support from the team.

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