Composio

Composio is a robust platform designed to enhance AI agents by equipping them with a wide array of tools to perform complex tasks seamlessly. Whether it's managing GitHub repositories, integrating with APIs, or automating workflows, Composio provides a versatile, scalable solution for developers. The platform offers support for over 100 tools and is compatible with major AI frameworks like OpenAI, Langchain, CrewAI, and many others. With features like managed authorization, high accuracy, and extensibility, Composio is tailored for developers looking to integrate sophisticated AI-powered functionalities into their applications.

General
AuthorComposioHQ
Release DateJuly 17, 2024
Websitehttps://composio.dev/
Repositoryhttps://github.com/ComposioHQ/composio
Documentationhttps://docs.composio.dev/introduction/intro/overview
Technology TypeAI Agent Integration Platform

Key Features

  • Extensive Toolset: Over 100 tools across various categories, including software, OS, browser, and search utilities.

  • Framework Compatibility: Works seamlessly with frameworks like Langchain, OpenAI, and more.

  • Managed Authorization: Simplifies authentication across multiple protocols (OAuth, API keys, etc.).

  • Accuracy: Enhanced tool accuracy, offering up to 40% better performance in AI agent tasks.

  • Extensibility: Easily add new tools, frameworks, and authorization protocols.

Start Building with Composio

Composio is a cutting-edge platform that empowers developers to integrate powerful AI agents into their applications with minimal effort. Whether you’re working with OpenAI or another major framework, Composio simplifies the process of creating intelligent workflows and automations. Dive into the documentation and explore the community-driven use cases to see how others are leveraging Composio to build innovative solutions.

👉 Start building with Composio

👉 Composio Guides

👉 Examples

Tutorials using Composio

Leveraging Composio for Advanced Multi-Agent AI Applications

Leveraging Composio for Advanced Multi-Agent AI Applications

Discover how to build a dynamic multi-agent system with Composio and AutoGen, automating data analysis and reporting with seamless AI integrations.

Composio top builders

Explore the top contributors showcasing the highest number of Composio app submissions within our community.

Composio AI technology page Hackathon projects

Discover innovative solutions crafted with Composio, developed by our community members during our engaging hackathons.

Muninn: Living Memory System for Technical Work

Muninn: Living Memory System for Technical Work

Engineering teams lose critical architectural decisions, context, and dependencies between meetings and informal huddles. Muninn solves this engineering amnesia through an opt-in living memory system built on an immutable principle: "You decide what gets heard. We decide what is worth remembering." Muninn captures technical speech via a high-fidelity 24kHz AudioWorklet stream or 1-tap quick notes. Rather than dumping raw audio or rambling summaries, an edge-orchestrated intelligence pipeline breaks speech into atomic, verifiable claims classified into five durable categories: observations, hypotheses, decisions, tasks, and questions. Each claim preserves its exact conversational provenance, speaker identity, confidence rating, and turn-by-turn timestamp. These claims populate a dynamic relational knowledge graph stored in Neon Serverless PostgreSQL with pgvector. A proactive background reasoning engine continuously audits the graph for open loops, unblocked tasks, and conflicting decisions. When a blocker clears or a new decision contradicts past consensus, Muninn autonomously triggers a resurfacing alert to keep projects aligned. Muninn introduces a dual storage privacy architecture. On the web, guest users operate in an ephemeral sandbox within sessionStorage that wipes immediately upon closing the tab, with optional cloud sync. On mobile and tablet hardware, native Android storage enables local-first persistence without mandatory cloud dependence. An integrated Sensitivity Review Gate quarantines credentials, confidential financial metrics, and off-the-record statements under a strict default-discard guarantee. The entire stack is engineered for speed with zero cold starts, featuring a Next.js 15 client on Cloudflare Pages, a Hono TypeScript edge API on Cloudflare Workers, real-time speech intelligence via AssemblyAI, and cross-platform native execution via Android.

Fresno LUX: A Living City

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

EscrowGuard

EscrowGuard

EscrowGuard is a multi-agent platform designed to resolve operational friction and strict regulatory compliance in high-value financial transactions or escrows. Developed by the "Script Hunters" team for the "Regulated & High-Stakes Workflows" track of the Band of Agents hackathon, the system turns validation into a real-time interactive negotiation room. - Instead of slow processes, EscrowGuard introduces a "Syndicate of Intelligent Agents" and robust technical orchestration: - Extractor AI (PydanticAI): Instantly processes passports and PDF documents to extract structured data with strict type validation. - Bank Retention (Mock SPEI): Simulates the preventive freezing of funds in escrow accounts while analyzing the profiles of those involved. - Risk OSINT Agent (CrewAI): Actively searches the web and scans international databases (OFAC, PEP, SAT) to identify money laundering networks or potential criminal sanctions. - Logic Orchestrator (LangGraph): A state machine that coordinates the agents and halts the financial flow if any level of risk is detected. Its biggest differentiator is its Human-in-the-Loop (HITL) architecture operated through Band Pro. If the agents detect an ambiguous case (such as a false positive due to a name collision with a sanctioned politician), LangGraph pauses the operation and calls a human Compliance Officer (DPO) into the room. The DPO can interrogate the OSINT agent about its sources, analyze discrepancies, and execute a manual approval to release the funds, bridging the scalability of AI with critical human judgment to close deals with complete security.

AlphaLens: The Quant Engine Thinks While You Sleep

AlphaLens: The Quant Engine Thinks While You Sleep

Quant hedge funds spend millions staffing teams to track alternative data signals — hiring velocity, pricing pressure, app sentiment, and regulatory filings — because the edge is in the data that doesn't appear in earnings calls. AlphaLens makes that edge accessible. It's a fully autonomous, continuously-learning signal engine powered by Bright Data, Claude, Cognee graph memory, and TriggerWare live-data triggers. Each night (or the moment an anomaly is detected intraday), AlphaLens wakes up, pulls live data across four alternative sources, runs each dataset through purpose-built Claude AI chains, and synthesizes everything into a composite earnings surprise score — from 0 to 100 — complete with analyst-style bullet points and a confidence interval. What makes v2 genuinely intelligent is its memory. Cognee replaces a flat signals database with a persistent knowledge graph: every signal event is linked to its company, its context, and its actual outcome. When AlphaLens analyzes NVIDIA this quarter, it recalls that the same GPU-validation hiring spike preceded a 16% beat three months ago — and weights the signal accordingly. The system gets sharper with every run. Built in six phases over ~48 hours on a $250 cloud budget, AlphaLens surfaces in a clean Next.js dashboard with watchlist rankings, sparkline trends, per-source evidence panels, and one-click CSV export for any backtesting platform.

Startme

Startme

StartME is a full-stack AI company operating system that replaces entire business departments with a coordinated swarm of 20 specialized AI agents. Built solo by a 17-year-old self-taught developer from Lagos, Nigeria — in 80,000+ lines of code — StartME isn't just an AI tool. It's a workforce. At its core is SOMA (Self-Organizing Multi-Agent Architecture) — a proprietary swarm intelligence layer enabling peer-to-peer agent communication, Byzantine fault-tolerant quorum voting, epigenetic memory, and autonomous self-correction. A master CEO agent orchestrates the swarm, escalating only critical decisions to human founders while agents handle execution. The platform covers the full operational stack: Sales, Marketing, Customer Support, Developer, Data Analyst, Legal, CFO, DevOps, Security, HR, Investor Relations, and 9 more agents — each with domain tools, persistent memory, and proactive reasoning loops. Key capabilities: — Human-in-the-loop controls across all critical agent actions — AI-powered spreadsheet generation from natural language — SOMA health dashboard with live swarm monitoring and anomaly detection — Hybrid MCP integration layer for internal ops, SaaS connections, and secure payment processing — Multi-currency payment infrastructure supporting global and emerging markets — Compliance-aware Legal agent adaptable to any regulatory environment SOMA is architected as a standalone enterprise layer targeting logistics, defense, healthcare, and smart cities — not just a startup tool, but infrastructure for the next generation of autonomous organizations. Any team, anywhere in the world, can deploy a full AI workforce in minutes. StartME is what happens when a teenager from Lagos decides the world doesn't need another chatbot. It needs an AI company. One founder. Twenty employees.