openagi
OpenAGI is an open-source Python framework for building autonomous multi-agent systems that can decompose tasks, coordinate worker agents, and execute actions via LLM integration. It supports manual orchestration and autonomous execution modes with features like long-term memory, task planning, and web search capabilities.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | aiplanethub/openagi |
| Owner | aiplanethub |
| Primary language | Jupyter Notebook |
| License | Apache-2.0 — OSI-approved |
| Stars | 621 |
| Forks | 129 |
| Open issues | 10 |
| Latest release | v0.3.0 (2025-02-15) |
| Last updated | 2025-02-25 |
| Source | https://github.com/aiplanethub/openagi |
What openagi is
OpenAGI provides a task decomposition planner, worker/admin agent architecture, LLM abstraction layer (OpenAI, Gemini), action tools (DuckDuckGo, Tavily search), and optional persistent memory. Built on Jupyter Notebook and Python 3.9+, installable via pip with examples for multi-agent trip planning and autonomous sports queries.
Get the openagi source
Clone the repository and explore it locally.
git clone https://github.com/aiplanethub/openagi.gitcd openagi# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires API keys for LLM providers (OpenAI, Gemini) and search tools (Tavily, DuckDuckGo); plan key rotation and environment management.
- Long-term memory feature requires persistent storage backend—implementation details and storage strategy not documented; verify schema and query performance.
- Worker role instructions and action bindings are text-based; no type safety or validation layer for malformed agent definitions.
- Task decomposition relies on LLM reasoning; output quality and cost are highly dependent on prompt engineering and model choice.
- Python 3.9–3.11 supported; verify compatibility with your runtime and dependency resolution for transitive packages.
When to avoid it — and what to weigh
- Production High-Scale Deployments — Project is at v0.3.0 (early-stage) with limited release history. Not suitable for mission-critical systems requiring SLA guarantees, audit trails, or battle-tested reliability.
- Real-Time or Low-Latency Requirements — LLM-based agents have inherent latency; no benchmarks provided for response times. Avoid time-sensitive operations or systems requiring <100ms execution.
- Sensitive Data Handling Without Review — No explicit security hardening, encryption, or data isolation guarantees documented. Requires thorough security review before processing PII, financial data, or regulated content.
- Complex State Management Across Agents — Framework focuses on task decomposition and sequential execution. Not designed for highly stateful, graph-based agent interactions or distributed consensus systems.
License & commercial use
Apache License 2.0 (Apache-2.0) is a permissive OSI-approved license. Allows commercial use, modification, and distribution with liability/trademark protections.
Apache-2.0 permits commercial use, derivative works, and private modification. However, verify that all transitive dependencies (LLM SDKs, search APIs) are compatible with your commercial use case. No warranty or indemnification clause; use at your own risk.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No explicit security posture documented. Considerations: LLM API keys exposed via environment variables (standard but requires careful CI/CD handling); user input passed to LLMs without documented sanitization or prompt injection defenses; long-term memory storage backend and encryption not detailed; no audit logging or access control mechanisms mentioned. Requires threat modeling before handling sensitive data.
Alternatives to consider
LangChain / LangGraph
Mature, widely-adopted agent orchestration framework with larger ecosystem, better documentation, and production-grade tooling. Steeper learning curve but more extensible.
AutoGen (Microsoft)
Multi-agent conversation framework with role-based agents and message passing. Focuses on agent-to-agent dialogue; less emphasis on task decomposition and external tool integration.
Crew AI
Lightweight, task-driven agent framework with role and goal abstractions. Simpler API than LangChain; smaller community but faster iteration for simple workflows.
Build on openagi with DEV.co software developers
OpenAGI offers a fast path to multi-agent prototyping, but production deployments require security review, state management planning, and cost governance. Let our AI engineering team help you assess fit, architect for scale, and integrate safely.
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openagi FAQ
Can I use OpenAGI with proprietary LLMs other than OpenAI and Gemini?
Is long-term memory persistent across process restarts?
What are the cost implications of autonomous agent execution?
Can agents run in parallel or do they execute sequentially?
Software developers & web developers for hire
Adopting openagi is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.
Ready to Build Autonomous Agents?
OpenAGI offers a fast path to multi-agent prototyping, but production deployments require security review, state management planning, and cost governance. Let our AI engineering team help you assess fit, architect for scale, and integrate safely.