Back to Open-Source AgentsOfficial overview: [AgentForge site](https://agentforge.net/) Package and releases: [AgentForge on PyPI](https://pypi.org/project/agentforge/) Community distribution and stack: [AgentForge (nbsp1221) on GitHub](https://github.com/nbsp1221/agentforge) Company footprint: [AgentForge on LinkedIn](https://www.linkedin.com/company/agentforge) Define agent personas and attach tools Add memory/storage for context and RAG Run test chats and batch task runs for evals Compose multi‑agent flows (coordinator/worker) Ship locally or in the cloud—no lock‑in to a single LLM LLM‑agnostic by design: Route to OpenAI, Anthropic, Google, and local models via adapters such as [LiteLLM](https://github.com/BerriAI/litellm) and [Ollama](https://ollama.com/). Community stacks frequently include [LM Studio](https://lmstudio.ai/) routes as well. Low‑code agent scaffolding: Personas, tool calling, and multi‑agent orchestration with simple configuration patterns. See the [PyPI feature list](https://pypi.org/project/agentforge/0.2.13/). Evaluation and rapid testing loops: Batch tests, compare outputs, and iterate prompts/tools quickly. Highlighted across [AgentForge on PyPI](https://pypi.org/project/agentforge/) and the [AgentForge site](https://agentforge.net/). Local‑first options: Private, cost‑controlled deployment via [Ollama](https://ollama.com/) and [Open WebUI](https://openwebui.com/) commonly paired in the ecosystem; see the [community repo](https://github.com/nbsp1221/agentforge). Storage and memory: PostgreSQL for persistence and [ChromaDB](https://www.trychroma.com/) for embeddings/RAG, as referenced in package listings and community stacks. Orchestration/UI integrations: [n8n](https://n8n.io/) for workflows; [Open WebUI](https://openwebui.com/) for chat/front‑end control. Packaging and updates: Actively maintained on [PyPI](https://pypi.org/project/agentforge/) with ongoing releases. Licensing: Open‑source; verify the specific license for the distribution you adopt. Flexible and OSS‑friendly for enterprise agent systems and hierarchical agents; see discussions on [r/javascript](https://www.reddit.com/r/javascript/comments/1kozd33/i_built_agentforge_a_free_enterpriseready/) and [r/ChatGPT](https://www.reddit.com/r/ChatGPT/comments/1kozns8/i_built_agentforge_a_free_enterpriseready/). Great for rapid iteration with low‑code scaffolding; highlighted on the [PyPI page](https://pypi.org/project/agentforge/0.2.13/) and [official site](https://agentforge.net/). Local‑first builds using [Ollama](https://ollama.com/) and [Open WebUI](https://openwebui.com/) receive positive feedback in the [community repo](https://github.com/nbsp1221/agentforge). Fragmented branding: Multiple “AgentForge” sites/repos can confuse what’s “official” (e.g., [AgentForge site](https://agentforge.net/) vs. forks and listings). Early docs/polish in places: Some templates may require extra setup; see the caution in [r/MistralAI](https://www.reddit.com/r/MistralAI/comments/1nyu6vq/agent_template_agent_forge_an_agent_that_helps/). Limited third‑party reviews: Minimal coverage on sites like G2/Capterra; searches often surface Salesforce “Agentforce” instead. Developers and research teams building multi‑LLM agent workflows Teams that prefer self‑hosting for privacy and cost control Startups/labs that need structured evaluation and repeatable testing of agent behavior Engineers favoring modular, open‑source stacks over vendor lock‑in Agent evaluation at scale: Run parallel tests, compare outcomes, refine prompts/tools . Multi‑agent systems: Coordinator/worker patterns for research, support, and ops . RAG and knowledge workflows: Vector memory for retrieval and context . Synthetic data generation and scenario simulation for QA/model training . Customer support and internal copilots with task‑specific personas . LLMs and access layer: OpenAI, Anthropic, Google, plus local models via [Ollama](https://ollama.com/) and routes through [LiteLLM](https://github.com/BerriAI/litellm); also seen with [LM Studio](https://lmstudio.ai/). Orchestration/UI: [n8n](https://n8n.io/) for workflows and [Open WebUI](https://openwebui.com/) for conversational interfaces. Storage/memory: PostgreSQL for persistence; [ChromaDB](https://www.trychroma.com/) for embeddings. Install from [PyPI](https://pypi.org/project/agentforge/) and start with the [community repo](https://github.com/nbsp1221/agentforge) for a working template. For local models, wire up [Ollama](https://ollama.com/) and use [Open WebUI](https://openwebui.com/) for fast iteration. For cloud providers, route via [LiteLLM](https://github.com/BerriAI/litellm) to standardize API access. Define an evaluation harness early: log prompts, tool calls, context, and outputs; track failure cases; iterate frequently. Verify the repository and license before production adoption due to naming overlaps across community builds. It’s open‑source; you install and run it yourself. There is no widely adopted hosted SaaS for the OSS framework. If you encounter a hosted “AgentForge,” verify it aligns with the OSS project and review its license/pricing carefully. Overview: [AgentForge site](https://agentforge.net/) Package and features: [AgentForge on PyPI](https://pypi.org/project/agentforge/) and [release details](https://pypi.org/project/agentforge/0.2.13/) Community stack with n8n, LiteLLM, PostgreSQL, Ollama, Open WebUI: [AgentForge (nbsp1221) on GitHub](https://github.com/nbsp1221/agentforge) Community sentiment: Enterprise/hierarchical framing on [r/javascript](https://www.reddit.com/r/javascript/comments/1kozd33/i_built_agentforge_a_free_enterpriseready/) Cross‑post on [r/ChatGPT](https://www.reddit.com/r/ChatGPT/comments/1kozns8/i_built_agentforge_a_free_enterpriseready/) Template caveat on [r/MistralAI](https://www.reddit.com/r/MistralAI/comments/1nyu6vq/agent_template_agent_forge_an_agent_that_helps/) Company footprint: [AgentForge on LinkedIn](https://www.linkedin.com/company/agentforge)
Funding
OSS
AgentForge: Open‑Source, LLM‑Agnostic Framework for Building and Testing AI Agents
AgentForge is an open‑source framework for designing, evaluating, and shipping AI agents across cloud and local LLMs. Its focus is fast iteration, robust evaluation tooling, and freedom from vendor lock‑in. Multiple community distributions exist under the “AgentForge” name, but the core value is consistent: low‑code agent construction, standardized multi‑model access, and practical dev tooling to move from prototype to production.
What You Can Build With AgentForge
If you need one line: AgentForge gives you a practical toolkit to build, evaluate, and ship agents across many LLMs—local and cloud—using open‑source parts.
Key Features
How It Works
1. Define an agent persona and capabilities.
2. Attach tools (APIs, retrieval functions) and configure memory/storage.
3. Select an LLM route (e.g., OpenAI/Anthropic via [LiteLLM](https://github.com/BerriAI/litellm) or local models via [Ollama](https://ollama.com/)).
4. Run test conversations or batch evals; log inputs/outputs and tool calls.
5. Iterate prompts and tools, then deploy via a simple UI or workflow orchestrator like [n8n](https://n8n.io/).
Pros (From Community Sentiment)
Cons (From Community Sentiment)
Who It’s For
Common Use Cases
Integrations and Stack Components
See the community distribution for a reference stack: [AgentForge (nbsp1221) on GitHub](https://github.com/nbsp1221/agentforge).