Forge
Forge is a Python reliability layer for self-hosted LLM tool-calling that can run as an OpenAI-compatible proxy, a workflow runner, or guardrails middleware inside an existing agentic loop.
Forge is page-worthy because it explains an important local-agent lesson: many failures blamed on small models are really harness failures around tool-call parsing, retries, step order, response validation, context compaction, and backend behavior.
The PyPI project page and GitHub repository describe Forge as a reliability layer for self-hosted LLM tool-calling with guardrails, context management, backend adapters, and multi-step workflows. PyPI lists version 0.7.0 released on May 22, 2026, Python 3.12+ support, MIT licensing, and tags for agents, guardrails, llama-cpp, local models, Ollama, and tool-calling. The project describes three usage modes: proxy server, WorkflowRunner, and guardrails middleware, with support for Ollama, llama-server, Llamafile, and Anthropic.
- Add guardrails to self-hosted LLM tool-calling workflows.
- Proxy existing coding-agent clients through a reliability layer.
- Run structured workflows with tool definitions, backend adapters, context management, and response validation.
- Compare model capability with and without harness-level reliability support.
Forge sits inside or in front of an agentic loop to improve tool-calling reliability. Its documented modes include an OpenAI-compatible proxy for existing clients, a WorkflowRunner for structured agent loops, and guardrails middleware for teams that already own their orchestration loop.
- Reliability features: rescue parsing, retry nudges, response validation, required steps, prerequisites, terminal tools, and context compaction.
- Backends: Ollama, llama-server, Llamafile, and Anthropic are documented as supported backend choices.
- Use cases: proxy existing tools such as OpenCode, Continue, Aider, or Cline, or build directly on Forge workflows.
Forge is not a full coding harness by itself. That is the point: it isolates one layer of the local-agent stack, the reliability layer that helps a model call tools in the right shape and recover from errors. Readers comparing small local models should ask whether the harness includes this kind of guardrail layer before concluding that model size alone explains the result.
Forge publishes project-provided evaluation claims. Treat those as useful signals but not universal benchmarks. The more durable page fact is the architecture: Forge can be used as proxy, workflow runner, or middleware around self-hosted and local-model tool-calling.
Forge is a harness layer focused on tool-call reliability and context management.
Forge supports local and self-hosted model workflows through Ollama, llama-server, and Llamafile.
Guardrails and tool permissions are part of the broader safety layer around agent execution.
Forge FAQ
Page-level questions for Forge.
Is Forge a coding agent?+
Forge is not primarily a coding agent. It is a reliability layer for self-hosted LLM tool-calling that can sit inside or in front of coding agents and other agentic workflows.
Why does Forge matter for small local models?+
Small local models can fail because tool calls are malformed, steps happen out of order, context is mismanaged, or retries are poor. Forge addresses those harness-level problems through proxy mode, workflow running, guardrails middleware, response validation, and context management.