---
title: "forge vs llm-strategy"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-blackhc-llm-strategy"
tools: ["antoinezambelli-forge", "blackhc-llm-strategy"]
---

# forge vs llm-strategy

*GraphCanon updated Aug 14, 2026*

## Verdict

Pick forge if developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge; pick llm-strategy if llm-strategy is a Python library promoting type safety in interactions with language models through its use of strongly typed functions and dataclasses.

[forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [llm-strategy](https://blackhc.github.io/llm-strategy/) has 400 stars, 22 forks, and 5 open issues, last pushed Mar 3, 2025. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [llm-strategy's repository](https://github.com/BlackHC/llm-strategy).

| | [forge](/tools/antoinezambelli-forge.md) | [llm-strategy](/tools/blackhc-llm-strategy.md) |
| --- | --- | --- |
| Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | Python library for strongly typed interaction with LLMs |
| Stars | 2,217 | 400 |
| Forks | 173 | 22 |
| Open issues | 4 | 5 |
| Language | Python | Python |
| Adopt for | Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge. | llm-strategy is a Python library promoting type safety in interactions with language models through its use of strongly typed functions and dataclasses. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | AI Agents, LLM Frameworks | LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [forge](/tools/antoinezambelli-forge.md) | [llm-strategy](/tools/blackhc-llm-strategy.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Dormant (18%) |
| Days since push | 0d | 522d |
| Open issues (now) | 4 | 5 |
| Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/blackhc-llm-strategy/trust.md) |

## Decision facts: forge

- **Requirements:** Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.
- **Adopt for:** Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge.

## Decision facts: llm-strategy

- **Adopt for:** llm-strategy is a Python library promoting type safety in interactions with language models through its use of strongly typed functions and dataclasses.

## Choose when

### Choose forge if…

- Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case..
- Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework.
- Also covers AI Agents.
- - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.

### Choose llm-strategy if…

- Tags unique to llm-strategy: gpt, langchain, llm, openai.
- You need to enforce strict type safety when working with LLMs

## When NOT to use forge

- - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup.
- - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management.
- - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.

## When NOT to use llm-strategy

- If loose or dynamic typing offers better flexibility for your application
- When you prefer frameworks that do not have a steep learning curve due to advanced type annotations

## Common questions

### What is the difference between forge and llm-strategy?

forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. llm-strategy: Python library for strongly typed interaction with LLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose forge over llm-strategy?

Choose forge over llm-strategy when Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.; Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework; Also covers AI Agents; - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic.

### When should I choose llm-strategy over forge?

Choose llm-strategy over forge when Tags unique to llm-strategy: gpt, langchain, llm, openai; You need to enforce strict type safety when working with LLMs.

### When should I avoid forge?

- If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup. - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management. - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services.

### When should I avoid llm-strategy?

If loose or dynamic typing offers better flexibility for your application When you prefer frameworks that do not have a steep learning curve due to advanced type annotations

### Is forge or llm-strategy more popular on GitHub?

forge has more GitHub stars (2,217 vs 400). Stars measure visibility, not whether either tool fits your constraints.

### Are forge and llm-strategy open source?

Yes - both are open-source projects on GitHub (forge: MIT, llm-strategy: MIT).

### Where can I find alternatives to forge or llm-strategy?

GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [llm-strategy alternatives](/tools/blackhc-llm-strategy/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [llm-strategy markdown twin](/tools/blackhc-llm-strategy/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/antoinezambelli-forge-vs-blackhc-llm-strategy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, forge or llm-strategy?

forge: Very active. llm-strategy: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for forge and llm-strategy?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [llm-strategy trust report](/tools/blackhc-llm-strategy/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=antoinezambelli-forge`](/api/graphcanon/graph?tool=antoinezambelli-forge)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_