AgentForge

ReAct agents on open-weight LLMs — tools, memory, and an eval harness.
Pairs with ragforge-ml for retrieval and turboquant-ml for quantized model serving.

Why AgentForge?

Most "agent framework" projects use proprietary models (GPT-4, Claude) behind a DSL of Runnable.invoke() chains nobody can debug. AgentForge is the opposite: ReAct loops on open-weight LLMs (Llama, Qwen, Mistral), with a small registry of well-bounded tools, and an evaluation harness so you can measure whether your agent is actually doing what you asked.

Three opinions:

1. Open models first. Defaults work on Qwen/Qwen2.5-3B-Instruct and any chat-template HF model. No API key required. Plug in turboquant-ml to serve the model quantized.
2. ReAct, not magic. The loop is a 60-line function (agent.py:run) that alternates Thought / Action / Observation steps. Easy to read, easy to debug.
3. Tools have hard boundaries. Python REPL runs in an AST-whitelisted sandbox; SQL is read-only; web search is rate-limited; RAG retrieval is delegated to ragforge-ml.

Features

Installation

The PyPI distribution is agentforge-ml (the unsuffixed agentforge name was taken by an unrelated project). Python import and CLI are just agentforge / af:

pip install agentforge-ml                       # core
pip install "agentforge-ml[tools]"              # + sympy + duckduckgo-search
pip install "agentforge-ml[rag]"                # + ragforge-ml integration
pip install "agentforge-ml[quantized]"          # + turboquant-ml NF4 path
pip install "agentforge-ml[serve]"              # + FastAPI
pip install "agentforge-ml[all]"                # everything

60-second tour

from agentforge import Agent
from agentforge.tools import Calculator, WebSearch, PythonREPL

agent = Agent.from_defaults(
    model_id="Qwen/Qwen2.5-3B-Instruct",
    tools=[Calculator(), PythonREPL(), WebSearch()],
)

result = agent.run("What is 47 * 1337, then take its square root?")
print(result.final_answer)
for step in result.steps:
    print(f"  [{step.tool}] {step.action_input!r} -> {step.observation!r}")

With RAG

from agentforge import Agent
from agentforge.tools import RAGTool
from ragforge import Pipeline

rag = Pipeline.from_defaults(model_id="Qwen/Qwen2.5-3B-Instruct")
rag.ingest(["docs/"])

agent = Agent.from_defaults(
    model_id="Qwen/Qwen2.5-3B-Instruct",
    tools=[RAGTool(rag)],
)
print(agent.run("What is our company refund policy?").final_answer)

CLI

af ask "What is 17 squared?" --tools calculator
af ask "Latest CVE for log4j?" --tools web_search
af eval data/eval_set.jsonl --tools calculator,python_repl
af serve --tools calculator,python_repl --port 8080

ReAct loop, in a picture

question -> [LLM] Thought + Action -> [Tool] Observation
            ^                                       |
            |_______________________________________|
                       up to max_steps

If the LLM emits Final Answer: the loop exits. Otherwise it loops until max_steps. The parser is forgiving: it tolerates whitespace and case but falls back to the last completed step on truncation.

Eval harness

Built-in, pure Python, no judge model required:

af eval examples/eval_set.jsonl --tools all

+--------------------+--------+
|  metric            |  mean  |
+--------------------+--------+
| task_completion    |  0.95  |
| final_answer_match |  0.81  |
| tool_accuracy      |  0.88  |
| step_efficiency    |  0.72  |
+--------------------+--------+
n=80  ·  p50=2.4s  ·  p95=8.1s

Architecture

agentforge/
├── core/         # ReAct loop + parser + prompts
├── tools/        # registry, calculator, python repl, web search, sql, rag
├── memory/       # conversation, persistent sqlite
├── llm/          # HuggingFace causal LM wrapper
├── eval/         # 4 metrics + orchestrator
├── serve/        # FastAPI app
└── cli.py        # af / agentforge

Every stage is a small module behind a small interface (LLM, Tool, Memory) — swap any of them in two lines.

Roadmap

• ReAct loop with structured parsing
• Tool protocol + registry
• 5 built-in tools (calculator, python, web, sql, rag)
• Persistent SQLite memory
• Eval: task completion, final-answer match, tool accuracy, step efficiency
• FastAPI server + Typer CLI
• turboquant-ml integration (NF4 / GPTQ / AWQ models)
• Plan-and-execute pattern alongside ReAct
• Streaming step output in /ask
• Tool-use chat templates (Qwen tool format, Llama-3 tool format)
• Multi-agent coordination

License

MIT.