I Built an Open-Source AI Agent That Benchmarks Itself (And It's Actually Good)

No API costs. No VC funding. Just 3,000 lines of Python and a llama.cpp backend.

Hello I've build yet another terminal IDE too that works out-of-the-box with a local LLM backed.

It's called open-agent. Running Qwen 3.6 35B on 6GB VRAM with pretty incredible results for a privacy centered setup/environment.

Here's the deep dive into Why's and How's that'll hopefully help some fellow builders.

The Problem With Every Agent Framework

I spent months testing LangChain, CrewAI, AutoGen, and the rest.

They all share the same DNA: they're API wrappers dressed up as agents. You configure a pipeline, wire it to GPT-4, and call it a day. The moment your credit card runs out, so does your agent.

And the benchmarks? Most frameworks cherry-pick numbers from someone else's paper. They don't run SWE-bench on their own code. They don't prove their agent can actually fix a bug in a repo it's never seen before.

I wanted something different.

A single-file agent that runs on my laptop. No API keys. No cloud dependencies. An agent that benchmarks itself using the same industry standards as OpenAI and DeepSeek — inside its own loop, not in some external harness that hides the cracks.

So I built one.

Meet Open-Agent

Three thousand lines of Python. A single file. Twenty-four tools. Eleven REPL commands. Four benchmarks. Zero API costs.

loop.py  ─  The whole thing
benchmark/
  ├── bigcodebench.py    Code synthesis (1140 problems)
  ├── swebench.py        Software engineering (Docker eval)
  ├── agentic_bench.py   Multi-step tool use (10 tasks)
  └── gaia.py            Meta's reasoning benchmark

It doesn't wrap an API. It is the agent. Every tool, every system prompt, every context management trick — it's all right there in one file you can read, modify, and understand.

How It Works

The core is a ReAct loop — Reason + Act, repeated until the task is done. But it's the details that matter.

The Loop

1. System prompt → injected with your bio, preferences, and 24 tool definitions
2. Preflight    → maps your project, searches the web for context
3. Think        → LLM decides what to do next
4. Act          → executes a tool (edit a file, search the web, run Python)
5. Observe      → feeds the result back into context
6. Repeat       → until the task is complete
7. Return       → final message

Nothing revolutionary on paper. The magic is in what happens between the steps.

Context Management That Actually Works

Small models (7B, 14B, 35B) fill their context window fast. The naive approach — keep appending turns until you hit the limit — works for about 20 minutes before the model forgets what it's doing.

Open-agent uses a rolling window:

┌─────────────────────────────────────────┐
│  System prompt          ─  always kept  │
│  Grounding context      ─  always kept  │
│  Memory / bio / prefs   ─  always kept  │
│  ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│  Middle turns           ─  archived     │
│  ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│  Last N turns           ─  preserved    │
└─────────────────────────────────────────┘

The first 3 messages (system, grounding, memory) stay. The last 9 turns stay. Everything in between gets compressed into a "Shadow Context" summary.

Result: the agent can run 500+ steps without losing the plot. On a 7B model.

The Web-First Philosophy

Large language models are frozen in time. Yours, mine, everyone's. Their training data is at least six months old, often older.

Open-agent treats the web as its primary reasoning engine, not a fallback.

Every non-trivial task starts with search_web — not as a checkbox feature, but as a hard requirement embedded in the system prompt:

"You are FORBIDDEN from writing any implementation code during Step 1 and Step 2. Your FIRST action MUST be to call search_web."

This discipline — research first, code second — is what makes small models punch above their weight. A 35B model with good search results beats a 70B model guessing from memory.

The 24 Tools

Tools are the agent's hands. Each one is a Python function registered as an LLM callable via function calling.

File Operations

Search

Execution

Planning & Memory

Meta & Self-Improvement

The Part That Actually Excites Me: Self-Benchmarking

Every agent framework claims performance numbers. Almost none of them run their own benchmarks inside their own agent loop.

Open-agent does.

from benchmark.bigcodebench import run_benchmark

# Same function used in interactive mode
run_benchmark(max_instances=50, subset="hard")

Or from the REPL:

/benchmark bigcodebench --instances 50 --subset hard

The agent calls run_agent() — the same function you use in interactive mode — on every benchmark problem. Same tools. Same context management. Same system prompts. No subprocess. No wrapper. No cheating.

Four Benchmarks

BigCodeBench — 1,140 code synthesis problems with embedded unittest test cases. Used by Qwen and DeepSeek. Evaluated locally — no external package needed.

SWE-bench Lite — 300 real GitHub bugs from 12 popular Python repos. The agent clones each repo, explores the codebase, applies a fix, and produces a git patch. Evaluated with swebench's official Docker harness.

Agentic Bench — 10 deterministic tool-use tasks: build an OpenAI-compatible proxy for llama.cpp, a model router, a log analyser, a context window visualiser, a skill generator. Everything about self-hosted LLM infrastructure.

GAIA — Meta's gold standard for multi-step reasoning. The agent searches the web, downloads files, processes data, and synthesises answers. Requires HuggingFace auth.

Each benchmark module is a standalone file:

benchmark/
  bigcodebench.py    ←  imports run_agent() directly
  swebench.py        ←  imports run_agent() in cloned repo
  agentic_bench.py   ←  imports run_agent() in temp dir
  gaia.py            ←  imports run_agent() directly

No dispatcher layer. No CLI runner. No abstraction indirection. Each benchmark is a self-contained function you can call from Python or from the REPL.

What 3,000 Lines Buys You

It runs on llama.cpp at localhost:8083. It falls back to any OpenAI-compatible endpoint. It never pays per token.

Why Open Source Matters Here

The agent framework space is crowded with:

• Vendor playthings — frameworks designed to sell you API credits
• Academic prototypes — papers with GitHub repos that haven't been touched in months
• Configuration nightmares — YAML files for days

Open-agent is none of those.

It's a single Python file you can read in an afternoon. It doesn't hide complexity behind abstractions — it puts everything in the open. The benchmarks are real. The evaluation is honest. The tools are practical.

And because it's a single file, you can fork it, gut it, rewrite the system prompts, add your own tools, and understand every line that runs on your machine.

The Roadmap

What comes next:

• Multi-agent orchestration — spawn sub-agents for parallel research
• Vision tools — process screenshots and diagrams
• Long-term memory — vector store for cross-session recall
• WebSocket bridge — attach to VS Code as a copilot alternative

But the foundation is already solid. An agent that runs locally, works reliably, and tells you honestly how it performs.

Try It

git clone https://github.com/your-username/open-agent
cd open-agent
pip install -r requirements.txt

# Start llama.cpp on port 8083
# Then:
python loop.py

For the benchmarks:

python -m benchmark.bigcodebench --instances 10
python -m benchmark.swebench --instances 5
python -m benchmark.agentic_bench

# Or from inside the REPL
# /benchmark bigcodebench --instances 10

Built with llama.cpp, Python, and the unshakeable belief that local AI is the future. No API keys were harmed in the making of this agent.