Agently
[GenAI Application Development Framework] 🚀 Build GenAI application quick and easy 💬 Easy to interact with GenAI agent in code using structure data and chained-calls syntax 🧩 Use Event-Driven Flow *TriggerFlow* to manage complex GenAI working logic 🔀 Switch to any model without rewrite application code
Runssource: GitHubhomepagePythonApache-2.0commit d58239501e7d
Python, Apache-2.0 licensed. The project labels itself: agent, agent based framework, agent framework, chatglm, claude, deepseek, ernie and framework.
Agently runs. An Argusic agent installed it in 5 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
The agently 4.1.4.8 package installs, imports, and creates agents via its fluent API. 3,776 of 3,826 tests pass. 9 failures are pre-existing characterization snapshot mismatches (expected on different package versions), and 1 requires a Docker daemon.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- No python binary on PATH — only python3 available, but code adapters and providers hardcoded python as argv[0]2 minutes
- Missing optional dependencies: python-dotenv, beautifulsoup4, fastapi, sqlmodel, aiosqlite, fastmcp1 minute
- 9 characterization tests fail due to baseline snapshot drift (event paths and prompt content differ across package versions)
- test_gvisor_direct_docker_run_emits_one_fixed_runtime_argv fails — Docker daemon not available in container and monkeypatch does not cover inspect_availability()
- Install time
- 5 minutes
- Cold machine to finish
- 45 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- pytest exited with code 1, 3776 passed, 10 failed (9 characterization baseline drift + 1 missing Docker), 39 skipped
- Model tokens used
- 344,619
- Exact commit tested
- d58239501e7d
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
FAILED tests/test_gvisor_execution_provider.py::test_gvisor_direct_docker_run_emits_one_fixed_runti… ============================== 1 failed in 0.48s =============================== exec /bin/sh -lc 'cd /work/repo && pip list 2>/dev/null | grep -i agently' in /work/repo exited 1 in 113ms: exec /bin/sh -lc 'cd /work/repo && .venv/bin/pip list 2>/dev/null | grep -i agently' in /work/repo succeeded in 54ms: agently 4.1.4.8 /work/repo agently-stage 0.3.8 tokens used 344,619
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 5 minutes, faster than the median of the 73 comparable projects Argusic has measured.
- Recovered from all 4 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 74 agent projects Argusic has installed and timed, Agently was the 29th fastest to reach a running state, and 50 of 74 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 5 minutes after the clone.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
agentagent-based-frameworkagent-frameworkchatglmclaudedeepseekernieframeworkgeminigoogle-geminigptllm-agentllm-applicationllm-appsllm-frameworkllmopsllmsminimaxpython
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/agently)Questions
- Does Agently run?
- Yes. Agently runs. Argusic installed and launched it on a clean machine in 5 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test Agently?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit d58239501e7d. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test Agently?
- The run that produced this verdict cost $0.32: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does Agently take to install?
- 5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does Agently need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing Agently?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does Agently compare with the alternatives?
- Of the 74 agent projects Argusic has installed and timed, Agently was the 29th fastest to reach a running state, and 50 of 74 reached one at all.
- Where is the evidence for Agently?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.