How ChatDev accelerates prototyping?
Imagine compressing a sprint into one day: a product manager describes an idea, and a developer spends half a day cutting a prototype. A week later the hypothesis is validated, but the prototype goes into the bin — 80% of the time went into routine. ChatDev is a framework from Tsinghua University where AI agents with roles of CEO, CTO, Programmer, Reviewer, and Tester mimic a waterfall process, generating working code from a natural language description. We use it in our project workflow to obtain a PoC in 30–90 minutes instead of 1–2 days.
How it works?
ChatDev builds a chain of dialogues: CEO sets the task, CTO chooses architecture, Programmer writes code, Reviewer and Tester find errors. Each phase is a separate dialogue with clear input/output. It is not a chatbot but a managed process where the result of one phase feeds into the next.
Why chat agents are more effective than a single LLM prompt?
A single run through GPT-4 with a prompt "create a scraper" often produces non-working code — mixes libraries, misses edge cases. ChatDev splits the task: CTO specifies architecture, Programmer writes to spec, Reviewer checks compliance. Each agent sees only its part of the context, reducing the load on the model's attention window. Practice shows: the percentage of immediately deployable code is ~15% for simple utilities, but time to first working commit decreases by 60–70%.
Examples of ChatDev integration
Basic launch and Python API
# Installation
git clone https://github.com/OpenBMB/ChatDev.git
cd ChatDev
pip install -r requirements.txt
# Launch development
python run.py \
--task "Develop a web scraper to extract prices from e-commerce sites. Python + BeautifulSoup + requests. Save to CSV." \
--name "PriceScraperApp" \
--model GPT_4_TURBO
from chatdev.chat_chain import ChatChain
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
chat_chain = ChatChain(
config_path="CompanyConfig/Default",
config_phase_path="PhaseConfig/Default",
config_role_path="RoleConfig/Default",
task_prompt="Create a utility for converting data formats (CSV, JSON, XML, YAML)",
project_name="DataConverter",
org_name="TechTeam",
model_type="GPT_4_TURBO",
)
chat_chain.pre_processing()
chat_chain.make_recruitment()
chat_chain.execute_chain()
chat_chain.post_processing()
Custom phases
{
"chain": [
{"phase": "DemandAnalysis", "phaseType": "SimplePhase"},
{"phase": "LanguageChoose", "phaseType": "SimplePhase"},
{"phase": "Coding", "phaseType": "SimplePhase"},
{"phase": "SecurityAudit", "phaseType": "SimplePhase"},
{"phase": "CodeCompleteAll", "phaseType": "ComposedPhase",
"cycleNum": 3,
"Composition": [
{"phase": "CodeReviewComment", "phaseType": "SimplePhase"},
{"phase": "CodeReviewModification", "phaseType": "SimplePhase"},
{"phase": "TestErrorSummary", "phaseType": "SimplePhase"},
{"phase": "TestModification", "phaseType": "SimplePhase"}
]
},
{"phase": "Manual", "phaseType": "SimplePhase"}
]
}
Iterative improvement via ExperiencePool
from chatdev.experience_pool import ExperiencePool
experience_pool = ExperiencePool.load("./experience_pool")
chat_chain = ChatChain(
task_prompt="...",
experience_pool=experience_pool,
use_experience=True,
)
experience_pool.update(chat_chain.get_experience())
experience_pool.save("./experience_pool")
Practical results and limitations
Case: rapid prototyping
Problem: a client team generated 2–3 prototypes per week for hypothesis validation. Each prototype required 1–2 developer days. We introduced ChatDev as a draft generator.
Usage: prototypes of simple utilities (converters, scrapers, report generators), CLI tools for internal use, basic CRUD APIs for PoCs.
Results from practice:
- Prototype creation time: 1–2 days → 30–90 minutes
- Review and refinement: 2–4 hours (vs. full write from scratch)
- Production readiness without refinement: ~15% (only the simplest utilities)
- Main value: fast concept validation — the team tested 12 hypotheses in a quarter instead of 6
- Time savings: ~15 person-days per month
Limitations: maximum efficiency on projects up to 500 lines of code. Complex multi-file projects with dependencies require significant post-processing. No native support for existing codebases.
Comparison with MetaGPT
| Aspect | ChatDev | MetaGPT |
|---|---|---|
| Approach | Dialogue-based (roles talk) | SOP-based (formal documents) |
| Code quality | Satisfactory for PoC | Higher for production code |
| Customisation | JSON configs | Python API + custom roles |
| Project size | Up to ~500 lines | Up to several thousand lines |
| Research-oriented | Yes | Less |
What the integration includes
When ordering ChatDev integration, you get: agent configuration for your typical tasks, a set of custom phases (SecurityAudit, PerformanceCheck, StyleLint), CI/CD integration (trigger generation from a button or from Jira), a basic ExperiencePool assembly with patterns from your projects, documentation (architecture, launch examples, troubleshooting) and a team training session (4 hours, remote).
Working process:
- Analytics: we analyse typical team tasks, determine the suitable volume for ChatDev (up to 500 lines).
- Design: configure roles, phases, ExperiencePool for your tech stack (Python, Go, TypeScript — any).
- Implementation: write configs, integrate API, add CI triggers.
- Testing: run through 10–15 real tickets, record generation success rate.
- Deployment: deploy on a dev stand or in the cloud, hand over refined templates.
Timelines: basic launch and setup — from 1 day; custom phases aligned with team processes — 3–5 days; full integration with CI and training — from 1 week. Cost is calculated individually — contact us to discuss details. Get a consultation: we will send a sample config for your project and estimate timelines.
Typical mistakes and ChatDev's role
- Overly long task descriptions (>200 words): agents lose focus — split the task into subtasks.
- Using for legacy refactoring: ChatDev does not see the code, generates from scratch. An agent-analyser is needed.
- Skipping the SecurityAudit phase: generated code may contain vulnerabilities (SQL injections, hardcoded tokens).
ChatDev will not replace developers but will accelerate them: it takes over routine (writing boilerplate, tests, documentation). 85% of results require refinement, but that refinement takes hours, not days. If you generate 20 prototypes per month, ChatDev saves ~15 person-days. That is enough to clear the backlog or launch a parallel experiment. Contact us — we will show how to embed ChatDev into your pipeline.