AgentCPM-Explore launched in January 2026, marking a significant milestone in the AI agent landscape. This 4B parameter model is the first open-source agent foundation model to rank on eight classic long-horizon agent benchmarks, including GAIA, HLE, and BrowserComp. What makes AgentCPM-Explore particularly impressive is its ability to match or surpass 8B models and even rival some 30B+ and closed-source LLMs, despite its compact size.
Developed jointly by THUNLP, Renmin University of China, ModelBest, and OpenBMB, AgentCPM-Explore represents a breakthrough in making powerful AI agents accessible for on-device deployment. The model's efficiency and performance make it an ideal choice for developers looking to implement AI agents without requiring massive computational resources.
What is AgentCPM-Explore?
AgentCPM-Explore is an agent foundation model designed specifically for long-horizon tasks that require sustained interaction with environments. Unlike traditional language models that excel at single-turn responses, AgentCPM-Explore can engage in over 100 rounds of continuous environment interaction, making it suitable for complex, multi-step tasks.
The model is built on the Qwen3-4B-Thinking-2507 base model and uses BF16 precision, striking a balance between performance and memory efficiency. With approximately 4 billion parameters, AgentCPM-Explore requires only about 8GB of GPU memory for inference, making it deployable on consumer-grade hardware.
Key Features of AgentCPM-Explore
1. Deep Exploration Capabilities
AgentCPM-Explore's standout feature is its ability to perform deep exploration tasks. The model supports:
- 100+ rounds of continuous interaction: Unlike models that struggle with extended conversations, AgentCPM-Explore maintains context and coherence across lengthy interactions
- Multi-source information cross-validation: The agent can verify information from multiple sources, ensuring accuracy and reliability
- Dynamic search strategy adjustment: The model adapts its approach based on task requirements and intermediate results
- Real-time information verification: AgentCPM-Explore can validate up-to-date information, crucial for tasks requiring current data
2. State-of-the-Art Performance
Despite being a 4B parameter model, AgentCPM-Explore achieves impressive benchmark scores:
| Benchmark | AgentCPM-Explore Score |
|---|---|
| GAIA (text-only) | 63.9% |
| BrowseComp | 25.0% |
| BrowseComp (Chinese) | 29.0% |
| HLE | 19.1% |
| Frames | 82.7% |
| WebWalker | 68.1% |
| Seal-0 | 40.0% |
| Xbench-DeepSearch | 70.0% |
These scores demonstrate that AgentCPM-Explore is competitive with much larger models. For context, the model's performance on GAIA (63.9%) is particularly noteworthy, as this benchmark tests complex reasoning and information retrieval capabilities.
3. Complete Open-Source Ecosystem
AgentCPM-Explore isn't just a model—it's a complete infrastructure for agent development. The project includes three essential components:
AgentRL: A fully asynchronous reinforcement learning framework designed specifically for agent training. This framework enables developers to train custom agents efficiently, supporting the unique requirements of agent-based learning.
AgentDock: A unified management and scheduling platform for tool sandboxes. AgentDock provides a standardized way to integrate and manage various tools that agents can use, from web browsers to specialized APIs.
AgentToLeaP: A one-click evaluation platform for assessing agent tool-learning capabilities. This platform simplifies the process of benchmarking and comparing agent performance across different tasks.
Hardware Requirements for AgentCPM-Explore
One of AgentCPM-Explore's most attractive features is its modest hardware requirements, making it accessible for a wide range of deployment scenarios.
Memory Requirements
For a 4B parameter model using BF16 precision:
- Inference: Approximately 8-9 GB of GPU memory
- Training/Fine-tuning: 16-24 GB of GPU memory (depending on batch size and optimization techniques)
Recommended Hardware Configurations
Minimum Configuration (Inference):
- GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
- RAM: 16GB system memory
- Storage: 20GB for model and dependencies
Recommended Configuration (Development):
- GPU: NVIDIA RTX 4090 (24GB VRAM) or A100 (40GB)
- RAM: 32GB system memory
- Storage: 50GB SSD for optimal performance
Production Deployment:
- Cloud platforms like FriendliAI offer optimized inference with advanced quantization and continuous batching
- Edge devices with 8GB+ GPU memory can run the model efficiently
Quantization Options
AgentCPM-Explore supports various quantization levels to further reduce memory requirements:
- INT8 quantization: ~4.5 GB memory, minimal performance loss
- INT4 quantization: ~2.2 GB memory, suitable for resource-constrained environments
- FP16/BF16: ~8.9 GB memory, optimal balance of performance and efficiency
AgentCPM-Explore vs. Competing Models
To understand AgentCPM-Explore's position in the AI agent landscape, let's compare it with other prominent models:
Performance Comparison
Based on benchmark results from early 2026:
| Model | Parameters | GAIA Score | BrowseComp | Deployment |
|---|---|---|---|---|
| AgentCPM-Explore | 4B | 63.9% | 25.0% | On-device |
| Claude 4.5 Sonnet | ~200B+ | 71.2% | 19.6% | Cloud-only |
| GPT-5 High | Unknown | 76.4% | 54.9% | Cloud-only |
| Typical 8B Models | 8B | ~55-65% | ~20-30% | Mixed |
Key Advantages
Size Efficiency: AgentCPM-Explore achieves 90% of the performance of models 2-4x its size, making it the most parameter-efficient agent model available.
Cost Effectiveness: With lower computational requirements, AgentCPM-Explore significantly reduces inference costs compared to larger models. Monthly download statistics show 1,830 downloads, indicating strong community adoption.
Privacy and Control: Unlike cloud-only models like Claude or GPT-5, AgentCPM-Explore can run entirely on-premises, ensuring data privacy and eliminating API dependencies.
Open Source Flexibility: The Apache 2.0 license allows for commercial use, modification, and distribution without restrictions.
Use Cases for AgentCPM-Explore
AgentCPM-Explore's unique capabilities make it suitable for various applications:
1. Research and Information Gathering
The model's deep exploration capabilities excel at:
- Academic research requiring multi-source verification
- Market research with dynamic information gathering
- Competitive analysis across multiple data sources
- Fact-checking and information validation
2. On-Device AI Assistants
With its modest hardware requirements, AgentCPM-Explore enables:
- Privacy-focused personal assistants running locally
- Offline AI agents for sensitive environments
- Edge computing applications in IoT devices
- Mobile AI agents for smartphones and tablets
3. Automated Task Execution
The model's 100+ round interaction capability supports:
- Complex workflow automation
- Multi-step problem-solving tasks
- Interactive debugging and troubleshooting
- Adaptive task planning and execution
4. Tool Integration and API Orchestration
Through AgentDock integration:
- Automated API testing and validation
- Multi-tool workflow coordination
- Dynamic tool selection based on task requirements
- Sandbox environment management
Getting Started with AgentCPM-Explore
Installation and Setup
Step 1: Download the Model
The model is available on multiple platforms:
- Hugging Face:
openbmb/AgentCPM-Explore - ModelScope:
OpenBMB/AgentCPM-Explore
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "openbmb/AgentCPM-Explore"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="bfloat16",
device_map="auto"
)Step 2: Configure Your Environment
Set up the AgentCPM infrastructure:
- Install AgentDock for tool management
- Configure AgentRL if you plan to fine-tune
- Set up AgentToLeaP for evaluation
Step 3: Run Your First Agent Task
Use the provided quickstart.py script:
- Configure your LLM API credentials
- Set up your MCP tool server address
- Execute the script to run agent tasks
- Review interaction traces in
outputs/quickstart_results/
Best Practices
Optimize for Your Hardware:
- Use INT8 quantization for GPUs with 8GB VRAM
- Enable gradient checkpointing for fine-tuning on limited memory
- Utilize batch processing for multiple concurrent tasks
Leverage the Ecosystem:
- Use AgentDock to standardize tool integration
- Implement custom evaluation metrics with AgentToLeaP
- Explore AgentRL for domain-specific fine-tuning
Monitor Performance:
- Track memory usage during extended interactions
- Measure latency for real-time applications
- Benchmark against your specific use cases
Technical Architecture Deep Dive
Model Foundation
AgentCPM-Explore builds upon the Qwen3-4B-Thinking-2507 base model, which provides:
- Strong reasoning capabilities optimized for agent tasks
- Efficient attention mechanisms for long-context processing
- Balanced parameter distribution for multi-task performance
Training Methodology
The model underwent specialized training using AgentRL:
- Reinforcement learning from agent feedback: The model learns from successful and failed agent interactions
- Multi-environment training: Exposure to diverse task environments improves generalization
- Continuous interaction optimization: Training specifically targets sustained multi-turn performance
Safetensors Format
AgentCPM-Explore uses the Safetensors format, offering:
- Faster loading times compared to traditional pickle-based formats
- Enhanced security against malicious model files
- Better memory efficiency during model loading
- Cross-platform compatibility
Limitations and Considerations
While AgentCPM-Explore represents a significant advancement, users should be aware of certain limitations:
Performance Trade-offs
Benchmark Gaps: On some benchmarks like BrowseComp (25.0%) and HLE (19.1%), AgentCPM-Explore trails larger models. For applications requiring absolute peak performance on these specific tasks, larger models may be more suitable.
Context Window: While supporting 100+ interaction rounds, the effective context window may be smaller than some competing models, potentially affecting very long-form tasks.
Resource Requirements
Minimum Viable Hardware: While 8GB GPU memory is sufficient for basic inference, complex multi-tool tasks may require more resources for optimal performance.
Inference Speed: Smaller models generally offer faster inference, but AgentCPM-Explore's agent-specific optimizations may introduce slight latency compared to pure language models.
Deployment Considerations
Tool Integration Complexity: Fully leveraging AgentDock and the tool ecosystem requires additional setup and configuration compared to simple API-based models.
Community Maturity: As a newly released model (January 2026), the community ecosystem and third-party integrations are still developing.
The Future of Agent Foundation Models
AgentCPM-Explore represents a crucial step toward democratizing AI agent technology. By proving that 4B parameter models can compete with much larger systems, it opens new possibilities for:
- Edge AI deployment: Running sophisticated agents on mobile devices and IoT hardware
- Privacy-preserving AI: Enabling on-premises agent deployment for sensitive applications
- Cost-effective scaling: Reducing infrastructure costs for agent-based applications
- Research accessibility: Allowing smaller research teams to experiment with agent technologies
The open-source nature of the entire infrastructure—from the model itself to the training framework and evaluation platform—ensures that the community can build upon this foundation, driving innovation in agent-based AI.
Conclusion
AgentCPM-Explore marks a turning point in agent foundation model development. With its 4B parameters, the model achieves performance comparable to systems many times its size, while maintaining hardware requirements accessible to a broad range of users. The combination of deep exploration capabilities, comprehensive open-source infrastructure, and strong benchmark performance makes AgentCPM-Explore a compelling choice for developers and researchers working on agent-based AI applications.
Whether you're building privacy-focused on-device assistants, conducting research on agent behaviors, or developing complex automation systems, AgentCPM-Explore provides a powerful, efficient, and accessible foundation. As the model and its ecosystem continue to mature, we can expect even more innovative applications and improvements in agent-based AI technology.
For those interested in exploring AgentCPM-Explore, the model is available now on Hugging Face and ModelScope under the Apache 2.0 license, with complete documentation and infrastructure available on the OpenBMB GitHub repository.