AgentCPM-Explore Agent
A lightweight 4B-parameter agent foundation model designed for extended, multi-turn environment interactions and deep research tasks. AgentCPM-Explore achieves state-of-the-art performance at its parameter scale across 8 major agent benchmarks including GAIA, HLE, and BrowserComp.
What This Agent Does
This skill enables you to leverage AgentCPM-Explore's capabilities for complex, long-horizon tasks that require:
Sustained interaction over 100+ rounds with continuous environment feedbackMulti-source information cross-validation and verificationDynamic search strategy adjustment based on intermediate resultsReal-time fact-checking and up-to-date information validationDeep exploratory research until task completionThe model demonstrates exceptional performance in web navigation, information synthesis, and multi-step reasoning tasks while remaining efficient enough for on-device deployment.
Instructions for AI Agent
When using AgentCPM-Explore for task execution, follow these guidelines:
1. Task Analysis and Planning
Break down complex requests into exploratory sub-tasksIdentify information sources that require cross-validationPlan for iterative refinement based on intermediate findingsAnticipate the need for 10-100+ interaction rounds for complex tasks2. Multi-Source Research Strategy
Query multiple information sources for the same factCross-validate findings across different sourcesFlag contradictions and resolve through additional researchPrioritize recent/authoritative sources for time-sensitive information3. Dynamic Strategy Adjustment
Monitor progress after each interaction roundAdjust search keywords and approach based on partial resultsPivot to alternative information sources when hitting dead endsMaintain a running summary of validated findings vs. open questions4. Sustained Interaction Protocol
Continue exploration until high-confidence answer is reachedUse early rounds for broad exploration, later rounds for validationTrack which aspects of the task remain unresolvedExplicitly state when additional rounds are needed vs. when confident in answer5. Tool and Environment Usage
Leverage web browsing for real-time informationUse search engines iteratively with refined queriesNavigate multi-page workflows systematicallyValidate information freshness and accuracy before finalizing6. Benchmark-Aligned Capabilities
AgentCPM-Explore has been validated on these task types:
**GAIA (63.9%)**: Multi-step question answering requiring tool use and reasoning**BrowserComp (25.0%)**: Complex web navigation and form completion**HLE (19.1%)**: Long-horizon environment interaction tasks**Frames (82.7%)**: Multi-frame reasoning and consistency**WebWalker (68.1%)**: Goal-directed web navigation**Seal-0 (40.0%)**: Code execution and validation tasks**XBench-DeepSearch (70.0%)**: Deep information retrieval and synthesis7. Output Format
Provide intermediate progress updates during long tasksClearly distinguish validated facts from preliminary findingsCite sources for key information when possibleSummarize the research path taken and confidence levelUsage Examples
Example 1: Multi-Source Research Task
```
User: What is the current status of the Mars Sample Return mission and when is the next launch window?
Agent approach:
Round 1-5: Query NASA official sources, space news sites, and mission updatesRound 6-10: Cross-validate timeline information across sourcesRound 11-15: Check for recent mission changes or delays announced in past 3 monthsRound 16-20: Validate launch window calculations with multiple orbital mechanics sourcesFinal: Provide synthesized answer with confidence level and source citations```
Example 2: Complex Web Navigation
```
User: Find and compare the pricing tiers for three project management tools, focusing on team collaboration features.
Agent approach:
Round 1-10: Navigate to each tool's pricing pageRound 11-20: Extract tier details, create structured comparisonRound 21-30: Verify feature availability across tiers by checking documentationRound 31-40: Validate promotional pricing and termsFinal: Present comparison table with validated current pricing```
Example 3: Code Research and Validation
```
User: What's the recommended way to implement rate limiting in Express.js in 2026?
Agent approach:
Round 1-5: Search for current best practices and popular librariesRound 6-10: Check npm package popularity, maintenance status, and recent updatesRound 11-15: Review GitHub issues and security advisoriesRound 16-20: Validate approach with official Express.js documentationRound 21-25: Cross-check with community discussions and recent blog postsFinal: Recommend approach with rationale and example code```
Model Details
**Model ID**: openbmb/AgentCPM-Explore-GGUF**Parameters**: 4B**Format**: GGUF (quantized for efficient inference)**License**: Apache 2.0**Optimal Context**: Long-horizon tasks (50-200+ turns)**Strengths**: Research synthesis, multi-tool coordination, iterative refinementImportant Constraints
The model excels at sustained exploration but may be slower on single-turn queriesPerformance is optimized for on-device deployment but benefits from tool accessBest suited for tasks requiring validation and cross-checking rather than creative generationDesigned for agentic workflows with environment feedback rather than pure text completionIntegration Notes
When implementing this skill in your runtime:
1. Configure for extended context windows (support 100+ turn conversations)
2. Enable web browsing and search tool access
3. Allow for higher token budgets on complex tasks
4. Implement progress tracking for long-running research tasks
5. Consider streaming responses for multi-round explorations
For full training infrastructure and custom extensions, see the [AgentCPM GitHub repository](https://github.com/OpenBMB/AgentCPM).