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Gemini Executive Synthesis

OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`.

Technical Positioning
An 'AI-Native Company' that relies on seamless integration with various LLM providers.
SaaS Insight & Market Implications
This issue reveals a critical LLM integration failure within OpenOPC. Despite `deepseek-v4-pro` being configured as the default model and its parameters explicitly defined in `litellm`'s `model_prices_and_context_window.json`, OpenOPC's `llm.provider` fails to resolve the model's context window. This indicates a disconnect between OpenOPC's internal model mapping logic and its external LLM dependency management. Such integration failures directly impede the platform's ability to leverage diverse AI models, limiting flexibility and potentially increasing operational costs if users are forced to rely on less optimal alternatives. This undermines OpenOPC's 'AI-Native' positioning by demonstrating fragility in its core LLM orchestration layer.
Proprietary Technical Taxonomy
opc.llm.provider error deepseek-v4-pro Unable to resolve context window Model deepseek-v4-pro isn't mapped yet litellm model_prices_and_context_window.json llm_config.yaml default_model

Raw Developer Origin & Technical Request

Source Icon GitHub Issue Jul 4, 2026
Repo: HKUDS/OpenOPC
opc.llm.provider error for deepseek-v4-pro

配置deepseek模型，运行会提示llcm.provider错误:
08:06:47 | WARNING | opc.llm.provider - Unable to resolve context window for model=deepseek/deepseek-v4-pro: Model deepseek-v4-pro isn't mapped yet. Add it here - github.com/BerriAI/litellm/b...

github.com/BerriAI/litellm/b...
"deepseek/deepseek-v4-pro": {
"cache_creation_input_token_cost": 0.0,
"cache_read_input_token_cost": 3.625e-09,
"input_cost_per_token": 4.35e-07,
"input_cost_per_token_cache_hit": 3.625e-09,
"litellm_provider": "deepseek",
"max_input_tokens": 1000000,
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
"output_cost_per_token": 8.7e-07,
"source": "api-docs.deepseek.com/quick_start/prici...
"supported_endpoints": [
"/v1/chat/completions"
],
"supports_assistant_prefill": true,
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_parallel_function_calling": true,
"supports_prompt_caching": true,
"supports_response_schema": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_vision": false
},

llm_config.yaml的内容如下：
(OpenOPC) xxx@ubuntu:~/workspace/OpenOPC$ cat .opc/config/llm_config.yaml
llm:
default_model: "deepseek/deepseek-v4-pro"
api_base...

Developer Debate & Comments

No active discussions extracted for this entry yet.

Adjacent Repository Pain Points

Other highly discussed features and pain points extracted from HKUDS/OpenOPC.

Extracted Positioning
OpenOPC's core task execution, UI/UX, and multi-agent orchestration capabilities, particularly for competitive analysis tasks.
An 'AI-Native Company' that is 'Self-Built, Self-Run, Self-Grown,' implying autonomous task completion and robust user experience, with high expectations for the Chinese market.
Top Replies
LZH-YS1998 • Jul 6, 2026
具体是什么任务呢，过程遇到哪些问题，还有使用的agent/模型可以分享下么
wiselinpm • Jul 7, 2026
uv run opc chat -p demo --mode company --company-profile corporate \ "给我做一份完整的 OpenOPC 竞品分析,含功能对比表和定价建议,输出到 .opc/projects/demo/competitive-analysis.md"。 代理使用codex ope...
wiselinpm • Jul 7, 2026
界面可视化行为，卡住在一个节点员工状态，不能完成任务 chat一直在刷新，UE是否不正常 状态面板是否更新锁死，点击没有反应 要重启系统 同时office经常不加载（任务图层zindex问题），任务被地板覆盖。任务最好...
Extracted Positioning
OpenOPC's multi-agent or 'Peercompany' collaboration mechanism.
An 'AI-Native Company' implying autonomous and collaborative AI agents.
Top Replies
LZH-YS1998 • Jul 6, 2026
This is expected behavior and commonly occurs when a mid-level role is waiting for work items delegated to its subordinate roles. Once all delegated work items are completed by the subordinates, th...
wiselinpm • Jul 7, 2026
这个预期卡这里好久。奇怪造成整个公司停摆
LZH-YS1998 • Jul 7, 2026
可以在ui的Execution Progress中，点击对应的角色查看下级角色是不是还有正常运作，以及kanban上显示是哪些工作项在进行（cto的下级是三个engineer）。如果分配给下级角色的工作没有做完，cto会等待。时间主要取...
Extracted Positioning
OpenOPC's performance and token consumption for AI-native application development.
An 'AI-Native Company' that is 'Self-Built, Self-Run, Self-Grown,' implying autonomous operation and efficient resource management.
Top Replies
wiselinpm • Jul 8, 2026
64378.9k / 1820.1k这个消耗：实际还未跑完第二阶段。
wiselinpm • Jul 8, 2026
任务面板加载不出列表
LZH-YS1998 • Jul 9, 2026
更新版修复了下前端卡顿的问题。减少token消耗是个值得探索的问题，我们后续的工作中会考虑升级这个点

Frequently Asked Questions

Market intelligence mapped to OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`..

How is OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`. positioned in the market?
Based on our AI analysis of the original developer request, its primary technical positioning is: An 'AI-Native Company' that relies on seamless integration with various LLM providers.
What is the general sentiment around OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`.?
Yes, we have tracked 1 direct responses and active debates regarding this specific topic originating from GitHub Issue.
Which technical concepts are associated with OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`.?
Our proprietary extraction maps OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`. to adjacent architectural concepts including opc.llm.provider error, deepseek-v4-pro, Unable to resolve context window, Model deepseek-v4-pro isn't mapped yet.
Are there startups building around OpenOPC's LLM provider integration, specifically with `deepseek-v4-pro`.?
Yes, market intelligence reveals commercial overlap. A product named 'DeepSeek-V4' focuses directly on this: The open-source era of 1M context intelligence

Engagement Signals

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Replies
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Issue Status

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like default_model and opc.llm.provider error by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.