Local inference on models like Llama 3.2 is now feasible, enabling on-device LLM use with internet search augmentation and reducing dependence on clou...Show more

Use Artificial Analysis’s metametric, which aggregates multiple intelligence benchmarks, to evaluate and compare the performance of open weight and pr...Show more

Prioritize LLM evaluation based on real-world task completion and cost efficiency rather than solely on benchmark scores.

Adopt Kimmy 2 via Ollama Cloud as a cost-effective workhorse model for general web research tasks, offering near-frontier performance for many use cas...Show more

Use large language models to automatically generate detailed product roadmaps or build plans for upcoming quarters by prompting for deliverables and t...Show more

Incorporate a ‘run critique and citations with hyperlinks’ step into your AI pipeline to validate outputs and provide traceable source links.

Demonstrate AI agent workflows by showing each planning and note-taking step, iteratively searching the web and extracting timestamped references for ...Show more

Develop systems for managing multiple AI agents at scale within organizations, moving beyond individual desktop deployments to centralized orchestrati...Show more

Use natural language prompts to meta-program agent behaviors instead of writing code to simplify updates and maintainability.

Connect authentication systems like Clerk to enable session-based user memory that continually self-improves agent personalization.

The Agent Builder allows free-text memory updates but currently lacks persistent session memory, requiring eventual integration of session-based stora...Show more

The LangGraph Agent CLI provides persistent memory across sessions, enabling agents to recall past interactions without manual reconfiguration.

Implement compliance and legal requirements as dynamic if-else logic within agents to ensure rapid updates when regulations change.

Embed repeatable user-specific patterns and preferences into agent responses via meta-programmed memory to tailor interactions to individuals like Tom...Show more

Agents should maintain system-level and user-level memory blocks to separate application state and individual user preferences for more nuanced intera...Show more

Deploying Deep Agents on Langgraph Cloud provides built-in persistent long-term memory, enabling agents to retain context and self-modify over time.

An extensible AI framework supporting mcp and LangChain integrations, with editable instruction files (e.g., research_agent.py) for system prompt cust...Show more

A tool that generates primary and sub-agents from plain-English descriptions and supports iterative output feedback to fine-tune behaviors.

You can attach any number of MCP servers (e.g., build, exa) as tools in Deep Agents to inject custom context pipelines into your agents.

The Deep Agents package supports both mcp and LangChain tools and lets you modify the system or research instructions directly (e.g., in research_agen...Show more