DSPy shipped dspy.RLM, a Recursive Language Model module in version 3.1.2, to address context rot — the tendency of models to lose performance on long inputs before reaching their advertised context w…
A developer contributed an open-source feature to DSPy, adding a Together AI fine-tuning provider. The provider allows users to fine-tune models like Llama directly within the DSPy framework, eliminat…
SigNoz announced support for monitoring DSPy programs via OpenTelemetry, enabling real-time visibility into DSPy module calls and language model requests. The integration uses the OpenInference DSPy i…
A new DSPy Dashboard monitors performance, cost, and reliability of DSPy programs using OpenTelemetry trace data, tracking span kinds CHAIN, LLM, and TOOL across model usage, latency percentiles, modu…
AMD has released the Primus Tuning Agent, a tool that automatically searches for optimal training configurations for large language models by using a projection engine as a fast scoring oracle. In a c…
Simon Willison used DSPy to evaluate and improve the system prompts for Datasette Agent, an AI tool that executes read-only SQL queries. The research identified that including column names in schema l…
A new benchmark, dspy-security-bench, reveals that DSPy prompt optimization degrades adversarial robustness against harder prompt-injection attacks. Testing with AgentDojo's attack suite, optimizers l…
A developer built dspyer, an open-source tool that wraps LLM calls in Pydantic schemas to automatically correct malformed JSON, missing fields, or fabricated citations. The tool compiles to a standard…
AxLLM launches Ax, a multi-language LLM framework that uses typed signatures to replace prompt glue with automated prompt generation, parsing, validation, and retry loops. Built on research from DSPy,…
Researchers introduced GEPA, a prompt optimizer that uses an LLM to read execution traces and rewrite prompts, outperforming reinforcement learning by leveraging natural language reasoning about failu…
Decagon deployed self-evolving AI agents that automatically improve their own prompts without human intervention, using a feedback loop that tests and promotes winning versions. The team found that th…
A new free eBook titled 'Building Pragmatic AI Agents That Use Tools and APIs' provides a practical guide to constructing production-grade AI agents, covering five frameworks including DSPy and OpenAI…
A developer built a data pipeline using DSPy and Claude Code to analyze whether AI discourse changed in academic research and policy discussions during the ChatGPT era. The pipeline processes NBER jou…
Three prompt injection attacks in 72 hours highlight structural vulnerabilities in agentic AI systems. A CVE against Cursor demonstrates that per-call validation fails when agents operate in poisoned …
Pydantic AI and DSPy are two Python frameworks for building LLM-powered applications that take fundamentally different approaches, with Pydantic AI emphasizing imperative agent design and type-safe st…
Stanford NLP's DSPy framework replaces manual prompt engineering with programmatic, optimizable modules that can be automatically tuned through closed-loop learning, treating prompts as variables that…
Developers are increasingly writing AI agent skills instead of traditional documentation, as skills offer immediate value and are easier to iterate. The shift raises questions about whether human-cent…
Seven leading AI agent frameworks have crystallized into distinct approaches for building autonomous systems as of mid-2026, with LangGraph leading production deployments at approximately 400 firms in…
Seven leading AI agent frameworks — DSPy, Claude Agent SDK, OpenAI Agents SDK, CrewAI, AutoGen/Microsoft Agent Framework, LangGraph, and Google ADK — now offer distinct trade-offs in abstraction level…