Tejas-TA/predikit

Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.

Build data processing and data analysis pipelines that leverage the power of LLMs 🧠

Build and run agents you can see, understand and trust.

AIDE: an LLM agent for machine learning engineering - the research Weco grew out of. Referenced in OpenAI MLE-bench.

Code, Build and Evaluate agents - excellent Model and Skills/MCP/ACP/A2A Support

An agentic Machine Learning Engineer

Run any ML model from any programming language.

Harness LLMs with Multi-Agent Programming

Cascading runtime for AI agents. Optimize cost, latency, quality, and policy decisions inside the agent loop.