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@codepawl

CodePawl

Infrastructure for autonomous coding agents

CodePawl

Infrastructure for autonomous coding agents.

CodePawl builds tools that help coding agents debug failures, remember useful context, coordinate work, recover from long-horizon execution errors, and optimize agent workloads.

Stack

  • TracePawl — failure diagnosis and replay for coding agents
  • MemPawl — persistent memory for agentic systems
  • OpenPawl — runtime for coordinated coding agents
  • CachePawl — optimization for long-horizon agent workloads

Current Focus

The current priority is TracePawl: a debugging and postmortem tool for coding-agent failures.

TracePawl focuses on answering questions such as:

  • Why did the agent fail?
  • Where did execution start to drift?
  • Which tool call or context update caused the failure?
  • What recovery action should be attempted next?
  • Has a similar failure happened before?

Research Direction

CodePawl is interested in four core problems:

  • Why coding agents fail
  • How agents remember useful operational knowledge
  • How multiple agents coordinate safely
  • How long-running agent execution becomes cheaper and more reliable

Status

CodePawl is early-stage research and engineering work. APIs, architecture, and project boundaries may change quickly.

Contact

Founder: An Nguyen
Email: founder@codepawl.com

Pinned Loading

  1. tracepawl tracepawl Public

    Failure diagnosis and replay for coding agents.

    TypeScript

  2. openpawl openpawl Public

    Stop pair-programming with one AI. Bring the whole team.

    TypeScript 3

  3. mempawl mempawl Public

    Hebbian memory for AI agents

    Python

  4. cachepawl cachepawl Public

    Research notes and scaffolding for cachepawl

    Python 1

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