Does CASS support searching across multiple computers?

Yes, CASS features a remote sources setup wizard that allows you to sync and search sessions from different machines via SSH and rsync.

How does CASS handle large LLM context limits?

CASS includes built-in token budget management flags like --max-tokens, --max-content-length, and field filtering to keep results within specific limits.

What agents does CASS support?

CASS aggregates sessions from over 11 agents including Claude Code, Cursor, Aider, Gemini CLI, Codex, ChatGPT, Pi-Agent, and Factory.

Can AI agents use CASS safely without blocking the terminal?

Yes, agents must use the --robot or --json flags. This ensures the output is machine-readable and prevents the interactive TUI from launching.

CASS Coding Agent Session Search

Name: CASS Coding Agent Session Search
Author: trungtnm

bytrungtnm

0•

Developer Tools

Indexes and searches local coding agent history across multiple platforms to provide a unified knowledge base for AI-assisted development.

CASS (Coding Agent Session Search) is a high-performance utility designed to centralize and search historical data from various AI coding agents like Claude Code, Cursor, Aider, and Codex. It provides a unified CLI/TUI that allows both human developers and AI agents to retrieve context from previous sessions, solving the problem of scattered knowledge across different tools. With its specialized 'Robot Mode' for machine-readable JSON output, token budget management, and multi-machine sync capabilities, CASS enables seamless knowledge transfer and context retrieval, making it easier for agents to learn from past interactions and solve complex debugging tasks.

Key Features

01Token budget management to truncate outputs and control LLM context usage

02Unified search across 11+ agents including Claude Code, Cursor, Aider, and Codex

03Robot Mode providing JSON/JSONL output optimized for AI agent consumption

04Multi-machine support via SSH/rsync to aggregate history from remote sources

050 GitHub stars

06Hybrid search modes combining lexical BM25 matching with vector similarity

Use Cases

01Reviewing daily activity timelines to track development progress across multiple AI tools

02Searching past sessions across different agents to find previously implemented solutions

03Managing agent-to-agent handoffs by extracting summaries of previous work from different environments

Key Features

01Token budget management to truncate outputs and control LLM context usage

02Unified search across 11+ agents including Claude Code, Cursor, Aider, and Codex

03Robot Mode providing JSON/JSONL output optimized for AI agent consumption

04Multi-machine support via SSH/rsync to aggregate history from remote sources

050 GitHub stars

06Hybrid search modes combining lexical BM25 matching with vector similarity

Use Cases

01Reviewing daily activity timelines to track development progress across multiple AI tools

02Searching past sessions across different agents to find previously implemented solutions

03Managing agent-to-agent handoffs by extracting summaries of previous work from different environments