● Covered by 1 source · 1 reportMedium impact1 neutral
Pensieve introduces local indexing for coding agents to reduce tool calls and token usage
Key points
- Pensieve offers local indexing for documents, folders, and Git repositories.
- Search functionality includes semantic and exact word matching.
- Agents use Pensieve to retrieve relevant code passages, reducing tool calls.
- Integration is available for agents like Claude Code, Codex, and Cursor.
Local Indexing for Comprehensive Search
Pensieve has introduced a local indexing capability that allows users to search across all their documents, folders, Git repositories, and conversations with coding agents. This feature enables search by meaning or by exact words, with options to exclude terms or narrow results by file type, folder, or repository.
Optimizing Agent Context and Efficiency
The primary goal of this indexing is to provide coding agents with more relevant context. By connecting over MCP, agents can query Pensieve and receive specific code passages with file and line numbers. This reduces the need for multiple tool calls and lowers token usage, as agents are directed to the precise information needed.
Performance Improvements Demonstrated
In a benchmark, agents utilizing Pensieve averaged 2.0 tool calls per lookup, compared to 3.4 tool calls with grep. Pensieve-equipped agents also answered after a single call in 82 out of 144 runs, indicating improved efficiency in information retrieval for coding tasks.
Integration with Existing Coding Agents
Pensieve integrates with various coding agents, including Claude Code, Codex, and Cursor, or any MCP client. Integration can be done via stdio or HTTP. The system also adds a "search before you grep" block and skills, including optional features that can identify teammates knowledgeable about specific code sections.
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Primary sources
GitHub TedHaley/pensieveReporting from
Pensieve has launched a local indexing feature that allows coding agents to search across files, repositories, and sessions. This aims to reduce the number of tool calls and token usage by providing agents with more precise context, improving efficiency in code-related tasks.