CocoIndex is an incremental data transformation engine that keeps AI agent context continuously fresh by reindexing only the delta when source data or code changes. It uses a declarative, state-driven programming model where developers write simple Python functions to declare target states, and the engine handles incremental syncing, memoization, and lineage tracking. The platform is designed for codebases, meeting notes, and other dynamic sources, enabling sub-second freshness at any repository scale.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
AI agents rely on context derived from codebases, documents, and communications, but this data changes constantly. Reprocessing entire datasets after every update is slow, expensive, and disruptive, leading to stale or incomplete context that degrades agent performance and reliability.
Solution
CocoIndex provides an incremental engine for long-horizon AI agents, using a declarative, state-driven programming model. Developers write pure Python functions that declare the desired target state, and the engine automatically diffs the source against the last run to compute and apply only the minimal changes needed. This approach, analogous to React for the frontend, ensures that targets like vector databases or Postgres stay in sync with source changes and code updates, with memoization at the function and transform level to skip unchanged work. The engine tracks end-to-end lineage, so every output can be traced back to its source item and code version, and it runs continuously at any scale, from a laptop to a petabyte corpus.
Target Audience
Primary users are AI engineers and data platform teams building coding, code-review, or security-review agents that need continuously fresh, explainable context from dynamic sources like codebases and meeting notes.
Features
- Declarative target state definitions: specify what the output should look like, not how to get there, with the engine handling inserts, updates, and deletes automatically.
- Function memoization: skips recomputation when both input data and code are unchanged, using content-addressed fingerprints and code hashes.
- End-to-end lineage: every byte in the target is tagged with source file, byte range, code commit, and run timestamp for full traceability.
- Incremental processing at any scale: runs the same code on a laptop or a shared daemon, processing only the delta and scaling with changes, not corpus size.
- Atomic batch commits: applies all target state changes (e.g., upserts and deletes) in a single transaction per processing component.
- Python-native development: write standard Python functions with an opt-in decorator, enabling breakpoints, testing, and notebook use without a DAG configuration.