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AI-Augmented Development

kcp-memory: Give Claude Code a Memory

Every Claude Code session starts the same way. The context window is empty. The agent has no recollection of what it did yesterday, which files it touched last week, or how it solved a similar problem three sessions ago. Each session is day one.

That is not a limitation of the model. It is a missing infrastructure layer.

Today we are releasing kcp-memory v0.1.0: a standalone Java daemon that indexes ~/.claude/projects/**/*.jsonl session transcripts into a local SQLite database with FTS5 full-text search. Ask it "what was I working on last week?" and it answers in milliseconds.

Update (same day — v0.2.0): kcp-memory now ships with tool-level granularity. kcp-commands v0.9.0 writes every Bash tool call to ~/.kcp/events.jsonl; kcp-memory ingests that stream and makes individual commands searchable via kcp-memory events search. Session-level and tool-level memory in one daemon, one database, zero additional dependencies.

Update (same day — v0.3.0): kcp-memory now ships as an MCP server. Run java -jar ~/.kcp/kcp-memory-daemon.jar mcp — registered in mcpServers in ~/.claude/settings.json — and Claude Code can call kcp_memory_search, kcp_memory_events_search, kcp_memory_list, and kcp_memory_stats inline during a session, without leaving the context window.

Update (same day — v0.4.0): Two new MCP tools close the remaining gaps. kcp_memory_session_detail(session_id) returns full session content — user messages, files touched, tools used — completing the search → read flow. kcp_memory_project_context() reads PWD from the process environment and returns the last 5 sessions and 20 tool events for the current project, with no query needed. Call it at the start of every session and Claude immediately knows what it was doing here last time.

KCP Comes to OpenCode: The First AI Coding Tool Plugin

kcp-commands recovers 33% of Claude Code's context window by intercepting Bash tool calls. Today we are extending that same principle to OpenCode — the 114K-star TypeScript alternative to Claude Code.

The result is opencode-kcp-plugin: a plugin that injects a knowledge.yaml knowledge map into every OpenCode session and annotates file search results with intent descriptions. The mechanism is different from kcp-commands, and the target is different, but the underlying idea is identical: give the agent a map so it does not have to rediscover the territory on every session.

The AI-Augmented Consultant: Knowledge Infrastructure Before Deliverables

A brief arrives on Friday afternoon. A compliance startup is building an AI-powered scoring engine. They have a working architecture. What they do not have are settled positions on five hard architectural questions. The deadline is Monday.

The question I asked myself was not "how do I answer these five questions?" It was "what knowledge infrastructure do I need to answer them well?"

That reframing is the entire methodology in one sentence.

KCP v0.1 to v0.5: How a Knowledge Standard Grows

KCP (Knowledge Context Protocol) has gone from a draft proposal to a v0.5 spec in one week. This post walks through what each version added, why those decisions were made, and where the spec is heading next.

The short version: every release promotes optional fields from community RFCs into the normative core. The spec is a strict superset at each step — a manifest written for v0.1 is still valid under v0.5.

The Evolution of KCP: From Minimal Draft to AI Knowledge Standard — v0.1 through v0.5 and Synthesis v1.20.0 implementation overview

KCP on Three Agent Frameworks: Same Pattern, Bigger Numbers

Five repos benchmarked, 73–80% reduction across three major AI agent frameworks. AutoGen leads at 80%, CrewAI at 76%, smolagents at 73%.

Today we applied KCP to three of the most widely-used AI agent frameworks — smolagents (HuggingFace, 25K stars), AutoGen (Microsoft, 55K stars), and CrewAI (44K stars). All three got the same treatment: a knowledge.yaml manifest, pre-built TL;DR summary files for the highest-traffic sections, and a before/after benchmark using the same model and methodology.

The results: 73%, 80%, and 76% reductions in agent tool calls. Open PRs are live on all three repositories.

73% smolagents (HuggingFace), 80% AutoGen (Microsoft), 76% CrewAI — KCP applied to these three widely-used AI frameworks yielded identical patterns of massive tool-call reduction.

KCP on Two Repos, Two Days: What the Numbers Actually Show

KCP benchmarking: 119 → 31 tool calls on application code, 53 → 25 on documentation. Two case studies, same methodology.

This week we applied KCP to two repositories back to back. Both got a knowledge.yaml manifest, pre-built TL;DR files for the highest-traffic sections, and a before/after benchmark using the same model and methodology.

The repos are very different. One is an application codebase — a plugin wizard for an AI-native design platform, 15 documentation units covering architecture, agent types, tools, shape schemas, and plugin protocols. The other is a pure documentation repository — a 13-chapter production guide for building safe infrastructure agents, 226 KB of structured decision frameworks and deployment checklists.

The question was whether KCP adds meaningful value in both cases, and whether the nature of the content changes the answer.

Without a manifest, agents wander. With one, they go straight to the answer.