Add memory agent and caching functionality 🧠✨

docs/src/components/BuiltinAgents.mdx

@@ -11,4 +11,5 @@ import { LinkCard } from '@astrojs/starlight/components';
 <LinkCard title="agent git" description="query a repository using Git to accomplish tasks. Provide all the context information available to execute git queries." href="/genaiscript/reference/scripts/system#systemagent_git" />
 <LinkCard title="agent github" description="query GitHub to accomplish tasks" href="/genaiscript/reference/scripts/system#systemagent_github" />
 <LinkCard title="agent interpreter" description="run code interpreters for Python, Math. Use this agent to ground computation questions." href="/genaiscript/reference/scripts/system#systemagent_interpreter" />
+<LinkCard title="agent memory" description="queries the memories created by other agent conversations." href="/genaiscript/reference/scripts/system#systemagent_memory" />
 <LinkCard title="agent user_input" description="ask user for input to confirm, select or answer the question in the query. The message should be very clear and provide all the context." href="/genaiscript/reference/scripts/system#systemagent_user_input" />

docs/src/content/docs/reference/scripts/system.mdx

@@ -137,7 +137,7 @@ defAgent(
         system: ["system.explanations", "system.github_info"],
         tools: [
             "md_find_files",
-            "md_read_frontmatterm",
+            "md_read_frontmatter",
             "fs_find_files",
             "fs_read_file",
         ],
@@ -290,6 +290,57 @@ defAgent(
 `````
 
 
+### `system.agent_memory`
+
+agent that retreives memories
+
+
+
+
+
+`````js wrap title="system.agent_memory"
+system({
+    title: "agent that retreives memories",
+})
+
+const cache = await workspace.cache("memory")
+
+defAgent(
+    "memory",
+    "queries the memories created by other agent conversations.",
+    async (ctx) => {
+        const memories = await cache.values()
+        ctx.$`Your are a helpfull LLM agent that acts as a knowledge base for memories created by other agents.
+
+    Answer the question in QUERY with the memories in MEMORY.
+
+    - Use the information in MEMORY exclusively to answer the question in QUERY.
+    - If the information in MEMORY is not enough to answer the question in QUERY, respond <NO_MEMORY>.
+    - The memory 
+    `
+        memories.reverse().forEach(
+            ({ agent, query, answer }) =>
+                ctx.def(
+                    "MEMORY",
+                    `${agent}> ${query}?
+                ${answer}
+                `
+                ),
+            {
+                flex: 1,
+            }
+        )
+    },
+    {
+        model: "openai:gpt-4o",
+        flexTokens: 30000,
+        system: ["system"],
+    }
+)
+
+`````
+
+
 ### `system.agent_user_input`
 
 Agent that can asks questions to the user.

packages/core/src/constants.ts

@@ -255,3 +255,5 @@ export const CLI_ENV_VAR_RX = /^genaiscript_var_/i
 
 export const GIT_DIFF_MAX_TOKENS = 8000
 export const MAX_TOOL_CONTENT_TOKENS = 4000
+
+export const MEMORY_CACHE_NAME = "memory"

packages/core/src/genaisrc/system.agent_memory.genai.mjs

@@ -0,0 +1,38 @@
+system({
+    title: "agent that retreives memories",
+})
+
+const cache = await workspace.cache("memory")
+
+defAgent(
+    "memory",
+    "queries the memories created by other agent conversations.",
+    async (ctx) => {
+        const memories = await cache.values()
+        ctx.$`Your are a helpfull LLM agent that acts as a knowledge base for memories created by other agents.
+
+    Answer the question in QUERY with the memories in MEMORY.
+
+    - Use the information in MEMORY exclusively to answer the question in QUERY.
+    - If the information in MEMORY is not enough to answer the question in QUERY, respond <NO_MEMORY>.
+    - The memory 
+    `
+        memories.reverse().forEach(
+            ({ agent, query, answer }) =>
+                ctx.def(
+                    "MEMORY",
+                    `${agent}> ${query}?
+                ${answer}
+                `
+                ),
+            {
+                flex: 1,
+            }
+        )
+    },
+    {
+        model: "openai:gpt-4o",
+        flexTokens: 30000,
+        system: ["system"],
+    }
+)

packages/core/src/runpromptcontext.ts

@@ -41,6 +41,7 @@ import {
 import { parseModelIdentifier, resolveModelConnectionInfo } from "./models"
 import {
     CHAT_REQUEST_PER_MODEL_CONCURRENT_LIMIT,
+    MEMORY_CACHE_NAME,
     MODEL_PROVIDER_AICI,
     SYSTEM_FENCE,
 } from "./constants"
@@ -59,6 +60,8 @@ import { Project } from "./ast"
 import { dedent } from "./indent"
 import { runtimeHost } from "./host"
 import { writeFileEdits } from "./fileedits"
+import { JSONLineCache } from "./cache"
+import { string } from "mathjs"
 
 export function createChatTurnGenerationContext(
     options: GenerationOptions,
@@ -347,7 +350,7 @@ export function createChatGenerationContext(
             },
             async (args) => {
                 const { context, query } = args
-                logVerbose(`${agentLabel}: ${query}`)
+                context.log(`${agentLabel}: ${query}`)
                 const res = await runPrompt(
                     async (_) => {
                         _.def("QUERY", query)
@@ -358,6 +361,17 @@ export function createChatGenerationContext(
                 - If you cannot answer the query, return "NO_ANSWER: <reason>".
                 - Be concise. Minimize output to the most relevant information to save context tokens.
                 `
+                        _.defOutputProcessor(async ({ text }) => {
+                            const agentMemory = JSONLineCache.byName<
+                                { agent: string; query: string },
+                                { answer: string }
+                            >(MEMORY_CACHE_NAME)
+                            const cacheKey = { agent: agentName, query }
+                            agentMemory.set(cacheKey, {
+                                ...cacheKey,
+                                answer: text,
+                            })
+                        })
                     },
                     {
                         label: agentLabel,