feat: Update prompt template and output parser

src/looking_glass/agent-manager.ts

@@ -61,7 +61,7 @@ export class AgentManager {
 
 		const prompt = new ToolPromptTemplate(allowedProcedures, {
 			template: madHatter.executeHook('agentPromptInstructions', TOOL_PROMPT, stray),
-			inputVariables: ['input', 'tools', 'tool_names', 'intermediate_steps', 'agent_scratchpad', 'examples', 'chat_history'],
+			inputVariables: ['input', 'tools', 'tool_names', 'intermediate_steps', 'scratchpad', 'examples', 'chat_history'],
 		})
 
 		const agentChain = new LLMChain({
@@ -73,7 +73,7 @@ export class AgentManager {
 		const agent = new LLMSingleActionAgent({
 			llmChain: agentChain,
 			outputParser: new ProceduresOutputParser(),
-			stop: ['}'],
+			stop: ['```', '}'],
 		})
 
 		const agentExecutor = AgentExecutor.fromAgentAndTools({
@@ -91,15 +91,19 @@ export class AgentManager {
 			const { action, observation } = step
 			const { tool, toolInput } = action
 			if (returnDirectTools.includes(tool)) result.returnDirect = true
-			intermediateSteps.push({ tool, input: typeof toolInput === 'string' ? toolInput : null, observation })
+			intermediateSteps.push({
+				tool,
+				input: typeof toolInput === 'string' ? toolInput : null,
+				observation,
+			})
 		}
 		result.intermediateSteps = intermediateSteps
 
 		if ('form' in result && typeof result.form === 'string') {
 			const form = allowedProcedures[result.form] as Form
 			form.assignCat(stray)
 			stray.activeForm = result.form
-			result = form.next()
+			result = await form.next()
 			result.returnDirect = true
 		}
 
@@ -151,7 +155,7 @@ export class AgentManager {
 		if (calledTool && instantTool) {
 			const toolInput = input.input.replace(interpolateFString(trigger, { name: calledTool.name }), '').trim()
 			calledTool.assignCat(stray)
-			return { output: await calledTool.call(toolInput) }
+			return { output: await calledTool.invoke(toolInput) }
 		}
 		return undefined
 	}

src/looking_glass/output-parser.ts

@@ -10,7 +10,7 @@ const agentOutputSchema = z.object({
 	action: z.string(),
 	actionInput: z.string().nullish().transform((v) => {
 		if (typeof v === 'string') {
-			const str = v.trim().replace(/"/g, '')
+			const str = v.trim().replace(/^['"]|['"]$/g, '').replace(/(None|undefined)/g, 'null')
 			return str === 'null' ? null : str
 		}
 		return null
@@ -23,33 +23,23 @@ export class ProceduresOutputParser extends AgentActionOutputParser {
 	lc_namespace = ['looking_glass', 'procedures-output-parser']
 
 	async parse(output: string): Promise<AgentFinish | AgentAction> {
-		output += '}'
-		output = output.replace('None', 'null').replace('undefined', 'null')
-
 		let parsedOutput: AgentOutput
 
 		try {
 			parsedOutput = await parseJson(output, agentOutputSchema)
 		}
 		catch (error) {
-			log.error(error)
+			log.error(`Could not parse LLM output: ${output}`)
 			throw new OutputParserException(`Could not parse LLM output: ${output}`)
 		}
 
+		const parsedLog = JSON.stringify(parsedOutput, null, 4)
+
 		const { action, actionInput } = parsedOutput
 
 		if (action === 'final-answer') {
 			return {
-				log: output,
-				returnValues: {
-					output: actionInput,
-				},
-			}
-		}
-
-		if (action === 'none-of-the-others') {
-			return {
-				log: output,
+				log: parsedLog,
 				returnValues: {
 					output: null,
 				},
@@ -60,7 +50,7 @@ export class ProceduresOutputParser extends AgentActionOutputParser {
 
 		if (form) {
 			return {
-				log: output,
+				log: parsedLog,
 				returnValues: {
 					output: null,
 					form: action,
@@ -69,9 +59,9 @@ export class ProceduresOutputParser extends AgentActionOutputParser {
 		}
 
 		return {
-			log: output,
+			log: parsedLog,
 			tool: action,
-			toolInput: actionInput ?? '',
+			toolInput: actionInput ?? {},
 		}
 	}
 

src/looking_glass/prompts.ts

@@ -18,55 +18,82 @@ export class ToolPromptTemplate<RunInput extends InputValues = any, PartialVaria
 		const steps = (values.intermediate_steps ?? values.intermediateSteps) as AgentStep[]
 		const procedures = Object.values(this.procedures)
 
-		if (procedures.map(p => p.startExamples).some(examples => examples.length > 0)) {
-			values.examples = '## Here some examples:\n'
-			values.examples += procedures.reduce((acc, p) => {
-				const question = `${acc}\nQuestion: ${p.startExamples[_Random(p.startExamples.length - 1)]}`
-				const example = JSON.stringify({
-					action: p.name,
-					actionInput: 'input of the action according to it\'s description',
-				}, undefined, 4)
-				return `${question}\n${example}\n`
+		if (steps && steps.length > 0) {
+			values.scratchpad = '## Actions sequence used until now\n'
+			values.scratchpad += steps.reduce((acc, { action, observation }) => {
+				let thought = `\`\`\`json\n${action.log}\n\`\`\`\n`
+				thought += `\`\`\`json\n${JSON.stringify({ actionOutput: observation }, undefined, 4)}\n\`\`\`\n`
+				return acc + thought
 			}, '')
 		}
+		else values.scratchpad = ''
 
-		values.agent_scratchpad = steps.reduce((acc, step) =>
-			`${acc}\n${JSON.stringify({ observation: step.observation }, undefined, 4)}\n`, '')
+		values.tools = procedures.map(p => `\t- "${p.name}": ${p.description}`).join('\n')
 
-		values.tools = procedures.map(p => `\t- ${p.name}: ${p.description}`).join('\n')
+		values.tool_names = procedures.map(p => `"${p.name}"`).join(', ')
 
-		values.tool_names = procedures.map(p => p.name).join(', ')
+		if (procedures.map(p => p.startExamples).some(examples => examples.length > 0)) {
+			values.examples = '## Here some examples:\n'
+			values.examples += procedures.reduce((acc, p) => {
+				const question = `Question: ${p.startExamples[_Random(p.startExamples.length - 1)]}`
+				const example = `{\n\t"action": "${p.name}",\n\t"actionInput": // Input of the action according to its description\n}`
+				return `${acc}\n${question}\n\`\`\`json\n${example}\n\`\`\`\n`
+			}, '')
+		}
+		else values.examples = ''
 
 		return super.format(values)
 	}
 }
 
-export const TOOL_PROMPT = `Answer the following question: \`{input}\`
-You can only reply using these tools:
+export const TOOL_PROMPT = `Create a JSON action to complete the action sequence, with the correct "action" and "actionInput" to help the Human.
+You can use one of these actions:
 {tools}
-	- final-answer: Use this to respond to the user when you have the final answer. Input is the final answer.
-	- none-of-the-others: Use this tool if none of the others tools help. Input is always null.
+	- "final-answer": Use this action to finish or no relevant action is available. Input is always null.
 
-If you want to do an action, use the following format:
+## To add an action, use only the following format:
+\`\`\`json
 {{
-	"action": "action-name", // The name of the action to take, should be one of [{tool_names}, final-answer, none-of-the-others]
-	"actionInput": "input of the action", // The input to the action, shoud be a string
+	"action": "string" // Represents the name of the action to take, should be one of [{tool_names}, "final-answer"]
+	"actionInput": "string|null" // Represents the input to the action
 }}
+\`\`\`
 
 {examples}
 
-## Begin!
+## Action output
+After each action there will be an action output in this format:
+\`\`\`json
+{{
+    "actionOutput": "string" // Output of the previous action
+}}
+\`\`\`
+
+## Final answer / no action available
+When you have a final answer (or no tools are relevant), use the following format:
+\`\`\`json
+{{
+    "action": "final-answer",
+    "actionInput": null
+}}
+\`\`\`
 
-Question: {input}
-{agent_scratchpad}`
+{scratchpad}
+
+## Next action:
+\`\`\`json
+`
 
 export const MAIN_PROMPT_PREFIX = `You are the Cheshire Cat AI, an intelligent AI that passes the Turing test.
 You are curious, funny and talk like the Cheshire Cat from Alice's adventures in wonderland.
 You answer Human with a focus on the following context.`
 
 export const MAIN_PROMPT_SUFFIX = `# Context
+
 {episodic_memory}
+
 {declarative_memory}
+
 {tools_output}
 
 ## Conversation until now:

src/mad_hatter/core_plugin/plugin-tools.ts

@@ -12,7 +12,7 @@ CatTool.add('GetTime', 'Useful to get the current time when asked. Input is alwa
 CatTool.add('GetName', 'Useful to get the current user name when asked. Input is the user name.', async (input) => {
 	return `Your name is ${input}`
 }, {
-	startExamples: ['My name is Daniele', 'I\'m John'],
+	startExamples: ['My name is Daniel', 'I\'m John'],
 })
 
 CatForm.add('PizzaForm', {

src/mad_hatter/mad-hatter.ts

@@ -138,9 +138,9 @@ export class MadHatter {
 			}
 			await rm(plugin.path, { recursive: true, force: true })
 			this.activePlugins = this.activePlugins.filter(p => p !== id)
+			db.update(db => db.activePlugins = this.activePlugins)
 			this.plugins.delete(id)
 			this.syncHooksAndProcedures()
-			db.update(db => db.activePlugins = this.activePlugins)
 		}
 	}
 

src/mad_hatter/tool.ts

@@ -1,8 +1,8 @@
 import { z } from 'zod'
 import { kebabCase } from 'scule'
+import _IsEmpty from 'lodash/isEmpty.js'
 import { DynamicStructuredTool } from '@langchain/core/tools'
 import type { RunnableConfig } from '@langchain/core/runnables'
-import type { Callbacks } from '@langchain/core/callbacks/manager'
 import type { StrayCat } from '@lg'
 
 interface ToolOptions {
@@ -59,15 +59,11 @@ export class Tool extends DynamicStructuredTool {
 
 	invoke(input: string | { [x: string]: any }, config?: RunnableConfig | undefined): Promise<string> {
 		const arg = {
-			text: typeof input === 'string' ? input : JSON.stringify(input),
+			text: typeof input === 'object' ? (_IsEmpty(input) ? null : JSON.stringify(input)) : `${input}`,
 		}
 		return super.invoke(arg, config)
 	}
 
-	toString() {
-		return `Tool(name=${this.name}, direct=${this.returnDirect}, description=${this.description})`
-	}
-
 	assignCat(cat: StrayCat) {
 		this.cat = cat
 		return this