Tip
Location within the framework bee-agent-framework/memory.
Memory in the context of an agent refers to the system's capability to store, recall, and utilize information from past interactions. This enables the agent to maintain context over time, improve its responses based on previous exchanges, and provide a more personalized experience.
import { UnconstrainedMemory } from "bee-agent-framework/memory/unconstrainedMemory";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
const memory = new UnconstrainedMemory();
// Single message
await memory.add(
BaseMessage.of({
role: "system",
text: `You are a helpful assistant.`,
}),
);
// Multiple messages
await memory.addMany([
BaseMessage.of({ role: "user", text: `What can you do?` }),
BaseMessage.of({ role: "assistant", text: `Everything!` }),
]);
console.info(memory.isEmpty()); // false
console.info(memory.messages); // prints all saved messages
console.info(memory.asReadOnly()); // returns a NEW read only instance
memory.reset(); // removes all messagesSource: examples/memory/base.ts
import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
import { UnconstrainedMemory } from "bee-agent-framework/memory/unconstrainedMemory";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
const memory = new UnconstrainedMemory();
await memory.addMany([
BaseMessage.of({
role: "system",
text: `Always respond very concisely.`,
}),
BaseMessage.of({ role: "user", text: `Give me first 5 prime numbers.` }),
]);
// Generate response
const llm = new OllamaChatLLM();
const response = await llm.generate(memory.messages);
await memory.add(BaseMessage.of({ role: "assistant", text: response.getTextContent() }));
console.log(`Conversation history`);
for (const message of memory) {
console.log(`${message.role}: ${message.text}`);
}Source: examples/memory/llmMemory.ts
Tip
Memory for non-chat LLMs works exactly the same way.
import { UnconstrainedMemory } from "bee-agent-framework/memory/unconstrainedMemory";
import { BeeAgent } from "bee-agent-framework/agents/bee/agent";
import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
const agent = new BeeAgent({
memory: new UnconstrainedMemory(),
llm: new OllamaChatLLM(),
tools: [],
});
await agent.run({ prompt: "Hello world!" });
console.info(agent.memory.messages.length); // 2
const userMessage = agent.memory.messages[0];
console.info(`User: ${userMessage.text}`); // User: Hello world!
const agentMessage = agent.memory.messages[1];
console.info(`Agent: ${agentMessage.text}`); // Agent: Hello! It's nice to chat with you.Source: examples/memory/agentMemory.ts
Tip
If your memory already contains the user message, run the agent with prompt: null.
Note
Bee Agent internally uses TokenMemory to store intermediate steps for a given run.
Note
Agent typically works with a memory similar to what was just shown.
The framework provides multiple out-of-the-box memory implementations.
Unlimited in size.
import { UnconstrainedMemory } from "bee-agent-framework/memory/unconstrainedMemory";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
const memory = new UnconstrainedMemory();
await memory.add(
BaseMessage.of({
role: "user",
text: `Hello world!`,
}),
);
console.info(memory.isEmpty()); // false
console.log(memory.messages.length); // 1
console.log(memory.messages);Source: examples/memory/unconstrainedMemory.ts
Keeps last k entries in the memory. The oldest ones are deleted (unless specified otherwise).
import { SlidingMemory } from "bee-agent-framework/memory/slidingMemory";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
const memory = new SlidingMemory({
size: 3, // (required) number of messages that can be in the memory at a single moment
handlers: {
// optional
// we select a first non-system message (default behaviour is to select the oldest one)
removalSelector: (messages) => messages.find((msg) => msg.role !== "system")!,
},
});
await memory.add(BaseMessage.of({ role: "system", text: "You are a guide through France." }));
await memory.add(BaseMessage.of({ role: "user", text: "What is the capital?" }));
await memory.add(BaseMessage.of({ role: "assistant", text: "Paris" }));
await memory.add(BaseMessage.of({ role: "user", text: "What language is spoken there?" })); // removes the first user's message
await memory.add(BaseMessage.of({ role: "assistant", text: "French" })); // removes the first assistant's message
console.info(memory.isEmpty()); // false
console.log(memory.messages.length); // 3
console.log(memory.messages);Source: examples/memory/slidingMemory.ts
Ensures that the token sum of all messages is below the given threshold. If overflow occurs, the oldest message will be removed.
import { TokenMemory } from "bee-agent-framework/memory/tokenMemory";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
const llm = new OllamaChatLLM();
const memory = new TokenMemory({
llm,
maxTokens: undefined, // optional (default is inferred from the passed LLM instance),
capacityThreshold: 0.75, // maxTokens*capacityThreshold = threshold where we start removing old messages
syncThreshold: 0.25, // maxTokens*syncThreshold = threshold where we start to use a real tokenization endpoint instead of guessing the number of tokens
handlers: {
// optional way to define which message should be deleted (default is the oldest one)
removalSelector: (messages) => messages.find((msg) => msg.role !== "system")!,
// optional way to estimate the number of tokens in a message before we use the actual tokenize endpoint (number of tokens < maxTokens*syncThreshold)
estimate: (msg) => Math.ceil((msg.role.length + msg.text.length) / 4),
},
});
await memory.add(BaseMessage.of({ role: "system", text: "You are a helpful assistant." }));
await memory.add(BaseMessage.of({ role: "user", text: "Hello world!" }));
console.info(memory.isDirty); // is the consumed token count estimated or retrieved via the tokenize endpoint?
console.log(memory.tokensUsed); // number of used tokens
console.log(memory.stats()); // prints statistics
await memory.sync(); // calculates real token usage for all messages marked as "dirty"Source: examples/memory/tokenMemory.ts
Only a single summarization of the conversation is preserved. Summarization is updated with every new message.
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
import { SummarizeMemory } from "bee-agent-framework/memory/summarizeMemory";
import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
const memory = new SummarizeMemory({
llm: new OllamaChatLLM({
modelId: "llama3.1",
parameters: {
temperature: 0,
num_predict: 250,
},
}),
});
await memory.addMany([
BaseMessage.of({ role: "system", text: "You are a guide through France." }),
BaseMessage.of({ role: "user", text: "What is the capital?" }),
BaseMessage.of({ role: "assistant", text: "Paris" }),
BaseMessage.of({ role: "user", text: "What language is spoken there?" }),
]);
console.info(memory.isEmpty()); // false
console.log(memory.messages.length); // 1
console.log(memory.messages[0].text); // The capital city of France is Paris, ...Source: examples/memory/summarizeMemory.ts
To create your memory implementation, you must implement the BaseMemory class.
import { BaseMemory } from "bee-agent-framework/memory/base";
import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
import { NotImplementedError } from "bee-agent-framework/errors";
export class MyMemory extends BaseMemory {
get messages(): readonly BaseMessage[] {
throw new NotImplementedError("Method not implemented.");
}
add(message: BaseMessage): Promise<void> {
throw new NotImplementedError("Method not implemented.");
}
reset(): void {
throw new NotImplementedError("Method not implemented.");
}
createSnapshot(): unknown {
throw new NotImplementedError("Method not implemented.");
}
loadSnapshot(state: ReturnType<typeof this.createSnapshot>): void {
throw new NotImplementedError("Method not implemented.");
}
}Source: examples/memory/custom.ts
The simplest implementation is UnconstrainedMemory, which can be found here.