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LLM Powered Autonomous Brokers | Lil’Log

LLM Powered Autonomous Brokers | Lil’Log

2023-06-26 23:22:32

Constructing brokers with LLM (massive language mannequin) as its core controller is a cool idea. A number of proof-of-concepts demos, reminiscent of AutoGPT, GPT-Engineer and BabAGI, function inspiring examples. The potentiality of LLM extends past producing well-written copies, tales, essays and packages; it may be framed as a robust common drawback solver.

In a LLM-powered autonomous agent system, LLM capabilities because the agent’s mind, complemented by a number of key elements:

  • Planning
    • Subgoal and decomposition: The agent breaks down massive duties into smaller, manageable subgoals, enabling environment friendly dealing with of advanced duties.
    • Reflection and refinement: The agent can do self-criticism and self-reflection over previous actions, be taught from errors and refine them for future steps, thereby enhancing the standard of ultimate outcomes.
  • Reminiscence
    • Quick-term reminiscence: I’d contemplate all of the in-context studying (See Prompt Engineering) as using short-term reminiscence of the mannequin to be taught.
    • Lengthy-term reminiscence: This gives the agent with the aptitude to retain and recall (infinite) data over prolonged intervals, typically by leveraging an exterior vector retailer and quick retrieval.
  • Instrument use
    • The agent learns to name exterior APIs for additional data that’s lacking from the mannequin weights (typically exhausting to alter after pre-training), together with present data, code execution functionality, entry to proprietary data sources and extra.

Fig. 1. Overview of a LLM-powered autonomous agent system.

A sophisticated process often includes many steps. An agent must know what they’re and plan forward.

Job Decomposition

Chain of thought (CoT; Wei et al. 2022) has turn into an ordinary prompting approach for enhancing mannequin efficiency on advanced duties. The mannequin is instructed to “assume step-by-step” to make the most of extra test-time computation to decompose exhausting duties into smaller and less complicated steps. CoT transforms large duties into a number of manageable duties and shed lights into an interpretation of the mannequin’s considering course of.

Tree of Ideas (Yao et al. 2023) extends CoT by exploring a number of reasoning prospects at every step. It first decomposes the issue into a number of thought steps and generates a number of ideas per step, making a tree construction. The search course of will be BFS (breadth-first search) or DFS (depth-first search) with every state evaluated by a classifier (by way of a immediate) or majority vote.

Job decomposition will be finished (1) by LLM with easy prompting like "Steps for XYZ.n1.", "What are the subgoals for reaching XYZ?", (2) by utilizing task-specific directions; e.g. "Write a narrative define." for writing a novel, or (3) with human inputs.

One other fairly distinct strategy, LLM+P (Liu et al. 2023), includes counting on an exterior classical planner to do long-horizon planning. This strategy makes use of the Planning Area Definition Language (PDDL) as an intermediate interface to explain the planning drawback. On this course of, LLM (1) interprets the issue into “Downside PDDL”, then (2) requests a classical planner to generate a PDDL plan primarily based on an present “Area PDDL”, and at last (3) interprets the PDDL plan again into pure language. Basically, the planning step is outsourced to an exterior software, assuming the supply of domain-specific PDDL and an appropriate planner which is widespread in sure robotic setups however not in lots of different domains.


Self-reflection is an important facet that enables autonomous brokers to enhance iteratively by refining previous motion selections and correcting earlier errors. It performs an important position in real-world duties the place trial and error are inevitable.

ReAct (Yao et al. 2023) integrates reasoning and performing inside LLM by extending the motion area to be a mixture of task-specific discrete actions and the language area. The previous permits LLM to work together with the surroundings (e.g. use Wikipedia search API), whereas the latter prompting LLM to generate reasoning traces in pure language.

The ReAct immediate template incorporates express steps for LLM to assume, roughly formatted as:

Thought: ...
Motion: ...
Statement: ...
... (Repeated many occasions)

Fig. 2. Examples of reasoning trajectories for knowledge-intensive duties (e.g. HotpotQA, FEVER) and decision-making duties (e.g. AlfWorld Env, WebShop). (Picture supply: Yao et al. 2023).

In each experiments on knowledge-intensive duties and decision-making duties, ReAct works higher than the Act-only baseline the place Thought: … step is eliminated.

Reflexion (Shinn & Labash 2023) is a framework to equips brokers with dynamic reminiscence and self-reflection capabilities to enhance reasoning abilities. Reflexion has an ordinary RL setup, by which the reward mannequin gives a easy binary reward and the motion area follows the setup in ReAct the place the task-specific motion area is augmented with language to allow advanced reasoning steps. After every motion $a_t$, the agent computes a heuristic $h_t$ and optionally could determine to reset the surroundings to begin a brand new trial relying on the self-reflection outcomes.

Fig. 3. Illustration of the Reflexion framework. (Picture supply: Shinn & Labash, 2023)

The heuristic operate determines when the trajectory is inefficient or comprises hallucination and ought to be stopped. Inefficient planning refers to trajectories that take too lengthy with out success. Hallucination is outlined as encountering a sequence of consecutive similar actions that result in the identical statement within the surroundings.

Self-reflection is created by exhibiting two-shot examples to LLM and every instance is a pair of (failed trajectory, best reflection for guiding future modifications within the plan). Then reflections are added into the agent’s working reminiscence, as much as three, for use as context for querying LLM.

Fig. 4. Experiments on AlfWorld Env and HotpotQA. Hallucination is a extra widespread failure than inefficient planning in AlfWorld. (Picture supply: Shinn & Labash, 2023)

Chain of Hindsight (CoH; Liu et al. 2023) encourages the mannequin to enhance by itself outputs by explicitly presenting it with a sequence of previous outputs, every annotated with suggestions. Human suggestions information is a set of $D_h = {(x, y_i , r_i , z_i)}_{i=1}^n$, the place $x$ is the immediate, every $y_i$ is a mannequin completion, $r_i$ is the human score of $y_i$, and $z_i$ is the corresponding human-provided hindsight suggestions. Assume the suggestions tuples are ranked by reward, $r_n geq r_{n-1} geq dots geq r_1$ The method is supervised fine-tuning the place the information is a sequence within the type of $tau_h = (x, z_i, y_i, z_j, y_j, dots, z_n, y_n)$, the place $leq i leq j leq n$. The mannequin is finetuned to solely predict $y_n$ the place conditioned on the sequence prefix, such that the mannequin can self-reflect to provide higher output primarily based on the suggestions sequence. The mannequin can optionally obtain a number of rounds of directions with human annotators at check time.

To keep away from overfitting, CoH provides a regularization time period to maximise the log-likelihood of the pre-training dataset. To keep away from shortcutting and copying (as a result of there are numerous widespread phrases in suggestions sequences), they randomly masks 0% – 5% of previous tokens throughout coaching.

The coaching dataset of their experiments is a mixture of WebGPT comparisons, summarization from human feedback and human preference dataset.

Fig. 5. After fine-tuning with CoH, the mannequin can observe directions to provide outputs with incremental enchancment in a sequence. (Picture supply: Liu et al. 2023)

The thought of CoH is to current a historical past of sequentially improved outputs in context and prepare the mannequin to tackle the development to provide higher outputs. Algorithm Distillation (AD; Laskin et al. 2023) applies the identical thought to cross-episode trajectories in reinforcement studying duties, the place an algorithm is encapsulated in a protracted history-conditioned coverage. Contemplating that an agent interacts with the surroundings many occasions and in every episode the agent will get a bit higher, AD concatenates this studying historical past and feeds that into the mannequin. Therefore we must always anticipate the following predicted motion to result in higher efficiency than earlier trials. The purpose is to be taught the method of RL as a substitute of coaching a task-specific coverage itself.

Fig. 6. Illustration of how Algorithm Distillation (AD) works.
(Picture supply: Laskin et al. 2023).

The paper hypothesizes that any algorithm that generates a set of studying histories will be distilled right into a neural community by performing behavioral cloning over actions. The historical past information is generated by a set of supply insurance policies, every educated for a particular process. On the coaching stage, throughout every RL run, a random process is sampled and a subsequence of multi-episode historical past is used for coaching, such that the realized coverage is task-agnostic.

In actuality, the mannequin has restricted context window size, so episodes ought to be quick sufficient to assemble multi-episode historical past. Multi-episodic contexts of 2-4 episodes are essential to be taught a near-optimal in-context RL algorithm. The emergence of in-context RL requires lengthy sufficient context.

As compared with three baselines, together with ED (professional distillation, habits cloning with professional trajectories as a substitute of studying historical past), supply coverage (used for producing trajectories for distillation by UCB), RL^2 (Duan et al. 2017; used as higher certain because it wants on-line RL), AD demonstrates in-context RL with efficiency getting near RL^2 regardless of solely utilizing offline RL and learns a lot quicker than different baselines. When conditioned on partial coaching historical past of the supply coverage, AD additionally improves a lot quicker than ED baseline.

Fig. 7. Comparability of AD, ED, supply coverage and RL^2 on environments that require reminiscence and exploration. Solely binary reward is assigned. The supply insurance policies are educated with A3C for “darkish” environments and DQN for watermaze.
(Picture supply: Laskin et al. 2023)

(Large thanks to ChatGPT for serving to me draft this part. I’ve realized loads concerning the human mind and information construction for quick MIPS in my conversations with ChatGPT.)

Forms of Reminiscence

Reminiscence will be outlined because the processes used to accumulate, retailer, retain, and later retrieve data. There are a number of varieties of reminiscence in human brains.

  1. Sensory Reminiscence: That is the earliest stage of reminiscence, offering the power to retain impressions of sensory data (visible, auditory, and so forth) after the unique stimuli have ended. Sensory reminiscence usually solely lasts for up to some seconds. Subcategories embrace iconic reminiscence (visible), echoic reminiscence (auditory), and haptic reminiscence (contact).

  2. Quick-Time period Reminiscence (STM) or Working Reminiscence: It shops data that we’re presently conscious of and wanted to hold out advanced cognitive duties reminiscent of studying and reasoning. Quick-term reminiscence is believed to have the capability of about 7 gadgets (Miller 1956) and lasts for 20-30 seconds.

  3. Lengthy-Time period Reminiscence (LTM): Lengthy-term reminiscence can retailer data for a remarkably very long time, starting from a number of days to many years, with an basically limitless storage capability. There are two subtypes of LTM:

    • Specific / declarative reminiscence: That is reminiscence of info and occasions, and refers to these recollections that may be consciously recalled, together with episodic reminiscence (occasions and experiences) and semantic reminiscence (info and ideas).
    • Implicit / procedural reminiscence: This kind of reminiscence is unconscious and includes abilities and routines which can be carried out robotically, like using a motorbike or typing on a keyboard.

Fig. 8. Categorization of human reminiscence.

We are able to roughly contemplate the next mappings:

  • Sensory reminiscence as studying embedding representations for uncooked inputs, together with textual content, picture or different modalities;
  • Quick-term reminiscence as in-context studying. It’s quick and finite, as it’s restricted by the finite context window size of Transformer.
  • Lengthy-term reminiscence because the exterior vector retailer that the agent can attend to at question time, accessible by way of quick retrieval.

Most Inside Product Search (MIPS)

The exterior reminiscence can alleviate the restriction of finite consideration span. A regular observe is to avoid wasting the embedding illustration of knowledge right into a vector retailer database that may assist quick most inner-product search (MIPS). To optimize the retrieval velocity, the widespread alternative is the approximate nearest neighbors (ANN)​ algorithm to return roughly prime ok nearest neighbors to commerce off a bit accuracy misplaced for an enormous speedup.

A pair widespread selections of ANN algorithms for quick MIPS:

  • LSH (Locality-Delicate Hashing): It introduces a hashing operate such that comparable enter gadgets are mapped to the identical buckets with excessive chance, the place the variety of buckets is far smaller than the variety of inputs.
  • ANNOY (Approximate Nearest Neighbors Oh Yeah): The core information construction are random projection bushes, a set of binary bushes the place every non-leaf node represents a hyperplane splitting the enter area into half and every leaf shops one information level. Bushes are constructed independently and at random, so to some extent, it mimics a hashing operate. ANNOY search occurs in all of the bushes to iteratively search by way of the half that’s closest to the question after which aggregates the outcomes. The thought is kind of associated to KD tree however much more scalable.
  • HNSW (Hierarchical Navigable Small World): It’s impressed by the thought of small world networks the place most nodes will be reached by every other nodes inside a small variety of steps; e.g. “six levels of separation” characteristic of social networks. HNSW builds hierarchical layers of those small-world graphs, the place the underside layers include the precise information factors. The layers within the center create shortcuts to hurry up search. When performing a search, HNSW begins from a random node within the prime layer and navigates in the direction of the goal. When it might probably’t get any nearer, it strikes all the way down to the following layer, till it reaches the underside layer. Every transfer within the higher layers can doubtlessly cowl a big distance within the information area, and every transfer within the decrease layers refines the search high quality.
  • FAISS (Fb AI Similarity Search): It operates on the idea that in excessive dimensional area, distances between nodes observe a Gaussian distribution and thus there ought to exist clustering of information factors. FAISS applies vector quantization by partitioning the vector area into clusters after which refining the quantization inside clusters. Search first appears to be like for cluster candidates with coarse quantization after which additional appears to be like into every cluster with finer quantization.
  • ScaNN (Scalable Nearest Neighbors): The principle innovation in ScaNN is anisotropic vector quantization. It quantizes a knowledge level $x_i$ to $tilde{x}_i$ such that the interior product $langle q, x_i rangle$ is as much like the unique distance of $angle q, tilde{x}_i$ as doable, as a substitute of choosing the closet quantization centroid factors.

Fig. 9. Categorization of human reminiscence. (Picture supply: Google Blog, 2020)

Examine extra MIPS algorithms and efficiency comparability in

Instrument use is a exceptional and distinguishing attribute of human beings. We create, modify and make the most of exterior objects to do issues that transcend our bodily and cognitive limits. Equipping LLMs with exterior instruments can considerably lengthen the mannequin capabilities.

Fig. 10. An image of a sea otter utilizing rock to crack open a seashell, whereas floating within the water. Whereas another animals can use instruments, the complexity is just not comparable with people. (Picture supply: Animals using tools)

MRKL (Karpas et al. 2022), quick for “Modular Reasoning, Information and Language”, is a neuro-symbolic structure for autonomous brokers. A MRKL system is proposed to include a set of “professional” modules and the general-purpose LLM works as a router to route inquiries to the perfect appropriate professional module. These modules will be neural (e.g. deep studying fashions) or symbolic (e.g. math calculator, forex converter, climate API).

They did an experiment on fine-tuning LLM to name a calculator, utilizing arithmetic as a check case. Their experiments confirmed that it was more durable to unravel verbal math issues than explicitly said math issues as a result of LLMs (7B Jurassic1-large mannequin) didn’t extract the best arguments for the essential arithmetic reliably. The outcomes spotlight when the exterior symbolic instruments can work reliably, realizing when to and tips on how to use the instruments are essential, decided by the LLM functionality.

Each TALM (Instrument Augmented Language Fashions; Parisi et al. 2022) and Toolformer (Schick et al. 2023) fine-tune a LM to be taught to make use of exterior software APIs. The dataset is expanded primarily based on whether or not a newly added API name annotation can enhance the standard of mannequin outputs. See extra particulars within the “External APIs” section of Immediate Engineering.

ChatGPT Plugins and OpenAI API function calling are good examples of LLMs augmented with software use functionality working in observe. The gathering of software APIs will be offered by different builders (as in Plugins) or self-defined (as in operate calls).

HuggingGPT (Shen et al. 2023) is a framework to make use of ChatGPT as the duty planner to pick out fashions obtainable in HuggingFace platform based on the mannequin descriptions and summarize the response primarily based on the execution outcomes.

Fig. 11. Illustration of how HuggingGPT works. (Picture supply: Shen et al. 2023)

The system includes of 4 levels:

(1) Job planning: LLM works because the mind and parses the consumer requests into a number of duties. There are 4 attributes related to every process: process kind, ID, dependencies, and arguments. They use few-shot examples to information LLM to do process parsing and planning.


The AI assistant can parse consumer enter to a number of duties: [{“task”: task, “id”, task_id, “dep”: dependency_task_ids, “args”: {“text”: text, “image”: URL, “audio”: URL, “video”: URL}}]. The “dep” area denotes the id of the earlier process which generates a brand new useful resource that the present process depends on. A particular tag “-task_id” refers back to the generated textual content picture, audio and video within the dependency process with id as task_id. The duty MUST be chosen from the next choices: {{ Accessible Job Record }}. There’s a logical relationship between duties, please observe their order. If the consumer enter cannot be parsed, it’s worthwhile to reply empty JSON. Listed here are a number of circumstances in your reference: {{ Demonstrations }}. The chat historical past is recorded as {{ Chat Historical past }}. From this chat historical past, you’ll find the trail of the user-mentioned sources in your process planning.

(2) Mannequin choice: LLM distributes the duties to professional fashions, the place the request is framed as a multiple-choice query. LLM is introduced with a listing of fashions to select from. As a result of restricted context size, process kind primarily based filtration is required.


Given the consumer request and the decision command, the AI assistant helps the consumer to pick out an appropriate mannequin from a listing of fashions to course of the consumer request. The AI assistant merely outputs the mannequin id of probably the most applicable mannequin. The output have to be in a strict JSON format: “id”: “id”, “purpose”: “your element purpose for the selection”. Now we have a listing of fashions so that you can select from {{ Candidate Fashions }}. Please choose one mannequin from the record.

(3) Job execution: Knowledgeable fashions execute on the precise duties and log outcomes.


With the enter and the inference outcomes, the AI assistant wants to explain the method and outcomes. The earlier levels will be shaped as – Consumer Enter: {{ Consumer Enter }}, Job Planning: {{ Duties }}, Mannequin Choice: {{ Mannequin Project }}, Job Execution: {{ Predictions }}. You will need to first reply the consumer’s request in a simple method. Then describe the duty course of and present your evaluation and mannequin inference outcomes to the consumer within the first particular person. If inference outcomes include a file path, should inform the consumer the entire file path.

(4) Response era: LLM receives the execution outcomes and gives summarized outcomes to customers.

To place HuggingGPT into actual world utilization, a pair challenges want to unravel: (1) Effectivity enchancment is required as each LLM inference rounds and interactions with different fashions decelerate the method; (2) It depends on a protracted context window to speak over sophisticated process content material; (3) Stability enchancment of LLM outputs and exterior mannequin companies.

API-Financial institution (Li et al. 2023) is a benchmark for evaluating the efficiency of tool-augmented LLMs. It comprises 53 generally used API instruments, a whole tool-augmented LLM workflow, and 264 annotated dialogues that contain 568 API calls. The number of APIs is kind of numerous, together with search engines like google and yahoo, calculator, calendar queries, sensible residence management, schedule administration, well being information administration, account authentication workflow and extra. As a result of there are a lot of APIs, LLM first has entry to API search engine to search out the best API to name after which makes use of the corresponding documentation to make a name.

Fig. 12. Pseudo code of how LLM makes an API name in API-Financial institution. (Picture supply: Li et al. 2023)

Within the API-Financial institution workflow, LLMs have to make a few selections and at every step we will consider how correct that call is. Choices embrace:

  1. Whether or not an API name is required.
  2. Determine the best API to name: if not adequate, LLMs have to iteratively modify the API inputs (e.g. deciding search key phrases for Search Engine API).
  3. Response primarily based on the API outcomes: the mannequin can select to refine and name once more if outcomes aren’t happy.

This benchmark evaluates the agent’s software use capabilities at three ranges:

  • Degree-1 evaluates the power to name the API. Given an API’s description, the mannequin wants to find out whether or not to name a given API, name it accurately, and reply correctly to API returns.
  • Degree-2 examines the power to retrieve the API. The mannequin must seek for doable APIs which will resolve the consumer’s requirement and discover ways to use them by studying documentation.
  • Degree-3 assesses the power to plan API past retrieve and name. Given unclear consumer requests (e.g. schedule group conferences, guide flight/resort/restaurant for a visit), the mannequin could need to conduct a number of API calls to unravel it.

Scientific Discovery Agent

ChemCrow (Bran et al. 2023) is a domain-specific instance by which LLM is augmented with 13 expert-designed instruments to perform duties throughout natural synthesis, drug discovery, and supplies design. The workflow, carried out in LangChain, displays what was beforehand described within the ReAct and MRKLs and combines CoT reasoning with instruments related to the duties:

  • The LLM is supplied with a listing of software names, descriptions of their utility, and particulars concerning the anticipated enter/output.
  • It’s then instructed to reply a user-given immediate utilizing the instruments offered when needed. The instruction suggests the mannequin to observe the ReAct format – Thought, Motion, Motion Enter, Statement.

One attention-grabbing statement is that whereas the LLM-based analysis concluded that GPT-4 and ChemCrow carry out almost equivalently, human evaluations with consultants oriented in the direction of the completion and chemical correctness of the options confirmed that ChemCrow outperforms GPT-4 by a big margin. This means a possible drawback with utilizing LLM to guage its personal efficiency on domains that requires deep experience. The lack of information could trigger LLMs not realizing its flaws and thus can not properly decide the correctness of process outcomes.

Boiko et al. (2023) additionally appeared into LLM-empowered brokers for scientific discovery, to deal with autonomous design, planning, and efficiency of advanced scientific experiments. This agent can use instruments to browse the Web, learn documentation, execute code, name robotics experimentation APIs and leverage different LLMs.

For instance, when requested to "develop a novel anticancer drug", the mannequin got here up with the next reasoning steps:

  1. inquired about present traits in anticancer drug discovery;
  2. chosen a goal;
  3. requested a scaffold focusing on these compounds;
  4. As soon as the compound was recognized, the mannequin tried its synthesis.

In addition they mentioned the dangers, particularly with illicit medication and bioweapons. They developed a check set containing a listing of identified chemical weapon brokers and requested the agent to synthesize them. 4 out of 11 requests (36%) have been accepted to acquire a synthesis resolution and the agent tried to seek the advice of documentation to execute the process. 7 out of 11 have been rejected and amongst these 7 rejected circumstances, 5 occurred after a Net search whereas 2 have been rejected primarily based on immediate solely.

Generative Brokers Simulation

Generative Brokers (Park, et al. 2023) is tremendous enjoyable experiment the place 25 digital characters, every managed by a LLM-powered agent, reside and interacting in a sandbox surroundings, impressed by The Sims. Generative brokers create plausible simulacra of human habits for interactive purposes.

The design of generative brokers combines LLM with reminiscence, planning and reflection mechanisms to allow brokers to behave conditioned on previous expertise, in addition to to work together with different brokers.

  • Reminiscence stream: is a long-term reminiscence module (exterior database) that data a complete record of brokers’ expertise in pure language.
    • Every aspect is an statement, an occasion immediately offered by the agent.
      – Inter-agent communication can set off new pure language statements.
  • Retrieval mannequin: surfaces the context to tell the agent’s habits, based on relevance, recency and significance.
    • Recency: current occasions have greater scores
    • Significance: distinguish mundane from core recollections. Ask LM immediately.
    • Relevance: primarily based on how associated it’s to the present scenario / question.
  • Reflection mechanism: synthesizes recollections into greater degree inferences over time and guides the agent’s future habits. They’re higher-level summaries of previous occasions (<- observe that it is a bit completely different from self-reflection above)
    • Immediate LM with 100 most up-to-date observations and to generate 3 most salient high-level questions given a set of observations/statements. Then ask LM to reply these questions.
  • Planning & Reacting: translate the reflections and the surroundings data into actions
    • Planning is actually as a way to optimize believability in the meanwhile vs in time.
    • Immediate template: {Intro of an agent X}. Right here is X's plan in the present day in broad strokes: 1)
    • Relationships between brokers and observations of 1 agent by one other are all considered for planning and reacting.
    • Surroundings data is current in a tree construction.

Fig. 13. The generative agent structure. (Picture supply: Park et al. 2023)

This enjoyable simulation leads to emergent social habits, reminiscent of data diffusion, relationship reminiscence (e.g. two brokers persevering with the dialog subject) and coordination of social occasions (e.g. host a celebration and invite many others).

Proof-of-Idea Examples

AutoGPT has drawn lots of consideration into the potential of establishing autonomous brokers with LLM as the principle controller. It has numerous reliability points given the pure language interface, however nonetheless a cool proof-of-concept demo. Numerous code in AutoGPT is about format parsing.

Right here is the system message utilized by AutoGPT, the place {{...}} are consumer inputs:

See Also

You might be {{ai-name}}, {{user-provided AI bot description}}.
Your selections should all the time be made independently with out in search of consumer help. Play to your strengths as an LLM and pursue easy methods with no authorized issues.


1. {{user-provided purpose 1}}
2. {{user-provided purpose 2}}
3. ...
4. ...
5. ...

1. ~4000 phrase restrict for brief time period reminiscence. Your quick time period reminiscence is brief, so instantly save vital data to information.
2. If you're not sure the way you beforehand did one thing or wish to recall previous occasions, eager about comparable occasions will enable you bear in mind.
3. No consumer help
4. Solely use the instructions listed in double quotes e.g. "command identify"
5. Use subprocesses for instructions that won't terminate inside a couple of minutes

1. Google Search: "google", args: "enter": "<search>"
2. Browse Web site: "browse_website", args: "url": "<url>", "query": "<what_you_want_to_find_on_website>"
3. Begin GPT Agent: "start_agent", args: "identify": "<identify>", "process": "<short_task_desc>", "immediate": "<immediate>"
4. Message GPT Agent: "message_agent", args: "key": "<key>", "message": "<message>"
5. Record GPT Brokers: "list_agents", args:
6. Delete GPT Agent: "delete_agent", args: "key": "<key>"
7. Clone Repository: "clone_repository", args: "repository_url": "<url>", "clone_path": "<listing>"
8. Write to file: "write_to_file", args: "file": "<file>", "textual content": "<textual content>"
9. Learn file: "read_file", args: "file": "<file>"
10. Append to file: "append_to_file", args: "file": "<file>", "textual content": "<textual content>"
11. Delete file: "delete_file", args: "file": "<file>"
12. Search Information: "search_files", args: "listing": "<listing>"
13. Analyze Code: "analyze_code", args: "code": "<full_code_string>"
14. Get Improved Code: "improve_code", args: "solutions": "<list_of_suggestions>", "code": "<full_code_string>"
15. Write Exams: "write_tests", args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Generate Picture: "generate_image", args: "immediate": "<immediate>"
18. Ship Tweet: "send_tweet", args: "textual content": "<textual content>"
19. Do Nothing: "do_nothing", args:
20. Job Full (Shutdown): "task_complete", args: "purpose": "<purpose>"

1. Web entry for searches and knowledge gathering.
2. Lengthy Time period reminiscence administration.
3. GPT-3.5 powered Brokers for delegation of straightforward duties.
4. File output.

Efficiency Analysis:
1. Repeatedly evaluate and analyze your actions to make sure you are performing to the perfect of your talents.
2. Constructively self-criticize your big-picture habits consistently.
3. Mirror on previous selections and methods to refine your strategy.
4. Each command has a value, so be sensible and environment friendly. Goal to finish duties within the least variety of steps.

You must solely reply in JSON format as described beneath
Response Format:
    "ideas": {
        "textual content": "thought",
        "reasoning": "reasoning",
        "plan": "- quick bulletedn- record that conveysn- long-term plan",
        "criticism": "constructive self-criticism",
        "converse": "ideas abstract to say to consumer"
    "command": {
        "identify": "command identify",
        "args": {
            "arg identify": "worth"
Make sure the response will be parsed by Python json.masses

GPT-Engineer is one other mission to create an entire repository of code given a process laid out in pure language. The GPT-Engineer is instructed to assume over a listing of smaller elements to construct and ask for consumer enter to make clear questions as wanted.

Listed here are a pattern dialog for process clarification despatched to OpenAI ChatCompletion endpoint utilized by GPT-Engineer:

    "role": "system",
    "content": "You will read instructions and not carry them out, only seek to clarify them.nSpecifically you will first summarise a list of super short bullets of areas that need clarification.nThen you will pick one clarifying question, and wait for an answer from the user.n"
    "role": "user",
    "content": "We are writing a Super Mario game in python. MVC components split in separate files. Keyboard control.n"
    "role": "assistant",
    "content": "Summary of areas that need clarification:n1. Specifics of the Super Mario game (e.g. level design, characters, gameplay mechanics)n2. Details about the MVC components (e.g. which components are in each file)n3. Keyboard control implementation (e.g. which keys to use, how to handle input)nnClarifying question:nCan you provide more details about the Super Mario game, such as level design, characters, and gameplay mechanics?"
    "role": "user",
    "content": "There are 10 levels in total. The main character is a plumber named Mario, who can walk and jump. It is a classical platform game just like Super Mario. The main character moves from left to right, trying to get to the destination, where there are many obstacles and attacks from enemies in the process.nnIs anything else unclear? If yes, only answer in the form:n{remaining unclear areas} remaining questions.n{Next question}nIf everything is sufficiently clear, only answer "Nothing more to clarify."."
    "role": "assistant",
    "content": "Remaining unclear areas: 2 remaining questions.nCan you provide more information about how the MVC components are split into separate files?"
    "role": "user",
    "content": "Make your own assumptions and state them explicitly before starting"

Then after these clarification, the agent moved into the code writing mode with a unique system message:

    "role": "system",
    "content": "You will get instructions for code to write.nYou will write a very long answer. Make sure that every detail of the architecture is, in the end, implemented as code.nMake sure that every detail of the architecture is, in the end, implemented as code.nnThink step by step and reason yourself to the right decisions to make sure we get it right.nYou will first lay out the names of the core classes, functions, methods that will be necessary, as well as a quick comment on their purpose.nnThen you will output the content of each file including ALL code.nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such thatnFILENAME is the lowercase file name including the file extension,nLANG is the markup code block language for the code's language, and CODE is the code:nnFILENAMEn```LANGnCODEn```nnYou will start with the "entrypoint" file, then go to the ones that are imported by that file, and so on.nPlease note that the code should be fully functional. No placeholders.nnFollow a language and framework appropriate best practice file naming convention.nMake sure that files contain all imports, types etc. Make sure that code in different files are compatible with each other.nEnsure to implement all code, if you are unsure, write a plausible implementation.nInclude module dependency or package manager dependency definition file.nBefore you finish, double check that all parts of the architecture is present in the files.nnUseful to know:nYou almost always put different classes in different files.nFor Python, you always create an appropriate requirements.txt file.nFor NodeJS, you always create an appropriate package.json file.nYou always add a comment briefly describing the purpose of the function definition.nYou try to add comments explaining very complex bits of logic.nYou always follow the best practices for the requested languages in terms of describing the code written as a definednpackage/project.nnnPython toolbelt preferences:n- pytestn- dataclassesn"
 #  … same conversation as earlier, ended with "Make your own assumptions and state them explicitly before starting".
    "role": "assistant",
    "content": "Assumptions:n1. Model: The model will contain the game's data, such as level information, character states, and enemy positions.n2. View: The view will handle the game's visuals, including rendering the game objects, backgrounds, and updating the display.n3. Controller: The controller will manage user input, such as keyboard controls, and update the model accordingly.nnWith these assumptions in mind, is there any specific aspect of the keyboard control implementation you would like me to clarify?"
    "role": "user",
    "content": "Please now remember the steps:nnThink step by step and reason yourself to the right decisions to make sure we get it right.nFirst lay out the names of the core classes, functions, methods that will be necessary, As well as a quick comment on their purpose.nnThen you will output the content of each file including ALL code.nEach file must strictly follow a markdown code block format, where the following tokens must be replaced such thatnFILENAME is the lowercase file name including the file extension,nLANG is the markup code block language for the code's language, and CODE is the code:nnFILENAMEn```LANGnCODEn```nnPlease note that the code should be fully functional. No placeholders.nnYou will start with the "entrypoint" file, then go to the ones that are imported by that file, and so on.nFollow a language and framework appropriate best practice file naming convention.nMake sure that files contain all imports, types etc. The code should be fully functional. Make sure that code in different files are compatible with each other.nBefore you finish, double check that all parts of the architecture is present in the files.n"

After going by way of key concepts and demos of constructing LLM-centered brokers, I begin to see a pair widespread limitations:

  • Finite context size: The restricted context capability limits the inclusion of historic data, detailed directions, API name context, and responses. The design of the system has to work with this restricted communication bandwidth, whereas mechanisms like self-reflection to be taught from previous errors would profit loads from lengthy or infinite context home windows. Though vector shops and retrieval can present entry to a bigger information pool, their illustration energy is just not as highly effective as full consideration.

  • Challenges in long-term planning and process decomposition: Planning over a prolonged historical past and successfully exploring the answer area stay difficult. LLMs battle to regulate plans when confronted with sudden errors, making them much less strong in comparison with people who be taught from trial and error.

  • Reliability of pure language interface: Present agent system depends on pure language as an interface between LLMs and exterior elements reminiscent of reminiscence and instruments. Nonetheless, the reliability of mannequin outputs is questionable, as LLMs could make formatting errors and infrequently exhibit rebellious habits (e.g. refuse to observe an instruction). Consequently, a lot of the agent demo code focuses on parsing mannequin output.

Cited as:

Weng, Lilian. (Jun 2023). LLM-powered Autonomous Brokers”. Lil’Log.


  title   = "LLM-powered Autonomous Brokers"",
  creator  = "Weng, Lilian",
  journal = "",
  yr    = "2023",
  month   = "Jun",
  url     = ""
[1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022

[2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023).

[3] Liu et al. “Chain of Hindsight Aligns Language Models with Feedback
arXiv preprint arXiv:2302.02676 (2023).

[4] Liu et al. “LLM+P: Empowering Large Language Models with Optimal Planning Proficiency” arXiv preprint arXiv:2304.11477 (2023).

[5] Yao et al. “ReAct: Synergizing reasoning and acting in language models.” ICLR 2023.

[6] Google Weblog. “Announcing ScaNN: Efficient Vector Similarity Search” July 28, 2020.


[8] Shinn & Labash. “Reflexion: an autonomous agent with dynamic memory and self-reflection” arXiv preprint arXiv:2303.11366 (2023).

[9] Laskin et al. “In-context Reinforcement Learning with Algorithm Distillation” ICLR 2023.

[10] Karpas et al. “MRKL Systems A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning.” arXiv preprint arXiv:2205.00445 (2022).

[11] Weaviate Weblog. Why is Vector Search so fast? Sep 13, 2022.

[12] Li et al. “API-Bank: A Benchmark for Tool-Augmented LLMs” arXiv preprint arXiv:2304.08244 (2023).

[13] Shen et al. “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace” arXiv preprint arXiv:2303.17580 (2023).

[14] Bran et al. “ChemCrow: Augmenting large-language models with chemistry tools.” arXiv preprint arXiv:2304.05376 (2023).

[15] Boiko et al. “Emergent autonomous scientific research capabilities of large language models.” arXiv preprint arXiv:2304.05332 (2023).

[16] Joon Sung Park, et al. “Generative Agents: Interactive Simulacra of Human Behavior.” arXiv preprint arXiv:2304.03442 (2023).

[17] AutoGPT.

[18] GPT-Engineer.

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