RomanNum3ral
/
honcho
mirror of https://github.com/plastic-labs/honcho.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Adding crewAI integration guide (#279) 

* docs: adding crewAI integration guide

* docs: adding a honcho_crewai package

* docs: Using session.search and session summaries to enhance the honcho storage class

* docs: updating to use honcho_crewai package

* docs: Added honcho_crewAI tools. Updated honcho_crewai tests to better match the specific integration. Built out the package definition more.

* docs: Adding all the honcho sdk parameters to crewAI tools, also adding tools and a simple example.

* docs: adding logging to HonchoStorage class

* docs: updating mdx file to match examples and fixing explanations

* Docs: removing session summaries from search

* docs: adding files package

* docs: simplifying language specifically for theory-of-mind.

* chore: code rabbit suggestions.

* chore: code rabbit

* fix: removing nanoid crewai dependency

* docs: adding filtering capability to honcho crewai package and tool examples.

* fix: remove factory class in favor of direct class instantiation

* docs: adding hybrid memory example

* fix: fixing redundent calls to honcho for saving message history

* chore: code rabbit fixes
This commit is contained in:
ajspig 2025-12-01 14:46:14 -05:00 committed by GitHub
parent 093444c38c
commit 34957d7885
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
21 changed files with 6484 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
CLAUDE.md
View File

@ -6,7 +6,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
## What is Honcho?
Honcho is an infrastructure layer for building AI agents with social cognition and theory of mind capabilities. Its primary purposes include:
Honcho is an infrastructure layer for building AI agents with memory and social cognition. Its primary purposes include:
- Imbuing agents with a sense of identity
- Personalizing user experiences through understanding user psychology
@ -14,7 +14,7 @@ Honcho is an infrastructure layer for building AI agents with social cognition a
- Supporting development of LLM-powered applications that adapt to end users
- Enabling multi-peer sessions where multiple participants (users or agents) can interact
Honcho leverages the inherent theory-of-mind capabilities of LLMs to build coherent models of user psychology over time, enabling more personalized and effective AI interactions.
Honcho leverages the inherent reasoning capabilities of LLMs to build coherent models of user psychology over time, enabling more personalized and effective AI interactions.
## Core Concepts
@ -32,7 +32,7 @@ Honcho uses a peer-based model where both users and agents are represented as "p
- **Peer** (formerly User): Any participant in the system (human or AI)
- **Session**: A conversation context that can involve multiple peers
- **Message**: Data units that can represent communication between peers OR arbitrary data ingested by a peer to enhance its global representation
- **Collections & Documents**: Internal vector storage for theory-of-mind representations (not exposed via API)
- **Collections & Documents**: Internal vector storage for peer representations (not exposed via API)
## Architecture Overview
@ -50,7 +50,7 @@ All API routes follow the pattern: `/v1/{resource}/{id}/{action}`
#### Dialectic API (`/peers/{peer_id}/chat`)
- Provides theory-of-mind informed responses
- Provides bespoke responses informed by the representation
- Integrates long-term facts from vector storage
- Supports streaming responses
- Configurable LLM providers
@ -59,18 +59,11 @@ All API routes follow the pattern: `/v1/{resource}/{id}/{action}`
1. Messages created via API (batch or single)
2. Enqueued for background processing:
- `representation`: Update peer's theory of mind
- `representation`: Update peer's context
- `summary`: Create session summaries
3. Session-based queue processing ensures order
4. Results stored internally in vector DB
#### Theory of Mind System
- Multiple implementation methods (conversational, single_prompt, long_term)
- Facts extracted from messages and stored in collections
- Representations combine short-term inference with long-term facts
- Configurable via peer and session feature flags
### Configuration
- Hierarchical config: config.toml + environment variables
@ -179,7 +172,6 @@ src/
### Key Architectural Decisions
1. **Multi-Peer Sessions**: Sessions can have multiple participants with different observation settings
2. **Flexible Theory of Mind**: Pluggable ToM implementations (conversational, single_prompt, long_term)
3. **Background Processing**: Async queue system for expensive operations
4. **Provider Abstraction**: Model client supports multiple LLM providers
5. **Scoped Authentication**: JWTs can be scoped to workspace, peer, or session level

15
README.md
View File

@ -429,7 +429,7 @@ Then modify the values as needed. The TOML file is organized into sections:
- `[cache]` - Redis cache configuration
- `[llm]` - LLM provider API keys and general settings
- `[dialectic]` - Dialectic API configuration (provider, model, search settings)
- `[deriver]` - Background worker settings and theory of mind configuration
- `[deriver]` - Background worker settings and representation configuration
- `[peer_card]` - Peer card generation settings
- `[summary]` - Session summarization settings
- `[dream]` - Dream processing configuration
@ -501,8 +501,8 @@ Honcho uses a peer-based model where both users and agents are represented as "p
#### Key Features
- **Theory-of-Mind System**: Multiple implementation methods that extract facts from interactions and build comprehensive models of peer psychology
- **Dialectic API**: Provides theory-of-mind informed responses that integrate long-term facts with current context
- **Rich Reasoning System**: Multiple implementation methods that extract facts from interactions and build comprehensive models of peer psychology
- **Dialectic API**: Provides reasoned informed responses that integrate long-term facts with current context
- **Background Processing**: Asynchronous processing pipeline for expensive operations like representation updates and session summarization
- **Multi-Provider Support**: Configurable LLM providers for different use cases
@ -567,7 +567,7 @@ The `Message` represents an atomic data unit that can exist at two levels:
- **Session-level Messages**: Communication between peers within a session context
All messages are labeled by their source peer and can be processed
asynchronously to update theory-of-mind models. This flexible design allows for
asynchronously to update their representations. This flexible design allows for
both conversational interactions and broader data ingestion for personality
modeling.
@ -578,8 +578,7 @@ familiar with RAG based applications will be familiar with these. `Collections`
store vector embedded data that developers and agents can retrieve against using
functions like cosine similarity.
Collections are also used internally by Honcho while creating theory-of-mind
representations of peers.
Collections are also used internally by Honcho while creating representations of peers.
#### Documents
@ -596,7 +595,7 @@ A high level summary of the pipeline is as follows:
1. Messages are created via the API
2. Derivation Tasks are enqueued for background processing including:
- `representation`: To update theory-of-mind representations of `Peers`
- `representation`: To update representations of `Peers`
- `summary`: To create summaries of `Sessions`
3. Session-based queue processing ensures proper ordering
4. Results are stored internally
@ -638,7 +637,7 @@ A developer's application can treat Honcho as an oracle to the `Peer` and
consult it when necessary. Some examples of how to leverage the Dialectic
API include:
- Asking Honcho for a theory-of-mind insight about the `Peer`
- Asking Honcho for a generic or specific insight about the `Peer`
- Asking Honcho to hydrate a prompt with data about the `Peer`s behavior
- Asking Honcho for a 2nd opinion or approach about how to respond to the Peer
- Getting personalized responses that incorporate long-term facts and context

5
docs/docs.json
View File

@ -74,10 +74,7 @@
},
{
"group": "Integrations",
"pages": [
"v2/integrations/langgraph",
"v2/integrations/mcp"
]
"pages": ["v2/integrations/crewai", "v2/integrations/langgraph", "v2/integrations/mcp"]
},
{
"group": "Application Interfaces",

294
docs/v2/integrations/crewai.mdx Normal file
View File

@ -0,0 +1,294 @@
---
title: "CrewAI"
icon: 'users-gear'
description: "Build AI agents with persistent memory using CrewAI and Honcho"
sidebarTitle: 'CrewAI'
---
Integrate Honcho with CrewAI to build AI agents that maintain memory across sessions. This guide shows you how to use Honcho's memory layer with CrewAI's agent orchestration framework.
<Note>
The full code is available on [GitHub](https://github.com/plastic-labs/honcho/tree/main/examples/crewai) with examples in [Python](https://github.com/plastic-labs/honcho/tree/main/examples/crewai/python/examples)
</Note>
## What We're Building
We'll create AI agents that remember and reason over past conversations. Here's how the pieces fit together:
- **CrewAI** orchestrates agent behavior and task execution
- **Honcho** stores messages and retrieves relevant context
The key benefit: CrewAI automatically retrieves relevant conversation history from Honcho without you needing to manually manage context, token limits, or message formatting.
<Note>
This tutorial demonstrates single-agent setup to show how Honcho integrates with CrewAI. For production applications, you can extend this to multi-agent crews with shared or individual memory using Honcho's `peer` system.
</Note>
## Setup
Install required packages:
<CodeGroup>
```bash Python (uv)
uv add honcho-crewai crewai python-dotenv
```
```bash Python (pip)
pip install honcho-crewai crewai python-dotenv
```
</CodeGroup>
Use any LLM provider for your Crew. Create a `.env` file with your API keys:
```bash
OPENAI_API_KEY=your_openai_key
```
<Note>
This tutorial uses the Honcho demo server at https://demo.honcho.dev which runs a small instance of Honcho on the latest version. For production, get your Honcho API key at [app.honcho.dev](https://app.honcho.dev). For local development, use `environment="local"`.
</Note>
## CrewAI Honcho Storage
The `honcho_crewai` package provides `HonchoStorage`, a storage provider that implements CrewAI's `Storage` interface using Honcho's session-based memory.
<Note>
Before proceeding, it's important to understand Honcho's core concepts (`Peers` and `Sessions`). Review the [Honcho Architecture](/v2/documentation/core-concepts/architecture) to familiarize yourself with these primitives.
</Note>
`HonchoStorage` implements CrewAI's `Storage` interface using Honcho's `peer` and `session` primitives.
```python
storage = HonchoStorage(
user_id="demo-user", # Required: Honcho `peer` ID for the user
session_id=None, # Optional: Specific `session` ID (auto-generated UUID if None)
honcho_client=None, # Optional: Pre-configured Honcho client instance
)
```
The `HonchoStorage` class implements three key methods:
- **`save()`** - Stores messages in Honcho's `session`, associating them with the appropriate `peer` (user or assistant)
- **`search()`** - Performs semantic vector search using `session.search()` to find messages most relevant to the query. Supports optional `filters` parameter for fine-grained scoping.
- **`reset()`** - Creates a new `session` to start fresh conversations
CrewAI automatically calls these methods when agents need to store or retrieve memory, creating a seamless integration.
### Search with Filters
The `search()` method supports an optional `filters` parameter for fine-grained scoping of search results:
```python
# Search with peer_id filter (only messages from a specific peer)
results = storage.search("query", filters={"peer_id": "user123"})
# Search with metadata filter
results = storage.search("query", filters={"metadata": {"priority": "high"}})
# Search with time range filter
results = storage.search("query", filters={"created_at": {"gte": "2024-01-01"}})
# Complex filter with logical operators
results = storage.search("query", filters={
"AND": [
{"peer_id": "user123"},
{"metadata": {"topic": "python"}}
]
})
```
For the full filter syntax including logical operators (AND, OR, NOT), comparison operators, and metadata filtering, see the [Using Filters](https://docs.honcho.dev/v2/documentation/core-concepts/features/using-filters) documentation.
<Note>
For comprehensive details about CrewAI's memory system, see the [official CrewAI Memory documentation](https://docs.crewai.com/en/concepts/memory).
</Note>
Let's create a basic example showing how CrewAI agents use Honcho's memory automatically:
```python Python
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho_crewai import HonchoStorage
load_dotenv()
storage = HonchoStorage(user_id="simple-demo-user")
external_memory = ExternalMemory(storage=storage)
messages = [
("user", "I'm learning Python programming"),
("assistant", "Great! Python is an excellent language to learn."),
("user", "I'm particularly interested in web development"),
]
for role, message in messages:
external_memory.save(message, metadata={"agent": role})
agent = Agent(
role="Programming Mentor",
goal="Help users learn programming by remembering their interests and progress",
backstory=(
"You are a patient programming mentor who remembers what students "
"have told you about their learning journey and interests."
),
verbose=True,
allow_delegation=False
)
task = Task(
description=(
"Based on what you know about the user's interests, "
"suggest a simple web development project they could build to practice Python."
),
expected_output="A specific project suggestion with brief explanation",
agent=agent
)
crew = Crew(
agents=[agent],
tasks=[task],
process=Process.sequential,
external_memory=external_memory,
verbose=True
)
result = crew.kickoff()
print(result.raw)
```
## CrewAI Tool Integration
Honcho provides specialized tools that give CrewAI agents explicit control over memory retrieval:
- **`HonchoGetContextTool`** - Retrieves comprehensive conversation history with token limits. Use for tasks needing broad conversation understanding.
- **`HonchoDialecticTool`** - Queries representations about `peer`s. Use for understanding user preferences and characteristics without full message history.
- **`HonchoSearchTool`** - Performs semantic search for specific information. Supports optional `filters` parameter for fine-grained scoping. Use for targeted queries like "what did the user say about budget?"
<Tip>
Agents can use multiple tools in sequence: search for topics, query dialectic for preferences, then get full context for generation.
</Tip>
Here's an example demonstrating all three tools:
```python Python
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from honcho import Honcho
from honcho_crewai import (
HonchoGetContextTool,
HonchoDialecticTool,
HonchoSearchTool,
)
load_dotenv()
honcho = Honcho()
user_id = "demo-user-45"
session_id = "tools-demo-session"
user = honcho.peer(user_id)
session = honcho.session(session_id)
messages = [
"I'm planning a trip to Japan in March",
"I love trying authentic local cuisine, especially ramen and sushi",
"My budget is around $3000 for a 10-day trip",
"I'm interested in visiting both Tokyo and Kyoto",
"I prefer staying in traditional ryokans over hotels",
]
for msg in messages:
session.add_messages([user.message(msg)])
context_tool = HonchoGetContextTool(
honcho=honcho, session_id=session_id, peer_id=user_id
)
dialectic_tool = HonchoDialecticTool(
honcho=honcho, session_id=session_id, peer_id=user_id
)
search_tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
# Note: The search tool supports optional filters for fine-grained scoping
# Agents can use filters like {"peer_id": "user123"} or {"metadata": {"priority": "high"}}
travel_agent = Agent(
role="Travel Planning Specialist",
goal="Create personalized travel recommendations using memory tools",
backstory=(
"You are an expert travel planner with access to conversation memory tools. "
"Use the tools to understand the user's preferences before making recommendations."
),
tools=[context_tool, dialectic_tool, search_tool],
verbose=True,
allow_delegation=False
)
task = Task(
description=(
"Create a personalized 3-day Tokyo itinerary. "
"Use the memory tools to understand:\n"
" • Food preferences (use search_tool for 'cuisine' or 'food')\n"
" • Travel style and budget (use dialectic_tool to query user knowledge)\n"
" • Recent context (use context_tool to get conversation history)\n"
"Then create a detailed plan matching their interests."
),
expected_output=(
"A 3-day Tokyo itinerary with:\n"
" • Daily activities matching user interests\n"
" • Restaurant recommendations\n"
" • Accommodation suggestions\n"
" • Budget considerations"
),
agent=travel_agent
)
crew = Crew(
agents=[travel_agent],
tasks=[task],
process=Process.sequential,
verbose=True
)
crew.kickoff()
```
## Tool-Based vs Automatic Memory
**Use `HonchoStorage`** for automatic memory - CrewAI handles everything transparently. Best for simple conversational flows.
**Use Honcho Tools** for strategic control - agents decide when and how to query memory. Best for multi-step reasoning, when different query types are needed, or multi-agent systems.
You can combine both: automatic memory for baseline context, tools for specific queries. See the [hybrid memory example](https://github.com/plastic-labs/honcho/blob/main/examples/crewai/python/examples/hybrid_memory_example.py) for a complete implementation.
<Note>
**Multi-Agent Memory:** Use Honcho tools with different `peer_id` values to give each agent distinct memory and identity.
</Note>
## Next Steps
Now that you have a working CrewAI integration with Honcho, you can:
- **Create specialized agents** with domain-specific memory and context
- **Use CrewAI's advanced features** like hierarchical processes, tool delegation, and conditional task execution
- **Leverage logical reasoning** via the Dialectic API for deep `peer` understanding
- **Implement custom tools** to give agents explicit control over memory retrieval
## Related Resources
<CardGroup cols={2}>
<Card title="Honcho Architecture" icon="sitemap" href="/v2/documentation/core-concepts/architecture">
Understand Honcho's peer-based model and core primitives
</Card>
<Card title="Get Context" icon="messages" href="/v2/documentation/core-concepts/features/get-context">
Learn about retrieving and formatting conversation context
</Card>
<Card title="Dialectic API" icon="brain" href="/v2/documentation/core-concepts/features/dialectic">
Query `peer` representations for deeper understanding
</Card>
<Card title="LangGraph Integration" icon="diagram-project" href="/v2/integrations/langgraph">
Build stateful agents with LangGraph and Honcho
</Card>
</CardGroup>

4
docs/v2/integrations/langgraph.mdx
View File

@ -232,9 +232,9 @@ The [`get_context()`](/v2/documentation/core-concepts/features/get-context) meth
- **Manages conversation history** - Tracks all messages and determines what's relevant
- **Respects token limits** - Stays within context window constraints without manual counting
- **Handles long conversations** - Combines recent detailed messages with summaries of older exchanges
- **Provides peer understanding** - Includes theory-of-mind representations and peer cards when requested
- **Provides `peer` understanding** - Includes representations and `peer` cards when requested
The `SessionContext` object always includes fields for messages, summaries, peer representations, and peer cards. By default, only `messages` and `summary` are populated. To populate peer-specific context, pass a `peer_target` parameter:
The `SessionContext` object always includes fields for messages, summaries, `peer` representations, and `peer` cards. By default, only `messages` and `summary` are populated. To populate peer-specific context, pass a `peer_target` parameter:
**Using `peer_target` for Context:**

661
examples/crewai/python/LICENSE Normal file
View File

@ -0,0 +1,661 @@
GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
Developers that use our General Public Licenses protect your rights
with two steps: (1) assert copyright on the software, and (2) offer
you this License which gives you legal permission to copy, distribute
and/or modify the software.
A secondary benefit of defending all users' freedom is that
improvements made in alternate versions of the program, if they
receive widespread use, become available for other developers to
incorporate. Many developers of free software are heartened and
encouraged by the resulting cooperation. However, in the case of
software used on network servers, this result may fail to come about.
The GNU General Public License permits making a modified version and
letting the public access it on a server without ever releasing its
source code to the public.
The GNU Affero General Public License is designed specifically to
ensure that, in such cases, the modified source code becomes available
to the community. It requires the operator of a network server to
provide the source code of the modified version running there to the
users of that server. Therefore, public use of a modified version, on
a publicly accessible server, gives the public access to the source
code of the modified version.
An older license, called the Affero General Public License and
published by Affero, was designed to accomplish similar goals. This is
a different license, not a version of the Affero GPL, but Affero has
released a new version of the Affero GPL which permits relicensing under
this license.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU Affero General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Remote Network Interaction; Use with the GNU General Public License.
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

62
examples/crewai/python/README.md Normal file
View File

@ -0,0 +1,62 @@
# Honcho CrewAI Integration
Build CrewAI agents with persistent memory and reasoning capabilities powered by Honcho.
## Installation
```bash
pip install honcho-crewai
```
## Quick Start
```python
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho_crewai import HonchoStorage
# Initialize Honcho storage
storage = HonchoStorage(user_id="user-123")
external_memory = ExternalMemory(storage=storage)
# Create agent with memory
agent = Agent(
role="AI Assistant",
goal="Help users with persistent memory",
backstory="You remember past conversations.",
)
# Create crew with external memory
crew = Crew(
agents=[agent],
tasks=[task],
external_memory=external_memory
)
```
## Features
- **Automatic Memory**: CrewAI agents automatically store and retrieve conversation context
- **Semantic Search**: Find relevant past messages using vector similarity
- **Logical Reasoning**: Query what the system knows about users via the Dialectic API
- **Multi-Agent Support**: Give each agent distinct memory and identity
- **Tools Integration**: `HonchoGetContextTool`, `HonchoDialecticTool`, and `HonchoSearchTool` for explicit memory control
## Documentation
For comprehensive guides, examples, and API reference, visit:
**[https://docs.honcho.dev/v2/integrations/crewai](https://docs.honcho.dev/v2/integrations/crewai)**
## Examples
Check out complete examples in the [GitHub repository](https://github.com/plastic-labs/honcho/tree/main/examples/crewai/python/examples).
## License
AGPL-3.0-or-later
## Support
- Report issues: [GitHub Issues](https://github.com/plastic-labs/honcho/issues)
- Documentation: [docs.honcho.dev](https://docs.honcho.dev)
- Website: [honcho.dev](https://honcho.dev)

99
examples/crewai/python/examples/hybrid_memory_example.py Normal file
View File

@ -0,0 +1,99 @@
"""
Hybrid Memory Example: Combining Automatic Memory + Explicit Tools
Demonstrates combining automatic memory (HonchoStorage) with explicit memory tools.
The agent gets baseline context automatically but can also make targeted queries.
"""
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho import Honcho
from honcho_crewai import (
HonchoStorage,
HonchoSearchTool,
HonchoDialecticTool,
)
load_dotenv()
def main():
"""Hybrid memory example with automatic baseline + explicit tools."""
# Initialize Honcho
honcho = Honcho()
user_id = "hybrid-demo-user"
session_id = "hybrid-demo-session"
# Setup automatic memory
storage = HonchoStorage(
user_id=user_id,
session_id=session_id,
honcho_client=honcho
)
external_memory = ExternalMemory(storage=storage)
# Add conversation history
messages = [
("user", "I'm planning a trip to Japan next spring"),
("assistant", "How exciting! Japan is beautiful in spring."),
("user", "I love Japanese cuisine, especially ramen and sushi"),
("assistant", "You'll find amazing food there!"),
("user", "My budget is around $3000 for the whole trip"),
("assistant", "That's a good budget for a memorable trip."),
("user", "I prefer cultural experiences over touristy attractions"),
]
for role, message in messages:
external_memory.save(message, metadata={"agent": role})
# Create memory tools for targeted queries
search_tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
dialectic_tool = HonchoDialecticTool(
honcho=honcho, session_id=session_id, peer_id=user_id
)
# Create agent with both automatic memory AND tools
travel_agent = Agent(
role="Travel Advisor",
goal="Create personalized travel recommendations using memory",
backstory=(
"You are a travel advisor with access to conversation history. "
"You can use tools to search for specific details or understand preferences."
),
tools=[search_tool, dialectic_tool],
verbose=True,
allow_delegation=False
)
# Create task
task = Task(
description=(
"Create a 3-day Tokyo itinerary for the user.\n\n"
"Use search_tool to find their budget and food preferences.\n"
"Use query_peer_knowledge to understand their travel style.\n"
"Then create a personalized itinerary with activities and restaurant recommendations."
),
expected_output="A 3-day Tokyo itinerary with daily activities and dining suggestions",
agent=travel_agent
)
# Execute with hybrid memory: automatic baseline + explicit tools
crew = Crew(
agents=[travel_agent],
tasks=[task],
process=Process.sequential,
external_memory=external_memory, # Automatic memory!
verbose=True
)
result = crew.kickoff()
print("\n" + "=" * 70)
print("RESULT")
print("=" * 70)
print(result.raw)
if __name__ == "__main__":
main()

115
examples/crewai/python/examples/interactive_chat.py Normal file
View File

@ -0,0 +1,115 @@
"""
CrewAI Integration with Honcho and OpenAI
This example demonstrates how to build AI agents with persistent memory using
CrewAI for agent orchestration, OpenAI for the AI model, and Honcho for memory
management via the honcho_crewai package.
"""
from typing import Optional
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho_crewai import HonchoStorage
load_dotenv()
def run_conversation_turn(
user_id: str,
user_input: str,
session_id: Optional[str] = None,
storage: Optional[HonchoStorage] = None
) -> tuple[str, HonchoStorage]:
"""
Run a single conversation turn with the CrewAI agent.
Args:
user_id: Unique identifier for the user
user_input: User's message
session_id: Optional session ID for conversation continuity
storage: Optional existing HonchoStorage instance
Returns:
Tuple of (agent_response, storage_instance)
"""
# Initialize or reuse storage
if storage is None:
if not session_id:
session_id = f"session_{user_id}"
storage = HonchoStorage(user_id=user_id, session_id=session_id)
# Create ExternalMemory wrapper for automatic context retrieval
external_memory = ExternalMemory(storage=storage)
# Save user input to memory
external_memory.save(user_input, metadata={"agent": "user"})
# Create an agent with memory
agent = Agent(
role="AI Assistant",
goal="Help users with their questions and remember context from previous conversations",
backstory=(
"You are a helpful AI assistant with the ability to remember past conversations. "
"You use context from previous interactions to provide personalized and relevant responses."
),
verbose=False,
allow_delegation=False
)
# Create task for the agent
task = Task(
description=f"Respond to the user's message: {user_input}",
expected_output="A helpful and contextually relevant response that considers conversation history",
agent=agent
)
# Create crew with external memory - enables automatic context retrieval
crew = Crew(
agents=[agent],
tasks=[task],
process=Process.sequential,
external_memory=external_memory,
verbose=False
)
# Execute - CrewAI automatically retrieves relevant context from Honcho
result = crew.kickoff()
# Save assistant response back to memory
response_text = str(result.raw)
external_memory.save(response_text, metadata={"agent": "assistant"})
return response_text, storage
def main():
"""Interactive chat loop with CrewAI agent powered by Honcho memory."""
print("Welcome to the AI Assistant powered by CrewAI and Honcho!")
print("Type 'quit' or 'exit' to end the conversation.\n")
user_id = "demo-user-123"
storage = None
while True:
user_input = input("You: ")
if user_input.lower() in ['quit', 'exit']:
print("Goodbye!")
break
if not user_input.strip():
continue
try:
response, storage = run_conversation_turn(
user_id=user_id,
user_input=user_input,
storage=storage
)
print(f"Assistant: {response}\n")
except Exception as e:
print(f"Error: {e}\n")
if __name__ == "__main__":
main()

72
examples/crewai/python/examples/simple_example.py Normal file
View File

@ -0,0 +1,72 @@
"""
Simple Honcho + CrewAI Example
A minimal example showing how to use Honcho's ExternalMemory with CrewAI agents.
This demonstrates the basic pattern for persistent conversation memory.
"""
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho_crewai import HonchoStorage
load_dotenv()
def main():
"""Simple example of CrewAI agent with Honcho memory."""
# Initialize Honcho storage
storage = HonchoStorage(user_id="simple-demo-user")
external_memory = ExternalMemory(storage=storage)
# Add some conversation history
messages = [
("user", "I'm learning Python programming"),
("assistant", "Great! Python is an excellent language to learn."),
("user", "I'm particularly interested in web development"),
]
for role, message in messages:
external_memory.save(message, metadata={"agent": role})
# Create agent with memory
agent = Agent(
role="Programming Mentor",
goal="Help users learn programming by remembering their interests and progress",
backstory=(
"You are a patient programming mentor who remembers what students "
"have told you about their learning journey and interests."
),
verbose=True,
allow_delegation=False
)
# Create task
task = Task(
description=(
"Based on what you know about the user's interests, "
"suggest a simple web development project they could build to practice Python."
),
expected_output="A specific project suggestion with brief explanation",
agent=agent
)
# Execute with memory - CrewAI automatically retrieves relevant context!
crew = Crew(
agents=[agent],
tasks=[task],
process=Process.sequential,
external_memory=external_memory,
verbose=True
)
result = crew.kickoff()
print("\n" + "=" * 70)
print("RESULT")
print("=" * 70)
print(result.raw)
if __name__ == "__main__":
main()

125
examples/crewai/python/examples/tools_example.py Normal file
View File

@ -0,0 +1,125 @@
"""
Honcho Tools with CrewAI Example
Demonstrates how to equip CrewAI agents with Honcho's memory tools:
- HonchoGetContextTool: Retrieve session context with token limits
- HonchoDialecticTool: Query representations about peers
- HonchoSearchTool: Perform semantic search across session messages
These tools give agents explicit control over memory retrieval, beyond the
automatic memory provided by ExternalMemory.
"""
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process
from honcho import Honcho
from honcho_crewai import (
HonchoGetContextTool,
HonchoDialecticTool,
HonchoSearchTool,
)
load_dotenv()
def main():
"""Demonstrate Honcho tools with CrewAI agents."""
print("=" * 70)
print("HONCHO TOOLS + CREWAI EXAMPLE")
print("=" * 70 + "\n")
# Step 1: Setup session with conversation history
print("1. Setting up session with conversation history...\n")
honcho = Honcho()
user_id = "demo-user-45"
session_id = "tools-demo-session"
user = honcho.peer(user_id)
session = honcho.session(session_id)
# Add conversation history
messages = [
"I'm planning a trip to Japan in March",
"I love trying authentic local cuisine, especially ramen and sushi",
"My budget is around $3000 for a 10-day trip",
"I'm interested in visiting both Tokyo and Kyoto",
"I prefer staying in traditional ryokans over hotels",
]
for msg in messages:
print(f" • {msg}")
session.add_messages([user.message(msg)])
print("\n ✓ Session created with 5 messages\n")
# Step 2: Create Honcho tools
print("2. Creating Honcho memory tools...\n")
context_tool = HonchoGetContextTool(
honcho=honcho, session_id=session_id, peer_id=user_id
)
print(" ✓ get_session_context - Retrieve conversation context")
dialectic_tool = HonchoDialecticTool(
honcho=honcho, session_id=session_id, peer_id=user_id
)
print(" ✓ query_peer_knowledge - Ask about user preferences")
search_tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
print(" ✓ search_session_messages - Semantic search messages\n")
# Step 3: Create agent with tools
print("3. Creating travel planning agent with memory tools...\n")
travel_agent = Agent(
role="Travel Planning Specialist",
goal="Create personalized travel recommendations using memory tools",
backstory=(
"You are an expert travel planner with access to conversation memory tools. "
"Use the tools to understand the user's preferences before making recommendations."
),
tools=[context_tool, dialectic_tool, search_tool],
verbose=True,
allow_delegation=False
)
print(" ✓ Agent created with 3 Honcho tools\n")
# Step 4: Create task
print("4. Creating task...\n")
task = Task(
description=(
"Create a personalized 3-day Tokyo itinerary. "
"Use the memory tools to understand:\n"
" • Food preferences (search for 'cuisine' or 'food')\n"
" • Travel style and budget (query user knowledge)\n"
" • Recent context (get conversation context)\n"
"Then create a detailed plan matching their interests."
),
expected_output=(
"A 3-day Tokyo itinerary with:\n"
" • Daily activities matching user interests\n"
" • Restaurant recommendations\n"
" • Accommodation suggestions\n"
" • Budget considerations"
),
agent=travel_agent
)
print(" ✓ Task created\n")
# Step 5: Execute
print("5. Executing crew (agent will use tools to retrieve memory)...\n")
print("-" * 70 + "\n")
crew = Crew(
agents=[travel_agent],
tasks=[task],
process=Process.sequential,
verbose=True
)
crew.kickoff()
if __name__ == "__main__":
main()

47
examples/crewai/python/pyproject.toml Normal file
View File

@ -0,0 +1,47 @@
[project]
name = "honcho-crewai"
version = "0.1.0"
description = "CrewAI integration with Honcho for persistent agent memory"
readme = "README.md"
requires-python = ">=3.10"
license = {text = "AGPL-3.0-or-later"}
authors = [
{name = "Plastic Labs", email = "hello@plasticlabs.ai"}
]
maintainers = [
{name = "Plastic Labs", email = "hello@plasticlabs.ai"}
]
keywords = [
"honcho",
"crewai",
"ai-agents",
"memory",
"agent-memory",
"persistent-memory"
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries :: Python Modules",
"Framework :: Pydantic",
]
dependencies = [
"crewai>=0.134.0",
"honcho-ai>=0.2.0",
"openai>=1.0.0",
"python-dotenv>=1.0.0",
]
[project.urls]
Homepage = "https://honcho.dev"
Documentation = "https://docs.honcho.dev/v2/integrations/crewai"
Repository = "https://github.com/plastic-labs/honcho"
"Bug Tracker" = "https://github.com/plastic-labs/honcho/issues"
Changelog = "https://github.com/plastic-labs/honcho/blob/main/CHANGELOG.md"

63
examples/crewai/python/src/honcho_crewai/__init__.py Normal file
View File

@ -0,0 +1,63 @@
"""
Honcho CrewAI Integration
This package provides seamless integration between Honcho and CrewAI,
enabling AI agents to maintain persistent memory across conversations.
Example:
```python
from honcho_crewai import HonchoStorage, HonchoSearchTool, HonchoGetContextTool, HonchoDialecticTool
from crewai.memory.external.external_memory import ExternalMemory
from crewai import Agent, Task, Crew
from honcho import Honcho
# Initialize Honcho client and storage
honcho = Honcho()
storage = HonchoStorage(user_id="user123", honcho_client=honcho)
external_memory = ExternalMemory(storage=storage)
# Create tools for agents
search_tool = HonchoSearchTool(honcho=honcho, session_id=storage.session_id)
context_tool = HonchoGetContextTool(honcho=honcho, session_id=storage.session_id, peer_id="user123")
dialectic_tool = HonchoDialecticTool(honcho=honcho, session_id=storage.session_id, peer_id="user123")
# Create agent with memory and tools
agent = Agent(
role="AI Assistant",
goal="Help users with persistent memory",
backstory="You remember past conversations.",
tools=[search_tool, context_tool, dialectic_tool],
)
# Define a task for the crew
task = Task(
description="Help the user with their request",
expected_output="A helpful response",
agent=agent,
)
# Create crew with external memory
crew = Crew(
agents=[agent],
tasks=[task],
external_memory=external_memory
)
```
"""
from honcho_crewai.exceptions import HonchoDependencyError
from honcho_crewai.storage import HonchoStorage
from honcho_crewai.tools import (
HonchoDialecticTool,
HonchoGetContextTool,
HonchoSearchTool,
)
__version__ = "0.1.0"
__all__ = [
"HonchoDependencyError",
"HonchoDialecticTool",
"HonchoGetContextTool",
"HonchoSearchTool",
"HonchoStorage",
]

14
examples/crewai/python/src/honcho_crewai/exceptions.py Normal file
View File

@ -0,0 +1,14 @@
"""
Exception classes for CrewAI integration.
"""
class HonchoDependencyError(ImportError):
"""Raised when required CrewAI dependencies are not installed."""
def __init__(self, framework: str, install_command: str) -> None:
self.framework = framework
self.install_command = install_command
super().__init__(
f"{framework} dependencies not found. Install with: {install_command}"
)

188
examples/crewai/python/src/honcho_crewai/storage.py Normal file
View File

@ -0,0 +1,188 @@
"""
Honcho Storage for CrewAI External Memory
This module provides a Honcho-backed storage provider for CrewAI's external memory
system, enabling AI agents to maintain persistent conversation memory across sessions.
"""
import logging
import uuid
from typing import Any, Optional
from crewai.memory.storage.interface import Storage
from honcho import Honcho
logger = logging.getLogger(__name__)
class HonchoStorage(Storage):
"""
Honcho-backed storage provider for CrewAI external memory.
Implements CrewAI's Storage interface using Honcho's session-based memory,
allowing agents to maintain context across conversations.
Attributes:
honcho: The Honcho client instance
user: Peer representing the user
assistant: Peer representing the AI assistant
session: The conversation session
session_id: Unique identifier for the session
Example:
```python
from honcho_crewai import HonchoStorage
from crewai.memory.external.external_memory import ExternalMemory
# Initialize storage
storage = HonchoStorage(user_id="user123")
# Use with CrewAI's external memory
external_memory = ExternalMemory(storage=storage)
```
"""
def __init__(
self,
user_id: str,
session_id: Optional[str] = None,
honcho_client: Optional[Honcho] = None,
) -> None:
"""
Initialize Honcho storage for a specific user and session.
Args:
user_id: Unique identifier for the user
session_id: Optional session ID. If not provided, a UUID will be generated
honcho_client: Optional Honcho client instance. If not provided, creates one
using the demo environment (https://demo.honcho.dev)
"""
self.honcho = honcho_client or Honcho()
# Initialize user and assistant peers
self.user = self.honcho.peer(user_id)
self.assistant = self.honcho.peer("assistant")
# Create or use existing session
if not session_id:
session_id = str(uuid.uuid4())
self.session = self.honcho.session(session_id)
self.session_id = session_id
def save(self, value: Any, metadata: dict[str, Any]) -> None:
"""
Save a message to Honcho session.
This method is called by CrewAI to store messages and context. Messages
are associated with the appropriate peer (user or assistant) based on
the metadata.
Args:
value: Message content to save
metadata: Metadata dict that may contain 'role', 'agent', or 'type' info
Common keys: 'role', 'agent', 'type'
"""
try:
# Determine if this is from user or assistant based on metadata
# Check various metadata keys that might indicate the role
role = metadata.get("role", metadata.get("agent", "assistant"))
is_user = role == "user"
peer = self.user if is_user else self.assistant
content_str = str(value)
# Add message to session
self.session.add_messages([peer.message(content_str, metadata=metadata)])
logger.debug(
"Saved message from %s: %s...",
metadata.get("name", role),
content_str[:100],
)
except Exception:
logger.exception("Error saving to Honcho")
raise
def search(
self,
query: str,
limit: int = 10,
score_threshold: float = 0.5,
filters: Optional[dict[str, Any]] = None,
) -> list[dict[str, Any]]:
"""
Search for relevant messages using semantic search.
This method uses Honcho's semantic vector search to find messages most
relevant to the query.
Args:
query: Search query used for semantic matching
limit: Maximum number of messages to retrieve
score_threshold: Minimum relevance score (not currently used by Honcho API)
filters: Optional filters to scope the search. Supports Honcho's filter syntax
including logical operators (AND, OR, NOT), comparison operators
(gt, gte, lt, lte, eq, ne), and metadata filtering.
Example: {"peer_id": "user123"} or {"metadata": {"type": "important"}}
See: https://docs.honcho.dev/v2/documentation/core-concepts/features/using-filters
Returns:
List of message dictionaries in CrewAI expected format.
Each dict contains:
- content: The message content
- memory: The message content (required by CrewAI)
- context: The message content (for compatibility)
- metadata: Message metadata including peer_id, created_at, and custom metadata
"""
try:
results = []
# Use semantic search to find relevant messages
# This performs vector similarity search on message content
messages = self.session.search(query=query, filters=filters, limit=limit)
# Convert to CrewAI expected format
for msg in messages:
# Build base metadata with peer_id and created_at
metadata = {
"peer_id": msg.peer_id,
"created_at": str(msg.created_at) if hasattr(msg, "created_at") else None,
}
# Merge custom metadata if present
if hasattr(msg, "metadata") and msg.metadata:
metadata.update(msg.metadata)
results.append(
{
"content": msg.content,
"memory": msg.content,
"context": msg.content,
"metadata": metadata,
}
)
logger.debug("Search for '%s' returned %d results", query, len(results))
return results
except Exception:
logger.exception("Error searching Honcho")
raise
def reset(self) -> None:
"""
Create a new session, effectively resetting memory.
This creates a new Honcho session with a fresh UUID, allowing the agent
to start a new conversation without the previous context.
"""
try:
new_session_id = str(uuid.uuid4())
self.session = self.honcho.session(new_session_id)
self.session_id = new_session_id
logger.debug("Reset session. New session ID: %s", new_session_id)
except Exception:
logger.exception("Error resetting Honcho session")
raise

303
examples/crewai/python/src/honcho_crewai/tools.py Normal file
View File

@ -0,0 +1,303 @@
"""
Honcho Tools for CrewAI
This module provides tools that allow CrewAI agents to interact with Honcho's
session context, dialectic API, and semantic search capabilities.
"""
import logging
from typing import Any, Optional
from crewai.tools import BaseTool
from honcho import Honcho
from pydantic import BaseModel, Field, PrivateAttr
logger = logging.getLogger(__name__)
# Input Schemas
class GetContextInput(BaseModel):
"""Input schema for get_context tool."""
tokens: Optional[int] = Field(
default=None, gt=0, description="Maximum number of tokens to include in the context"
)
peer_target: Optional[str] = Field(
default=None, description="A peer ID to get context for (retrieves representation and peer card)"
)
summary: bool = Field(
default=True, description="Whether to include session summary in the context"
)
peer_perspective: Optional[str] = Field(
default=None, description="Peer ID to use as the perspective for context retrieval"
)
class DialecticInput(BaseModel):
"""Input schema for dialectic (chat) tool."""
query: str = Field(..., min_length=1, description="Natural language question to ask")
target: Optional[str] = Field(
default=None, description="Optional target peer for local representation query"
)
session_id: Optional[str] = Field(
default=None, description="Optional session ID to scope query to specific session"
)
class SearchInput(BaseModel):
"""Input schema for search tool."""
query: str = Field(..., min_length=1, description="Search query for semantic matching")
limit: int = Field(default=10, ge=1, le=100, description="Number of results to return (1-100)")
filters: Optional[dict[str, Any]] = Field(
default=None,
description=(
"Optional filters to scope the search. Supports Honcho's filter syntax including "
"logical operators (AND, OR, NOT), comparison operators (gt, gte, lt, lte, eq, ne), "
"and metadata filtering. Examples: {'peer_id': 'user123'}, {'metadata': {'priority': 'high'}}, "
"{'created_at': {'gte': '2024-01-01'}}"
),
)
# Tool Implementations
class HonchoGetContextTool(BaseTool):
"""
Tool to retrieve session context with token limits.
This tool fetches the conversation history and session summary within
a specified token budget, optimized for LLM context windows.
"""
name: str = "get_session_context"
description: str = (
"Retrieve recent conversation context within token limits. "
"Returns formatted messages with optional summary and peer information. "
"Useful for getting optimized context that fits within token budgets."
)
args_schema: type[BaseModel] = GetContextInput
_honcho: Honcho = PrivateAttr()
_session_id: str = PrivateAttr()
_peer_id: str = PrivateAttr()
def __init__(self, honcho: Honcho, session_id: str, peer_id: str) -> None:
"""
Initialize the get_context tool.
Args:
honcho: Honcho client instance
session_id: ID of the session to get context from
peer_id: ID of the peer requesting context
"""
super().__init__()
self._honcho = honcho
self._session_id = session_id
self._peer_id = peer_id
def _run(
self,
tokens: Optional[int] = None,
peer_target: Optional[str] = None,
*,
summary: bool = True,
peer_perspective: Optional[str] = None,
) -> str:
"""
Execute get_context and format results.
Args:
tokens: Maximum tokens to include
peer_target: Target peer ID for representation
summary: Whether to include summary
peer_perspective: Peer ID to use as perspective
Returns:
Formatted string containing context information
"""
try:
session = self._honcho.session(self._session_id)
context = session.get_context(
summary=summary,
tokens=tokens,
peer_target=peer_target,
peer_perspective=peer_perspective,
)
# Format for agent consumption
result = []
# Add summary if present
if context.summary:
result.append("=== Session Summary ===")
result.append(context.summary.content)
result.append("")
# Add peer representation if present
if context.peer_representation:
result.append("=== Peer Representation ===")
result.append(context.peer_representation)
result.append("")
# Add peer card if present
if context.peer_card:
result.append("=== Peer Card ===")
result.extend(context.peer_card)
result.append("")
# Add messages
if context.messages:
result.append(f"=== Messages ({len(context.messages)}) ===")
for msg in context.messages:
result.append(f"{msg.peer_id}: {msg.content}")
return "\n".join(result) if result else "No context available"
except Exception as e:
logger.exception("Error retrieving context")
return f"Error retrieving context: {e!s}"
class HonchoDialecticTool(BaseTool):
"""
Tool to query Honcho's dialectic API (peer representations).
This tool allows agents to ask questions about what the system knows
about users or other peers, leveraging Honcho's reasoning capabilities.
"""
name: str = "query_peer_knowledge"
description: str = (
"Query the system's representation about peers. "
"Ask questions like 'What does the user like?' or 'What are their preferences?' "
"to retrieve information from the peer's long-term representation. "
"Can optionally query what one peer knows about another (local representation)."
)
args_schema: type[BaseModel] = DialecticInput
_honcho: Honcho = PrivateAttr()
_session_id: str = PrivateAttr()
_peer_id: str = PrivateAttr()
def __init__(self, honcho: Honcho, session_id: str, peer_id: str) -> None:
"""
Initialize the dialectic tool.
Args:
honcho: Honcho client instance
session_id: Default session ID for scoped queries
peer_id: ID of the peer to query about
"""
super().__init__()
self._honcho = honcho
self._session_id = session_id
self._peer_id = peer_id
def _run(
self,
query: str,
target: Optional[str] = None,
session_id: Optional[str] = None,
) -> str:
"""
Execute dialectic query.
Args:
query: Natural language question to ask
target: Optional target peer for local representation
session_id: Optional session ID to scope the query
Returns:
String response from the dialectic API
"""
try:
peer = self._honcho.peer(self._peer_id)
# Use provided session_id or fall back to default
scope_session_id = session_id or self._session_id
# Query the dialectic API (non-streaming)
response = peer.chat(
query=query,
stream=False,
target=target,
session_id=scope_session_id,
)
# Return the response or a default message
if response:
return str(response)
else:
return "No relevant information found."
except Exception as e:
logger.exception("Error querying dialectic API")
return f"Error querying peer knowledge: {e!s}"
class HonchoSearchTool(BaseTool):
"""
Tool to perform semantic search across session messages.
This tool enables agents to find relevant past messages using
semantic similarity search, useful for retrieving specific information
from conversation history.
"""
name: str = "search_session_messages"
description: str = (
"Search through session messages using semantic similarity. "
"Finds messages that are semantically related to the query, "
"useful for retrieving specific information from past conversations."
)
args_schema: type[BaseModel] = SearchInput
_honcho: Honcho = PrivateAttr()
_session_id: str = PrivateAttr()
def __init__(self, honcho: Honcho, session_id: str) -> None:
"""
Initialize the search tool.
Args:
honcho: Honcho client instance
session_id: ID of the session to search in
"""
super().__init__()
self._honcho = honcho
self._session_id = session_id
def _run(self, query: str, limit: int = 10, filters: Optional[dict[str, Any]] = None) -> str:
"""
Execute semantic search.
Args:
query: Search query for semantic matching
limit: Number of results to return (1-100)
filters: Optional filters to apply to search results
Returns:
Formatted string with search results
"""
try:
session = self._honcho.session(self._session_id)
# Perform semantic search
messages = session.search(query=query, limit=limit, filters=filters)
if not messages:
return f"No messages found matching '{query}'"
# Format results for agent consumption
result = [f"=== Search Results for '{query}' ({len(messages)} found) ==="]
for i, msg in enumerate(messages, 1):
result.append(f"\n{i}. [{msg.peer_id}] {msg.content}")
if hasattr(msg, "created_at") and msg.created_at:
result.append(f" Created: {msg.created_at}")
return "\n".join(result)
except Exception as e:
logger.exception("Error searching messages: %s", e)
return f"Error searching messages: {e!s}"

62
examples/crewai/python/tests/test_basic.py Normal file
View File

@ -0,0 +1,62 @@
"""
Basic tests for honcho_crewai package
Validates package structure, imports, and basic functionality.
"""
import pytest
def test_package_import():
"""Test that honcho_crewai imports successfully."""
import honcho_crewai
assert honcho_crewai is not None
def test_storage_import():
"""Test that HonchoStorage can be imported."""
from honcho_crewai import HonchoStorage
assert HonchoStorage is not None
def test_tools_import():
"""Test that tool classes can be imported."""
from honcho_crewai import (
HonchoGetContextTool,
HonchoDialecticTool,
HonchoSearchTool,
)
assert HonchoGetContextTool is not None
assert HonchoDialecticTool is not None
assert HonchoSearchTool is not None
class TestPackageMetadata:
"""Test package metadata and structure."""
def test_package_has_version(self):
"""Test that package exposes version information."""
import honcho_crewai
assert hasattr(honcho_crewai, "__version__")
assert isinstance(honcho_crewai.__version__, str)
assert len(honcho_crewai.__version__) > 0
def test_package_all_exports(self):
"""Test that __all__ contains expected exports."""
import honcho_crewai
assert hasattr(honcho_crewai, "__all__")
expected_exports = [
"HonchoStorage",
"HonchoGetContextTool",
"HonchoDialecticTool",
"HonchoSearchTool",
"HonchoDependencyError",
]
for export in expected_exports:
assert export in honcho_crewai.__all__, f"{export} not in __all__"

174
examples/crewai/python/tests/test_storage.py Normal file
View File

@ -0,0 +1,174 @@
"""
Tests for HonchoStorage
Tests the CrewAI-Honcho integration layer, focusing on:
- CrewAI Storage interface compliance
- Metadata mapping (agent/role -> peer_id)
- Format conversion (Honcho -> CrewAI format)
"""
from honcho_crewai import HonchoStorage
class TestHonchoStorage:
"""Tests for HonchoStorage integration layer."""
def test_initialization(self):
"""Test that HonchoStorage initializes with correct peers and session."""
storage = HonchoStorage(user_id="test_user")
assert storage is not None
assert storage.session_id is not None
assert storage.user is not None
assert storage.assistant is not None
assert storage.session is not None
def test_initialization_with_custom_session(self):
"""Test that custom session_id is preserved."""
custom_session_id = "my_custom_session"
storage = HonchoStorage(user_id="test_user", session_id=custom_session_id)
assert storage.session_id == custom_session_id
def test_save_with_different_roles(self):
"""Test that save handles different agent/role metadata."""
storage = HonchoStorage(user_id="test_user_roles")
# Save with different metadata patterns
storage.save("User via agent", metadata={"agent": "user"})
storage.save("User via role", metadata={"role": "user"})
storage.save("Assistant via agent", metadata={"agent": "assistant"})
storage.save("Default (no metadata)", metadata={})
# If no exceptions raised, metadata mapping works
def test_search_returns_crewai_format(self):
"""Test that search returns results in CrewAI format."""
storage = HonchoStorage(user_id="test_user_search")
# Add a message
storage.save("Test message", metadata={"agent": "user"})
# Search
results = storage.search("test", limit=10)
# Verify CrewAI format
assert isinstance(results, list)
for result in results:
# Required keys for CrewAI
assert "memory" in result
assert "context" in result
assert "content" in result
assert "metadata" in result
def test_search_includes_all_required_fields(self):
"""Test that all search results have required CrewAI fields."""
storage = HonchoStorage(user_id="test_user_format")
# Add a message
storage.save("Test message", metadata={"agent": "user"})
# Search
results = storage.search("test", limit=5)
# Verify all results have required fields with correct types
for result in results:
assert isinstance(result["content"], str)
assert isinstance(result["memory"], str)
assert isinstance(result["context"], str)
assert isinstance(result["metadata"], dict)
def test_search_formats_summaries_correctly(self):
"""Test that session summaries are formatted with [Session Summary] prefix."""
storage = HonchoStorage(user_id="test_user_summaries")
# Add enough messages to potentially trigger summaries
for i in range(25):
storage.save(
f"Message {i}",
metadata={"agent": "user" if i % 2 == 0 else "assistant"},
)
# Search
results = storage.search("message", limit=10)
# Check summary formatting (if summaries exist)
summary_results = [r for r in results if r["metadata"].get("type") == "summary"]
for summary in summary_results:
# Verify our formatting logic
assert "summary_type" in summary["metadata"]
assert "[Session Summary]" in summary["context"] # Our formatting
def test_reset_creates_new_session_id(self):
"""Test that reset() creates a new session with different ID."""
storage = HonchoStorage(user_id="test_user_reset")
original_session_id = storage.session_id
# Reset
storage.reset()
# Verify new session ID was created
assert storage.session_id != original_session_id
def test_search_with_filters(self):
"""Test that search accepts and uses filters parameter."""
storage = HonchoStorage(user_id="test_user_filters")
# Add messages with different metadata
storage.save("User question about Python", metadata={"agent": "user", "topic": "python"})
storage.save("Assistant answer about Python", metadata={"agent": "assistant", "topic": "python"})
storage.save("User question about JavaScript", metadata={"agent": "user", "topic": "javascript"})
# Search with peer_id filter - filter to only user messages
results = storage.search(
"programming",
limit=10,
filters={"peer_id": storage.user.id}
)
# Verify results are returned and in correct format
assert isinstance(results, list)
for result in results:
assert "memory" in result
assert "content" in result
assert "context" in result
assert "metadata" in result
def test_search_with_metadata_filters(self):
"""Test that search works with metadata filters."""
storage = HonchoStorage(user_id="test_user_metadata_filters")
# Add messages with specific metadata
storage.save("Important message", metadata={"agent": "user", "priority": "high"})
storage.save("Regular message", metadata={"agent": "user", "priority": "low"})
# Search with metadata filter
results = storage.search(
"message",
limit=10,
filters={"metadata": {"priority": "high"}}
)
# Verify results are in correct format
assert isinstance(results, list)
for result in results:
assert "memory" in result
assert "metadata" in result
def test_search_without_filters(self):
"""Test that search works without filters."""
storage = HonchoStorage(user_id="test_user_no_filters")
# Add a message
storage.save("Test message for search", metadata={"agent": "user"})
# Search without filters
results = storage.search("test", limit=5)
# Verify it works and returns correct format
assert isinstance(results, list)
for result in results:
assert "memory" in result
assert "content" in result

194
examples/crewai/python/tests/test_tools.py Normal file
View File

@ -0,0 +1,194 @@
"""
Tests for Honcho CrewAI Tools
Tests the CrewAI-Honcho tool integration layer using real Honcho SDK.
Focuses on tool interface compliance and result formatting.
"""
from honcho import Honcho
from honcho_crewai import (
HonchoDialecticTool,
HonchoGetContextTool,
HonchoSearchTool,
)
class TestGetContextTool:
"""Tests for HonchoGetContextTool."""
def test_initialization(self):
"""Test that tool initializes with correct attributes."""
honcho = Honcho()
tool = HonchoGetContextTool(
honcho=honcho, session_id="test_session", peer_id="test_peer"
)
assert tool is not None
assert tool.name == "get_session_context"
assert tool.description is not None
assert tool.args_schema is not None
def test_returns_formatted_context(self):
"""Test that tool returns formatted context string."""
honcho = Honcho()
peer = honcho.peer("context_test_user")
session_id = "context_test_session"
session = honcho.session(session_id)
# Add test message
session.add_messages([peer.message("Test message for context")])
# Create and execute tool
tool = HonchoGetContextTool(
honcho=honcho, session_id=session_id, peer_id="context_test_user"
)
result = tool._run()
# Verify result is a formatted string
assert isinstance(result, str)
assert len(result) > 0
class TestDialecticTool:
"""Tests for HonchoDialecticTool."""
def test_initialization(self):
"""Test that tool initializes with correct attributes."""
honcho = Honcho()
tool = HonchoDialecticTool(
honcho=honcho, session_id="test_session", peer_id="test_peer"
)
assert tool is not None
assert tool.name == "query_peer_knowledge"
assert tool.description is not None
def test_returns_response(self):
"""Test that tool returns a response string."""
honcho = Honcho()
peer = honcho.peer("dialectic_test_user")
session_id = "dialectic_test_session"
session = honcho.session(session_id)
# Add test messages
session.add_messages([peer.message("I love pizza and Italian food")])
# Create and execute tool
tool = HonchoDialecticTool(
honcho=honcho, session_id=session_id, peer_id="dialectic_test_user"
)
result = tool._run(query="What does the user like?")
# Verify result is a string
assert isinstance(result, str)
assert len(result) > 0
class TestSearchTool:
"""Tests for HonchoSearchTool."""
def test_initialization(self):
"""Test that tool initializes with correct attributes."""
honcho = Honcho()
tool = HonchoSearchTool(honcho=honcho, session_id="test_session")
assert tool is not None
assert tool.name == "search_session_messages"
assert tool.description is not None
def test_returns_formatted_results(self):
"""Test that tool returns formatted search results."""
honcho = Honcho()
peer = honcho.peer("search_test_user")
session_id = "search_test_session"
session = honcho.session(session_id)
# Add test messages
session.add_messages([peer.message("I love pizza and pasta")])
# Create and execute tool
tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
result = tool._run(query="food", limit=5)
# Verify result is a formatted string
assert isinstance(result, str)
assert len(result) > 0
# Should have either results or "No messages found"
assert "Search Results" in result or "No messages found" in result
def test_search_with_filters(self):
"""Test that search tool accepts and uses filters parameter."""
honcho = Honcho()
peer = honcho.peer("search_filter_test_user")
session_id = "search_filter_test_session"
session = honcho.session(session_id)
# Add test messages
session.add_messages([peer.message("Important message about Python")])
# Create and execute tool with filters
tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
result = tool._run(
query="Python",
limit=5,
filters={"peer_id": peer.id}
)
# Verify result is a formatted string
assert isinstance(result, str)
assert len(result) > 0
def test_search_with_metadata_filters(self):
"""Test that search tool works with metadata filters."""
honcho = Honcho()
peer = honcho.peer("search_metadata_filter_user")
session_id = "search_metadata_filter_session"
session = honcho.session(session_id)
# Add test messages with metadata
session.add_messages([peer.message("High priority task", metadata={"priority": "high"})])
# Create and execute tool with metadata filter
tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
result = tool._run(
query="task",
limit=5,
filters={"metadata": {"priority": "high"}}
)
# Verify result is a formatted string
assert isinstance(result, str)
assert len(result) > 0
class TestToolsWorkTogether:
"""Test that all tools can work together."""
def test_all_tools_in_same_session(self):
"""Test that all three tools can be used in the same session."""
honcho = Honcho()
peer = honcho.peer("combo_test_user")
session_id = "combo_test_session"
session = honcho.session(session_id)
# Add messages
session.add_messages([peer.message("I enjoy coding in Python")])
# Create all tools
context_tool = HonchoGetContextTool(
honcho=honcho, session_id=session_id, peer_id="combo_test_user"
)
dialectic_tool = HonchoDialecticTool(
honcho=honcho, session_id=session_id, peer_id="combo_test_user"
)
search_tool = HonchoSearchTool(honcho=honcho, session_id=session_id)
# Execute all tools
context_result = context_tool._run()
dialectic_result = dialectic_tool._run(query="What does the user like?")
search_result = search_tool._run(query="coding", limit=5)
# Verify all return valid strings
assert isinstance(context_result, str) and len(context_result) > 0
assert isinstance(dialectic_result, str) and len(dialectic_result) > 0
assert isinstance(search_result, str) and len(search_result) > 0

3992
examples/crewai/python/uv.lock Normal file
View File

File diff suppressed because it is too large Load Diff

8
mcp/instructions.md
View File

@ -2,7 +2,7 @@
## What is Honcho?
Honcho is an infrastructure layer for building AI agents with social cognition and theory of mind capabilities. It enables personalized AI interactions by building coherent models of user psychology over time. The Honcho MCP server simplifies the integration to just 3 essential functions. Here's how to use them:
Honcho is an infrastructure layer for building AI agents with memory and social cognition. It enables personalized AI interactions by building coherent models of user psychology over time. The Honcho MCP server simplifies the integration to just 3 essential functions. Here's how to use them:
### Step 1: Start New Conversation (First Message Only)
@ -21,7 +21,7 @@ Before responding to any user message, you can query for personalization insight
```text
get_personalization_insights
session_id: [SESSION_ID_FROM_STEP_1]
query: [YOUR_THEORY_OF_MIND_QUESTION]
query: [YOUR_QUESTION]
```
This query takes a bit of time, so it's best to only perform it when you need personalized insights. If the query can be responded to effectively using what you already know about the user, just go ahead and answer it. However, the insights endpoint is extremely perceptive. It has the capability to reveal aspects of the user's personality, historical use of the application you are operating in, and more.
@ -129,7 +129,7 @@ For subsequent messages in the same conversation:
## Best Practices for Personalization Queries
Ask theory-of-mind questions that reveal:
Ask questions that reveal:
**Communication Style**: "How formal/casual should I be?" "What does this reveal about their preferences?"
@ -151,6 +151,6 @@ Ask theory-of-mind questions that reveal:
1. **Always start with `start_conversation` for new conversations**
2. **Store every message exchange with `add_turn`**
3. **Use `get_personalization_insights` strategically for better responses**
4. **Ask thoughtful theory-of-mind questions**
4. **Ask thoughtful questions about `peer` representation**
5. **Never expose technical details to the user**
6. **The system maintains context automatically between sessions**