AgentFoundry
AgentFoundry is Syntheticore's proprietary Python library for building and orchestrating enterprise AI agents. It provides LLM orchestration, governed tool execution, multi-tenant memory, vector and knowledge-graph integration, workflow lifecycle management, and execution tracing.
Start Here
| You want | Read |
|---|---|
| Coding standards, TDD, IP rules, and agent workflow | AGENTS.md |
| Documentation map and current priorities | docs/README.md |
| Current backlog | docs/TODO.md |
| Configuration and environment variables | docs/reference/Configuration_Guide.md |
| Supported LLM providers | docs/reference/LLM_Providers.md |
| Memory architecture and operations | docs/reference/Memory_Management_Guide.md |
What It Provides
- Flat, hierarchical, and deep-agent orchestration over LangGraph
- Registry-based tool discovery and governed execution
- Thread, user, organization, and global memory scopes
- Milvus and FAISS vector-store providers
- DuckDB/SQLite, PostgreSQL, and Neo4j knowledge-graph providers
- Dependency-aware workflow execution with pause, resume, cancel, retry, and replan
- Request tracing, cost accounting, execution budgets, and audit export
- Cython-compiled distribution for performance and IP protection
- Fail-fast initialization and operation: required backends do not silently degrade to substitute providers or pseudo-embeddings
Requirements
- Python 3.11+
- The dependencies in
requirements.txt - A valid AgentFoundry license for enforced runtime use
Quick Start
Use the top-level public API for host-facing classes and the documented
agentfoundry.config API for configuration:
from agentfoundry import Orchestrator, ToolRegistry
from agentfoundry.config import AgentConfig, set_module_config
config = AgentConfig.from_dict({
"scope": {
"org_id": "myorg",
"user_id": "u1",
"thread_id": "t1",
},
"llm": {
"provider": "openai",
"model_name": "gpt-5.4",
"api_key": "sk-...",
},
"vector_store": {
"provider": "milvus",
"milvus": {"uri": "http://localhost:19530"},
},
})
set_module_config(config)
registry = ToolRegistry(config=config)
registry.load_tools_from_directory()
orchestrator = Orchestrator(registry, config=config)
result = orchestrator.process_request(
"Summarize recent activity",
config={
"configurable": {
"org_id": "myorg",
"user_id": "u1",
"thread_id": "t1",
}
},
)
For a TOML deployment, start from
agentfoundry.template.toml:
config = AgentConfig.from_toml("agentfoundry.toml", apply_env=True)
Canonical environment overrides use nested AF__* paths, for example
AF__LLM__PROVIDER and AF__VECTOR_STORE__MILVUS__URI. See the
Configuration Guide for precedence,
secret aliases, and migration from legacy flat keys.
Development
AgentFoundry follows mandatory test-driven development. Most production modules are Cython compiled, so rebuild extensions after changing a Python source that has a matching shared object:
python setup.py build_ext --inplace
python -m pytest tests/test_<module>.py -v
See AGENTS.md for the complete workflow and
docs/workflows/tdd-workflow.md for the TDD
guide.
Licensing
Copyright (c) Syntheticore, Inc. All rights reserved.
This software is proprietary and confidential. Use, reproduction,
modification, distribution, or commercial deployment requires explicit written
authorization from Syntheticore, Inc. Licensing inquiries:
csteel@syntheticore.com.
1 maintainer
Metadata
1 maintainer
Release files for agentfoundry 1.5.93
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| agentfoundry-1.5.93-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.17+ x86-64 | Details |
| agentfoundry-1.5.93-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.17+ ARM64, Linux glibc 2.28+ ARM64 | Details |
Total release size: 15.5 MB
Release files / agentfoundry-1.5.93-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
| Download URL | agentfoundry-1.5.93-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl |
|---|---|
| Size | 7.8 MB |
| Tags | CPython 3.12 Linux glibc 2.17+ x86-64 |
|
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No |
| Uploaded via |
twine/7.0.0 CPython/3.12.12
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Release files / agentfoundry-1.5.93-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
| Download URL | agentfoundry-1.5.93-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl |
|---|---|
| Size | 7.7 MB |
| Tags | CPython 3.12 Linux glibc 2.17+ ARM64 Linux glibc 2.28+ ARM64 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.12
|
